Conservative Tests under Satisficing Models of Publication Bias.

PubMed

McCrary, Justin; Christensen, Garret; Fanelli, Daniele

2016-01-01

Publication bias leads consumers of research to observe a selected sample of statistical estimates calculated by producers of research. We calculate critical values for statistical significance that could help to adjust after the fact for the distortions created by this selection effect, assuming that the only source of publication bias is file drawer bias. These adjusted critical values are easy to calculate and differ from unadjusted critical values by approximately 50%-rather than rejecting a null hypothesis when the t-ratio exceeds 2, the analysis suggests rejecting a null hypothesis when the t-ratio exceeds 3. Samples of published social science research indicate that on average, across research fields, approximately 30% of published t-statistics fall between the standard and adjusted cutoffs.

Conservative Tests under Satisficing Models of Publication Bias

PubMed Central

McCrary, Justin; Christensen, Garret; Fanelli, Daniele

2016-01-01

Publication bias leads consumers of research to observe a selected sample of statistical estimates calculated by producers of research. We calculate critical values for statistical significance that could help to adjust after the fact for the distortions created by this selection effect, assuming that the only source of publication bias is file drawer bias. These adjusted critical values are easy to calculate and differ from unadjusted critical values by approximately 50%—rather than rejecting a null hypothesis when the t-ratio exceeds 2, the analysis suggests rejecting a null hypothesis when the t-ratio exceeds 3. Samples of published social science research indicate that on average, across research fields, approximately 30% of published t-statistics fall between the standard and adjusted cutoffs. PMID:26901834

On the nature and correction of the spurious S-wise spiral galaxy winding bias in Galaxy Zoo 1

NASA Astrophysics Data System (ADS)

Hayes, Wayne B.; Davis, Darren; Silva, Pedro

2017-04-01

The Galaxy Zoo 1 catalogue displays a bias towards the S-wise winding direction in spiral galaxies, which has yet to be explained. The lack of an explanation confounds our attempts to verify the Cosmological Principle, and has spurred some debate as to whether a bias exists in the real Universe. The bias manifests not only in the obvious case of trying to decide if the universe as a whole has a winding bias, but also in the more insidious case of selecting which Galaxies to include in a winding direction survey. While the former bias has been accounted for in a previous image-mirroring study, the latter has not. Furthermore, the bias has never been corrected in the GZ1 catalogue, as only a small sample of the GZ1 catalogue was reexamined during the mirror study. We show that the existing bias is a human selection effect rather than a human chirality bias. In effect, the excess S-wise votes are spuriously 'stolen' from the elliptical and edge-on-disc categories, not the Z-wise category. Thus, when selecting a set of spiral galaxies by imposing a threshold T so that max (PS, PZ) > T or PS + PZ > T, we spuriously select more S-wise than Z-wise galaxies. We show that when a provably unbiased machine selects which galaxies are spirals independent of their chirality, the S-wise surplus vanishes, even if humans still determine the chirality. Thus, when viewed across the entire GZ1 sample (and by implication, the Sloan catalogue), the winding direction of arms in spiral galaxies as viewed from Earth is consistent with the flip of a fair coin.

Attention Bias toward Threat in Pediatric Anxiety Disorders

ERIC Educational Resources Information Center

Roy, Amy Krain; Vasa, Roma A.; Bruck, Maggie; Mogg, Karin; Bradley, Brendan P.; Sweeney, Michael; Bergman, R. Lindsey; McClure-Tone, Erin B.; Pine, Daniel S.

2008-01-01

Attention bias towards threat faces is examined for a large sample of anxiety-disordered youths using visual probe task. The results showed that anxious individuals showed a selective bias towards threat due to perturbation in neural mechanisms that control vigilance.

The impact of selection bias on vaccine effectiveness estimates from test-negative studies.

PubMed

Jackson, Michael L; Phillips, C Hallie; Benoit, Joyce; Kiniry, Erika; Madziwa, Lawrence; Nelson, Jennifer C; Jackson, Lisa A

2018-01-29

Estimates of vaccine effectiveness (VE) from test-negative studies may be subject to selection bias. In the context of influenza VE, we used simulations to identify situations in which meaningful selection bias can occur. We also analyzed observational study data for evidence of selection bias. For the simulation study, we defined a hypothetical population whose members are at risk for acute respiratory illness (ARI) due to influenza and other pathogens. An unmeasured "healthcare seeking proclivity" affects both probability of vaccination and probability of seeking care for an ARI. We varied the direction and magnitude of these effects and identified situations where meaningful bias occurred. For the observational study, we reanalyzed data from the United States Influenza VE Network, an ongoing test-negative study. We compared "bias-naïve" VE estimates to bias-adjusted estimates, which used data from the source populations to correct for sampling bias. In the simulation study, an unmeasured care-seeking proclivity could create selection bias if persons with influenza ARI were more (or less) likely to seek care than persons with non-influenza ARI. However, selection bias was only meaningful when rates of care seeking between influenza ARI and non-influenza ARI were very different. In the observational study, the bias-naïve VE estimate of 55% (95% CI, 47--62%) was trivially different from the bias-adjusted VE estimate of 57% (95% CI, 49--63%). In combination, these studies suggest that while selection bias is possible in test-negative VE studies, this bias in unlikely to be meaningful under conditions likely to be encountered in practice. Researchers and public health officials can continue to rely on VE estimates from test-negative studies. Copyright © 2017 Elsevier Ltd. All rights reserved.

Sampling considerations for disease surveillance in wildlife populations

USGS Publications Warehouse

Nusser, S.M.; Clark, W.R.; Otis, D.L.; Huang, L.

2008-01-01

Disease surveillance in wildlife populations involves detecting the presence of a disease, characterizing its prevalence and spread, and subsequent monitoring. A probability sample of animals selected from the population and corresponding estimators of disease prevalence and detection provide estimates with quantifiable statistical properties, but this approach is rarely used. Although wildlife scientists often assume probability sampling and random disease distributions to calculate sample sizes, convenience samples (i.e., samples of readily available animals) are typically used, and disease distributions are rarely random. We demonstrate how landscape-based simulation can be used to explore properties of estimators from convenience samples in relation to probability samples. We used simulation methods to model what is known about the habitat preferences of the wildlife population, the disease distribution, and the potential biases of the convenience-sample approach. Using chronic wasting disease in free-ranging deer (Odocoileus virginianus) as a simple illustration, we show that using probability sample designs with appropriate estimators provides unbiased surveillance parameter estimates but that the selection bias and coverage errors associated with convenience samples can lead to biased and misleading results. We also suggest practical alternatives to convenience samples that mix probability and convenience sampling. For example, a sample of land areas can be selected using a probability design that oversamples areas with larger animal populations, followed by harvesting of individual animals within sampled areas using a convenience sampling method.

Racial and Ethnic Bias in Test Construction. Final Report.

ERIC Educational Resources Information Center

Green, Donald Ross

To determine if tryout samples typically used for item selection contribute to test bias against minority groups, item analyses were made of the California Achievement Tests using seven subgroups of the standardization sample: Northern White Suburban, Northern Black Urban, Southern White Suburban, Southern Black Rural, Southern White Rural,…

Racial and Ethnic Bias in Test Construction.

ERIC Educational Resources Information Center

Green, Donald Ross

To determine if tryout samples typically used for item selection contribute to test bias against minority groups, item analyses were made of the California Achievement Tests using seven sub-groups of the standardization sample: Northern White Suburban, Northern Black Urban, Southern White Suburban, Southern Black Rural, Southern White Rural,…

Accounting for selection bias in association studies with complex survey data.

PubMed

Wirth, Kathleen E; Tchetgen Tchetgen, Eric J

2014-05-01

Obtaining representative information from hidden and hard-to-reach populations is fundamental to describe the epidemiology of many sexually transmitted diseases, including HIV. Unfortunately, simple random sampling is impractical in these settings, as no registry of names exists from which to sample the population at random. However, complex sampling designs can be used, as members of these populations tend to congregate at known locations, which can be enumerated and sampled at random. For example, female sex workers may be found at brothels and street corners, whereas injection drug users often come together at shooting galleries. Despite the logistical appeal, complex sampling schemes lead to unequal probabilities of selection, and failure to account for this differential selection can result in biased estimates of population averages and relative risks. However, standard techniques to account for selection can lead to substantial losses in efficiency. Consequently, researchers implement a variety of strategies in an effort to balance validity and efficiency. Some researchers fully or partially account for the survey design, whereas others do nothing and treat the sample as a realization of the population of interest. We use directed acyclic graphs to show how certain survey sampling designs, combined with subject-matter considerations unique to individual exposure-outcome associations, can induce selection bias. Finally, we present a novel yet simple maximum likelihood approach for analyzing complex survey data; this approach optimizes statistical efficiency at no cost to validity. We use simulated data to illustrate this method and compare it with other analytic techniques.

Measuring coverage in MNCH: design, implementation, and interpretation challenges associated with tracking vaccination coverage using household surveys.

PubMed

Cutts, Felicity T; Izurieta, Hector S; Rhoda, Dale A

2013-01-01

Vaccination coverage is an important public health indicator that is measured using administrative reports and/or surveys. The measurement of vaccination coverage in low- and middle-income countries using surveys is susceptible to numerous challenges. These challenges include selection bias and information bias, which cannot be solved by increasing the sample size, and the precision of the coverage estimate, which is determined by the survey sample size and sampling method. Selection bias can result from an inaccurate sampling frame or inappropriate field procedures and, since populations likely to be missed in a vaccination coverage survey are also likely to be missed by vaccination teams, most often inflates coverage estimates. Importantly, the large multi-purpose household surveys that are often used to measure vaccination coverage have invested substantial effort to reduce selection bias. Information bias occurs when a child's vaccination status is misclassified due to mistakes on his or her vaccination record, in data transcription, in the way survey questions are presented, or in the guardian's recall of vaccination for children without a written record. There has been substantial reliance on the guardian's recall in recent surveys, and, worryingly, information bias may become more likely in the future as immunization schedules become more complex and variable. Finally, some surveys assess immunity directly using serological assays. Sero-surveys are important for assessing public health risk, but currently are unable to validate coverage estimates directly. To improve vaccination coverage estimates based on surveys, we recommend that recording tools and practices should be improved and that surveys should incorporate best practices for design, implementation, and analysis.

Selection within households in health surveys

PubMed Central

Alves, Maria Cecilia Goi Porto; Escuder, Maria Mercedes Loureiro; Claro, Rafael Moreira; da Silva, Nilza Nunes

2014-01-01

OBJECTIVE To compare the efficiency and accuracy of sampling designs including and excluding the sampling of individuals within sampled households in health surveys. METHODS From a population survey conducted in Baixada Santista Metropolitan Area, SP, Southeastern Brazil, lowlands between 2006 and 2007, 1,000 samples were drawn for each design and estimates for people aged 18 to 59 and 18 and over were calculated for each sample. In the first design, 40 census tracts, 12 households per sector, and one person per household were sampled. In the second, no sampling within the household was performed and 40 census sectors and 6 households for the 18 to 59-year old group and 5 or 6 for the 18 and over age group or more were sampled. Precision and bias of proportion estimates for 11 indicators were assessed in the two final sets of the 1000 selected samples with the two types of design. They were compared by means of relative measurements: coefficient of variation, bias/mean ratio, bias/standard error ratio, and relative mean square error. Comparison of costs contrasted basic cost per person, household cost, number of people, and households. RESULTS Bias was found to be negligible for both designs. A lower precision was found in the design including individuals sampling within households, and the costs were higher. CONCLUSIONS The design excluding individual sampling achieved higher levels of efficiency and accuracy and, accordingly, should be first choice for investigators. Sampling of household dwellers should be adopted when there are reasons related to the study subject that may lead to bias in individual responses if multiple dwellers answer the proposed questionnaire. PMID:24789641

Accounting protesting and warm glow bidding in Contingent Valuation surveys considering the management of environmental goods--an empirical case study assessing the value of protecting a Natura 2000 wetland area in Greece.

PubMed

Grammatikopoulou, Ioanna; Olsen, Søren Bøye

2013-11-30

Based on a Contingent Valuation survey aiming to reveal the willingness to pay (WTP) for conservation of a wetland area in Greece, we show how protest and warm glow motives can be taken into account when modeling WTP. In a sample of more than 300 respondents, we find that 54% of the positive bids are rooted to some extent in warm glow reasoning while 29% of the zero bids can be classified as expressions of protest rather than preferences. In previous studies, warm glow bidders are only rarely identified while protesters are typically identified and excluded from further analysis. We test for selection bias associated with simple removal of both protesters and warm glow bidders in our data. Our findings show that removal of warm glow bidders does not significantly distort WTP whereas we find strong evidence of selection bias associated with removal of protesters. We show how to correct for such selection bias by using a sample selection model. In our empirical sample, using the typical approach of removing protesters from the analysis, the value of protecting the wetland is significantly underestimated by as much as 46% unless correcting for selection bias. Copyright © 2013 Elsevier Ltd. All rights reserved.

An Analysis of Selectivity Bias in the Medicare AAPCC

PubMed Central

Dowd, Bryan; Feldman, Roger; Moscovice, Ira; Wisner, Catherine; Bland, Pat; Finch, Mike

1996-01-01

Using econometric models of endogenous sample selection, we examine possible payment bias to Medicare Tax Equity and Fiscal Responsibility Act of 1982 (TEFRA)-risk health maintenance organizations (HMOs) in the Twin Cities in 1988. We do not find statistically significant evidence of favorable HMO selection. In fact, the sign of the selection term indicates adverse selection into HMOs. This finding is interesting, in view of the fact that three of the five risk HMOs in the study have since converted to non-risk contracts. PMID:10158735

[Study on correction of data bias caused by different missing mechanisms in survey of medical expenditure among students enrolling in Urban Resident Basic Medical Insurance].

PubMed

Zhang, Haixia; Zhao, Junkang; Gu, Caijiao; Cui, Yan; Rong, Huiying; Meng, Fanlong; Wang, Tong

2015-05-01

The study of the medical expenditure and its influencing factors among the students enrolling in Urban Resident Basic Medical Insurance (URBMI) in Taiyuan indicated that non response bias and selection bias coexist in dependent variable of the survey data. Unlike previous studies only focused on one missing mechanism, a two-stage method to deal with two missing mechanisms simultaneously was suggested in this study, combining multiple imputation with sample selection model. A total of 1 190 questionnaires were returned by the students (or their parents) selected in child care settings, schools and universities in Taiyuan by stratified cluster random sampling in 2012. In the returned questionnaires, 2.52% existed not missing at random (NMAR) of dependent variable and 7.14% existed missing at random (MAR) of dependent variable. First, multiple imputation was conducted for MAR by using completed data, then sample selection model was used to correct NMAR in multiple imputation, and a multi influencing factor analysis model was established. Based on 1 000 times resampling, the best scheme of filling the random missing values is the predictive mean matching (PMM) method under the missing proportion. With this optimal scheme, a two stage survey was conducted. Finally, it was found that the influencing factors on annual medical expenditure among the students enrolling in URBMI in Taiyuan included population group, annual household gross income, affordability of medical insurance expenditure, chronic disease, seeking medical care in hospital, seeking medical care in community health center or private clinic, hospitalization, hospitalization canceled due to certain reason, self medication and acceptable proportion of self-paid medical expenditure. The two-stage method combining multiple imputation with sample selection model can deal with non response bias and selection bias effectively in dependent variable of the survey data.

Method and apparatus for differential spectroscopic atomic-imaging using scanning tunneling microscopy

DOEpatents

Kazmerski, Lawrence L.

1990-01-01

A Method and apparatus for differential spectroscopic atomic-imaging is disclosed for spatial resolution and imaging for display not only individual atoms on a sample surface, but also bonding and the specific atomic species in such bond. The apparatus includes a scanning tunneling microscope (STM) that is modified to include photon biasing, preferably a tuneable laser, modulating electronic surface biasing for the sample, and temperature biasing, preferably a vibration-free refrigerated sample mounting stage. Computer control and data processing and visual display components are also included. The method includes modulating the electronic bias voltage with and without selected photon wavelengths and frequency biasing under a stabilizing (usually cold) bias temperature to detect bonding and specific atomic species in the bonds as the STM rasters the sample. This data is processed along with atomic spatial topography data obtained from the STM raster scan to create a real-time visual image of the atoms on the sample surface.

State-dependent biasing method for importance sampling in the weighted stochastic simulation algorithm.

PubMed

Roh, Min K; Gillespie, Dan T; Petzold, Linda R

2010-11-07

The weighted stochastic simulation algorithm (wSSA) was developed by Kuwahara and Mura [J. Chem. Phys. 129, 165101 (2008)] to efficiently estimate the probabilities of rare events in discrete stochastic systems. The wSSA uses importance sampling to enhance the statistical accuracy in the estimation of the probability of the rare event. The original algorithm biases the reaction selection step with a fixed importance sampling parameter. In this paper, we introduce a novel method where the biasing parameter is state-dependent. The new method features improved accuracy, efficiency, and robustness.

Do men and women report their sexual partnerships differently? Evidence from Kisumu, Kenya.

PubMed

Clark, Shelley; Kabiru, Caroline; Zulu, Eliya

2011-12-01

It is generally believed that men and women misreport their sexual behaviors, which undermines the ability of researchers, program designers and health care providers to assess whether these behaviors compromise individuals' sexual and reproductive health. Data on 1,299 recent sexual partnerships were collected in a 2007 survey of 1,275 men and women aged 18-24 and living in Kisumu, Kenya. Chi-square and t tests were used to examine how sample selection bias and selective partnership reporting may result in gender differences in reported sexual behaviors. Correlation coefficients and kappa statistics were calculated in further analysis of a sample of 280 matched marital and nonmarital couples to assess agreement on reported behaviors. Even after adjustment for sample selection bias, men reported twice as many partnerships as women (0.5 vs. 0.2), as well as more casual partnerships. However, when selective reporting was controlled for, aggregate gender differences in sexual behaviors almost entirely disappeared. In the matched-couples sample, men and women exhibited moderate to substantial levels of agreement for most relationship characteristics and behaviors, including type of relationship, frequency of sex and condom use. Finally, men and women tended to agree about whether men had other nonmarital partners, but disagreed about women's nonmarital partners. Both sample selection bias and selective partnership reporting can influence the level of agreement between men's and women's reports of sexual behaviors. Although men report more casual partners than do women, accounts of sexual behavior within reported relationships are generally reliable.

Selection Bias in Students' Evaluation of Teaching: Causes of Student Absenteeism and Its Consequences for Course Ratings and Rankings

ERIC Educational Resources Information Center

Wolbring, Tobias; Treischl, Edgar

2016-01-01

Systematic sampling error due to self-selection is a common topic in methodological research and a key challenge for every empirical study. Since selection bias is often not sufficiently considered as a potential flaw in research on and evaluations in higher education, the aim of this paper is to raise awareness for the topic using the case of…

Survey Response-Related Biases in Contingent Valuation: Concepts, Remedies, and Empirical Application to Valuing Aquatic Plant Management

Treesearch

Mark L. Messonnier; John C. Bergstrom; Chrisopher M. Cornwell; R. Jeff Teasley; H. Ken Cordell

2000-01-01

Simple nonresponse and selection biases that may occur in survey research such as contingent valuation applications are discussed and tested. Correction mechanisms for these types of biases are demonstrated. Results indicate the importance of testing and correcting for unit and item nonresponse bias in contingent valuation survey data. When sample nonresponse and...

Are most samples of animals systematically biased? Consistent individual trait differences bias samples despite random sampling.

PubMed

Biro, Peter A

2013-02-01

Sampling animals from the wild for study is something nearly every biologist has done, but despite our best efforts to obtain random samples of animals, 'hidden' trait biases may still exist. For example, consistent behavioral traits can affect trappability/catchability, independent of obvious factors such as size and gender, and these traits are often correlated with other repeatable physiological and/or life history traits. If so, systematic sampling bias may exist for any of these traits. The extent to which this is a problem, of course, depends on the magnitude of bias, which is presently unknown because the underlying trait distributions in populations are usually unknown, or unknowable. Indeed, our present knowledge about sampling bias comes from samples (not complete population censuses), which can possess bias to begin with. I had the unique opportunity to create naturalized populations of fish by seeding each of four small fishless lakes with equal densities of slow-, intermediate-, and fast-growing fish. Using sampling methods that are not size-selective, I observed that fast-growing fish were up to two-times more likely to be sampled than slower-growing fish. This indicates substantial and systematic bias with respect to an important life history trait (growth rate). If correlations between behavioral, physiological and life-history traits are as widespread as the literature suggests, then many animal samples may be systematically biased with respect to these traits (e.g., when collecting animals for laboratory use), and affect our inferences about population structure and abundance. I conclude with a discussion on ways to minimize sampling bias for particular physiological/behavioral/life-history types within animal populations.

Selection bias and patterns of confounding in cohort studies: the case of the NINFEA web-based birth cohort.

PubMed

Pizzi, Costanza; De Stavola, Bianca L; Pearce, Neil; Lazzarato, Fulvio; Ghiotti, Paola; Merletti, Franco; Richiardi, Lorenzo

2012-11-01

Several studies have examined the effects of sample selection on the exposure-outcome association estimates in cohort studies, but the reasons why this selection may induce bias have not been fully explored. To investigate how sample selection of the web-based NINFEA birth cohort may change the confounding patterns present in the source population. The characteristics of the NINFEA participants (n=1105) were compared with those of the wider source population-the Piedmont Birth Registry (PBR)-(n=36 092), and the association of two exposures (parity and educational level) with two outcomes (low birth weight and birth by caesarean section), while controlling for other risk factors, was studied. Specifically the associations among measured risk factors within each dataset were examined and the exposure-outcome estimates compared in terms of relative ORs. The associations of educational level with the other risk factors (alcohol consumption, folic acid intake, maternal age, pregnancy weight gain, previous miscarriages) partly differed between PBR and NINFEA. This was not observed for parity. Overall, the exposure-outcome estimates derived from NINFEA only differed moderately from those obtained in PBR, with relative ORs ranging between 0.74 and 1.03. Sample selection in cohort studies may alter the confounding patterns originally present in the general population. However, this does not necessarily introduce selection bias in the exposure-outcome estimates, as sample selection may reduce some of the residual confounding present in the general population.

Galaxy And Mass Assembly (GAMA): Improved emission lines measurements in four representative samples at 0.07

NASA Astrophysics Data System (ADS)

Rodrigues, M.; Foster, C.; Taylor, E. N.; Wright, A. H.; Hopkins, A. M.; Baldry, I.; Brough, S.; Bland-Hawthorn, J.; Cluver, M. E.; Lara-López, M. A.; Liske, J.; López-Sánchez, Á. R.; Pimbblet, K. A.

2016-05-01

This paper presents a new catalog of emission lines based on the GAMA II data for galaxies between 0.07 9.4 at z ~ 0.1 and log M∗> 10.6 at z ~ 0.30. We have developed a dedicated code called MARVIN that automates the main steps of the data analysis, but imposes visual individual quality control of each measurement. We use this catalog to investigate how the sample selection influences the shape of the stellar mass - metallicity relation. We find that commonly used selection criteria on line detections and by AGN rejection could affect the shape and dispersion of the high-mass end of the M - Z relation. For log M∗> 10.6, common selection criteria reject about 65% of the emission-line galaxies. We also find that the relation does not evolve significantly from z = 0.07 to z = 0.34 in the range of stellar mass for which the samples are representative (log M∗> 10.6). For lower stellar masses (log M∗< 10.2) we are able to show that the observed 0.15 dex metallicity decrease in the same redshift range is a consequence of a color bias arising from selecting targets in the r-band. We highlight that this color selection bias affects all samples selected in r-band (e.g., GAMA and SDSS), even those drawn from volume-limited samples. Previously reported evolution of the M - Z relation at various redshifts may need to be revised to evaluate the effect of this selection bias.

Measuring Coverage in MNCH: Design, Implementation, and Interpretation Challenges Associated with Tracking Vaccination Coverage Using Household Surveys

PubMed Central

Cutts, Felicity T.; Izurieta, Hector S.; Rhoda, Dale A.

2013-01-01

Vaccination coverage is an important public health indicator that is measured using administrative reports and/or surveys. The measurement of vaccination coverage in low- and middle-income countries using surveys is susceptible to numerous challenges. These challenges include selection bias and information bias, which cannot be solved by increasing the sample size, and the precision of the coverage estimate, which is determined by the survey sample size and sampling method. Selection bias can result from an inaccurate sampling frame or inappropriate field procedures and, since populations likely to be missed in a vaccination coverage survey are also likely to be missed by vaccination teams, most often inflates coverage estimates. Importantly, the large multi-purpose household surveys that are often used to measure vaccination coverage have invested substantial effort to reduce selection bias. Information bias occurs when a child's vaccination status is misclassified due to mistakes on his or her vaccination record, in data transcription, in the way survey questions are presented, or in the guardian's recall of vaccination for children without a written record. There has been substantial reliance on the guardian's recall in recent surveys, and, worryingly, information bias may become more likely in the future as immunization schedules become more complex and variable. Finally, some surveys assess immunity directly using serological assays. Sero-surveys are important for assessing public health risk, but currently are unable to validate coverage estimates directly. To improve vaccination coverage estimates based on surveys, we recommend that recording tools and practices should be improved and that surveys should incorporate best practices for design, implementation, and analysis. PMID:23667334

The Evaluation of Bias of the Weighted Random Effects Model Estimators. Research Report. ETS RR-11-13

ERIC Educational Resources Information Center

Jia, Yue; Stokes, Lynne; Harris, Ian; Wang, Yan

2011-01-01

Estimation of parameters of random effects models from samples collected via complex multistage designs is considered. One way to reduce estimation bias due to unequal probabilities of selection is to incorporate sampling weights. Many researchers have been proposed various weighting methods (Korn, & Graubard, 2003; Pfeffermann, Skinner,…

Quantifying recent erosion and sediment delivery using probability sampling: A case study

Treesearch

Jack Lewis

2002-01-01

Abstract - Estimates of erosion and sediment delivery have often relied on measurements from locations that were selected to be representative of particular terrain types. Such judgement samples are likely to overestimate or underestimate the mean of the quantity of interest. Probability sampling can eliminate the bias due to sample selection, and it permits the...

Associations among Selective Attention, Memory Bias, Cognitive Errors and Symptoms of Anxiety in Youth

ERIC Educational Resources Information Center

Watts, Sarah E.; Weems, Carl F.

2006-01-01

The purpose of this study was to examine the linkages among selective attention, memory bias, cognitive errors, and anxiety problems by testing a model of the interrelations among these cognitive variables and childhood anxiety disorder symptoms. A community sample of 81 youth (38 females and 43 males) aged 9-17 years and their parents completed…

The Effects of Sample Selection Bias on Racial Differences in Child Abuse Reporting.

ERIC Educational Resources Information Center

Ards, Sheila; Chung, Chanjin; Myers, Samuel L., Jr.

1998-01-01

Data from the National Incidence Study (NIS) of Child Abuse and Neglect suggest no racial difference in child maltreatment, although there are more black children within the child welfare population. This study found selection bias in the NIS design caused by the exclusion of family, friends, and neighbors that resulted in differences in NIS cases…

An experimental investigation of recruitment bias in eating pathology research.

PubMed

Moss, Erin L; von Ranson, Kristin M

2006-04-01

Previous, uncontrolled research has suggested a bias may exist in recruiting participants for eating disorder research. Recruitment biases may affect sample representativeness and generalizability of findings. This experiment investigated whether revealing that a study's topic was related to eating disorders created a self-selection bias. Young women at a university responded to advertisements containing contrasting information about the nature of a single study. We recruited one group by advertising the study under the title "Disordered Eating in Young Women" (n = 251) and another group using the title "Consumer Preferences" (n = 259). Results indicated similar levels of eating pathology in both groups, so the different recruitment techniques did not engender self-selection. However, the consumer preferences group scored higher in self-reported social desirability. The level of information conveyed in study advertising does not impact reporting of eating disturbances among nonclinical samples, although there is evidence social desirability might. 2006 by Wiley Periodicals, Inc.

A morphological basis for orientation tuning in primary visual cortex.

PubMed

Mooser, François; Bosking, William H; Fitzpatrick, David

2004-08-01

Feedforward connections are thought to be important in the generation of orientation-selective responses in visual cortex by establishing a bias in the sampling of information from regions of visual space that lie along a neuron's axis of preferred orientation. It remains unclear, however, which structural elements-dendrites or axons-are ultimately responsible for conveying this sampling bias. To explore this question, we have examined the spatial arrangement of feedforward axonal connections that link non-oriented neurons in layer 4 and orientation-selective neurons in layer 2/3 of visual cortex in the tree shrew. Target sites of labeled boutons in layer 2/3 resulting from focal injections of biocytin in layer 4 show an orientation-specific axial bias that is sufficient to confer orientation tuning to layer 2/3 neurons. We conclude that the anisotropic arrangement of axon terminals is the principal source of the orientation bias contributed by feedforward connections.

Do Men and Women Report Their Sexual Partnerships Differently? Evidence from Kisumu, Kenya

PubMed Central

Clark, Shelley; Kabiru, Caroline; Zulu, Eliya

2012-01-01

CONTEXT It is generally believed that men and women misreport their sexual behaviors, which undermines the ability of researchers, program designers and health care providers to assess whether these behaviors compromise individuals’ sexual and reproductive health. METHODS Data on 1,299 recent sexual partnerships were collected in a 2007 survey of 1,275 men and women aged 18–24 and living in Kisumu, Kenya. Chi-square and t tests were used to examine how sample selection bias and selective partnership reporting may result in gender differences in reported sexual behaviors. Correlation coefficients and kappa statistics were calculated in further analysis of a sample of 280 matched marital and nonmarital couples to assess agreement on reported behaviors. RESULTS Even after adjustment for sample selection bias, men reported twice as many partnerships as women (0.5 vs. 0.2), as well as more casual partnerships. However, when selective reporting was controlled for, aggregate gender differences in sexual behaviors almost entirely disappeared. In the matched-couples sample, men and women exhibited moderate to substantial levels of agreement for most relationship characteristics and behaviors, including type of relationship, frequency of sex and condom use. Finally, men and women tended to agree about whether men had other nonmarital partners, but disagreed about women’s nonmarital partners. CONCLUSIONS Both sample selection bias and selective partnership reporting can influence the level of agreement between men’s and women’s reports of sexual behaviors. Although men report more casual partners than do women, accounts of sexual behavior within reported relationships are generally reliable. PMID:22227625

Effects of Sample Selection on Estimates of Economic Impacts of Outdoor Recreation

Treesearch

Donald B.K. English

1997-01-01

Estimates of the economic impacts of recreation often come from spending data provided by a self-selected subset of a random sample of site visitors. The subset is frequently less than half the onsite sample. Biased vectors of per trip spending and impact estimates can result if self-selection is related to spending pattctns, and proper corrective procedures arc not...

HMO marketing and selection bias: are TEFRA HMOs skimming?

PubMed

Lichtenstein, R; Thomas, J W; Watkins, B; Puto, C; Lepkowski, J; Adams-Watson, J; Simone, B; Vest, D

1992-04-01

The research evidence indicates that health maintenance organizations (HMOs) participating in the Tax Equity and Fiscal Responsibility Act of 1982 (TEFRA) At-Risk Program tend to experience favorable selection. Although favorable selection might result from patient decisions, a common conjecture is that it can be induced by HMOs through their marketing activities. The purpose of this study is to examine the relationship between HMO marketing strategies and selection bias in TEFRA At-Risk HMOs. A purposive sample of 22 HMOs that were actively marketing their TEFRA programs was selected and data on organizational characteristics, market area characteristics, and HMO marketing decisions were collected. To measure selection bias in these HMOs, the functional health status of approximately 300 enrollees in each HMO was compared to that of 300 non-enrolling beneficiaries in the same area. Three dependent variables, reflecting selection bias at the mean, the low health tail, and the high health tail of the health status distribution were created. Weighted least squares regressions were then used to identify relationships between marketing elements and selection bias. Subject to the statistical limitations of the study, our conclusion is that it is doubtful that HMO marketing decisions are responsible for the prevalence of favorable selection in HMO enrollment. It also appears unlikely that HMOs were differentially targeting healthy and unhealthy segments of the Medicare market.

Rethinking the assessment of risk of bias due to selective reporting: a cross-sectional study.

PubMed

Page, Matthew J; Higgins, Julian P T

2016-07-08

Selective reporting is included as a core domain of Cochrane's tool for assessing risk of bias in randomised trials. There has been no evaluation of review authors' use of this domain. We aimed to evaluate assessments of selective reporting in a cross-section of Cochrane reviews and to outline areas for improvement. We obtained data on selective reporting judgements for 8434 studies included in 586 Cochrane reviews published from issue 1-8, 2015. One author classified the reasons for judgements of high risk of selective reporting bias. We randomly selected 100 reviews with at least one trial rated at high risk of outcome non-reporting bias (non-/partial reporting of an outcome on the basis of its results). One author recorded whether the authors of these reviews incorporated the selective reporting assessment when interpreting results. Of the 8434 studies, 1055 (13 %) were rated at high risk of bias on the selective reporting domain. The most common reason was concern about outcome non-reporting bias. Few studies were rated at high risk because of concerns about bias in selection of the reported result (e.g. reporting of only a subset of measurements, analysis methods or subsets of the data that were pre-specified). Review authors often specified in the risk of bias tables the study outcomes that were not reported (84 % of studies) but less frequently specified the outcomes that were partially reported (61 % of studies). At least one study was rated at high risk of outcome non-reporting bias in 31 % of reviews. In the random sample of these reviews, only 30 % incorporated this information when interpreting results, by acknowledging that the synthesis of an outcome was missing data that were not/partially reported. Our audit of user practice in Cochrane reviews suggests that the assessment of selective reporting in the current risk of bias tool does not work well. It is not always clear which outcomes were selectively reported or what the corresponding risk of bias is in the synthesis with missing outcome data. New tools that will make it easier for reviewers to convey this information are being developed.

Adaptively biased sequential importance sampling for rare events in reaction networks with comparison to exact solutions from finite buffer dCME method

PubMed Central

Cao, Youfang; Liang, Jie

2013-01-01

Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. By adopting a look-ahead strategy and by enumerating short paths from the current state, we estimate the reaction-specific and state-specific forward and backward moving probabilities of the system, which are then used to bias reaction selections. The ABSIS algorithm can automatically detect barrier-crossing regions, and can adjust bias adaptively at different steps of the sampling process, with bias determined by the outcome of exhaustively generated short paths. In addition, there are only two bias parameters to be determined, regardless of the number of the reactions and the complexity of the network. We have applied the ABSIS method to four biochemical networks: the birth-death process, the reversible isomerization, the bistable Schlögl model, and the enzymatic futile cycle model. For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively by comparing simulation results with true answers. Overall, ABSIS can accurately and efficiently estimate rare event probabilities for all examples, often with smaller variance than other importance sampling algorithms. The ABSIS method is general and can be applied to study rare events of other stochastic networks with complex probability landscape. PMID:23862966

Adaptively biased sequential importance sampling for rare events in reaction networks with comparison to exact solutions from finite buffer dCME method

NASA Astrophysics Data System (ADS)

Cao, Youfang; Liang, Jie

2013-07-01

Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. By adopting a look-ahead strategy and by enumerating short paths from the current state, we estimate the reaction-specific and state-specific forward and backward moving probabilities of the system, which are then used to bias reaction selections. The ABSIS algorithm can automatically detect barrier-crossing regions, and can adjust bias adaptively at different steps of the sampling process, with bias determined by the outcome of exhaustively generated short paths. In addition, there are only two bias parameters to be determined, regardless of the number of the reactions and the complexity of the network. We have applied the ABSIS method to four biochemical networks: the birth-death process, the reversible isomerization, the bistable Schlögl model, and the enzymatic futile cycle model. For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively by comparing simulation results with true answers. Overall, ABSIS can accurately and efficiently estimate rare event probabilities for all examples, often with smaller variance than other importance sampling algorithms. The ABSIS method is general and can be applied to study rare events of other stochastic networks with complex probability landscape.

Adaptively biased sequential importance sampling for rare events in reaction networks with comparison to exact solutions from finite buffer dCME method.

PubMed

Cao, Youfang; Liang, Jie

2013-07-14

Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. By adopting a look-ahead strategy and by enumerating short paths from the current state, we estimate the reaction-specific and state-specific forward and backward moving probabilities of the system, which are then used to bias reaction selections. The ABSIS algorithm can automatically detect barrier-crossing regions, and can adjust bias adaptively at different steps of the sampling process, with bias determined by the outcome of exhaustively generated short paths. In addition, there are only two bias parameters to be determined, regardless of the number of the reactions and the complexity of the network. We have applied the ABSIS method to four biochemical networks: the birth-death process, the reversible isomerization, the bistable Schlögl model, and the enzymatic futile cycle model. For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively by comparing simulation results with true answers. Overall, ABSIS can accurately and efficiently estimate rare event probabilities for all examples, often with smaller variance than other importance sampling algorithms. The ABSIS method is general and can be applied to study rare events of other stochastic networks with complex probability landscape.

Selection biases in empirical p(z) methods for weak lensing

DOE PAGES

Gruen, D.; Brimioulle, F.

2017-02-23

To measure the mass of foreground objects with weak gravitational lensing, one needs to estimate the redshift distribution of lensed background sources. This is commonly done in an empirical fashion, i.e. with a reference sample of galaxies of known spectroscopic redshift, matched to the source population. In this paper, we develop a simple decision tree framework that, under the ideal conditions of a large, purely magnitude-limited reference sample, allows an unbiased recovery of the source redshift probability density function p(z), as a function of magnitude and colour. We use this framework to quantify biases in empirically estimated p(z) caused bymore » selection effects present in realistic reference and weak lensing source catalogues, namely (1) complex selection of reference objects by the targeting strategy and success rate of existing spectroscopic surveys and (2) selection of background sources by the success of object detection and shape measurement at low signal to noise. For intermediate-to-high redshift clusters, and for depths and filter combinations appropriate for ongoing lensing surveys, we find that (1) spectroscopic selection can cause biases above the 10 per cent level, which can be reduced to ≈5 per cent by optimal lensing weighting, while (2) selection effects in the shape catalogue bias mass estimates at or below the 2 per cent level. Finally, this illustrates the importance of completeness of the reference catalogues for empirical redshift estimation.« less

Fitting distributions to microbial contamination data collected with an unequal probability sampling design.

PubMed

Williams, M S; Ebel, E D; Cao, Y

2013-01-01

The fitting of statistical distributions to microbial sampling data is a common application in quantitative microbiology and risk assessment applications. An underlying assumption of most fitting techniques is that data are collected with simple random sampling, which is often times not the case. This study develops a weighted maximum likelihood estimation framework that is appropriate for microbiological samples that are collected with unequal probabilities of selection. A weighted maximum likelihood estimation framework is proposed for microbiological samples that are collected with unequal probabilities of selection. Two examples, based on the collection of food samples during processing, are provided to demonstrate the method and highlight the magnitude of biases in the maximum likelihood estimator when data are inappropriately treated as a simple random sample. Failure to properly weight samples to account for how data are collected can introduce substantial biases into inferences drawn from the data. The proposed methodology will reduce or eliminate an important source of bias in inferences drawn from the analysis of microbial data. This will also make comparisons between studies and the combination of results from different studies more reliable, which is important for risk assessment applications. © 2012 No claim to US Government works.

Use of Bayes theorem to correct size-specific sampling bias in growth data.

PubMed

Troynikov, V S

1999-03-01

The bayesian decomposition of posterior distribution was used to develop a likelihood function to correct bias in the estimates of population parameters from data collected randomly with size-specific selectivity. Positive distributions with time as a parameter were used for parametrization of growth data. Numerical illustrations are provided. The alternative applications of the likelihood to estimate selectivity parameters are discussed.

Estimating the efficacy of Alcoholics Anonymous without self-selection bias: An instrumental variables re-analysis of randomized clinical trials

PubMed Central

Humphreys, Keith; Blodgett, Janet C.; Wagner, Todd H.

2014-01-01

Background Observational studies of Alcoholics Anonymous’ (AA) effectiveness are vulnerable to self-selection bias because individuals choose whether or not to attend AA. The present study therefore employed an innovative statistical technique to derive a selection bias-free estimate of AA’s impact. Methods Six datasets from 5 National Institutes of Health-funded randomized trials (one with two independent parallel arms) of AA facilitation interventions were analyzed using instrumental variables models. Alcohol dependent individuals in one of the datasets (n = 774) were analyzed separately from the rest of sample (n = 1582 individuals pooled from 5 datasets) because of heterogeneity in sample parameters. Randomization itself was used as the instrumental variable. Results Randomization was a good instrument in both samples, effectively predicting increased AA attendance that could not be attributed to self-selection. In five of the six data sets, which were pooled for analysis, increased AA attendance that was attributable to randomization (i.e., free of self-selection bias) was effective at increasing days of abstinence at 3-month (B = .38, p = .001) and 15-month (B = 0.42, p = .04) follow-up. However, in the remaining dataset, in which pre-existing AA attendance was much higher, further increases in AA involvement caused by the randomly assigned facilitation intervention did not affect drinking outcome. Conclusions For most individuals seeking help for alcohol problems, increasing AA attendance leads to short and long term decreases in alcohol consumption that cannot be attributed to self-selection. However, for populations with high pre-existing AA involvement, further increases in AA attendance may have little impact. PMID:25421504

Estimating the efficacy of Alcoholics Anonymous without self-selection bias: an instrumental variables re-analysis of randomized clinical trials.

PubMed

Humphreys, Keith; Blodgett, Janet C; Wagner, Todd H

2014-11-01

Observational studies of Alcoholics Anonymous' (AA) effectiveness are vulnerable to self-selection bias because individuals choose whether or not to attend AA. The present study, therefore, employed an innovative statistical technique to derive a selection bias-free estimate of AA's impact. Six data sets from 5 National Institutes of Health-funded randomized trials (1 with 2 independent parallel arms) of AA facilitation interventions were analyzed using instrumental variables models. Alcohol-dependent individuals in one of the data sets (n = 774) were analyzed separately from the rest of sample (n = 1,582 individuals pooled from 5 data sets) because of heterogeneity in sample parameters. Randomization itself was used as the instrumental variable. Randomization was a good instrument in both samples, effectively predicting increased AA attendance that could not be attributed to self-selection. In 5 of the 6 data sets, which were pooled for analysis, increased AA attendance that was attributable to randomization (i.e., free of self-selection bias) was effective at increasing days of abstinence at 3-month (B = 0.38, p = 0.001) and 15-month (B = 0.42, p = 0.04) follow-up. However, in the remaining data set, in which preexisting AA attendance was much higher, further increases in AA involvement caused by the randomly assigned facilitation intervention did not affect drinking outcome. For most individuals seeking help for alcohol problems, increasing AA attendance leads to short- and long-term decreases in alcohol consumption that cannot be attributed to self-selection. However, for populations with high preexisting AA involvement, further increases in AA attendance may have little impact. Copyright © 2014 by the Research Society on Alcoholism.

Spitzer Observations of GRB Hosts: A Legacy Approach

NASA Astrophysics Data System (ADS)

Perley, Daniel; Tanvir, Nial; Hjorth, Jens; Berger, Edo; Laskar, Tanmoy; Michalowski, Michal; Chary, Ranga-Ram; Fynbo, Johan; Levan, Andrew

2012-09-01

The host galaxies of long-duration GRBs are drawn from uniquely broad range of luminosities and redshifts. Thus they offer the possibility of studying the evolution of star-forming galaxies without the limitations of other luminosity-selected samples, which typically are increasingly biased towards the most massive systems at higher redshift. However, reaping the full benefits of this potential requires careful attention to the selection biases affecting host identification. To this end, we propose observations of a Legacy sample of 70 GRB host galaxies (an additional 70 have already been observed by Spitzer), in order to constrain the mass and luminosity function in GRB-selected galaxies at high redshift, including its dependence on redshift and on properties of the afterglow. Crucially, and unlike previous Spitzer surveys, this sample is carefully designed to be uniform and free of optical selection biases that have caused previous surveys to systematically under-represent the role of luminous, massive hosts. We also propose to extend to larger, more powerfully constraining samples the study of two science areas where Spitzer observations have recently shown spectacular success: the hosts of dust-obscured GRBs (which promise to further our understanding of the connection between GRBs and star-formation in the most luminous galaxies), and the evolution of the mass-metallicity relation at z>2 (for which GRB host observations provide particularly powerful constraints on high-z chemical evolution).

Observational selection biases in time-delay strong lensing and their impact on cosmography

NASA Astrophysics Data System (ADS)

Collett, Thomas E.; Cunnington, Steven D.

2016-11-01

Inferring cosmological parameters from time-delay strong lenses requires a significant investment of telescope time; it is therefore tempting to focus on the systems with the brightest sources, the highest image multiplicities and the widest image separations. We investigate if this selection bias can influence the properties of the lenses studied and the cosmological parameters inferred. Using an ellipsoidal power-law deflector population, we build a sample of double- and quadruple-image systems. Assuming reasonable thresholds on image separation and flux, based on current lens monitoring campaigns, we find that the typical density profile slopes of monitorable lenses are significantly shallower than the input ensemble. From a sample of quads, we find that this selection function can introduce a 3.5 per cent bias on the inferred time-delay distances if the properties of the input ensemble are (incorrectly) used as priors on the lens model. This bias remains at the 2.4 per cent level when high-resolution imaging of the quasar host is used to precisely infer the properties of individual lenses. We also investigate if the lines of sight for monitorable strong lenses are biased. The expectation value for the line-of-sight convergence is increased by 0.009 (0.004) for quads (doubles) implying a 0.9 per cent (0.4 per cent) bias on H0. We therefore conclude that whilst the properties of typical quasar lenses and their lines of sight do deviate from the global population, the total magnitude of this effect is likely to be a subdominant effect for current analyses, but has the potential to be a major systematic for samples of ˜25 or more lenses.

HICOSMO - cosmology with a complete sample of galaxy clusters - I. Data analysis, sample selection and luminosity-mass scaling relation

NASA Astrophysics Data System (ADS)

Schellenberger, G.; Reiprich, T. H.

2017-08-01

The X-ray regime, where the most massive visible component of galaxy clusters, the intracluster medium, is visible, offers directly measured quantities, like the luminosity, and derived quantities, like the total mass, to characterize these objects. The aim of this project is to analyse a complete sample of galaxy clusters in detail and constrain cosmological parameters, like the matter density, Ωm, or the amplitude of initial density fluctuations, σ8. The purely X-ray flux-limited sample (HIFLUGCS) consists of the 64 X-ray brightest galaxy clusters, which are excellent targets to study the systematic effects, that can bias results. We analysed in total 196 Chandra observations of the 64 HIFLUGCS clusters, with a total exposure time of 7.7 Ms. Here, we present our data analysis procedure (including an automated substructure detection and an energy band optimization for surface brightness profile analysis) that gives individually determined, robust total mass estimates. These masses are tested against dynamical and Planck Sunyaev-Zeldovich (SZ) derived masses of the same clusters, where good overall agreement is found with the dynamical masses. The Planck SZ masses seem to show a mass-dependent bias to our hydrostatic masses; possible biases in this mass-mass comparison are discussed including the Planck selection function. Furthermore, we show the results for the (0.1-2.4) keV luminosity versus mass scaling relation. The overall slope of the sample (1.34) is in agreement with expectations and values from literature. Splitting the sample into galaxy groups and clusters reveals, even after a selection bias correction, that galaxy groups exhibit a significantly steeper slope (1.88) compared to clusters (1.06).

The quasar luminosity function from a variability-selected sample

NASA Astrophysics Data System (ADS)

Hawkins, M. R. S.; Veron, P.

1993-01-01

A sample of quasars is selected from a 10-yr sequence of 30 UK Schmidt plates. Luminosity functions are derived in several redshift intervals, which in each case show a featureless power-law rise towards low luminosities. There is no sign of the 'break' found in the recent UVX sample of Boyle et al. It is suggested that reasons for the disagreement are connected with biases in the selection of the UVX sample. The question of the nature of quasar evolution appears to be still unresolved.

The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models.

PubMed

Syfert, Mindy M; Smith, Matthew J; Coomes, David A

2013-01-01

Species distribution models (SDMs) trained on presence-only data are frequently used in ecological research and conservation planning. However, users of SDM software are faced with a variety of options, and it is not always obvious how selecting one option over another will affect model performance. Working with MaxEnt software and with tree fern presence data from New Zealand, we assessed whether (a) choosing to correct for geographical sampling bias and (b) using complex environmental response curves have strong effects on goodness of fit. SDMs were trained on tree fern data, obtained from an online biodiversity data portal, with two sources that differed in size and geographical sampling bias: a small, widely-distributed set of herbarium specimens and a large, spatially clustered set of ecological survey records. We attempted to correct for geographical sampling bias by incorporating sampling bias grids in the SDMs, created from all georeferenced vascular plants in the datasets, and explored model complexity issues by fitting a wide variety of environmental response curves (known as "feature types" in MaxEnt). In each case, goodness of fit was assessed by comparing predicted range maps with tree fern presences and absences using an independent national dataset to validate the SDMs. We found that correcting for geographical sampling bias led to major improvements in goodness of fit, but did not entirely resolve the problem: predictions made with clustered ecological data were inferior to those made with the herbarium dataset, even after sampling bias correction. We also found that the choice of feature type had negligible effects on predictive performance, indicating that simple feature types may be sufficient once sampling bias is accounted for. Our study emphasizes the importance of reducing geographical sampling bias, where possible, in datasets used to train SDMs, and the effectiveness and essentialness of sampling bias correction within MaxEnt.

Is First-Order Vector Autoregressive Model Optimal for fMRI Data?

PubMed

Ting, Chee-Ming; Seghouane, Abd-Krim; Khalid, Muhammad Usman; Salleh, Sh-Hussain

2015-09-01

We consider the problem of selecting the optimal orders of vector autoregressive (VAR) models for fMRI data. Many previous studies used model order of one and ignored that it may vary considerably across data sets depending on different data dimensions, subjects, tasks, and experimental designs. In addition, the classical information criteria (IC) used (e.g., the Akaike IC (AIC)) are biased and inappropriate for the high-dimensional fMRI data typically with a small sample size. We examine the mixed results on the optimal VAR orders for fMRI, especially the validity of the order-one hypothesis, by a comprehensive evaluation using different model selection criteria over three typical data types--a resting state, an event-related design, and a block design data set--with varying time series dimensions obtained from distinct functional brain networks. We use a more balanced criterion, Kullback's IC (KIC) based on Kullback's symmetric divergence combining two directed divergences. We also consider the bias-corrected versions (AICc and KICc) to improve VAR model selection in small samples. Simulation results show better small-sample selection performance of the proposed criteria over the classical ones. Both bias-corrected ICs provide more accurate and consistent model order choices than their biased counterparts, which suffer from overfitting, with KICc performing the best. Results on real data show that orders greater than one were selected by all criteria across all data sets for the small to moderate dimensions, particularly from small, specific networks such as the resting-state default mode network and the task-related motor networks, whereas low orders close to one but not necessarily one were chosen for the large dimensions of full-brain networks.

Occupational noise exposure and age correction: the problem of selection bias.

PubMed

Dobie, Robert A

2009-12-01

Selection bias often invalidates conclusions about populations based on clinical convenience samples. A recent paper in this journal makes two surprising assertions about noise-induced permanent threshold shift (NIPTS): first, that there is more NIPTS at 2 kHz than at higher frequencies; second, that NIPTS declines with advancing age. Neither assertion can be supported with the data presented, which were obtained from a clinical sample; both are consistent with the hypothesis that people who choose to attend an audiology clinic have worse hearing, especially at 2 kHz, than people of the same age and gender who choose not to attend.

The Effect of Selection Bias in Studies of Fads and Fashions

PubMed Central

Denrell, Jerker; Kovács, Balázs

2015-01-01

Most studies of fashion and fads focus on objects and practices that once were popular. We argue that limiting the sample to such trajectories generates a selection bias that obscures the underlying process and generates biased estimates. Through simulations and the analysis of a data set that has previously not been used to analyze the rise and fall of cultural practices, the New York Times text archive, we show that studying a whole range of cultural objects, both popular and less popular, is essential for understanding the drivers of popularity. In particular, we show that estimates of statistical models of the drivers of popularity will be biased if researchers use only trajectories of those practices that once were popular. PMID:25886158

Accounting for length-bias and selection effects in estimating the distribution of menstrual cycle length

PubMed Central

Lum, Kirsten J.; Sundaram, Rajeshwari; Louis, Thomas A.

2015-01-01

Prospective pregnancy studies are a valuable source of longitudinal data on menstrual cycle length. However, care is needed when making inferences of such renewal processes. For example, accounting for the sampling plan is necessary for unbiased estimation of the menstrual cycle length distribution for the study population. If couples can enroll when they learn of the study as opposed to waiting for the start of a new menstrual cycle, then due to length-bias, the enrollment cycle will be stochastically larger than the general run of cycles, a typical property of prevalent cohort studies. Furthermore, the probability of enrollment can depend on the length of time since a woman’s last menstrual period (a backward recurrence time), resulting in selection effects. We focus on accounting for length-bias and selection effects in the likelihood for enrollment menstrual cycle length, using a recursive two-stage approach wherein we first estimate the probability of enrollment as a function of the backward recurrence time and then use it in a likelihood with sampling weights that account for length-bias and selection effects. To broaden the applicability of our methods, we augment our model to incorporate a couple-specific random effect and time-independent covariate. A simulation study quantifies performance for two scenarios of enrollment probability when proper account is taken of sampling plan features. In addition, we estimate the probability of enrollment and the distribution of menstrual cycle length for the study population of the Longitudinal Investigation of Fertility and the Environment Study. PMID:25027273

Examinations of Home Economics Textbooks for Sex Bias.

ERIC Educational Resources Information Center

Weis, Susan F.

1979-01-01

Four analyses were conducted on a sample of 100 randomly selected, secondary home economics textbooks published between 1964 and 1974. Results indicated that the contents presented sex bias in language usage, in pictures portraying male and female role environments, and in role behaviors and expectations emphasized. (Author/JH)

Suspected survivor bias in case-control studies: stratify on survival time and use a negative control.

PubMed

van Rein, Nienke; Cannegieter, Suzanne C; Rosendaal, Frits R; Reitsma, Pieter H; Lijfering, Willem M

2014-02-01

Selection bias in case-control studies occurs when control selection is inappropriate. However, selection bias due to improper case sampling is less well recognized. We describe how to recognize survivor bias (i.e., selection on exposed cases) and illustrate this with an example study. A case-control study was used to analyze the effect of statins on major bleedings during treatment with vitamin K antagonists. A total of 110 patients who experienced such bleedings were included 18-1,018 days after the bleeding complication and matched to 220 controls. A protective association of major bleeding for exposure to statins (odds ratio [OR]: 0.56; 95% confidence interval: 0.29-1.08) was found, which did not become stronger after adjustment for confounding factors. These observations lead us to suspect survivor bias. To identify this bias, results were stratified on time between bleeding event and inclusion, and repeated for a negative control (an exposure not related to survival): blood group non-O. The ORs for exposure to statins increased gradually to 1.37 with shorter time between outcome and inclusion, whereas ORs for the negative control remained constant, confirming our hypothesis. We recommend the presented method to check for overoptimistic results, that is, survivor bias in case-control studies. Copyright © 2014 Elsevier Inc. All rights reserved.

Field-based random sampling without a sampling frame: control selection for a case-control study in rural Africa.

PubMed

Crampin, A C; Mwinuka, V; Malema, S S; Glynn, J R; Fine, P E

2001-01-01

Selection bias, particularly of controls, is common in case-control studies and may materially affect the results. Methods of control selection should be tailored both for the risk factors and disease under investigation and for the population being studied. We present here a control selection method devised for a case-control study of tuberculosis in rural Africa (Karonga, northern Malawi) that selects an age/sex frequency-matched random sample of the population, with a geographical distribution in proportion to the population density. We also present an audit of the selection process, and discuss the potential of this method in other settings.

Worry or craving? A selective review of evidence for food-related attention biases in obese individuals, eating-disorder patients, restrained eaters and healthy samples.

PubMed

Werthmann, Jessica; Jansen, Anita; Roefs, Anne

2015-05-01

Living in an 'obesogenic' environment poses a serious challenge for weight maintenance. However, many people are able to maintain a healthy weight indicating that not everybody is equally susceptible to the temptations of this food environment. The way in which someone perceives and reacts to food cues, that is, cognitive processes, could underlie differences in susceptibility. An attention bias for food could be such a cognitive factor that contributes to overeating. However, an attention bias for food has also been implicated with restrained eating and eating-disorder symptomatology. The primary aim of the present review was to determine whether an attention bias for food is specifically related to obesity while also reviewing evidence for attention biases in eating-disorder patients, restrained eaters and healthy-weight individuals. Another aim was to systematically examine how selective attention for food relates (causally) to eating behaviour. Current empirical evidence on attention bias for food within obese samples, eating-disorder patients, and, even though to a lesser extent, in restrained eaters is contradictory. However, present experimental studies provide relatively consistent evidence that an attention bias for food contributes to subsequent food intake. This review highlights the need to distinguish not only between different (temporal) attention bias components, but also to take different motivations (craving v. worry) and their impact on attentional processing into account. Overall, the current state of research suggests that biased attention could be one important cognitive mechanism by which the food environment tempts us into overeating.

Sex Bias in Research and Measurement: A Type III Error.

ERIC Educational Resources Information Center

Project on Sex Stereotyping in Education, Red Bank, NJ.

The module described in this document is part of a series of instructional modules on sex-role stereotyping in education. This document (including all but the cassette tape) is the module that examines how sex bias influences selection of research topics, sampling techniques, interpretation of data, and conclusions. Suggestions for designing…

Improved variance estimation of classification performance via reduction of bias caused by small sample size.

PubMed

Wickenberg-Bolin, Ulrika; Göransson, Hanna; Fryknäs, Mårten; Gustafsson, Mats G; Isaksson, Anders

2006-03-13

Supervised learning for classification of cancer employs a set of design examples to learn how to discriminate between tumors. In practice it is crucial to confirm that the classifier is robust with good generalization performance to new examples, or at least that it performs better than random guessing. A suggested alternative is to obtain a confidence interval of the error rate using repeated design and test sets selected from available examples. However, it is known that even in the ideal situation of repeated designs and tests with completely novel samples in each cycle, a small test set size leads to a large bias in the estimate of the true variance between design sets. Therefore different methods for small sample performance estimation such as a recently proposed procedure called Repeated Random Sampling (RSS) is also expected to result in heavily biased estimates, which in turn translates into biased confidence intervals. Here we explore such biases and develop a refined algorithm called Repeated Independent Design and Test (RIDT). Our simulations reveal that repeated designs and tests based on resampling in a fixed bag of samples yield a biased variance estimate. We also demonstrate that it is possible to obtain an improved variance estimate by means of a procedure that explicitly models how this bias depends on the number of samples used for testing. For the special case of repeated designs and tests using new samples for each design and test, we present an exact analytical expression for how the expected value of the bias decreases with the size of the test set. We show that via modeling and subsequent reduction of the small sample bias, it is possible to obtain an improved estimate of the variance of classifier performance between design sets. However, the uncertainty of the variance estimate is large in the simulations performed indicating that the method in its present form cannot be directly applied to small data sets.

Accounting for length-bias and selection effects in estimating the distribution of menstrual cycle length.

PubMed

Lum, Kirsten J; Sundaram, Rajeshwari; Louis, Thomas A

2015-01-01

Prospective pregnancy studies are a valuable source of longitudinal data on menstrual cycle length. However, care is needed when making inferences of such renewal processes. For example, accounting for the sampling plan is necessary for unbiased estimation of the menstrual cycle length distribution for the study population. If couples can enroll when they learn of the study as opposed to waiting for the start of a new menstrual cycle, then due to length-bias, the enrollment cycle will be stochastically larger than the general run of cycles, a typical property of prevalent cohort studies. Furthermore, the probability of enrollment can depend on the length of time since a woman's last menstrual period (a backward recurrence time), resulting in selection effects. We focus on accounting for length-bias and selection effects in the likelihood for enrollment menstrual cycle length, using a recursive two-stage approach wherein we first estimate the probability of enrollment as a function of the backward recurrence time and then use it in a likelihood with sampling weights that account for length-bias and selection effects. To broaden the applicability of our methods, we augment our model to incorporate a couple-specific random effect and time-independent covariate. A simulation study quantifies performance for two scenarios of enrollment probability when proper account is taken of sampling plan features. In addition, we estimate the probability of enrollment and the distribution of menstrual cycle length for the study population of the Longitudinal Investigation of Fertility and the Environment Study. Published by Oxford University Press 2014. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Active Learning to Overcome Sample Selection Bias: Application to Photometric Variable Star Classification

NASA Astrophysics Data System (ADS)

Richards, Joseph W.; Starr, Dan L.; Brink, Henrik; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; James, J. Berian; Long, James P.; Rice, John

2012-01-01

Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because (1) standard assumptions for machine-learned model selection procedures break down and (2) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting, co-training, and active learning (AL). We argue that AL—where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up—is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and Optical Gravitational Lensing Experiment, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a Web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply AL to classify variable stars in the All Sky Automated Survey, finding dramatic improvement in our agreement with the ASAS Catalog of Variable Stars, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.

A Critical Assessment of Bias in Survey Studies Using Location-Based Sampling to Recruit Patrons in Bars

PubMed Central

Morrison, Christopher; Lee, Juliet P.; Gruenewald, Paul J.; Marzell, Miesha

2015-01-01

Location-based sampling is a method to obtain samples of people within ecological contexts relevant to specific public health outcomes. Random selection increases generalizability, however in some circumstances (such as surveying bar patrons) recruitment conditions increase risks of sample bias. We attempted to recruit representative samples of bars and patrons in six California cities, but low response rates precluded meaningful analysis. A systematic review of 24 similar studies revealed that none addressed the key shortcomings of our study. We recommend steps to improve studies that use location-based sampling: (i) purposively sample places of interest, (ii) utilize recruitment strategies appropriate to the environment, and (iii) provide full information on response rates at all levels of sampling. PMID:26574657

Estimating the "impact" of out-of-home placement on child well-being: approaching the problem of selection bias.

PubMed

Berger, Lawrence M; Bruch, Sarah K; Johnson, Elizabeth I; James, Sigrid; Rubin, David

2009-01-01

This study used data on 2,453 children aged 4-17 from the National Survey of Child and Adolescent Well-Being and 5 analytic methods that adjust for selection factors to estimate the impact of out-of-home placement on children's cognitive skills and behavior problems. Methods included ordinary least squares (OLS) regressions and residualized change, simple change, difference-in-difference, and fixed effects models. Models were estimated using the full sample and a matched sample generated by propensity scoring. Although results from the unmatched OLS and residualized change models suggested that out-of-home placement is associated with increased child behavior problems, estimates from models that more rigorously adjust for selection bias indicated that placement has little effect on children's cognitive skills or behavior problems.

Implicit Social Biases in People with Autism

PubMed Central

Birmingham, Elina; Stanley, Damian; Nair, Remya; Adolphs, Ralph

2015-01-01

Implicit social biases are ubiquitous and are known to influence social behavior. A core diagnostic criterion of Autism Spectrum Disorder (ASD) is abnormal social behavior. Here we investigated the extent to which individuals with ASD might show a specific attenuation of implicit social biases, using the Implicit Association Test (IAT) across Social (gender, race) and Nonsocial (flowers/insect, shoes) categories. High-functioning adults with ASD showed intact but reduced IAT effects relative to healthy controls. Importantly, we observed no selective attenuation of implicit social (vs. nonsocial) biases in our ASD population. To extend these results, we collected data from a large online sample of the general population, and explored correlations between autistic traits and IAT effects. No associations were found between autistic traits and IAT effects for any of the categories tested in our online sample. Taken together, these results suggest that implicit social biases, as measured by the IAT, are largely intact in ASD. PMID:26386014

Sampling for Patient Exit Interviews: Assessment of Methods Using Mathematical Derivation and Computer Simulations.

PubMed

Geldsetzer, Pascal; Fink, Günther; Vaikath, Maria; Bärnighausen, Till

2018-02-01

(1) To evaluate the operational efficiency of various sampling methods for patient exit interviews; (2) to discuss under what circumstances each method yields an unbiased sample; and (3) to propose a new, operationally efficient, and unbiased sampling method. Literature review, mathematical derivation, and Monte Carlo simulations. Our simulations show that in patient exit interviews it is most operationally efficient if the interviewer, after completing an interview, selects the next patient exiting the clinical consultation. We demonstrate mathematically that this method yields a biased sample: patients who spend a longer time with the clinician are overrepresented. This bias can be removed by selecting the next patient who enters, rather than exits, the consultation room. We show that this sampling method is operationally more efficient than alternative methods (systematic and simple random sampling) in most primary health care settings. Under the assumption that the order in which patients enter the consultation room is unrelated to the length of time spent with the clinician and the interviewer, selecting the next patient entering the consultation room tends to be the operationally most efficient unbiased sampling method for patient exit interviews. © 2016 The Authors. Health Services Research published by Wiley Periodicals, Inc. on behalf of Health Research and Educational Trust.

Spatial clustering of dark matter haloes: secondary bias, neighbour bias, and the influence of massive neighbours on halo properties

NASA Astrophysics Data System (ADS)

Salcedo, Andrés N.; Maller, Ariyeh H.; Berlind, Andreas A.; Sinha, Manodeep; McBride, Cameron K.; Behroozi, Peter S.; Wechsler, Risa H.; Weinberg, David H.

2018-04-01

We explore the phenomenon commonly known as halo assembly bias, whereby dark matter haloes of the same mass are found to be more or less clustered when a second halo property is considered, for haloes in the mass range 3.7 × 1011-5.0 × 1013 h-1 M⊙. Using the Large Suite of Dark Matter Simulations (LasDamas) we consider nine commonly used halo properties and find that a clustering bias exists if haloes are binned by mass or by any other halo property. This secondary bias implies that no single halo property encompasses all the spatial clustering information of the halo population. The mean values of some halo properties depend on their halo's distance to a more massive neighbour. Halo samples selected by having high values of one of these properties therefore inherit a neighbour bias such that they are much more likely to be close to a much more massive neighbour. This neighbour bias largely accounts for the secondary bias seen in haloes binned by mass and split by concentration or age. However, haloes binned by other mass-like properties still show a secondary bias even when the neighbour bias is removed. The secondary bias of haloes selected by their spin behaves differently than that for other halo properties, suggesting that the origin of the spin bias is different than of other secondary biases.

A comparison of two sampling designs for fish assemblage assessment in a large river

USGS Publications Warehouse

Kiraly, Ian A.; Coghlan, Stephen M.; Zydlewski, Joseph D.; Hayes, Daniel

2014-01-01

We compared the efficiency of stratified random and fixed-station sampling designs to characterize fish assemblages in anticipation of dam removal on the Penobscot River, the largest river in Maine. We used boat electrofishing methods in both sampling designs. Multiple 500-m transects were selected randomly and electrofished in each of nine strata within the stratified random sampling design. Within the fixed-station design, up to 11 transects (1,000 m) were electrofished, all of which had been sampled previously. In total, 88 km of shoreline were electrofished during summer and fall in 2010 and 2011, and 45,874 individuals of 34 fish species were captured. Species-accumulation and dissimilarity curve analyses indicated that all sampling effort, other than fall 2011 under the fixed-station design, provided repeatable estimates of total species richness and proportional abundances. Overall, our sampling designs were similar in precision and efficiency for sampling fish assemblages. The fixed-station design was negatively biased for estimating the abundance of species such as Common Shiner Luxilus cornutus and Fallfish Semotilus corporalis and was positively biased for estimating biomass for species such as White Sucker Catostomus commersonii and Atlantic Salmon Salmo salar. However, we found no significant differences between the designs for proportional catch and biomass per unit effort, except in fall 2011. The difference observed in fall 2011 was due to limitations on the number and location of fixed sites that could be sampled, rather than an inherent bias within the design. Given the results from sampling in the Penobscot River, application of the stratified random design is preferable to the fixed-station design due to less potential for bias caused by varying sampling effort, such as what occurred in the fall 2011 fixed-station sample or due to purposeful site selection.

Monitoring the aftermath of Flint drinking water contamination crisis: Another case of sampling bias?

PubMed

Goovaerts, Pierre

2017-07-15

The delay in reporting high levels of lead in Flint drinking water, following the city's switch to the Flint River as its water supply, was partially caused by the biased selection of sampling sites away from the lead pipe network. Since Flint returned to its pre-crisis source of drinking water, the State has been monitoring water lead levels (WLL) at selected "sentinel" sites. In a first phase that lasted two months, 739 residences were sampled, most of them bi-weekly, to determine the general health of the distribution system and to track temporal changes in lead levels. During the same period, water samples were also collected through a voluntary program whereby concerned citizens received free testing kits and conducted sampling on their own. State officials relied on the former data to demonstrate the steady improvement in water quality. A recent analysis of data collected by voluntary sampling revealed, however, an opposite trend with lead levels increasing over time. This paper looks at potential sampling bias to explain such differences. Although houses with higher WLL were more likely to be sampled repeatedly, voluntary sampling turned out to reproduce fairly well the main characteristics (i.e. presence of lead service lines (LSL), construction year) of Flint housing stock. State-controlled sampling was less representative; e.g., sentinel sites with LSL were mostly built between 1935 and 1950 in lower poverty areas, which might hamper our ability to disentangle the effects of LSL and premise plumbing (lead fixtures and pipes present within old houses) on WLL. Also, there was no sentinel site with LSL in two of the most impoverished wards, including where the percentage of children with elevated blood lead levels tripled following the switch in water supply. Correcting for sampling bias narrowed the gap between sampling programs, yet overall temporal trends are still opposite. Copyright © 2017 Elsevier B.V. All rights reserved.

Race-Related Cognitive Test Bias in the ACTIVE Study: A MIMIC Model Approach

PubMed Central

Aiken Morgan, Adrienne T.; Marsiske, Michael; Dzierzewski, Joseph; Jones, Richard N.; Whitfield, Keith E.; Johnson, Kathy E.; Cresci, Mary K.

2010-01-01

The present study investigated evidence for race-related test bias in cognitive measures used in the baseline assessment of the ACTIVE clinical trial. Test bias against African Americans has been documented in both cognitive aging and early lifespan studies. Despite significant mean performance differences, Multiple Indicators Multiple Causes (MIMIC) models suggested most differences were at the construct level. There was little evidence that specific measures put either group at particular advantage or disadvantage and little evidence of cognitive test bias in this sample. Small group differences in education, cognitive status, and health suggest positive selection may have attenuated possible biases. PMID:20845121

Sampling Biases in MODIS and SeaWiFS Ocean Chlorophyll Data

NASA Technical Reports Server (NTRS)

Gregg, Watson W.; Casey, Nancy W.

2007-01-01

Although modem ocean color sensors, such as MODIS and SeaWiFS are often considered global missions, in reality it takes many days, even months, to sample the ocean surface enough to provide complete global coverage. The irregular temporal sampling of ocean color sensors can produce biases in monthly and annual mean chlorophyll estimates. We quantified the biases due to sampling using data assimilation to create a "truth field", which we then sub-sampled using the observational patterns of MODIS and SeaWiFS. Monthly and annual mean chlorophyll estimates from these sub-sampled, incomplete daily fields were constructed and compared to monthly and annual means from the complete daily fields of the assimilation model, at a spatial resolution of 1.25deg longitude by 0.67deg latitude. The results showed that global annual mean biases were positive, reaching nearly 8% (MODIS) and >5% (SeaWiFS). For perspective the maximum interannual variability in the SeaWiFS chlorophyll record was about 3%. Annual mean sampling biases were low (<3%) in the midlatitudes (between -40deg and 40deg). Low interannual variability in the global annual mean sampling biases suggested that global scale trend analyses were valid. High latitude biases were much higher than the global annual means, up to 20% as a basin annual mean, and over 80% in some months. This was the result of the high solar zenith angle exclusion in the processing algorithms. Only data where the solar angle is <75deg are permitted, in contrast to the assimilation which samples regularly over the entire area and month. High solar zenith angles do not facilitate phytoplankton photosynthesis and consequently low chlorophyll concentrations occurring here are missed by the data sets. Ocean color sensors selectively sample in locations and times of favorable phytoplankton growth, producing overestimates of chlorophyll. The biases derived from lack of sampling in the high latitudes varied monthly, leading to artifacts in the apparent seasonal cycle from ocean color sensors. A false secondary peak in chlorophyll occurred in May-August, which resulted from lack of sampling in the Antarctic.

Little Evidence That Time in Child Care Causes Externalizing Problems During Early Childhood in Norway

PubMed Central

Zachrisson, Henrik Daae; Dearing, Eric; Lekhal, Ratib; Toppelberg, Claudio O.

2012-01-01

Associations between maternal reports of hours in child care and children’s externalizing problems at 18 and 36 months of age were examined in a population-based Norwegian sample (n = 75,271). Within a sociopolitical context of homogenously high-quality child care, there was little evidence that high quantity of care causes externalizing problems. Using conventional approaches to handling selection bias and listwise deletion for substantial attrition in this sample, more hours in care predicted higher problem levels, yet with small effect sizes. The finding, however, was not robust to using multiple imputation for missing values. Moreover, when sibling and individual fixed-effects models for handling selection bias were used, no relation between hours and problems was evident. PMID:23311645

Accidental deep field bias in CMB T and SNe z correlation

NASA Astrophysics Data System (ADS)

Friday, Tracey; Clowes, Roger G.; Raghunathan, Srinivasan; Williger, Gerard M.

2018-05-01

Evidence presented by Yershov, Orlov and Raikov apparently showed that the WMAP/Planck cosmic microwave background (CMB) pixel-temperatures (T) at supernovae (SNe) locations tend to increase with increasing redshift (z). They suggest this correlation could be caused by the Integrated Sachs-Wolfe effect and/or by some unrelated foreground emission. Here, we assess this correlation independently using Planck 2015 SMICA R2.01 data and, following Yershov et al., a sample of 2783 SNe from the Sternberg Astronomical Institute. Our analysis supports the prima facie existence of the correlation but attributes it to a composite selection bias (high CMB T × high SNe z) caused by the accidental alignment of seven deep survey fields with CMB hotspots. These seven fields contain 9.2 per cent of the SNe sample (256 SNe). Spearman's rank-order correlation coefficient indicates the correlation present in the whole sample (ρs = 0.5, p-value =6.7 × 10-9) is insignificant for a sub-sample of the seven fields together (ρs = 0.2, p-value =0.2) and entirely absent for the remainder of the SNe (ρs = 0.1, p-value =0.6). We demonstrate the temperature and redshift biases of these seven deep fields, and estimate the likelihood of their falling on CMB hotspots by chance is at least ˜ 6.8 per cent (approximately 1 in 15). We show that a sample of 7880 SNe from the Open Supernova Catalogue exhibits the same effect and we conclude that the correlation is an accidental but not unlikely selection bias.

Two Decades into the LCR: What We Do and Still Don’t Know to Solve Lead Problems

EPA Science Inventory

Site selection and sampling protocol biases in LCR samplingunderestimate peak lead and copper concentrations whilemissing erratic lead release episodes resulting from distributionsystem chemical and physical disturbances. Possible sitetargeting and sampling protocol changes could...

Two Decades into the LCR: What We Do and Still Don’t Know to Solve Lead Problems - abstract

EPA Science Inventory

Site selection and sampling protocol biases in LCR samplingunderestimate peak lead and copper concentrations whilemissing erratic lead release episodes resulting from distributionsystem chemical and physical disturbances. Possible sitetargeting and sampling protocol changes could...

Spatial clustering of dark matter haloes: secondary bias, neighbour bias, and the influence of massive neighbours on halo properties

DOE PAGES

Salcedo, Andres N.; Maller, Ariyeh H.; Berlind, Andreas A.; ...

2018-01-15

Here, we explore the phenomenon commonly known as halo assembly bias, whereby dark matter haloes of the same mass are found to be more or less clustered when a second halo property is considered, for haloes in the mass range 3.7 × 10 11–5.0 × 10 13 h –1 M ⊙. Using the Large Suite of Dark Matter Simulations (LasDamas) we consider nine commonly used halo properties and find that a clustering bias exists if haloes are binned by mass or by any other halo property. This secondary bias implies that no single halo property encompasses all the spatial clusteringmore » information of the halo population. The mean values of some halo properties depend on their halo's distance to a more massive neighbour. Halo samples selected by having high values of one of these properties therefore inherit a neighbour bias such that they are much more likely to be close to a much more massive neighbour. This neighbour bias largely accounts for the secondary bias seen in haloes binned by mass and split by concentration or age. However, haloes binned by other mass-like properties still show a secondary bias even when the neighbour bias is removed. The secondary bias of haloes selected by their spin behaves differently than that for other halo properties, suggesting that the origin of the spin bias is different than of other secondary biases.« less

Spatial clustering of dark matter haloes: secondary bias, neighbour bias, and the influence of massive neighbours on halo properties

DOE Office of Scientific and Technical Information (OSTI.GOV)

Salcedo, Andres N.; Maller, Ariyeh H.; Berlind, Andreas A.

Here, we explore the phenomenon commonly known as halo assembly bias, whereby dark matter haloes of the same mass are found to be more or less clustered when a second halo property is considered, for haloes in the mass range 3.7 × 10 11–5.0 × 10 13 h –1 M ⊙. Using the Large Suite of Dark Matter Simulations (LasDamas) we consider nine commonly used halo properties and find that a clustering bias exists if haloes are binned by mass or by any other halo property. This secondary bias implies that no single halo property encompasses all the spatial clusteringmore » information of the halo population. The mean values of some halo properties depend on their halo's distance to a more massive neighbour. Halo samples selected by having high values of one of these properties therefore inherit a neighbour bias such that they are much more likely to be close to a much more massive neighbour. This neighbour bias largely accounts for the secondary bias seen in haloes binned by mass and split by concentration or age. However, haloes binned by other mass-like properties still show a secondary bias even when the neighbour bias is removed. The secondary bias of haloes selected by their spin behaves differently than that for other halo properties, suggesting that the origin of the spin bias is different than of other secondary biases.« less

Is Knowledge Random? Introducing Sampling and Bias through Outdoor Inquiry

ERIC Educational Resources Information Center

Stier, Sam

2010-01-01

Sampling, very generally, is the process of learning about something by selecting and assessing representative parts of that population or object. In the inquiry activity described here, students learned about sampling techniques as they estimated the number of trees greater than 12 cm dbh (diameter at breast height) in a wooded, discrete area…

Performance of Random Effects Model Estimators under Complex Sampling Designs

ERIC Educational Resources Information Center

Jia, Yue; Stokes, Lynne; Harris, Ian; Wang, Yan

2011-01-01

In this article, we consider estimation of parameters of random effects models from samples collected via complex multistage designs. Incorporation of sampling weights is one way to reduce estimation bias due to unequal probabilities of selection. Several weighting methods have been proposed in the literature for estimating the parameters of…

Multilocus patterns of polymorphism and selection across the X chromosome of Caenorhabditis remanei.

PubMed

Cutter, Asher D

2008-03-01

Natural selection and neutral processes such as demography, mutation, and gene conversion all contribute to patterns of polymorphism within genomes. Identifying the relative importance of these varied components in evolution provides the principal challenge for population genetics. To address this issue in the nematode Caenorhabditis remanei, I sampled nucleotide polymorphism at 40 loci across the X chromosome. The site-frequency spectrum for these loci provides no evidence for population size change, and one locus presents a candidate for linkage to a target of balancing selection. Selection for codon usage bias leads to the non-neutrality of synonymous sites, and despite its weak magnitude of effect (N(e)s approximately 0.1), is responsible for profound patterns of diversity and divergence in the C. remanei genome. Although gene conversion is evident for many loci, biased gene conversion is not identified as a significant evolutionary process in this sample. No consistent association is observed between synonymous-site diversity and linkage-disequilibrium-based estimators of the population recombination parameter, despite theoretical predictions about background selection or widespread genetic hitchhiking, but genetic map-based estimates of recombination are needed to rigorously test for a diversity-recombination relationship. Coalescent simulations also illustrate how a spurious correlation between diversity and linkage-disequilibrium-based estimators of recombination can occur, due in part to the presence of unbiased gene conversion. These results illustrate the influence that subtle natural selection can exert on polymorphism and divergence, in the form of codon usage bias, and demonstrate the potential of C. remanei for detecting natural selection from genomic scans of polymorphism.

Quality and methodological challenges in Internet-based mental health trials.

PubMed

Ye, Xibiao; Bapuji, Sunita Bayyavarapu; Winters, Shannon; Metge, Colleen; Raynard, Mellissa

2014-08-01

To review the quality of Internet-based mental health intervention studies and their methodological challenges. We searched multiple literature databases to identify relevant studies according to the Population, Interventions, Comparators, Outcomes, and Study Design framework. Two reviewers independently assessed selection bias, allocation bias, confounding bias, blinding, data collection methods, and withdrawals/dropouts, using the Quality Assessment Tool for Quantitative Studies. We rated each component as strong, moderate, or weak and assigned a global rating (strong, moderate, or weak) to each study. We discussed methodological issues related to the study quality. Of 122 studies included, 31 (25%), 44 (36%), and 47 (39%) were rated strong, moderate, and weak, respectively. Only five studies were rated strong for all of the six quality components (three of them were published by the same group). Lack of blinding, selection bias, and low adherence were the top three challenges in Internet-based mental health intervention studies. The overall quality of Internet-based mental health intervention needs to improve. In particular, studies need to improve sample selection, intervention allocation, and blinding.

Some Insights into Analytical Bias Involved in the Application of Grab Sampling for Volatile Organic Compounds: A Case Study against Used Tedlar Bags

PubMed Central

Ghosh, Samik; Kim, Ki-Hyun; Sohn, Jong Ryeul

2011-01-01

In this study, we have examined the patterns of VOCs released from used Tedlar bags that were once used for the collection under strong source activities. In this way, we attempted to account for the possible bias associated with the repetitive use of Tedlar bags. To this end, we selected the bags that were never heated. All of these target bags were used in ambient temperature (typically at or below 30°C). These bags were also dealt carefully to avoid any mechanical abrasion. This study will provide the essential information regarding the interaction between VOCs and Tedlar bag materials as a potential source of bias in bag sampling approaches. PMID:22235175

Some insights into analytical bias involved in the application of grab sampling for volatile organic compounds: a case study against used Tedlar bags.

PubMed

Ghosh, Samik; Kim, Ki-Hyun; Sohn, Jong Ryeul

2011-01-01

In this study, we have examined the patterns of VOCs released from used Tedlar bags that were once used for the collection under strong source activities. In this way, we attempted to account for the possible bias associated with the repetitive use of Tedlar bags. To this end, we selected the bags that were never heated. All of these target bags were used in ambient temperature (typically at or below 30°C). These bags were also dealt carefully to avoid any mechanical abrasion. This study will provide the essential information regarding the interaction between VOCs and Tedlar bag materials as a potential source of bias in bag sampling approaches.

Limitation of Inverse Probability-of-Censoring Weights in Estimating Survival in the Presence of Strong Selection Bias

PubMed Central

Howe, Chanelle J.; Cole, Stephen R.; Chmiel, Joan S.; Muñoz, Alvaro

2011-01-01

In time-to-event analyses, artificial censoring with correction for induced selection bias using inverse probability-of-censoring weights can be used to 1) examine the natural history of a disease after effective interventions are widely available, 2) correct bias due to noncompliance with fixed or dynamic treatment regimens, and 3) estimate survival in the presence of competing risks. Artificial censoring entails censoring participants when they meet a predefined study criterion, such as exposure to an intervention, failure to comply, or the occurrence of a competing outcome. Inverse probability-of-censoring weights use measured common predictors of the artificial censoring mechanism and the outcome of interest to determine what the survival experience of the artificially censored participants would be had they never been exposed to the intervention, complied with their treatment regimen, or not developed the competing outcome. Even if all common predictors are appropriately measured and taken into account, in the context of small sample size and strong selection bias, inverse probability-of-censoring weights could fail because of violations in assumptions necessary to correct selection bias. The authors used an example from the Multicenter AIDS Cohort Study, 1984–2008, regarding estimation of long-term acquired immunodeficiency syndrome-free survival to demonstrate the impact of violations in necessary assumptions. Approaches to improve correction methods are discussed. PMID:21289029

Causal inference and the data-fusion problem

PubMed Central

Bareinboim, Elias; Pearl, Judea

2016-01-01

We review concepts, principles, and tools that unify current approaches to causal analysis and attend to new challenges presented by big data. In particular, we address the problem of data fusion—piecing together multiple datasets collected under heterogeneous conditions (i.e., different populations, regimes, and sampling methods) to obtain valid answers to queries of interest. The availability of multiple heterogeneous datasets presents new opportunities to big data analysts, because the knowledge that can be acquired from combined data would not be possible from any individual source alone. However, the biases that emerge in heterogeneous environments require new analytical tools. Some of these biases, including confounding, sampling selection, and cross-population biases, have been addressed in isolation, largely in restricted parametric models. We here present a general, nonparametric framework for handling these biases and, ultimately, a theoretical solution to the problem of data fusion in causal inference tasks. PMID:27382148

The Role of Self-reports and Behavioral Measures of Interpretation Biases in Children with Varying Levels of Anxiety.

PubMed

Klein, Anke M; Flokstra, Emmelie; van Niekerk, Rianne; Klein, Steven; Rapee, Ronald M; Hudson, Jennifer L; Bögels, Susan M; Becker, Eni S; Rinck, Mike

2018-04-21

We investigated the role of self-reports and behavioral measures of interpretation biases and their content-specificity in children with varying levels of spider fear and/or social anxiety. In total, 141 selected children from a community sample completed an interpretation bias task with scenarios that were related to either spider threat or social threat. Specific interpretation biases were found; only spider-related interpretation bias and self-reported spider fear predicted unique variance in avoidance behavior on the Behavior Avoidance Task for spiders. Likewise, only social-threat related interpretation bias and self-reported social anxiety predicted anxiety during the Social Speech Task. These findings support the hypothesis that fearful children display cognitive biases that are specific to particular fear-relevant stimuli. Clinically, this insight might be used to improve treatments for anxious children by targeting content-specific interpretation biases related to individual disorders.

A cautionary note on substituting spatial subunits for repeated temporal sampling in studies of site occupancy

USGS Publications Warehouse

Kendall, William L.; White, Gary C.

2009-01-01

1. Assessing the probability that a given site is occupied by a species of interest is important to resource managers, as well as metapopulation or landscape ecologists. Managers require accurate estimates of the state of the system, in order to make informed decisions. Models that yield estimates of occupancy, while accounting for imperfect detection, have proven useful by removing a potentially important source of bias. To account for detection probability, multiple independent searches per site for the species are required, under the assumption that the species is available for detection during each search of an occupied site. 2. We demonstrate that when multiple samples per site are defined by searching different locations within a site, absence of the species from a subset of these spatial subunits induces estimation bias when locations are exhaustively assessed or sampled without replacement. 3. We further demonstrate that this bias can be removed by choosing sampling locations with replacement, or if the species is highly mobile over a short period of time. 4. Resampling an existing data set does not mitigate bias due to exhaustive assessment of locations or sampling without replacement. 5. Synthesis and applications. Selecting sampling locations for presence/absence surveys with replacement is practical in most cases. Such an adjustment to field methods will prevent one source of bias, and therefore produce more robust statistical inferences about species occupancy. This will in turn permit managers to make resource decisions based on better knowledge of the state of the system.

Migration, self-selection and earnings in Philippine rural communities.

PubMed

Lanzona, L A

1998-06-01

"Estimated returns to schooling investments can be misleading if migration causes significant shifts in population distribution across time. Data gathered in rural Philippine communities show that the more educated and experienced individuals are more likely to outmigrate, causing a sample selection bias in the estimation of wage equations. The observed wages were then lower than the conditional population mean of an entire cohort residing originally in the area. Controlling for self-selection, the wage returns to schooling and experience were higher, Finally, the sample selectivity variable accounts substantially for the difference in the wages of men and women." excerpt

Using local multiplicity to improve effect estimation from a hypothesis-generating pharmacogenetics study.

PubMed

Zou, W; Ouyang, H

2016-02-01

We propose a multiple estimation adjustment (MEA) method to correct effect overestimation due to selection bias from a hypothesis-generating study (HGS) in pharmacogenetics. MEA uses a hierarchical Bayesian approach to model individual effect estimates from maximal likelihood estimation (MLE) in a region jointly and shrinks them toward the regional effect. Unlike many methods that model a fixed selection scheme, MEA capitalizes on local multiplicity independent of selection. We compared mean square errors (MSEs) in simulated HGSs from naive MLE, MEA and a conditional likelihood adjustment (CLA) method that model threshold selection bias. We observed that MEA effectively reduced MSE from MLE on null effects with or without selection, and had a clear advantage over CLA on extreme MLE estimates from null effects under lenient threshold selection in small samples, which are common among 'top' associations from a pharmacogenetics HGS.

Social Groups Prioritize Selective Attention to Faces: How Social Identity Shapes Distractor Interference

PubMed Central

Hill, LaBarron K.; Williams, DeWayne P.; Thayer, Julian F.

2016-01-01

Human faces automatically attract visual attention and this process appears to be guided by social group memberships. In two experiments, we examined how social groups guide selective attention toward in-group and out-group faces. Black and White participants detected a target letter among letter strings superimposed on faces (Experiment 1). White participants were less accurate on trials with racial out-group (Black) compared to in-group (White) distractor faces. Likewise, Black participants were less accurate on trials with racial out-group (White) compared to in-group (Black) distractor faces. However, this pattern of out-group bias was only evident under high perceptual load—when the task was visually difficult. To examine the malleability of this pattern of racial bias, a separate sample of participants were assigned to mixed-race minimal groups (Experiment 2). Participants assigned to groups were less accurate on trials with their minimal in-group members compared to minimal out-group distractor faces, regardless of race. Again, this pattern of out-group bias was only evident under high perceptual load. Taken together, these results suggest that social identity guides selective attention toward motivationally relevant social groups—shifting from out-group bias in the domain of race to in-group bias in the domain of minimal groups—when perceptual resources are scarce. PMID:27556646

Enhanced conformational sampling using replica exchange with concurrent solute scaling and hamiltonian biasing realized in one dimension.

PubMed

Yang, Mingjun; Huang, Jing; MacKerell, Alexander D

2015-06-09

Replica exchange (REX) is a powerful computational tool for overcoming the quasi-ergodic sampling problem of complex molecular systems. Recently, several multidimensional extensions of this method have been developed to realize exchanges in both temperature and biasing potential space or the use of multiple biasing potentials to improve sampling efficiency. However, increased computational cost due to the multidimensionality of exchanges becomes challenging for use on complex systems under explicit solvent conditions. In this study, we develop a one-dimensional (1D) REX algorithm to concurrently combine the advantages of overall enhanced sampling from Hamiltonian solute scaling and the specific enhancement of collective variables using Hamiltonian biasing potentials. In the present Hamiltonian replica exchange method, termed HREST-BP, Hamiltonian solute scaling is applied to the solute subsystem, and its interactions with the environment to enhance overall conformational transitions and biasing potentials are added along selected collective variables associated with specific conformational transitions, thereby balancing the sampling of different hierarchical degrees of freedom. The two enhanced sampling approaches are implemented concurrently allowing for the use of a small number of replicas (e.g., 6 to 8) in 1D, thus greatly reducing the computational cost in complex system simulations. The present method is applied to conformational sampling of two nitrogen-linked glycans (N-glycans) found on the HIV gp120 envelope protein. Considering the general importance of the conformational sampling problem, HREST-BP represents an efficient procedure for the study of complex saccharides, and, more generally, the method is anticipated to be of general utility for the conformational sampling in a wide range of macromolecular systems.

Do nonexercisers also share the positive exerciser stereotype?: An elicitation and comparison of beliefs about exercisers.

PubMed

Rodgers, Wendy M; Hall, Craig R; Wilson, Philip M; Berry, Tanya R

2009-02-01

The purpose of this research was to examine whether exercisers and nonexercisers are rated similarly on a variety of characteristics by a sample of randomly selected regular exercisers, nonexercisers who intend to exercise, and nonexercisers with no intention to exercise. Previous research by Martin Ginis et al. (2003) has demonstrated an exerciser stereotype that advantages exercisers. It is unknown, however, the extent to which an exerciser stereotype is shared by nonexercisers, particularly nonintenders. Following an item-generation procedure, a sample of 470 (n=218 men; n=252 women) people selected using random digit dialing responded to a questionnaire assessing the extent to which they agreed that exercisers and nonexercisers possessed 24 characteristics, such as "happy," "fit," "fat," and "lazy." The results strongly support a positive exerciser bias, with exercisers rated more favorably on 22 of the 24 items. The degree of bias was equivalent in all groups of respondents. Examination of the demographic characteristics revealed no differences among the three groups on age, work status, or child-care responsibilities, suggesting that there is a pervasive positive exerciser bias.

An investigation of bias in a study of nuclear shipyard workers.

PubMed

Greenberg, E R; Rosner, B; Hennekens, C; Rinsky, R; Colton, T

1985-02-01

The authors examined discrepant findings between a 1978 proportional mortality study and a 1981 cohort study of workers at the Portsmouth, New Hampshire, Naval Shipyard to determine whether the healthy worker effect, selection bias, or measurement bias could explain why only the proportional mortality study found excess cancer deaths among nuclear workers. Lower mortality from noncancer causes in nuclear workers (the healthy worker effect) partly accounted for the observed elevated cancer proportional mortality. More important, however, was measurement bias which occurred in the proportional mortality study when nuclear workers who had not died of cancer were misclassified as not being nuclear workers based on information from their next of kin, thereby creating a spurious association. Although the proportional mortality study was based on a small sample of all deaths occurring in the cohort, selection bias did not contribute materially to the discrepant results for total cancer deaths. With regard to leukemia, misclassification of occupation in the proportional mortality study and disagreement about cause of death accounted for some of the reported excess deaths.

Sample Selection for Training Cascade Detectors.

PubMed

Vállez, Noelia; Deniz, Oscar; Bueno, Gloria

2015-01-01

Automatic detection systems usually require large and representative training datasets in order to obtain good detection and false positive rates. Training datasets are such that the positive set has few samples and/or the negative set should represent anything except the object of interest. In this respect, the negative set typically contains orders of magnitude more images than the positive set. However, imbalanced training databases lead to biased classifiers. In this paper, we focus our attention on a negative sample selection method to properly balance the training data for cascade detectors. The method is based on the selection of the most informative false positive samples generated in one stage to feed the next stage. The results show that the proposed cascade detector with sample selection obtains on average better partial AUC and smaller standard deviation than the other compared cascade detectors.

Nutrition surveillance using a small open cohort: experience from Burkina Faso.

PubMed

Altmann, Mathias; Fermanian, Christophe; Jiao, Boshen; Altare, Chiara; Loada, Martin; Myatt, Mark

2016-01-01

Nutritional surveillance remains generally weak and early warning systems are needed in areas with high burden of acute under-nutrition. In order to enhance insight into nutritional surveillance, a community-based sentinel sites approach, known as the Listening Posts (LP) Project, was piloted in Burkina Faso by Action Contre la Faim (ACF). This paper presents ACF's experience with the LP approach and investigates potential selection and observational biases. Six primary sampling units (PSUs) were selected in each livelihood zone using the centric systematic area sampling methodology. In each PSU, 22 children aged between 6 and 24 months were selected by proximity sampling. The prevalence of GAM for each month from January 2011 to December 2013 was estimated using a Bayesian normal-normal conjugate analysis followed by PROBIT estimation. To validate the LP approach in detecting changes over time, the time trends of MUAC from LP and from five cross-sectional surveys were modelled using polynomial regression and compared by using a Wald test. The differences between prevalence estimates from the two data sources were used to assess selection and observational biases. The 95 % credible interval around GAM prevalence estimates using LP approach ranged between +6.5 %/-6.0 % on a prevalence of 36.1 % and +3.5 %/-2.9 % on a prevalence of 10.8 %. LP and cross-sectional surveys time trend models were well correlated (p = 0.6337). Although LP showed a slight but significant trend for GAM to decrease over time at a rate of -0.26 %/visit, the prevalence estimates from the two data sources showed good agreement over a 3-year period. The LP methodology has proved to be valid in following trends of GAM prevalence for a period of 3 years without selection bias. However, a slight observational bias was observed, requiring a periodical reselection of the sentinel sites. This kind of surveillance project is suited to use in areas with high burden of acute under-nutrition where early warning systems are strongly needed. Advocacy is necessary to develop sustainable nutrition surveillance system and to support the use of surveillance data in guiding nutritional programs.

VizieR Online Data Catalog: Quasar luminosity function (Hawkins+, 1993)

NASA Astrophysics Data System (ADS)

Hawkins, M. R. S.; Veron, P.

1994-11-01

A sample of quasars is selected from a 10-yr sequence of 30 UK Schmidt plates. Luminosity functions are derived in several redshift intervals, which in each case show a featureless power-law rise towards low luminosities. There is no sigh of the 'break' found in the recent UVX sample of Boyle, Shanks & Peterson. It is suggested that reasons for the disagreement are connected with biases in the selection of the UVX sample. The question of the nature of quasar evolution appears to be still unresolved. (1 data file).

The CogBIAS longitudinal study protocol: cognitive and genetic factors influencing psychological functioning in adolescence.

PubMed

Booth, Charlotte; Songco, Annabel; Parsons, Sam; Heathcote, Lauren; Vincent, John; Keers, Robert; Fox, Elaine

2017-12-29

Optimal psychological development is dependent upon a complex interplay between individual and situational factors. Investigating the development of these factors in adolescence will help to improve understanding of emotional vulnerability and resilience. The CogBIAS longitudinal study (CogBIAS-L-S) aims to combine cognitive and genetic approaches to investigate risk and protective factors associated with the development of mood and impulsivity-related outcomes in an adolescent sample. CogBIAS-L-S is a three-wave longitudinal study of typically developing adolescents conducted over 4 years, with data collection at age 12, 14 and 16. At each wave participants will undergo multiple assessments including a range of selective cognitive processing tasks (e.g. attention bias, interpretation bias, memory bias) and psychological self-report measures (e.g. anxiety, depression, resilience). Saliva samples will also be collected at the baseline assessment for genetic analyses. Multilevel statistical analyses will be performed to investigate the developmental trajectory of cognitive biases on psychological functioning, as well as the influence of genetic moderation on these relationships. CogBIAS-L-S represents the first longitudinal study to assess multiple cognitive biases across adolescent development and the largest study of its kind to collect genetic data. It therefore provides a unique opportunity to understand how genes and the environment influence the development and maintenance of cognitive biases and provide insight into risk and protective factors that may be key targets for intervention.

Non-Gaussian shape discrimination with spectroscopic galaxy surveys

DOE Office of Scientific and Technical Information (OSTI.GOV)

Byun, Joyce; Bean, Rachel, E-mail: byun@astro.cornell.edu, E-mail: rbean@astro.cornell.edu

2015-03-01

We consider how galaxy clustering data, from Mpc to Gpc scales, from upcoming large scale structure surveys, such as Euclid and DESI, can provide discriminating information about the bispectrum shape arising from a variety of inflationary scenarios. Through exploring in detail the weighting of shape properties in the calculation of the halo bias and halo mass function we show how they probe a broad range of configurations, beyond those in the squeezed limit, that can help distinguish between shapes with similar large scale bias behaviors. We assess the impact, on constraints for a diverse set of non-Gaussian shapes, of galaxymore » clustering information in the mildly non-linear regime, and surveys that span multiple redshifts and employ different galactic tracers of the dark matter distribution. Fisher forecasts are presented for a Euclid-like spectroscopic survey of Hα-selected emission line galaxies (ELGs), and a DESI-like survey, of luminous red galaxies (LRGs) and [O-II] doublet-selected ELGs, in combination with Planck-like CMB temperature and polarization data.While ELG samples provide better probes of shapes that are divergent in the squeezed limit, LRG constraints, centered below z<1, yield stronger constraints on shapes with scale-independent large-scale halo biases, such as the equilateral template. The ELG and LRG samples provide complementary degeneracy directions for distinguishing between different shapes. For Hα-selected galaxies, we note that recent revisions of the expected Hα luminosity function reduce the halo bias constraints on the local shape, relative to the CMB. For galaxy clustering constraints to be comparable to those from the CMB, additional information about the Gaussian galaxy bias is needed, such as can be determined from the galaxy clustering bispectrum or probing the halo power spectrum directly through weak lensing. If the Gaussian galaxy bias is constrained to better than a percent level then the LSS and CMB data could provide complementary constraints that will enable differentiation of bispectrum with distinct theoretical origins but with similar large scale, squeezed-limit properties.« less

Large biases in regression-based constituent flux estimates: causes and diagnostic tools

USGS Publications Warehouse

Hirsch, Robert M.

2014-01-01

It has been documented in the literature that, in some cases, widely used regression-based models can produce severely biased estimates of long-term mean river fluxes of various constituents. These models, estimated using sample values of concentration, discharge, and date, are used to compute estimated fluxes for a multiyear period at a daily time step. This study compares results of the LOADEST seven-parameter model, LOADEST five-parameter model, and the Weighted Regressions on Time, Discharge, and Season (WRTDS) model using subsampling of six very large datasets to better understand this bias problem. This analysis considers sample datasets for dissolved nitrate and total phosphorus. The results show that LOADEST-7 and LOADEST-5, although they often produce very nearly unbiased results, can produce highly biased results. This study identifies three conditions that can give rise to these severe biases: (1) lack of fit of the log of concentration vs. log discharge relationship, (2) substantial differences in the shape of this relationship across seasons, and (3) severely heteroscedastic residuals. The WRTDS model is more resistant to the bias problem than the LOADEST models but is not immune to them. Understanding the causes of the bias problem is crucial to selecting an appropriate method for flux computations. Diagnostic tools for identifying the potential for bias problems are introduced, and strategies for resolving bias problems are described.

Normalization Approaches for Removing Systematic Biases Associated with Mass Spectrometry and Label-Free Proteomics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Callister, Stephen J.; Barry, Richard C.; Adkins, Joshua N.

2006-02-01

Central tendency, linear regression, locally weighted regression, and quantile techniques were investigated for normalization of peptide abundance measurements obtained from high-throughput liquid chromatography-Fourier transform ion cyclotron resonance mass spectrometry (LC-FTICR MS). Arbitrary abundances of peptides were obtained from three sample sets, including a standard protein sample, two Deinococcus radiodurans samples taken from different growth phases, and two mouse striatum samples from control and methamphetamine-stressed mice (strain C57BL/6). The selected normalization techniques were evaluated in both the absence and presence of biological variability by estimating extraneous variability prior to and following normalization. Prior to normalization, replicate runs from each sample setmore » were observed to be statistically different, while following normalization replicate runs were no longer statistically different. Although all techniques reduced systematic bias, assigned ranks among the techniques revealed significant trends. For most LC-FTICR MS analyses, linear regression normalization ranked either first or second among the four techniques, suggesting that this technique was more generally suitable for reducing systematic biases.« less

Entropy-based gene ranking without selection bias for the predictive classification of microarray data.

PubMed

Furlanello, Cesare; Serafini, Maria; Merler, Stefano; Jurman, Giuseppe

2003-11-06

We describe the E-RFE method for gene ranking, which is useful for the identification of markers in the predictive classification of array data. The method supports a practical modeling scheme designed to avoid the construction of classification rules based on the selection of too small gene subsets (an effect known as the selection bias, in which the estimated predictive errors are too optimistic due to testing on samples already considered in the feature selection process). With E-RFE, we speed up the recursive feature elimination (RFE) with SVM classifiers by eliminating chunks of uninteresting genes using an entropy measure of the SVM weights distribution. An optimal subset of genes is selected according to a two-strata model evaluation procedure: modeling is replicated by an external stratified-partition resampling scheme, and, within each run, an internal K-fold cross-validation is used for E-RFE ranking. Also, the optimal number of genes can be estimated according to the saturation of Zipf's law profiles. Without a decrease of classification accuracy, E-RFE allows a speed-up factor of 100 with respect to standard RFE, while improving on alternative parametric RFE reduction strategies. Thus, a process for gene selection and error estimation is made practical, ensuring control of the selection bias, and providing additional diagnostic indicators of gene importance.

Correcting Classifiers for Sample Selection Bias in Two-Phase Case-Control Studies

PubMed Central

Theis, Fabian J.

2017-01-01

Epidemiological studies often utilize stratified data in which rare outcomes or exposures are artificially enriched. This design can increase precision in association tests but distorts predictions when applying classifiers on nonstratified data. Several methods correct for this so-called sample selection bias, but their performance remains unclear especially for machine learning classifiers. With an emphasis on two-phase case-control studies, we aim to assess which corrections to perform in which setting and to obtain methods suitable for machine learning techniques, especially the random forest. We propose two new resampling-based methods to resemble the original data and covariance structure: stochastic inverse-probability oversampling and parametric inverse-probability bagging. We compare all techniques for the random forest and other classifiers, both theoretically and on simulated and real data. Empirical results show that the random forest profits from only the parametric inverse-probability bagging proposed by us. For other classifiers, correction is mostly advantageous, and methods perform uniformly. We discuss consequences of inappropriate distribution assumptions and reason for different behaviors between the random forest and other classifiers. In conclusion, we provide guidance for choosing correction methods when training classifiers on biased samples. For random forests, our method outperforms state-of-the-art procedures if distribution assumptions are roughly fulfilled. We provide our implementation in the R package sambia. PMID:29312464

The Discovery of Single-Nucleotide Polymorphisms—and Inferences about Human Demographic History

PubMed Central

Wakeley, John; Nielsen, Rasmus; Liu-Cordero, Shau Neen; Ardlie, Kristin

2001-01-01

A method of historical inference that accounts for ascertainment bias is developed and applied to single-nucleotide polymorphism (SNP) data in humans. The data consist of 84 short fragments of the genome that were selected, from three recent SNP surveys, to contain at least two polymorphisms in their respective ascertainment samples and that were then fully resequenced in 47 globally distributed individuals. Ascertainment bias is the deviation, from what would be observed in a random sample, caused either by discovery of polymorphisms in small samples or by locus selection based on levels or patterns of polymorphism. The three SNP surveys from which the present data were derived differ both in their protocols for ascertainment and in the size of the samples used for discovery. We implemented a Monte Carlo maximum-likelihood method to fit a subdivided-population model that includes a possible change in effective size at some time in the past. Incorrectly assuming that ascertainment bias does not exist causes errors in inference, affecting both estimates of migration rates and historical changes in size. Migration rates are overestimated when ascertainment bias is ignored. However, the direction of error in inferences about changes in effective population size (whether the population is inferred to be shrinking or growing) depends on whether either the numbers of SNPs per fragment or the SNP-allele frequencies are analyzed. We use the abbreviation “SDL,” for “SNP-discovered locus,” in recognition of the genomic-discovery context of SNPs. When ascertainment bias is modeled fully, both the number of SNPs per SDL and their allele frequencies support a scenario of growth in effective size in the context of a subdivided population. If subdivision is ignored, however, the hypothesis of constant effective population size cannot be rejected. An important conclusion of this work is that, in demographic or other studies, SNP data are useful only to the extent that their ascertainment can be modeled. PMID:11704929

Investigations of potential bias in the estimation of lambda using Pradel's (1996) model for capture-recapture data

USGS Publications Warehouse

Hines, James E.; Nichols, James D.

2002-01-01

Pradel's (1996) temporal symmetry model permitting direct estimation and modelling of population growth rate, u i , provides a potentially useful tool for the study of population dynamics using marked animals. Because of its recent publication date, the approach has not seen much use, and there have been virtually no investigations directed at robustness of the resulting estimators. Here we consider several potential sources of bias, all motivated by specific uses of this estimation approach. We consider sampling situations in which the study area expands with time and present an analytic expression for the bias in u i We next consider trap response in capture probabilities and heterogeneous capture probabilities and compute large-sample and simulation-based approximations of resulting bias in u i . These approximations indicate that trap response is an especially important assumption violation that can produce substantial bias. Finally, we consider losses on capture and emphasize the importance of selecting the estimator for u i that is appropriate to the question being addressed. For studies based on only sighting and resighting data, Pradel's (1996) u i ' is the appropriate estimator.

Mate-sampling costs and sexy sons.

PubMed

Kokko, H; Booksmythe, I; Jennions, M D

2015-01-01

Costly female mating preferences for purely Fisherian male traits (i.e. sexual ornaments that are genetically uncorrelated with inherent viability) are not expected to persist at equilibrium. The indirect benefit of producing 'sexy sons' (Fisher process) disappears: in some models, the male trait becomes fixed; in others, a range of male trait values persist, but a larger trait confers no net fitness advantage because it lowers survival. Insufficient indirect selection to counter the direct cost of producing fewer offspring means that preferences are lost. The only well-cited exception assumes biased mutation on male traits. The above findings generally assume constant direct selection against female preferences (i.e. fixed costs). We show that if mate-sampling costs are instead derived based on an explicit account of how females acquire mates, an initially costly mating preference can coevolve with a male trait so that both persist in the presence or absence of biased mutation. Our models predict that empirically detecting selection at equilibrium will be difficult, even if selection was responsible for the location of the current equilibrium. In general, it appears useful to integrate mate sampling theory with models of genetic consequences of mating preferences: being explicit about the process by which individuals select mates can alter equilibria. © 2014 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2014 European Society For Evolutionary Biology.

Choosing the Allometric Exponent in Covariate Model Building.

PubMed

Sinha, Jaydeep; Al-Sallami, Hesham S; Duffull, Stephen B

2018-04-27

Allometric scaling is often used to describe the covariate model linking total body weight (WT) to clearance (CL); however, there is no consensus on how to select its value. The aims of this study were to assess the influence of between-subject variability (BSV) and study design on (1) the power to correctly select the exponent from a priori choices, and (2) the power to obtain unbiased exponent estimates. The influence of WT distribution range (randomly sampled from the Third National Health and Nutrition Examination Survey, 1988-1994 [NHANES III] database), sample size (N = 10, 20, 50, 100, 200, 500, 1000 subjects), and BSV on CL (low 20%, normal 40%, high 60%) were assessed using stochastic simulation estimation. A priori exponent values used for the simulations were 0.67, 0.75, and 1, respectively. For normal to high BSV drugs, it is almost impossible to correctly select the exponent from an a priori set of exponents, i.e. 1 vs. 0.75, 1 vs. 0.67, or 0.75 vs. 0.67 in regular studies involving < 200 adult participants. On the other hand, such regular study designs are sufficient to appropriately estimate the exponent. However, regular studies with < 100 patients risk potential bias in estimating the exponent. Those study designs with limited sample size and narrow range of WT (e.g. < 100 adult participants) potentially risk either selection of a false value or yielding a biased estimate of the allometric exponent; however, such bias is only relevant in cases of extrapolating the value of CL outside the studied population, e.g. analysis of a study of adults that is used to extrapolate to children.

[Participation of migrants in health surveys conducted by telephone: potential and limits].

PubMed

Schenk, L; Neuhauser, H

2005-10-01

Migrants living in Germany are a both large and vulnerable population subgroup. They are not easily induced to participate in health surveys, Hence, achieving high participation rates of migrants in health surveys and avoiding selection bias is a difficult task. In this study, we report on the participation of migrants in the German National Health Telephone Survey 2003 (GSTel03), the first comprehensive national health survey conducted by telephone in Germany. Three migrant groups were identified: individuals with non-German citizenship (foreigners), naturalized migrants, and ethnic German immigrants (Spätaussiedler). The aim of this study is to evaluate the degree to which the GSTel03 subsample of foreigners is representative for foreigners living in Germany. We compare the prevalence of sociodemographic characteristics and selected health indicators of foreigners in the GNTel03 subsample with prevalences from national statistics and from a large national household survey ("Mikrozensus 2003"). The proportion of participants with non-German nationality in the overall GSTel03 sample was significantly lower than the proportion of foreigners in the residential population in Germany (3.7 % vs. 8.9 %). While there was no evidence of selection bias with regard to age and sex distribution, we found significant differences with regard to other factors, including nationality, length of stay in Germany, unemployment rate and education. The comparison of health indicators showed only moderate differences between GSTel03 sample and "Mikrozensus" results. However, these differences did not consistently point to a better or worse health status in the GSTel03 sample of foreigners and should therefore not be generalised in respect of other health indicators. Our study emphasises the importance of a continuous effort to improve migrant participation in health studies and of a thorough analysis of selection bias when interpreting results.

Sampling in epidemiological research: issues, hazards and pitfalls.

PubMed

Tyrer, Stephen; Heyman, Bob

2016-04-01

Surveys of people's opinions are fraught with difficulties. It is easier to obtain information from those who respond to text messages or to emails than to attempt to obtain a representative sample. Samples of the population that are selected non-randomly in this way are termed convenience samples as they are easy to recruit. This introduces a sampling bias. Such non-probability samples have merit in many situations, but an epidemiological enquiry is of little value unless a random sample is obtained. If a sufficient number of those selected actually complete a survey, the results are likely to be representative of the population. This editorial describes probability and non-probability sampling methods and illustrates the difficulties and suggested solutions in performing accurate epidemiological research.

Sampling in epidemiological research: issues, hazards and pitfalls

PubMed Central

Tyrer, Stephen; Heyman, Bob

2016-01-01

Surveys of people's opinions are fraught with difficulties. It is easier to obtain information from those who respond to text messages or to emails than to attempt to obtain a representative sample. Samples of the population that are selected non-randomly in this way are termed convenience samples as they are easy to recruit. This introduces a sampling bias. Such non-probability samples have merit in many situations, but an epidemiological enquiry is of little value unless a random sample is obtained. If a sufficient number of those selected actually complete a survey, the results are likely to be representative of the population. This editorial describes probability and non-probability sampling methods and illustrates the difficulties and suggested solutions in performing accurate epidemiological research. PMID:27087985

Does self-selection affect samples' representativeness in online surveys? An investigation in online video game research.

PubMed

Khazaal, Yasser; van Singer, Mathias; Chatton, Anne; Achab, Sophia; Zullino, Daniele; Rothen, Stephane; Khan, Riaz; Billieux, Joel; Thorens, Gabriel

2014-07-07

The number of medical studies performed through online surveys has increased dramatically in recent years. Despite their numerous advantages (eg, sample size, facilitated access to individuals presenting stigmatizing issues), selection bias may exist in online surveys. However, evidence on the representativeness of self-selected samples in online studies is patchy. Our objective was to explore the representativeness of a self-selected sample of online gamers using online players' virtual characters (avatars). All avatars belonged to individuals playing World of Warcraft (WoW), currently the most widely used online game. Avatars' characteristics were defined using various games' scores, reported on the WoW's official website, and two self-selected samples from previous studies were compared with a randomly selected sample of avatars. We used scores linked to 1240 avatars (762 from the self-selected samples and 478 from the random sample). The two self-selected samples of avatars had higher scores on most of the assessed variables (except for guild membership and exploration). Furthermore, some guilds were overrepresented in the self-selected samples. Our results suggest that more proficient players or players more involved in the game may be more likely to participate in online surveys. Caution is needed in the interpretation of studies based on online surveys that used a self-selection recruitment procedure. Epidemiological evidence on the reduced representativeness of sample of online surveys is warranted.

Using Propensity Scores for Estimating Causal Effects: A Study in the Development of Moral Reasoning

ERIC Educational Resources Information Center

Grunwald, Heidi E.; Mayhew, Matthew J.

2008-01-01

The purpose of this study was to illustrate the use of propensity scores for creating comparison groups, partially controlling for pretreatment course selection bias, and estimating the treatment effects of selected courses on the development of moral reasoning in undergraduate students. Specifically, we used a sample of convenience for comparing…

Do You See What I See? Exploring the Consequences of Luminosity Limits in Black Hole-Galaxy Evolution Studies

NASA Astrophysics Data System (ADS)

Jones, Mackenzie L.; Hickox, Ryan C.; Mutch, Simon J.; Croton, Darren J.; Ptak, Andrew F.; DiPompeo, Michael A.

2017-07-01

In studies of the connection between active galactic nuclei (AGNs) and their host galaxies, there is widespread disagreement on some key aspects of the connection. These disagreements largely stem from a lack of understanding of the nature of the full underlying AGN population. Recent attempts to probe this connection utilize both observations and simulations to correct for a missed population, but presently are limited by intrinsic biases and complicated models. We take a simple simulation for galaxy evolution and add a new prescription for AGN activity to connect galaxy growth to dark matter halo properties and AGN activity to star formation. We explicitly model selection effects to produce an “observed” AGN population for comparison with observations and empirically motivated models of the local universe. This allows us to bypass the difficulties inherent in models that attempt to infer the AGN population by inverting selection effects. We investigate the impact of selecting AGNs based on thresholds in luminosity or Eddington ratio on the “observed” AGN population. By limiting our model AGN sample in luminosity, we are able to recreate the observed local AGN luminosity function and specific star formation-stellar mass distribution, and show that using an Eddington ratio threshold introduces less bias into the sample by selecting the full range of growing black holes, despite the challenge of selecting low-mass black holes. We find that selecting AGNs using these various thresholds yield samples with different AGN host galaxy properties.

SpArcFiRe: morphological selection effects due to reduced visibility of tightly winding arms in distant spiral galaxies

NASA Astrophysics Data System (ADS)

Peng, Tianrui Rae; Edward English, John; Silva, Pedro; Davis, Darren R.; Hayes, Wayne B.

2018-03-01

The Galaxy Zoo project has provided a plethora of valuable morphological data on a large number of galaxies from various surveys, and their team have identified and/or corrected for many biases. Here we study a new bias related to spiral arm pitch angles, which first requires selecting a sample of spiral galaxies that show observable structure. One obvious way is to select galaxies using a threshold in spirality, which we define as the fraction of Galaxy Zoo humans who have reported seeing spiral structure. Using such a threshold, we use the automated tool SpArcFiRe (SPiral ARC FInder and REporter) to measure spiral arm pitch angles. We observe that the mean pitch angle of spiral arms increases linearly with redshift for 0.05 < z < 0.085. We hypothesize that this is a selection effect due to tightly-wound arms becoming less visible as image quality degrades, leading to fewer such galaxies being above the spirality threshold as redshift increases. We corroborate this hypothesis by first artificially degrading images of nearby galaxies, and then using a machine learning algorithm trained on Galaxy Zoo data to provide a spirality for each artificially degraded image. We find that SpARcFiRe's ability to accurately measure pitch angles decreases as the image degrades, but that spirality decreases more quickly in galaxies with tightly wound arms, leading to the selection effect. This new bias means one must be careful in selecting a sample on which to measure spiral structure. Finally, we also include a sensitivity analysis of SpArcFiRe's internal parameters.

Feature Grouping and Selection Over an Undirected Graph.

PubMed

Yang, Sen; Yuan, Lei; Lai, Ying-Cheng; Shen, Xiaotong; Wonka, Peter; Ye, Jieping

2012-01-01

High-dimensional regression/classification continues to be an important and challenging problem, especially when features are highly correlated. Feature selection, combined with additional structure information on the features has been considered to be promising in promoting regression/classification performance. Graph-guided fused lasso (GFlasso) has recently been proposed to facilitate feature selection and graph structure exploitation, when features exhibit certain graph structures. However, the formulation in GFlasso relies on pairwise sample correlations to perform feature grouping, which could introduce additional estimation bias. In this paper, we propose three new feature grouping and selection methods to resolve this issue. The first method employs a convex function to penalize the pairwise l ∞ norm of connected regression/classification coefficients, achieving simultaneous feature grouping and selection. The second method improves the first one by utilizing a non-convex function to reduce the estimation bias. The third one is the extension of the second method using a truncated l 1 regularization to further reduce the estimation bias. The proposed methods combine feature grouping and feature selection to enhance estimation accuracy. We employ the alternating direction method of multipliers (ADMM) and difference of convex functions (DC) programming to solve the proposed formulations. Our experimental results on synthetic data and two real datasets demonstrate the effectiveness of the proposed methods.

Monitoring landscape metrics by point sampling: accuracy in estimating Shannon's diversity and edge density.

PubMed

Ramezani, Habib; Holm, Sören; Allard, Anna; Ståhl, Göran

2010-05-01

Environmental monitoring of landscapes is of increasing interest. To quantify landscape patterns, a number of metrics are used, of which Shannon's diversity, edge length, and density are studied here. As an alternative to complete mapping, point sampling was applied to estimate the metrics for already mapped landscapes selected from the National Inventory of Landscapes in Sweden (NILS). Monte-Carlo simulation was applied to study the performance of different designs. Random and systematic samplings were applied for four sample sizes and five buffer widths. The latter feature was relevant for edge length, since length was estimated through the number of points falling in buffer areas around edges. In addition, two landscape complexities were tested by applying two classification schemes with seven or 20 land cover classes to the NILS data. As expected, the root mean square error (RMSE) of the estimators decreased with increasing sample size. The estimators of both metrics were slightly biased, but the bias of Shannon's diversity estimator was shown to decrease when sample size increased. In the edge length case, an increasing buffer width resulted in larger bias due to the increased impact of boundary conditions; this effect was shown to be independent of sample size. However, we also developed adjusted estimators that eliminate the bias of the edge length estimator. The rates of decrease of RMSE with increasing sample size and buffer width were quantified by a regression model. Finally, indicative cost-accuracy relationships were derived showing that point sampling could be a competitive alternative to complete wall-to-wall mapping.

Attention bias in adults with anorexia nervosa, obsessive-compulsive disorder, and social anxiety disorder

PubMed Central

Schneier, Franklin R.; Kimeldorf, Marcia B.; Choo, Tse; Steinglass, Joanna E.; Wall, Melanie; Fyer, Abby J.; Simpson, H. Blair

2016-01-01

Background Attention bias to threat (selective attention toward threatening stimuli) has been frequently found in anxiety disorder samples, but its distribution both within and beyond this category is unclear. Attention bias has been studied extensively in social anxiety disorder (SAD) but relatively little in obsessive compulsive disorder (OCD), historically considered an anxiety disorder, or anorexia nervosa (AN), which is often characterized by interpersonal as well as body image/eating fears. Methods Medication-free adults with SAD (n=43), OCD (n=50), or AN (n=30), and healthy control volunteers (HC, n=74) were evaluated for attention bias with an established dot probe task presenting images of angry and neutral faces. Additional outcomes included attention bias variability (ABV), which summarizes fluctuation in attention between vigilance and avoidance, and has been reported to have superior reliability. We hypothesized that attention bias would be elevated in SAD and associated with SAD severity. Results Attention bias in each disorder did not differ from HC, but within the SAD group attention bias correlated significantly with severity of social avoidance. ABV was significantly lower in OCD versus HC, and it correlated positively with severity of OCD symptoms within the OCD group. Conclusions Findings do not support differences from HC in attention bias to threat faces for SAD, OCD, or AN. Within the SAD sample, the association of attention bias with severity of social avoidance is consistent with evidence that attention bias moderates development of social withdrawal. The association of ABV with OCD diagnosis and severity is novel and deserves further study. PMID:27174402

Uncovering racial bias in nursing fundamentals textbooks.

PubMed

Byrne, M M

2001-01-01

This article describes research that sought to identify and critique selected content areas from three nursing fundamentals textbooks for the presence or absence of racial bias embedded in the portrayal of African Americans. The analyzed content areas were the history of nursing, cultural content, and physical assessment/hygiene parameters. A researcher-developed guide was used for data collection and analysis of textual language, illustrations, linguistics, and references. A thematic analysis resulted in I I themes reflecting the portrayal of African Americans in these sampled textbooks. An interpretive analysis with a lens of Sadker and Sadker's categories of bias, along with other literary and theoretical contexts, were used to explore for the presence or absence of racial bias. Recommendations for nursing education are provided.

The search for loci under selection: trends, biases and progress.

PubMed

Ahrens, Collin W; Rymer, Paul D; Stow, Adam; Bragg, Jason; Dillon, Shannon; Umbers, Kate D L; Dudaniec, Rachael Y

2018-03-01

Detecting genetic variants under selection using F ST outlier analysis (OA) and environmental association analyses (EAAs) are popular approaches that provide insight into the genetic basis of local adaptation. Despite the frequent use of OA and EAA approaches and their increasing attractiveness for detecting signatures of selection, their application to field-based empirical data have not been synthesized. Here, we review 66 empirical studies that use Single Nucleotide Polymorphisms (SNPs) in OA and EAA. We report trends and biases across biological systems, sequencing methods, approaches, parameters, environmental variables and their influence on detecting signatures of selection. We found striking variability in both the use and reporting of environmental data and statistical parameters. For example, linkage disequilibrium among SNPs and numbers of unique SNP associations identified with EAA were rarely reported. The proportion of putatively adaptive SNPs detected varied widely among studies, and decreased with the number of SNPs analysed. We found that genomic sampling effort had a greater impact than biological sampling effort on the proportion of identified SNPs under selection. OA identified a higher proportion of outliers when more individuals were sampled, but this was not the case for EAA. To facilitate repeatability, interpretation and synthesis of studies detecting selection, we recommend that future studies consistently report geographical coordinates, environmental data, model parameters, linkage disequilibrium, and measures of genetic structure. Identifying standards for how OA and EAA studies are designed and reported will aid future transparency and comparability of SNP-based selection studies and help to progress landscape and evolutionary genomics. © 2018 John Wiley & Sons Ltd.

Sample selection may bias the outcome of an adolescent mental health survey: results from a five-year follow-up of 4171 adolescents.

PubMed

Kekkonen, V; Kivimäki, P; Valtonen, H; Hintikka, J; Tolmunen, T; Lehto, S M; Laukkanen, E

2015-02-01

The representativeness of the data is one of the main issues in evaluating the significance of research findings. Dropping out is common in adolescent mental health research, and may distort the results. Nevertheless, very little is known about the types of systematic bias that may affect studies in a) the informed consent phase and b) later in follow-up phases. The authors addressed this gap in knowledge in a five-year follow-up study on a sample of adolescents aged 13-18 years. The data were collected using self-report questionnaires. The baseline sample consisted of 4171 adolescents, 1827 (43.8%) of whom gave consent to be contacted for a follow-up survey, but only 797 (19.1%) participated in the follow-up. Binary logistic regression models were used to explain the participation. Young age, female gender, a high number of hobbies, good performance at school in the native language and general subjects, family disintegration such as divorce, high parental employment, and symptoms of depression and anxiety were associated with both consent and participation. However, the effect of mental health aspects was smaller than the effect of age and gender. This study confirmed the possibility of systematic selection bias by adolescents' sociodemographic characteristics. The representativeness of the study sample might have been improved by more intense recruitment strategies. Copyright © 2014 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Scolnic, D.; Kessler, R., E-mail: dscolnic@kicp.uchicago.edu, E-mail: kessler@kicp.uchicago.edu

Simulations of Type Ia supernovae (SNe Ia) surveys are a critical tool for correcting biases in the analysis of SNe Ia to infer cosmological parameters. Large-scale Monte Carlo simulations include a thorough treatment of observation history, measurement noise, intrinsic scatter models, and selection effects. In this Letter, we improve simulations with a robust technique to evaluate the underlying populations of SN Ia color and stretch that correlate with luminosity. In typical analyses, the standardized SN Ia brightness is determined from linear “Tripp” relations between the light curve color and luminosity and between stretch and luminosity. However, this solution produces Hubblemore » residual biases because intrinsic scatter and measurement noise result in measured color and stretch values that do not follow the Tripp relation. We find a 10 σ bias (up to 0.3 mag) in Hubble residuals versus color and 5 σ bias (up to 0.2 mag) in Hubble residuals versus stretch in a joint sample of 920 spectroscopically confirmed SN Ia from PS1, SNLS, SDSS, and several low- z surveys. After we determine the underlying color and stretch distributions, we use simulations to predict and correct the biases in the data. We show that removing these biases has a small impact on the low- z sample, but reduces the intrinsic scatter σ {sub int} from 0.101 to 0.083 in the combined PS1, SNLS, and SDSS sample. Past estimates of the underlying populations were too broad, leading to a small bias in the equation of state of dark energy w of Δ w = 0.005.« less

Breast Cancer and Women's Labor Supply

PubMed Central

Bradley, Cathy J; Bednarek, Heather L; Neumark, David

2002-01-01

Objective To investigate the effect of breast cancer on women's labor supply. Date Source/Study Setting Using the 1992 Health and Retirement Study, we estimate the probability of working using probit regression and then, for women who are employed, we estimate regressions for average weekly hours worked using ordinary least squares (OLS). We control for health status by using responses to perceived health status and comorbidities. For a sample of married women, we control for spouses' employer-based health insurance. We also perform additional analyses to detect selection bias in our sample. Principal Findings We find that the probability of breast cancer survivors working is 10 percentage points less than that for women without breast cancer. Among women who work, breast cancer survivors work approximately three more hours per week than women who do not have cancer. Results of similar magnitude persist after health status is controlled in the analysis, and although we could not definitively rule out selection bias, we could not find evidence that our results are attributable to selection bias. Conclusions For some women, breast cancer may impose an economic hardship because it causes them to leave their jobs. However, for women who survive and remain working, this study failed to show a negative effect on hours worked associated with breast cancer. Perhaps the morbidity associated with certain types and stages of breast cancer and its treatment does not interfere with work. PMID:12479498

Brighter galaxy bias: underestimating the velocity dispersions of galaxy clusters

NASA Astrophysics Data System (ADS)

Old, L.; Gray, M. E.; Pearce, F. R.

2013-09-01

We study the systematic bias introduced when selecting the spectroscopic redshifts of brighter cluster galaxies to estimate the velocity dispersion of galaxy clusters from both simulated and observational galaxy catalogues. We select clusters with Ngal ≥ 50 at five low-redshift snapshots from the publicly available De Lucia & Blaziot semi-analytic model galaxy catalogue. Clusters are also selected from the Tempel Sloan Digital Sky Survey Data Release 8 groups and clusters catalogue across the redshift range 0.021 ≤ z ≤ 0.098. We employ various selection techniques to explore whether the velocity dispersion bias is simply due to a lack of dynamical information or is the result of an underlying physical process occurring in the cluster, for example, dynamical friction experienced by the brighter cluster members. The velocity dispersions of the parent dark matter (DM) haloes are compared to the galaxy cluster dispersions and the stacked distribution of DM particle velocities is examined alongside the corresponding galaxy velocity distribution. We find a clear bias between the halo and the semi-analytic galaxy cluster velocity dispersion on the order of σgal/σDM ˜ 0.87-0.95 and a distinct difference in the stacked galaxy and DM particle velocities distribution. We identify a systematic underestimation of the velocity dispersions when imposing increasing absolute I-band magnitude limits. This underestimation is enhanced when using only the brighter cluster members for dynamical analysis on the order of 5-35 per cent, indicating that dynamical friction is a serious source of bias when using galaxy velocities as tracers of the underlying gravitational potential. In contrast to the literature we find that the resulting bias is not only halo mass dependent but also that the nature of the dependence changes according to the galaxy selection strategy. We make a recommendation that, in the realistic case of limited availability of spectral observations, a strictly magnitude-limited sample should be avoided to ensure an unbiased estimate of the velocity dispersion.

Electric Field-aided Selective Activation for Indium-Gallium-Zinc-Oxide Thin Film Transistors.

PubMed

Lee, Heesoo; Chang, Ki Soo; Tak, Young Jun; Jung, Tae Soo; Park, Jeong Woo; Kim, Won-Gi; Chung, Jusung; Jeong, Chan Bae; Kim, Hyun Jae

2016-10-11

A new technique is proposed for the activation of low temperature amorphous InGaZnO thin film transistor (a-IGZO TFT) backplanes through application of a bias voltage and annealing at 130 °C simultaneously. In this 'electrical activation', the effects of annealing under bias are selectively focused in the channel region. Therefore, electrical activation can be an effective method for lower backplane processing temperatures from 280 °C to 130 °C. Devices fabricated with this method exhibit equivalent electrical properties to those of conventionally-fabricated samples. These results are analyzed electrically and thermodynamically using infrared microthermography. Various bias voltages are applied to the gate, source, and drain electrodes while samples are annealed at 130 °C for 1 hour. Without conventional high temperature annealing or electrical activation, current-voltage curves do not show transfer characteristics. However, electrically activated a-IGZO TFTs show superior electrical characteristics, comparable to the reference TFTs annealed at 280 °C for 1 hour. This effect is a result of the lower activation energy, and efficient transfer of electrical and thermal energy to a-IGZO TFTs. With this approach, superior low-temperature a-IGZO TFTs are fabricated successfully.

The impact of non-response bias due to sampling in public health studies: A comparison of voluntary versus mandatory recruitment in a Dutch national survey on adolescent health.

PubMed

Cheung, Kei Long; Ten Klooster, Peter M; Smit, Cees; de Vries, Hein; Pieterse, Marcel E

2017-03-23

In public health monitoring of young people it is critical to understand the effects of selective non-response, in particular when a controversial topic is involved like substance abuse or sexual behaviour. Research that is dependent upon voluntary subject participation is particularly vulnerable to sampling bias. As respondents whose participation is hardest to elicit on a voluntary basis are also more likely to report risk behaviour, this potentially leads to underestimation of risk factor prevalence. Inviting adolescents to participate in a home-sent postal survey is a typical voluntary recruitment strategy with high non-response, as opposed to mandatory participation during school time. This study examines the extent to which prevalence estimates of adolescent health-related characteristics are biased due to different sampling methods, and whether this also biases within-subject analyses. Cross-sectional datasets collected in 2011 in Twente and IJsselland, two similar and adjacent regions in the Netherlands, were used. In total, 9360 youngsters in a mandatory sample (Twente) and 1952 youngsters in a voluntary sample (IJsselland) participated in the study. To test whether the samples differed on health-related variables, we conducted both univariate and multivariable logistic regression analyses controlling for any demographic difference between the samples. Additional multivariable logistic regressions were conducted to examine moderating effects of sampling method on associations between health-related variables. As expected, females, older individuals, as well as individuals with higher education levels, were over-represented in the voluntary sample, compared to the mandatory sample. Respondents in the voluntary sample tended to smoke less, consume less alcohol (ever, lifetime, and past four weeks), have better mental health, have better subjective health status, have more positive school experiences and have less sexual intercourse than respondents in the mandatory sample. No moderating effects were found for sampling method on associations between variables. This is one of first studies to provide strong evidence that voluntary recruitment may lead to a strong non-response bias in health-related prevalence estimates in adolescents, as compared to mandatory recruitment. The resulting underestimation in prevalence of health behaviours and well-being measures appeared large, up to a four-fold lower proportion for self-reported alcohol consumption. Correlations between variables, though, appeared to be insensitive to sampling bias.

Infrared properties of serendipitous X-ray quasars

NASA Technical Reports Server (NTRS)

Neugebauer, G.; Soifer, B. T.; Matthews, K.; Margon, B.; Chanan, G. A.

1982-01-01

Near infrared measurements were obtained of 30 quasars originally found serendipitously as X-ray sources in fields of other objects. The observations show that the infrared characteristics of these quasars do not differ significantly from those of quasars selected by other criteria. Because this X-ray selected sample is subject to different selection biases than previous radio and optical surveys, this conclusion is useful in validating previous inferences regarding the infrared colors of 'typical' quasars.

Attentional Bias towards Positive Emotion Predicts Stress Resilience.

PubMed

Thoern, Hanna A; Grueschow, Marcus; Ehlert, Ulrike; Ruff, Christian C; Kleim, Birgit

2016-01-01

There is extensive evidence for an association between an attentional bias towards emotionally negative stimuli and vulnerability to stress-related psychopathology. Less is known about whether selective attention towards emotionally positive stimuli relates to mental health and stress resilience. The current study used a modified Dot Probe task to investigate if individual differences in attentional biases towards either happy or angry emotional stimuli, or an interaction between these biases, are related to self-reported trait stress resilience. In a nonclinical sample (N = 43), we indexed attentional biases as individual differences in reaction time for stimuli preceded by either happy or angry (compared to neutral) face stimuli. Participants with greater attentional bias towards happy faces (but not angry faces) reported higher trait resilience. However, an attentional bias towards angry stimuli moderated this effect: The attentional bias towards happy faces was only predictive for resilience in those individuals who also endorsed an attentional bias towards angry stimuli. An attentional bias towards positive emotional stimuli may thus be a protective factor contributing to stress resilience, specifically in those individuals who also endorse an attentional bias towards negative emotional stimuli. Our findings therefore suggest a novel target for prevention and treatment interventions addressing stress-related psychopathology.

Supernovae - A new selection effect. [statistical distribution in and radial distance from center of parent galaxy

NASA Technical Reports Server (NTRS)

Shaw, R. L.

1979-01-01

A sample of 228 supernovae that occurred in galaxies with known redshifts is used to show that the mean projected linear supernova distance from the center of the parent galaxy increases with increasing redshift. This effect is interpreted as an observational bias: the discovery rate of supernovae is reduced in the inner parts of distant, poorly resolved galaxies. Even under the optimistic assumption that no selection effects work in galaxies closer than 33 Mpc, about 50% of all supernovae are lost in the inner regions of galaxies beyond 150 Mpc. This observational bias must be taken into account in the derivation of statistical properties of supernovae.

Randomized clinical trials in dentistry: Risks of bias, risks of random errors, reporting quality, and methodologic quality over the years 1955–2013

PubMed Central

Armijo-Olivo, Susan; Cummings, Greta G.; Amin, Maryam; Flores-Mir, Carlos

2017-01-01

Objectives To examine the risks of bias, risks of random errors, reporting quality, and methodological quality of randomized clinical trials of oral health interventions and the development of these aspects over time. Methods We included 540 randomized clinical trials from 64 selected systematic reviews. We extracted, in duplicate, details from each of the selected randomized clinical trials with respect to publication and trial characteristics, reporting and methodologic characteristics, and Cochrane risk of bias domains. We analyzed data using logistic regression and Chi-square statistics. Results Sequence generation was assessed to be inadequate (at unclear or high risk of bias) in 68% (n = 367) of the trials, while allocation concealment was inadequate in the majority of trials (n = 464; 85.9%). Blinding of participants and blinding of the outcome assessment were judged to be inadequate in 28.5% (n = 154) and 40.5% (n = 219) of the trials, respectively. A sample size calculation before the initiation of the study was not performed/reported in 79.1% (n = 427) of the trials, while the sample size was assessed as adequate in only 17.6% (n = 95) of the trials. Two thirds of the trials were not described as double blinded (n = 358; 66.3%), while the method of blinding was appropriate in 53% (n = 286) of the trials. We identified a significant decrease over time (1955–2013) in the proportion of trials assessed as having inadequately addressed methodological quality items (P < 0.05) in 30 out of the 40 quality criteria, or as being inadequate (at high or unclear risk of bias) in five domains of the Cochrane risk of bias tool: sequence generation, allocation concealment, incomplete outcome data, other sources of bias, and overall risk of bias. Conclusions The risks of bias, risks of random errors, reporting quality, and methodological quality of randomized clinical trials of oral health interventions have improved over time; however, further efforts that contribute to the development of more stringent methodology and detailed reporting of trials are still needed. PMID:29272315

Randomized clinical trials in dentistry: Risks of bias, risks of random errors, reporting quality, and methodologic quality over the years 1955-2013.

PubMed

Saltaji, Humam; Armijo-Olivo, Susan; Cummings, Greta G; Amin, Maryam; Flores-Mir, Carlos

2017-01-01

To examine the risks of bias, risks of random errors, reporting quality, and methodological quality of randomized clinical trials of oral health interventions and the development of these aspects over time. We included 540 randomized clinical trials from 64 selected systematic reviews. We extracted, in duplicate, details from each of the selected randomized clinical trials with respect to publication and trial characteristics, reporting and methodologic characteristics, and Cochrane risk of bias domains. We analyzed data using logistic regression and Chi-square statistics. Sequence generation was assessed to be inadequate (at unclear or high risk of bias) in 68% (n = 367) of the trials, while allocation concealment was inadequate in the majority of trials (n = 464; 85.9%). Blinding of participants and blinding of the outcome assessment were judged to be inadequate in 28.5% (n = 154) and 40.5% (n = 219) of the trials, respectively. A sample size calculation before the initiation of the study was not performed/reported in 79.1% (n = 427) of the trials, while the sample size was assessed as adequate in only 17.6% (n = 95) of the trials. Two thirds of the trials were not described as double blinded (n = 358; 66.3%), while the method of blinding was appropriate in 53% (n = 286) of the trials. We identified a significant decrease over time (1955-2013) in the proportion of trials assessed as having inadequately addressed methodological quality items (P < 0.05) in 30 out of the 40 quality criteria, or as being inadequate (at high or unclear risk of bias) in five domains of the Cochrane risk of bias tool: sequence generation, allocation concealment, incomplete outcome data, other sources of bias, and overall risk of bias. The risks of bias, risks of random errors, reporting quality, and methodological quality of randomized clinical trials of oral health interventions have improved over time; however, further efforts that contribute to the development of more stringent methodology and detailed reporting of trials are still needed.

Evidence of sex-bias in gene expression in the brain transcriptome of two populations of rainbow trout (Oncorhynchus mykiss) with divergent life histories.

PubMed

Hale, Matthew C; McKinney, Garrett J; Thrower, Frank P; Nichols, Krista M

2018-01-01

Sex-bias in gene expression is a mechanism that can generate phenotypic variance between the sexes, however, relatively little is known about how patterns of sex-bias vary during development, and how variable sex-bias is between different populations. To that end, we measured sex-bias in gene expression in the brain transcriptome of rainbow trout (Oncorhynchus mykiss) during the first two years of development. Our sampling included from the fry stage through to when O. mykiss either migrate to the ocean or remain resident and undergo sexual maturation. Samples came from two F1 lines: One from migratory steelhead trout and one from resident rainbow trout. All samples were reared in a common garden environment and RNA sequencing (RNA-seq) was used to estimate patterns of gene expression. A total of 1,716 (4.6% of total) genes showed evidence of sex-bias in gene expression in at least one time point. The majority (96.7%) of sex-biased genes were differentially expressed during the second year of development, indicating that patterns of sex-bias in expression are tied to key developmental events, such as migration and sexual maturation. Mapping of differentially expressed genes to the O. mykiss genome revealed that the X chromosome is enriched for female upregulated genes, and this may indicate a lack of dosage compensation in rainbow trout. There were many more sex-biased genes in the migratory line than the resident line suggesting differences in patterns of gene expression in the brain between populations subjected to different forces of selection. Overall, our results suggest that there is considerable variation in the extent and identity of genes exhibiting sex-bias during the first two years of life. These differentially expressed genes may be connected to developmental differences between the sexes, and/or between adopting a resident or migratory life history.

redMaGiC: Selecting luminous red galaxies from the DES Science Verification data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rozo, E.; Rykoff, E. S.; Abate, A.

Here, we introduce redMaGiC, an automated algorithm for selecting luminous red galaxies (LRGs). The algorithm was specifically developed to minimize photometric redshift uncertainties in photometric large-scale structure studies. redMaGiC achieves this by self-training the colour cuts necessary to produce a luminosity-thresholded LRG sample of constant comoving density. We demonstrate that redMaGiC photo-zs are very nearly as accurate as the best machine learning-based methods, yet they require minimal spectroscopic training, do not suffer from extrapolation biases, and are very nearly Gaussian. We apply our algorithm to Dark Energy Survey (DES) Science Verification (SV) data to produce a redMaGiC catalogue sampling themore » redshift range z ϵ [0.2, 0.8]. Our fiducial sample has a comoving space density of 10 –3 (h –1 Mpc) –3, and a median photo-z bias (zspec – zphoto) and scatter (σz/(1 + z)) of 0.005 and 0.017, respectively. The corresponding 5σ outlier fraction is 1.4 per cent. We also test our algorithm with Sloan Digital Sky Survey Data Release 8 and Stripe 82 data, and discuss how spectroscopic training can be used to control photo-z biases at the 0.1 per cent level.« less

redMaGiC: Selecting luminous red galaxies from the DES Science Verification data

DOE PAGES

Rozo, E.; Rykoff, E. S.; Abate, A.; ...

2016-05-30

Here, we introduce redMaGiC, an automated algorithm for selecting luminous red galaxies (LRGs). The algorithm was specifically developed to minimize photometric redshift uncertainties in photometric large-scale structure studies. redMaGiC achieves this by self-training the colour cuts necessary to produce a luminosity-thresholded LRG sample of constant comoving density. We demonstrate that redMaGiC photo-zs are very nearly as accurate as the best machine learning-based methods, yet they require minimal spectroscopic training, do not suffer from extrapolation biases, and are very nearly Gaussian. We apply our algorithm to Dark Energy Survey (DES) Science Verification (SV) data to produce a redMaGiC catalogue sampling themore » redshift range z ϵ [0.2, 0.8]. Our fiducial sample has a comoving space density of 10 –3 (h –1 Mpc) –3, and a median photo-z bias (zspec – zphoto) and scatter (σz/(1 + z)) of 0.005 and 0.017, respectively. The corresponding 5σ outlier fraction is 1.4 per cent. We also test our algorithm with Sloan Digital Sky Survey Data Release 8 and Stripe 82 data, and discuss how spectroscopic training can be used to control photo-z biases at the 0.1 per cent level.« less

Variations in the promoter region of the serotonin transporter gene and biased attention for emotional information: a meta-analysis.

PubMed

Pergamin-Hight, Lee; Bakermans-Kranenburg, Marian J; van Ijzendoorn, Marinus H; Bar-Haim, Yair

2012-02-15

Selective attention to negative information has been strongly implicated in the etiology and maintenance of anxiety and offered as a potential intermediate phenotype for anxiety disorders. Attention biases have been studied in relation to a polymorphism in the promoter region of the serotonin transporter gene (5-HTTLPR) offering equivocal findings. The present meta-analysis tested whether the extant published data support the notion that variation in the 5-HTTLPR genotype modulates selective attention to negative information. Eleven relevant samples from 10 published articles were identified through a systematic literature search (total n = 807). Relevant attention bias and 5-HTTLPR data were extracted based on specific coding rules, and Cohen's d effect size index was used to calculate all outcome measures. Publication bias was assessed using various methods. Carriers of the low (SS, SL(G), L(G)L(G)) transmission efficacy genotype display attentional vigilance toward negatively valenced stimuli, a pattern not found in the intermediate (SL(A), L(A)L(G)) and high (L(A)L(A)) efficacy genotypes. This phenomenon emerges as of medium effect size. The meta-analysis supports the notion that allele variants of the 5-HTTLPR are associated with selective attention to negative stimuli. More studies are needed to fully establish the consistency of this effect. Future studies applying systematic attention bias modification may shed further light on the role of 5-HTTLPR in the development of anxiety disorders and in the prediction of clinical response to attention bias modification treatments. Copyright © 2012 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Selection bias in rheumatic disease research.

PubMed

Choi, Hyon K; Nguyen, Uyen-Sa; Niu, Jingbo; Danaei, Goodarz; Zhang, Yuqing

2014-07-01

The identification of modifiable risk factors for the development of rheumatic conditions and their sequelae is crucial for reducing the substantial worldwide burden of these diseases. However, the validity of such research can be threatened by sources of bias, including confounding, measurement and selection biases. In this Review, we discuss potentially major issues of selection bias--a type of bias frequently overshadowed by other bias and feasibility issues, despite being equally or more problematic--in key areas of rheumatic disease research. We present index event bias (a type of selection bias) as one of the potentially unifying reasons behind some unexpected findings, such as the 'risk factor paradox'--a phenomenon exemplified by the discrepant effects of certain risk factors on the development versus the progression of osteoarthritis (OA) or rheumatoid arthritis (RA). We also discuss potential selection biases owing to differential loss to follow-up in RA and OA research, as well as those due to the depletion of susceptibles (prevalent user bias) and immortal time bias. The lesson remains that selection bias can be ubiquitous and, therefore, has the potential to lead the field astray. Thus, we conclude with suggestions to help investigators avoid such issues and limit the impact on future rheumatology research.

Potential sources of analytical bias and error in selected trace element data-quality analyses

USGS Publications Warehouse

Paul, Angela P.; Garbarino, John R.; Olsen, Lisa D.; Rosen, Michael R.; Mebane, Christopher A.; Struzeski, Tedmund M.

2016-09-28

Potential sources of analytical bias and error associated with laboratory analyses for selected trace elements where concentrations were greater in filtered samples than in paired unfiltered samples were evaluated by U.S. Geological Survey (USGS) Water Quality Specialists in collaboration with the USGS National Water Quality Laboratory (NWQL) and the Branch of Quality Systems (BQS).Causes for trace-element concentrations in filtered samples to exceed those in associated unfiltered samples have been attributed to variability in analytical measurements, analytical bias, sample contamination either in the field or laboratory, and (or) sample-matrix chemistry. These issues have not only been attributed to data generated by the USGS NWQL but have been observed in data generated by other laboratories. This study continues the evaluation of potential analytical bias and error resulting from matrix chemistry and instrument variability by evaluating the performance of seven selected trace elements in paired filtered and unfiltered surface-water and groundwater samples collected from 23 sampling sites of varying chemistries from six States, matrix spike recoveries, and standard reference materials.Filtered and unfiltered samples have been routinely analyzed on separate inductively coupled plasma-mass spectrometry instruments. Unfiltered samples are treated with hydrochloric acid (HCl) during an in-bottle digestion procedure; filtered samples are not routinely treated with HCl as part of the laboratory analytical procedure. To evaluate the influence of HCl on different sample matrices, an aliquot of the filtered samples was treated with HCl. The addition of HCl did little to differentiate the analytical results between filtered samples treated with HCl from those samples left untreated; however, there was a small, but noticeable, decrease in the number of instances where a particular trace-element concentration was greater in a filtered sample than in the associated unfiltered sample for all trace elements except selenium. Accounting for the small dilution effect (2 percent) from the addition of HCl, as required for the in-bottle digestion procedure for unfiltered samples, may be one step toward decreasing the number of instances where trace-element concentrations are greater in filtered samples than in paired unfiltered samples.The laboratory analyses of arsenic, cadmium, lead, and zinc did not appear to be influenced by instrument biases. These trace elements showed similar results on both instruments used to analyze filtered and unfiltered samples. The results for aluminum and molybdenum tended to be higher on the instrument designated to analyze unfiltered samples; the results for selenium tended to be lower. The matrices used to prepare calibration standards were different for the two instruments. The instrument designated for the analysis of unfiltered samples was calibrated using standards prepared in a nitric:hydrochloric acid (HNO3:HCl) matrix. The instrument designated for the analysis of filtered samples was calibrated using standards prepared in a matrix acidified only with HNO3. Matrix chemistry may have influenced the responses of aluminum, molybdenum, and selenium on the two instruments. The best analytical practice is to calibrate instruments using calibration standards prepared in matrices that reasonably match those of the samples being analyzed.Filtered and unfiltered samples were spiked over a range of trace-element concentrations from less than 1 to 58 times ambient concentrations. The greater the magnitude of the trace-element spike concentration relative to the ambient concentration, the greater the likelihood spike recoveries will be within data control guidelines (80–120 percent). Greater variability in spike recoveries occurred when trace elements were spiked at concentrations less than 10 times the ambient concentration. Spike recoveries that were considerably lower than 90 percent often were associated with spiked concentrations substantially lower than what was present in the ambient sample. Because the main purpose of spiking natural water samples with known quantities of a particular analyte is to assess possible matrix effects on analytical results, the results of this study stress the importance of spiking samples at concentrations that are reasonably close to what is expected but sufficiently high to exceed analytical variability. Generally, differences in spike recovery results between paired filtered and unfiltered samples were minimal when samples were analyzed on the same instrument.Analytical results for trace-element concentrations in ambient filtered and unfiltered samples greater than 10 and 40 μg/L, respectively, were within the data-quality objective for precision of ±25 percent. Ambient trace-element concentrations in filtered samples greater than the long-term method detection limits but less than 10 μg/L failed to meet the data-quality objective for precision for at least one trace element in about 54 percent of the samples. Similarly, trace-element concentrations in unfiltered samples greater than the long-term method detection limits but less than 40 μg/L failed to meet this data-quality objective for at least one trace-element analysis in about 58 percent of the samples. Although, aluminum and zinc were particularly problematic, limited re-analyses of filtered and unfiltered samples appeared to improve otherwise failed analytical precision.The evaluation of analytical bias using standard reference materials indicate a slight low bias for results for arsenic, cadmium, selenium, and zinc. Aluminum and molybdenum show signs of high bias. There was no observed bias, as determined using the standard reference materials, during the analysis of lead.

Bias due to differential participation in case-control studies and review of available approaches for adjustment.

PubMed

Aigner, Annette; Grittner, Ulrike; Becher, Heiko

2018-01-01

Low response rates in epidemiologic research potentially lead to the recruitment of a non-representative sample of controls in case-control studies. Problems in the unbiased estimation of odds ratios arise when characteristics causing the probability of participation are associated with exposure and outcome. This is a specific setting of selection bias and a realistic hazard in many case-control studies. This paper formally describes the problem and shows its potential extent, reviews existing approaches for bias adjustment applicable under certain conditions, compares and applies them. We focus on two scenarios: a characteristic C causing differential participation of controls is linked to the outcome through its association with risk factor E (scenario I), and C is additionally a genuine risk factor itself (scenario II). We further assume external data sources are available which provide an unbiased estimate of C in the underlying population. Given these scenarios, we (i) review available approaches and their performance in the setting of bias due to differential participation; (ii) describe two existing approaches to correct for the bias in both scenarios in more detail; (iii) present the magnitude of the resulting bias by simulation if the selection of a non-representative sample is ignored; and (iv) demonstrate the approaches' application via data from a case-control study on stroke. The bias of the effect measure for variable E in scenario I and C in scenario II can be large and should therefore be adjusted for in any analysis. It is positively associated with the difference in response rates between groups of the characteristic causing differential participation, and inversely associated with the total response rate in the controls. Adjustment in a standard logistic regression framework is possible in both scenarios if the population distribution of the characteristic causing differential participation is known or can be approximated well.

New perspectives on microbial community distortion after whole-genome amplification

EPA Science Inventory

Whole-genome amplification (WGA) has become an important tool to explore the genomic information of microorganisms in an environmental sample with limited biomass, however potential selective biases during the amplification processes are poorly understood. Here, we describe the e...

Investigations of potential bias in the estimation of lambda using Pradel's (1996) model for capture-recapture data

USGS Publications Warehouse

Hines, J.E.; Nichols, J.D.

2002-01-01

Pradel's (1996) temporal symmetry model permitting direct estimation and modelling of population growth rate, lambda sub i provides a potentially useful tool for the study of population dynamics using marked animals. Because of its recent publication date, the approach has not seen much use, and there have been virtually no investigations directed at robustness of the resulting estimators. Here we consider several potential sources of bias, all motivated by specific uses of this estimation approach. We consider sampling situations in which the study area expands with time and present an analytic expression for the bias in lambda hat sub i. We next consider trap response in capture probabilities and heterogeneous capture probabilities and compute large-sample and simulation-based approximations of resulting bias in lambda hat sub i. These approximations indicate that trap response is an especially important assumption violation that can produce substantial bias. Finally, we consider losses on capture and emphasize the importance of selecting the estimator for lambda sub i that is appropriate to the question being addressed. For studies based on only sighting and resighting data, Pradel's (1996) lambda hat prime sub i is the appropriate estimator.

MBE System for Antimonide Based Semiconductor Lasers

DTIC Science & Technology

1999-01-31

selectivity are reported as a function of plasma chemistry and DC self-bias. Experiment The samples used in this study are undoped bulk GaSb, InSb...Phys. Lett. 64(13), 1673-1675 (1994). 8. J. W. Lee, J. Hong, E. S. Lambers, C. R. Abernathy, S. J. Pearton, W. S. Hobson, and F. Ren, Plasma Chemistry and...AlGaAsSb are reported as functions of plasma chemistry , ICP power, RF self-bias, and chamber pressure. It is found that physical sputtering desorption of

Selective outcome reporting and sponsorship in randomized controlled trials in IVF and ICSI.

PubMed

Braakhekke, M; Scholten, I; Mol, F; Limpens, J; Mol, B W; van der Veen, F

2017-10-01

Are randomized controlled trials (RCTs) on IVF and ICSI subject to selective outcome reporting and is this related to sponsorship? There are inconsistencies, independent from sponsorship, in the reporting of primary outcome measures in the majority of IVF and ICSI trials, indicating selective outcome reporting. RCTs are subject to bias at various levels. Of these biases, selective outcome reporting is particularly relevant to IVF and ICSI trials since there is a wide variety of outcome measures to choose from. An established cause of reporting bias is sponsorship. It is, at present, unknown whether RCTs in IVF/ICSI are subject to selective outcome reporting and whether this is related with sponsorship. We systematically searched RCTs on IVF and ICSI published between January 2009 and March 2016 in MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials and the publisher subset of PubMed. We analysed 415 RCTs. Per included RCT, we extracted data on impact factor of the journal, sample size, power calculation, and trial registry and thereafter data on primary outcome measure, the direction of trial results and sponsorship. Of the 415 identified RCTs, 235 were excluded for our primary analysis, because the sponsorship was not reported. Of the 180 RCTs included in our analysis, 7 trials did not report on any primary outcome measure and 107 of the remaining 173 trials (62%) reported on surrogate primary outcome measures. Of the 114 registered trials, 21 trials (18%) provided primary outcomes in their manuscript that were different from those in the trial registry. This indicates selective outcome reporting. We found no association between selective outcome reporting and sponsorship. We ran additional analyses to include the trials that had not reported sponsorship and found no outcomes that differed from our primary analysis. Since the majority of the trials did not report on sponsorship, there is a risk on sampling bias. IVF and ICSI trials are subject, to a large extent, to selective outcome reporting. Readers should be aware of this to avoid implementation of false or misleading results in clinical practice. No funding received and there are no conflicts of interest. N/A. © The Author 2017. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Attrition Bias in Panel Data: A Sheep in Wolf's Clothing? A Case Study Based on the Mabel Survey.

PubMed

Cheng, Terence C; Trivedi, Pravin K

2015-09-01

This paper investigates the nature and consequences of sample attrition in a unique longitudinal survey of medical doctors. We describe the patterns of non-response and examine if attrition affects the econometric analysis of medical labour market outcomes using the estimation of physician earnings equations as a case study. We compare the econometric gestimates obtained from a number of different modelling strategies, which are as follows: balanced versus unbalanced samples; an attrition model for panel data based on the classic sample selection model; and a recently developed copula-based selection model. Descriptive evidence shows that doctors who work longer hours, have lower years of experience, are overseas trained and have changed their work location are more likely to drop out. Our analysis suggests that the impact of attrition on inference about the earnings of general practitioners is small. For specialists, there appears to be some evidence for an economically significant bias. Finally, we discuss how the top-up samples in the Medicine in Australia: Balancing Employment and Life survey can be used to address the problem of panel attrition. Copyright © 2015 John Wiley & Sons, Ltd.

Does Self-Selection Affect Samples’ Representativeness in Online Surveys? An Investigation in Online Video Game Research

PubMed Central

van Singer, Mathias; Chatton, Anne; Achab, Sophia; Zullino, Daniele; Rothen, Stephane; Khan, Riaz; Billieux, Joel; Thorens, Gabriel

2014-01-01

Background The number of medical studies performed through online surveys has increased dramatically in recent years. Despite their numerous advantages (eg, sample size, facilitated access to individuals presenting stigmatizing issues), selection bias may exist in online surveys. However, evidence on the representativeness of self-selected samples in online studies is patchy. Objective Our objective was to explore the representativeness of a self-selected sample of online gamers using online players’ virtual characters (avatars). Methods All avatars belonged to individuals playing World of Warcraft (WoW), currently the most widely used online game. Avatars’ characteristics were defined using various games’ scores, reported on the WoW’s official website, and two self-selected samples from previous studies were compared with a randomly selected sample of avatars. Results We used scores linked to 1240 avatars (762 from the self-selected samples and 478 from the random sample). The two self-selected samples of avatars had higher scores on most of the assessed variables (except for guild membership and exploration). Furthermore, some guilds were overrepresented in the self-selected samples. Conclusions Our results suggest that more proficient players or players more involved in the game may be more likely to participate in online surveys. Caution is needed in the interpretation of studies based on online surveys that used a self-selection recruitment procedure. Epidemiological evidence on the reduced representativeness of sample of online surveys is warranted. PMID:25001007

Do You See What I See? Exploring the Consequences of Luminosity Limits in Black Hole–Galaxy Evolution Studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jones, Mackenzie L.; Hickox, Ryan C.; DiPompeo, Michael A.

In studies of the connection between active galactic nuclei (AGNs) and their host galaxies, there is widespread disagreement on some key aspects of the connection. These disagreements largely stem from a lack of understanding of the nature of the full underlying AGN population. Recent attempts to probe this connection utilize both observations and simulations to correct for a missed population, but presently are limited by intrinsic biases and complicated models. We take a simple simulation for galaxy evolution and add a new prescription for AGN activity to connect galaxy growth to dark matter halo properties and AGN activity to starmore » formation. We explicitly model selection effects to produce an “observed” AGN population for comparison with observations and empirically motivated models of the local universe. This allows us to bypass the difficulties inherent in models that attempt to infer the AGN population by inverting selection effects. We investigate the impact of selecting AGNs based on thresholds in luminosity or Eddington ratio on the “observed” AGN population. By limiting our model AGN sample in luminosity, we are able to recreate the observed local AGN luminosity function and specific star formation-stellar mass distribution, and show that using an Eddington ratio threshold introduces less bias into the sample by selecting the full range of growing black holes, despite the challenge of selecting low-mass black holes. We find that selecting AGNs using these various thresholds yield samples with different AGN host galaxy properties.« less

Assessment of the reliability of data collected for the Department of Veterans Affairs national surgical quality improvement program.

PubMed

Davis, Chester L; Pierce, John R; Henderson, William; Spencer, C David; Tyler, Christine; Langberg, Robert; Swafford, Jennan; Felan, Gladys S; Kearns, Martha A; Booker, Brigitte

2007-04-01

The Office of the Medical Inspector of the Department of Veterans Affairs (VA) studied the reliability of data collected by the VA's National Surgical Quality Improvement Program (NSQIP). The study focused on case selection bias, accuracy of reports on patients who died, and interrater reliability measurements of patient risk variables and outcomes. Surgical data from a sample of 15 VA medical centers were analyzed. For case selection bias, reviewers applied NSQIP criteria to include or exclude 2,460 patients from the database, comparing their results with those of NSQIP staff. For accurate reporting of patients who died, reviewers compared Social Security numbers of 10,444 NSQIP records with those found in the VA Beneficiary Identification and Records Locator Subsystem, VA Patient Treatment Files, and Social Security Administration death files. For measurement of interrater reliability, reviewers reabstracted 59 variables in each of 550 patient medical records that also were recorded in the NSQIP database. On case selection bias, the reviewers agreed with NSQIP decisions on 2,418 (98%) of the 2,460 cases. Computer record matching identified 4 more deaths than the NSQIP total of 198, a difference of about 2%. For 52 of the categorical variables, agreement, uncorrected for chance, was 96%. For 48 of 52 categorical variables, kappas ranged from 0.61 to 1.0 (substantial to almost perfect agreement); none of the variables had kappas of less than 0.20 (slight to poor agreement). This sample of medical centers shows adherence to criteria in selecting cases for the NSQIP database, for reporting deaths, and for collecting patient risk variables.

A KiDS weak lensing analysis of assembly bias in GAMA galaxy groups

NASA Astrophysics Data System (ADS)

Dvornik, Andrej; Cacciato, Marcello; Kuijken, Konrad; Viola, Massimo; Hoekstra, Henk; Nakajima, Reiko; van Uitert, Edo; Brouwer, Margot; Choi, Ami; Erben, Thomas; Fenech Conti, Ian; Farrow, Daniel J.; Herbonnet, Ricardo; Heymans, Catherine; Hildebrandt, Hendrik; Hopkins, Andrew M.; McFarland, John; Norberg, Peder; Schneider, Peter; Sifón, Cristóbal; Valentijn, Edwin; Wang, Lingyu

2017-07-01

We investigate possible signatures of halo assembly bias for spectroscopically selected galaxy groups from the Galaxy And Mass Assembly (GAMA) survey using weak lensing measurements from the spatially overlapping regions of the deeper, high-imaging-quality photometric Kilo-Degree Survey. We use GAMA groups with an apparent richness larger than 4 to identify samples with comparable mean host halo masses but with a different radial distribution of satellite galaxies, which is a proxy for the formation time of the haloes. We measure the weak lensing signal for groups with a steeper than average and with a shallower than average satellite distribution and find no sign of halo assembly bias, with the bias ratio of 0.85^{+0.37}_{-0.25}, which is consistent with the Λ cold dark matter prediction. Our galaxy groups have typical masses of 1013 M⊙ h-1, naturally complementing previous studies of halo assembly bias on galaxy cluster scales.

Selection bias in rheumatic disease research

PubMed Central

Choi, Hyon K.; Nguyen, Uyen-Sa; Niu, Jingbo; Danaei, Goodarz; Zhang, Yuqing

2014-01-01

The identification of modifiable risk factors for the development of rheumatic conditions and their sequelae is crucial for reducing the substantial worldwide burden of these diseases. However, the validity of such research can be threatened by sources of bias, including confounding, measurement and selection biases. In this Review, we discuss potentially major issues of selection bias—a type of bias frequently overshadowed by other bias and feasibility issues, despite being equally or more problematic—in key areas of rheumatic disease research. We present index event bias (a type of selection bias) as one of the potentially unifying reasons behind some unexpected findings, such as the ‘risk factor paradox’—a phenomenon exemplified by the discrepant effects of certain risk factors on the development versus the progression of osteoarthritis (OA) or rheumatoid arthritis (RA). We also discuss potential selection biases owing to differential loss to follow-up in RA and OA research, as well as those due to the depletion of susceptibles (prevalent user bias) and immortal time bias. The lesson remains that selection bias can be ubiquitous and, therefore, has the potential to lead the field astray. Thus, we conclude with suggestions to help investigators avoid such issues and limit the impact on future rheumatology research. PMID:24686510

Bias correction for selecting the minimal-error classifier from many machine learning models.

PubMed

Ding, Ying; Tang, Shaowu; Liao, Serena G; Jia, Jia; Oesterreich, Steffi; Lin, Yan; Tseng, George C

2014-11-15

Supervised machine learning is commonly applied in genomic research to construct a classifier from the training data that is generalizable to predict independent testing data. When test datasets are not available, cross-validation is commonly used to estimate the error rate. Many machine learning methods are available, and it is well known that no universally best method exists in general. It has been a common practice to apply many machine learning methods and report the method that produces the smallest cross-validation error rate. Theoretically, such a procedure produces a selection bias. Consequently, many clinical studies with moderate sample sizes (e.g. n = 30-60) risk reporting a falsely small cross-validation error rate that could not be validated later in independent cohorts. In this article, we illustrated the probabilistic framework of the problem and explored the statistical and asymptotic properties. We proposed a new bias correction method based on learning curve fitting by inverse power law (IPL) and compared it with three existing methods: nested cross-validation, weighted mean correction and Tibshirani-Tibshirani procedure. All methods were compared in simulation datasets, five moderate size real datasets and two large breast cancer datasets. The result showed that IPL outperforms the other methods in bias correction with smaller variance, and it has an additional advantage to extrapolate error estimates for larger sample sizes, a practical feature to recommend whether more samples should be recruited to improve the classifier and accuracy. An R package 'MLbias' and all source files are publicly available. tsenglab.biostat.pitt.edu/software.htm. ctseng@pitt.edu Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Immortal time bias: a frequently unrecognized threat to validity in the evaluation of postoperative radiotherapy.

PubMed

Park, Henry S; Gross, Cary P; Makarov, Danil V; Yu, James B

2012-08-01

To evaluate the influence of immortal time bias on observational cohort studies of postoperative radiotherapy (PORT) and the effectiveness of sequential landmark analysis to account for this bias. First, we reviewed previous studies of the Surveillance, Epidemiology, and End Results (SEER) database to determine how frequently this bias was considered. Second, we used SEER to select three tumor types (glioblastoma multiforme, Stage IA-IVM0 gastric adenocarcinoma, and Stage II-III rectal carcinoma) for which prospective trials demonstrated an improvement in survival associated with PORT. For each tumor type, we calculated conditional survivals and adjusted hazard ratios of PORT vs. postoperative observation cohorts while restricting the sample at sequential monthly landmarks. Sixty-two percent of previous SEER publications evaluating PORT failed to use a landmark analysis. As expected, delivery of PORT for all three tumor types was associated with improved survival, with the largest associated benefit favoring PORT when all patients were included regardless of survival. Preselecting a cohort with a longer minimum survival sequentially diminished the apparent benefit of PORT. Although the majority of previous SEER articles do not correct for it, immortal time bias leads to altered estimates of PORT effectiveness, which are very sensitive to landmark selection. We suggest the routine use of sequential landmark analysis to account for this bias. Copyright © 2012 Elsevier Inc. All rights reserved.

Immortal Time Bias: A Frequently Unrecognized Threat to Validity in the Evaluation of Postoperative Radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Park, Henry S.; Gross, Cary P.; Makarov, Danil V.

2012-08-01

Purpose: To evaluate the influence of immortal time bias on observational cohort studies of postoperative radiotherapy (PORT) and the effectiveness of sequential landmark analysis to account for this bias. Methods and Materials: First, we reviewed previous studies of the Surveillance, Epidemiology, and End Results (SEER) database to determine how frequently this bias was considered. Second, we used SEER to select three tumor types (glioblastoma multiforme, Stage IA-IVM0 gastric adenocarcinoma, and Stage II-III rectal carcinoma) for which prospective trials demonstrated an improvement in survival associated with PORT. For each tumor type, we calculated conditional survivals and adjusted hazard ratios of PORTmore » vs. postoperative observation cohorts while restricting the sample at sequential monthly landmarks. Results: Sixty-two percent of previous SEER publications evaluating PORT failed to use a landmark analysis. As expected, delivery of PORT for all three tumor types was associated with improved survival, with the largest associated benefit favoring PORT when all patients were included regardless of survival. Preselecting a cohort with a longer minimum survival sequentially diminished the apparent benefit of PORT. Conclusions: Although the majority of previous SEER articles do not correct for it, immortal time bias leads to altered estimates of PORT effectiveness, which are very sensitive to landmark selection. We suggest the routine use of sequential landmark analysis to account for this bias.« less

Variation in the Intensity of Selection on Codon Bias over Time Causes Contrasting Patterns of Base Composition Evolution in Drosophila

PubMed Central

Jackson, Benjamin C.; Campos, José L.; Haddrill, Penelope R.; Charlesworth, Brian

2017-01-01

Four-fold degenerate coding sites form a major component of the genome, and are often used to make inferences about selection and demography, so that understanding their evolution is important. Despite previous efforts, many questions regarding the causes of base composition changes at these sites in Drosophila remain unanswered. To shed further light on this issue, we obtained a new whole-genome polymorphism data set from D. simulans. We analyzed samples from the putatively ancestral range of D. simulans, as well as an existing polymorphism data set from an African population of D. melanogaster. By using D. yakuba as an outgroup, we found clear evidence for selection on 4-fold sites along both lineages over a substantial period, with the intensity of selection increasing with GC content. Based on an explicit model of base composition evolution, we suggest that the observed AT-biased substitution pattern in both lineages is probably due to an ancestral reduction in selection intensity, and is unlikely to be the result of an increase in mutational bias towards AT alone. By using two polymorphism-based methods for estimating selection coefficients over different timescales, we show that the selection intensity on codon usage has been rather stable in D. simulans in the recent past, but the long-term estimates in D. melanogaster are much higher than the short-term ones, indicating a continuing decline in selection intensity, to such an extent that the short-term estimates suggest that selection is only active in the most GC-rich parts of the genome. Finally, we provide evidence for complex evolutionary patterns in the putatively neutral short introns, which cannot be explained by the standard GC-biased gene conversion model. These results reveal a dynamic picture of base composition evolution. PMID:28082609

Bias and precision of selected analytes reported by the National Atmospheric Deposition Program and National Trends Network, 1984

USGS Publications Warehouse

Brooks, M.H.; Schroder, L.J.; Willoughby, T.C.

1987-01-01

The U.S. Geological Survey operated a blind audit sample program during 1974 to test the effects of the sample handling and shipping procedures used by the National Atmospheric Deposition Program and National Trends Network on the quality of wet deposition data produced by the combined networks. Blind audit samples, which were dilutions of standard reference water samples, were submitted by network site operators to the central analytical laboratory disguised as actual wet deposition samples. Results from the analyses of blind audit samples were used to calculate estimates of analyte bias associated with all network wet deposition samples analyzed in 1984 and to estimate analyte precision. Concentration differences between double blind samples that were submitted to the central analytical laboratory and separate analyses of aliquots of those blind audit samples that had not undergone network sample handling and shipping were used to calculate analyte masses that apparently were added to each blind audit sample by routine network handling and shipping procedures. These calculated masses indicated statistically significant biases for magnesium, sodium , potassium, chloride, and sulfate. Median calculated masses were 41.4 micrograms (ug) for calcium, 14.9 ug for magnesium, 23.3 ug for sodium, 0.7 ug for potassium, 16.5 ug for chloride and 55.3 ug for sulfate. Analyte precision was estimated using two different sets of replicate measures performed by the central analytical laboratory. Estimated standard deviations were similar to those previously reported. (Author 's abstract)

How and how much does RAD-seq bias genetic diversity estimates?

PubMed

Cariou, Marie; Duret, Laurent; Charlat, Sylvain

2016-11-08

RAD-seq is a powerful tool, increasingly used in population genomics. However, earlier studies have raised red flags regarding possible biases associated with this technique. In particular, polymorphism on restriction sites results in preferential sampling of closely related haplotypes, so that RAD data tends to underestimate genetic diversity. Here we (1) clarify the theoretical basis of this bias, highlighting the potential confounding effects of population structure and selection, (2) confront predictions to real data from in silico digestion of full genomes and (3) provide a proof of concept toward an ABC-based correction of the RAD-seq bias. Under a neutral and panmictic model, we confirm the previously established relationship between the true polymorphism and its RAD-based estimation, showing a more pronounced bias when polymorphism is high. Using more elaborate models, we show that selection, resulting in heterogeneous levels of polymorphism along the genome, exacerbates the bias and leads to a more pronounced underestimation. On the contrary, spatial genetic structure tends to reduce the bias. We confront the neutral and panmictic model to "ideal" empirical data (in silico RAD-sequencing) using full genomes from natural populations of the fruit fly Drosophila melanogaster and the fungus Shizophyllum commune, harbouring respectively moderate and high genetic diversity. In D. melanogaster, predictions fit the model, but the small difference between the true and RAD polymorphism makes this comparison insensitive to deviations from the model. In the highly polymorphic fungus, the model captures a large part of the bias but makes inaccurate predictions. Accordingly, ABC corrections based on this model improve the estimations, albeit with some imprecisions. The RAD-seq underestimation of genetic diversity associated with polymorphism in restriction sites becomes more pronounced when polymorphism is high. In practice, this means that in many systems where polymorphism does not exceed 2 %, the bias is of minor importance in the face of other sources of uncertainty, such as heterogeneous bases composition or technical artefacts. The neutral panmictic model provides a practical mean to correct the bias through ABC, albeit with some imprecisions. More elaborate ABC methods might integrate additional parameters, such as population structure and selection, but their opposite effects could hinder accurate corrections.

Bias, Confounding, and Interaction: Lions and Tigers, and Bears, Oh My!

PubMed

Vetter, Thomas R; Mascha, Edward J

2017-09-01

Epidemiologists seek to make a valid inference about the causal effect between an exposure and a disease in a specific population, using representative sample data from a specific population. Clinical researchers likewise seek to make a valid inference about the association between an intervention and outcome(s) in a specific population, based upon their randomly collected, representative sample data. Both do so by using the available data about the sample variable to make a valid estimate about its corresponding or underlying, but unknown population parameter. Random error in an experiment can be due to the natural, periodic fluctuation or variation in the accuracy or precision of virtually any data sampling technique or health measurement tool or scale. In a clinical research study, random error can be due to not only innate human variability but also purely chance. Systematic error in an experiment arises from an innate flaw in the data sampling technique or measurement instrument. In the clinical research setting, systematic error is more commonly referred to as systematic bias. The most commonly encountered types of bias in anesthesia, perioperative, critical care, and pain medicine research include recall bias, observational bias (Hawthorne effect), attrition bias, misclassification or informational bias, and selection bias. A confounding variable is a factor associated with both the exposure of interest and the outcome of interest. A confounding variable (confounding factor or confounder) is a variable that correlates (positively or negatively) with both the exposure and outcome. Confounding is typically not an issue in a randomized trial because the randomized groups are sufficiently balanced on all potential confounding variables, both observed and nonobserved. However, confounding can be a major problem with any observational (nonrandomized) study. Ignoring confounding in an observational study will often result in a "distorted" or incorrect estimate of the association or treatment effect. Interaction among variables, also known as effect modification, exists when the effect of 1 explanatory variable on the outcome depends on the particular level or value of another explanatory variable. Bias and confounding are common potential explanations for statistically significant associations between exposure and outcome when the true relationship is noncausal. Understanding interactions is vital to proper interpretation of treatment effects. These complex concepts should be consistently and appropriately considered whenever one is not only designing but also analyzing and interpreting data from a randomized trial or observational study.

How good is crude MDL for solving the bias-variance dilemma? An empirical investigation based on Bayesian networks.

PubMed

Cruz-Ramírez, Nicandro; Acosta-Mesa, Héctor Gabriel; Mezura-Montes, Efrén; Guerra-Hernández, Alejandro; Hoyos-Rivera, Guillermo de Jesús; Barrientos-Martínez, Rocío Erandi; Gutiérrez-Fragoso, Karina; Nava-Fernández, Luis Alonso; González-Gaspar, Patricia; Novoa-del-Toro, Elva María; Aguilera-Rueda, Vicente Josué; Ameca-Alducin, María Yaneli

2014-01-01

The bias-variance dilemma is a well-known and important problem in Machine Learning. It basically relates the generalization capability (goodness of fit) of a learning method to its corresponding complexity. When we have enough data at hand, it is possible to use these data in such a way so as to minimize overfitting (the risk of selecting a complex model that generalizes poorly). Unfortunately, there are many situations where we simply do not have this required amount of data. Thus, we need to find methods capable of efficiently exploiting the available data while avoiding overfitting. Different metrics have been proposed to achieve this goal: the Minimum Description Length principle (MDL), Akaike's Information Criterion (AIC) and Bayesian Information Criterion (BIC), among others. In this paper, we focus on crude MDL and empirically evaluate its performance in selecting models with a good balance between goodness of fit and complexity: the so-called bias-variance dilemma, decomposition or tradeoff. Although the graphical interaction between these dimensions (bias and variance) is ubiquitous in the Machine Learning literature, few works present experimental evidence to recover such interaction. In our experiments, we argue that the resulting graphs allow us to gain insights that are difficult to unveil otherwise: that crude MDL naturally selects balanced models in terms of bias-variance, which not necessarily need be the gold-standard ones. We carry out these experiments using a specific model: a Bayesian network. In spite of these motivating results, we also should not overlook three other components that may significantly affect the final model selection: the search procedure, the noise rate and the sample size.

How Good Is Crude MDL for Solving the Bias-Variance Dilemma? An Empirical Investigation Based on Bayesian Networks

PubMed Central

Cruz-Ramírez, Nicandro; Acosta-Mesa, Héctor Gabriel; Mezura-Montes, Efrén; Guerra-Hernández, Alejandro; Hoyos-Rivera, Guillermo de Jesús; Barrientos-Martínez, Rocío Erandi; Gutiérrez-Fragoso, Karina; Nava-Fernández, Luis Alonso; González-Gaspar, Patricia; Novoa-del-Toro, Elva María; Aguilera-Rueda, Vicente Josué; Ameca-Alducin, María Yaneli

2014-01-01

The bias-variance dilemma is a well-known and important problem in Machine Learning. It basically relates the generalization capability (goodness of fit) of a learning method to its corresponding complexity. When we have enough data at hand, it is possible to use these data in such a way so as to minimize overfitting (the risk of selecting a complex model that generalizes poorly). Unfortunately, there are many situations where we simply do not have this required amount of data. Thus, we need to find methods capable of efficiently exploiting the available data while avoiding overfitting. Different metrics have been proposed to achieve this goal: the Minimum Description Length principle (MDL), Akaike’s Information Criterion (AIC) and Bayesian Information Criterion (BIC), among others. In this paper, we focus on crude MDL and empirically evaluate its performance in selecting models with a good balance between goodness of fit and complexity: the so-called bias-variance dilemma, decomposition or tradeoff. Although the graphical interaction between these dimensions (bias and variance) is ubiquitous in the Machine Learning literature, few works present experimental evidence to recover such interaction. In our experiments, we argue that the resulting graphs allow us to gain insights that are difficult to unveil otherwise: that crude MDL naturally selects balanced models in terms of bias-variance, which not necessarily need be the gold-standard ones. We carry out these experiments using a specific model: a Bayesian network. In spite of these motivating results, we also should not overlook three other components that may significantly affect the final model selection: the search procedure, the noise rate and the sample size. PMID:24671204

Clustering of quasars in a wide luminosity range at redshift 4 with Subaru Hyper Suprime-Cam Wide-field imaging

NASA Astrophysics Data System (ADS)

He, Wanqiu; Akiyama, Masayuki; Bosch, James; Enoki, Motohiro; Harikane, Yuichi; Ikeda, Hiroyuki; Kashikawa, Nobunari; Kawaguchi, Toshihiro; Komiyama, Yutaka; Lee, Chien-Hsiu; Matsuoka, Yoshiki; Miyazaki, Satoshi; Nagao, Tohru; Nagashima, Masahiro; Niida, Mana; Nishizawa, Atsushi J.; Oguri, Masamune; Onoue, Masafusa; Oogi, Taira; Ouchi, Masami; Schulze, Andreas; Shirasaki, Yuji; Silverman, John D.; Tanaka, Manobu M.; Tanaka, Masayuki; Toba, Yoshiki; Uchiyama, Hisakazu; Yamashita, Takuji

2018-01-01

We examine the clustering of quasars over a wide luminosity range, by utilizing 901 quasars at \\overline{z}_phot˜ 3.8 with -24.73 < M1450 < -22.23 photometrically selected from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) S16A Wide2 date release and 342 more luminous quasars at 3.4 < zspec < 4.6 with -28.0 < M1450 < -23.95 from the Sloan Digital Sky Survey that fall in the HSC survey fields. We measure the bias factors of two quasar samples by evaluating the cross-correlation functions (CCFs) between the quasar samples and 25790 bright z ˜ 4 Lyman break galaxies in M1450 < -21.25 photometrically selected from the HSC dataset. Over an angular scale of 10.0" to 1000.0", the bias factors are 5.93+1.34-1.43 and 2.73+2.44-2.55 for the low- and high-luminosity quasars, respectively, indicating no significant luminosity dependence of quasar clustering at z ˜ 4. It is noted that the bias factor of the luminous quasars estimated by the CCF is smaller than that estimated by the auto-correlation function over a similar redshift range, especially on scales below 40.0". Moreover, the bias factor of the less-luminous quasars implies the minimal mass of their host dark matter halos is 0.3-2 × 1012 h-1 M⊙, corresponding to a quasar duty cycle of 0.001-0.06.

The ALHAMBRA survey: evolution of galaxy clustering since z ˜ 1

NASA Astrophysics Data System (ADS)

Arnalte-Mur, P.; Martínez, V. J.; Norberg, P.; Fernández-Soto, A.; Ascaso, B.; Merson, A. I.; Aguerri, J. A. L.; Castander, F. J.; Hurtado-Gil, L.; López-Sanjuan, C.; Molino, A.; Montero-Dorta, A. D.; Stefanon, M.; Alfaro, E.; Aparicio-Villegas, T.; Benítez, N.; Broadhurst, T.; Cabrera-Caño, J.; Cepa, J.; Cerviño, M.; Cristóbal-Hornillos, D.; del Olmo, A.; González Delgado, R. M.; Husillos, C.; Infante, L.; Márquez, I.; Masegosa, J.; Moles, M.; Perea, J.; Pović, M.; Prada, F.; Quintana, J. M.

2014-06-01

We study the clustering of galaxies as function of luminosity and redshift in the range 0.35 < z < 1.25 using data from the Advanced Large Homogeneous Area Medium-Band Redshift Astronomical (ALHAMBRA) survey. The ALHAMBRA data used in this work cover 2.38 deg2 in seven independent fields, after applying a detailed angular selection mask, with accurate photometric redshifts, σz ≲ 0.014(1 + z), down to IAB < 24. Given the depth of the survey, we select samples in B-band luminosity down to Lth ≃ 0.16L* at z = 0.9. We measure the real-space clustering using the projected correlation function, accounting for photometric redshifts uncertainties. We infer the galaxy bias, and study its evolution with luminosity. We study the effect of sample variance, and confirm earlier results that the Cosmic Evolution Survey (COSMOS) and European Large Area ISO Survey North 1 (ELAIS-N1) fields are dominated by the presence of large structures. For the intermediate and bright samples, Lmed ≳ 0.6L*, we obtain a strong dependence of bias on luminosity, in agreement with previous results at similar redshift. We are able to extend this study to fainter luminosities, where we obtain an almost flat relation, similar to that observed at low redshift. Regarding the evolution of bias with redshift, our results suggest that the different galaxy populations studied reside in haloes covering a range in mass between log10[Mh/( h-1 M⊙)] ≳ 11.5 for samples with Lmed ≃ 0.3L* and log10[Mh/( h-1 M⊙)] ≳ 13.0 for samples with Lmed ≃ 2L*, with typical occupation numbers in the range of ˜1-3 galaxies per halo.

Improving the collection of knowledge, attitude and practice data with community surveys: a comparison of two second-stage sampling methods.

PubMed

Davis, Rosemary H; Valadez, Joseph J

2014-12-01

Second-stage sampling techniques, including spatial segmentation, are widely used in community health surveys when reliable household sampling frames are not available. In India, an unresearched technique for household selection is used in eight states, which samples the house with the last marriage or birth as the starting point. Users question whether this last-birth or last-marriage (LBLM) approach introduces bias affecting survey results. We conducted two simultaneous population-based surveys. One used segmentation sampling; the other used LBLM. LBLM sampling required modification before assessment was possible and a more systematic approach was tested using last birth only. We compared coverage proportions produced by the two independent samples for six malaria indicators and demographic variables (education, wealth and caste). We then measured the level of agreement between the caste of the selected participant and the caste of the health worker making the selection. No significant difference between methods was found for the point estimates of six malaria indicators, education, caste or wealth of the survey participants (range of P: 0.06 to >0.99). A poor level of agreement occurred between the caste of the health worker used in household selection and the caste of the final participant, (Κ = 0.185), revealing little association between the two, and thereby indicating that caste was not a source of bias. Although LBLM was not testable, a systematic last-birth approach was tested. If documented concerns of last-birth sampling are addressed, this new method could offer an acceptable alternative to segmentation in India. However, inter-state caste variation could affect this result. Therefore, additional assessment of last birth is required before wider implementation is recommended. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine © The Author 2013; all rights reserved.

A two-phase sampling survey for nonresponse and its paradata to correct nonresponse bias in a health surveillance survey.

PubMed

Santin, G; Bénézet, L; Geoffroy-Perez, B; Bouyer, J; Guéguen, A

2017-02-01

The decline in participation rates in surveys, including epidemiological surveillance surveys, has become a real concern since it may increase nonresponse bias. The aim of this study is to estimate the contribution of a complementary survey among a subsample of nonrespondents, and the additional contribution of paradata in correcting for nonresponse bias in an occupational health surveillance survey. In 2010, 10,000 workers were randomly selected and sent a postal questionnaire. Sociodemographic data were available for the whole sample. After data collection of the questionnaires, a complementary survey among a random subsample of 500 nonrespondents was performed using a questionnaire administered by an interviewer. Paradata were collected for the complete subsample of the complementary survey. Nonresponse bias in the initial sample and in the combined samples were assessed using variables from administrative databases available for the whole sample, not subject to differential measurement errors. Corrected prevalences by reweighting technique were estimated by first using the initial survey alone and then the initial and complementary surveys combined, under several assumptions regarding the missing data process. Results were compared by computing relative errors. The response rates of the initial and complementary surveys were 23.6% and 62.6%, respectively. For the initial and the combined surveys, the relative errors decreased after correction for nonresponse on sociodemographic variables. For the combined surveys without paradata, relative errors decreased compared with the initial survey. The contribution of the paradata was weak. When a complex descriptive survey has a low response rate, a short complementary survey among nonrespondents with a protocol which aims to maximize the response rates, is useful. The contribution of sociodemographic variables in correcting for nonresponse bias is important whereas the additional contribution of paradata in correcting for nonresponse bias is questionable. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

The left perceptual bias for adult and infant faces in adults and 5-year-old children: face age matters.

PubMed

Proietti, Valentina; Pavone, Sarah; Ricciardelli, Paola; Macchi Cassia, Viola

2015-01-01

A large number of studies have shown that adults rely more heavily on information conveyed by the left side of the face in judging emotional state, gender and identity. This phenomenon, called left perceptual bias (LPB), suggests a right hemisphere lateralization of face processing mechanisms. Although specialization of neural mechanisms for processing over-experienced face categories begins during the first year of life, little is known about the developmental trajectory of the LPB and whether or when the bias becomes selective for specific face categories as a result of experience. To address these questions we tested adults (Experiment 1) and 5-year-old children (Experiment 2) with null or limited experience with infants in an identity matching-to-sample task with chimeric adult and infant faces, for which both adults and children have been shown to manifest differential processing abilities. Results showed that 5-year-olds manifest a leftward bias selective for adult faces, and the magnitude of the bias is larger for adult compared to infant faces in adults. This evidence is in line with earlier demonstrations of a perceptual processing advantage for adult faces in adults and children and points to the role of experience in shaping neurocognitive specialization for face processing.

Neither fixed nor random: weighted least squares meta-regression.

PubMed

Stanley, T D; Doucouliagos, Hristos

2017-03-01

Our study revisits and challenges two core conventional meta-regression estimators: the prevalent use of 'mixed-effects' or random-effects meta-regression analysis and the correction of standard errors that defines fixed-effects meta-regression analysis (FE-MRA). We show how and explain why an unrestricted weighted least squares MRA (WLS-MRA) estimator is superior to conventional random-effects (or mixed-effects) meta-regression when there is publication (or small-sample) bias that is as good as FE-MRA in all cases and better than fixed effects in most practical applications. Simulations and statistical theory show that WLS-MRA provides satisfactory estimates of meta-regression coefficients that are practically equivalent to mixed effects or random effects when there is no publication bias. When there is publication selection bias, WLS-MRA always has smaller bias than mixed effects or random effects. In practical applications, an unrestricted WLS meta-regression is likely to give practically equivalent or superior estimates to fixed-effects, random-effects, and mixed-effects meta-regression approaches. However, random-effects meta-regression remains viable and perhaps somewhat preferable if selection for statistical significance (publication bias) can be ruled out and when random, additive normal heterogeneity is known to directly affect the 'true' regression coefficient. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Different hunting strategies select for different weights in red deer.

PubMed

Martínez, María; Rodríguez-Vigal, Carlos; Jones, Owen R; Coulson, Tim; San Miguel, Alfonso

2005-09-22

Much insight can be derived from records of shot animals. Most researchers using such data assume that their data represents a random sample of a particular demographic class. However, hunters typically select a non-random subset of the population and hunting is, therefore, not a random process. Here, with red deer (Cervus elaphus) hunting data from a ranch in Toledo, Spain, we demonstrate that data collection methods have a significant influence upon the apparent relationship between age and weight. We argue that a failure to correct for such methodological bias may have significant consequences for the interpretation of analyses involving weight or correlated traits such as breeding success, and urge researchers to explore methods to identify and correct for such bias in their data.

Towards Identifying and Reducing the Bias of Disease Information Extracted from Search Engine Data

PubMed Central

Huang, Da-Cang; Wang, Jin-Feng; Huang, Ji-Xia; Sui, Daniel Z.; Zhang, Hong-Yan; Hu, Mao-Gui; Xu, Cheng-Dong

2016-01-01

The estimation of disease prevalence in online search engine data (e.g., Google Flu Trends (GFT)) has received a considerable amount of scholarly and public attention in recent years. While the utility of search engine data for disease surveillance has been demonstrated, the scientific community still seeks ways to identify and reduce biases that are embedded in search engine data. The primary goal of this study is to explore new ways of improving the accuracy of disease prevalence estimations by combining traditional disease data with search engine data. A novel method, Biased Sentinel Hospital-based Area Disease Estimation (B-SHADE), is introduced to reduce search engine data bias from a geographical perspective. To monitor search trends on Hand, Foot and Mouth Disease (HFMD) in Guangdong Province, China, we tested our approach by selecting 11 keywords from the Baidu index platform, a Chinese big data analyst similar to GFT. The correlation between the number of real cases and the composite index was 0.8. After decomposing the composite index at the city level, we found that only 10 cities presented a correlation of close to 0.8 or higher. These cities were found to be more stable with respect to search volume, and they were selected as sample cities in order to estimate the search volume of the entire province. After the estimation, the correlation improved from 0.8 to 0.864. After fitting the revised search volume with historical cases, the mean absolute error was 11.19% lower than it was when the original search volume and historical cases were combined. To our knowledge, this is the first study to reduce search engine data bias levels through the use of rigorous spatial sampling strategies. PMID:27271698

Towards Identifying and Reducing the Bias of Disease Information Extracted from Search Engine Data.

PubMed

Huang, Da-Cang; Wang, Jin-Feng; Huang, Ji-Xia; Sui, Daniel Z; Zhang, Hong-Yan; Hu, Mao-Gui; Xu, Cheng-Dong

2016-06-01

The estimation of disease prevalence in online search engine data (e.g., Google Flu Trends (GFT)) has received a considerable amount of scholarly and public attention in recent years. While the utility of search engine data for disease surveillance has been demonstrated, the scientific community still seeks ways to identify and reduce biases that are embedded in search engine data. The primary goal of this study is to explore new ways of improving the accuracy of disease prevalence estimations by combining traditional disease data with search engine data. A novel method, Biased Sentinel Hospital-based Area Disease Estimation (B-SHADE), is introduced to reduce search engine data bias from a geographical perspective. To monitor search trends on Hand, Foot and Mouth Disease (HFMD) in Guangdong Province, China, we tested our approach by selecting 11 keywords from the Baidu index platform, a Chinese big data analyst similar to GFT. The correlation between the number of real cases and the composite index was 0.8. After decomposing the composite index at the city level, we found that only 10 cities presented a correlation of close to 0.8 or higher. These cities were found to be more stable with respect to search volume, and they were selected as sample cities in order to estimate the search volume of the entire province. After the estimation, the correlation improved from 0.8 to 0.864. After fitting the revised search volume with historical cases, the mean absolute error was 11.19% lower than it was when the original search volume and historical cases were combined. To our knowledge, this is the first study to reduce search engine data bias levels through the use of rigorous spatial sampling strategies.

Selection bias in dynamically measured supermassive black hole samples: its consequences and the quest for the most fundamental relation

NASA Astrophysics Data System (ADS)

Shankar, Francesco; Bernardi, Mariangela; Sheth, Ravi K.; Ferrarese, Laura; Graham, Alister W.; Savorgnan, Giulia; Allevato, Viola; Marconi, Alessandro; Läsker, Ronald; Lapi, Andrea

2016-08-01

We compare the set of local galaxies having dynamically measured black holes with a large, unbiased sample of galaxies extracted from the Sloan Digital Sky Survey. We confirm earlier work showing that the majority of black hole hosts have significantly higher velocity dispersions σ than local galaxies of similar stellar mass. We use Monte Carlo simulations to illustrate the effect on black hole scaling relations if this bias arises from the requirement that the black hole sphere of influence must be resolved to measure black hole masses with spatially resolved kinematics. We find that this selection effect artificially increases the normalization of the Mbh-σ relation by a factor of at least ˜3; the bias for the Mbh-Mstar relation is even larger. Our Monte Carlo simulations and analysis of the residuals from scaling relations both indicate that σ is more fundamental than Mstar or effective radius. In particular, the Mbh-Mstar relation is mostly a consequence of the Mbh-σ and σ-Mstar relations, and is heavily biased by up to a factor of 50 at small masses. This helps resolve the discrepancy between dynamically based black hole-galaxy scaling relations versus those of active galaxies. Our simulations also disfavour broad distributions of black hole masses at fixed σ. Correcting for this bias suggests that the calibration factor used to estimate black hole masses in active galaxies should be reduced to values of fvir ˜ 1. Black hole mass densities should also be proportionally smaller, perhaps implying significantly higher radiative efficiencies/black hole spins. Reducing black hole masses also reduces the gravitational wave signal expected from black hole mergers.

Selection bias in dynamically-measured super-massive black hole samples: its consequences and the quest for the most fundamental relation

NASA Astrophysics Data System (ADS)

Shankar, Francesco; Bernardi, M.; Sheth, R. K.; Weinberg, D. H.; Miralda-Escudé, J.; Ferrarese, L.; Graham, A.; Sesana, A.; Lapi, A.; Marconi, A.; Allevato, V.; Savorgnan, G.; Laesker, R.

2016-08-01

We compare the set of local galaxies having dynamically measured black holes with a large, unbiased sample of galaxies extracted from the Sloan Digital Sky Survey. We confirm earlier work showing that the majority of black hole hosts have significantly higher velocity dispersions sigma than local galaxies of similar stellar mass. We use Monte-Carlo simulations to illustrate the effect on black hole scaling relations if this bias arises from the requirement that the black hole sphere of influence must be resolved to measure black hole masses with spatially resolved kinematics. We find that this selection effect artificially increases the normalization of the Mbh-sigma relation by a factor of at least ~3; the bias for the Mbh-Mstar relation is even larger. Our Monte Carlo simulations and analysis of the residuals from scaling relations both indicate that sigma is more fundamental than Mstar or effective radius. In particular, the Mbh-Mstar relation is mostly a consequence of the Mbh-sigma and sigma-Mstar relations, and is heavily biased by up to a factor of 50 at small masses. This helps resolve the discrepancy between dynamically-based black hole-galaxy scaling relations versus those of active galaxies. Our simulations also disfavour broad distributions of black hole masses at fixed sigma. Correcting for this bias suggests that the calibration factor used to estimate black hole masses in active galaxies should be reduced to values of fvir~1. Black hole mass densities should also be proportionally smaller, perhaps implying significantly higher radiative efficiencies/black hole spins. Reducing black hole masses also reduces the gravitational wave signal expected from black hole mergers.

Spot Sampling and Exposure Surrogate Selection as Sources of Bias in Environmental Epidemiology Studies

EPA Science Inventory

Spot measurements of chemical biomarkers are often used as quantitative exposure surrogates in environmental epidemiology studies. These measures can be expressed a number of different ways – for example, urinary biomarkers can be expressed in units of concentration (&micr...

Soil sampling strategies: evaluation of different approaches.

PubMed

de Zorzi, Paolo; Barbizzi, Sabrina; Belli, Maria; Mufato, Renzo; Sartori, Giuseppe; Stocchero, Giulia

2008-11-01

The National Environmental Protection Agency of Italy (APAT) performed a soil sampling intercomparison, inviting 14 regional agencies to test their own soil sampling strategies. The intercomparison was carried out at a reference site, previously characterised for metal mass fraction distribution. A wide range of sampling strategies, in terms of sampling patterns, type and number of samples collected, were used to assess the mean mass fraction values of some selected elements. The different strategies led in general to acceptable bias values (D) less than 2sigma, calculated according to ISO 13258. Sampling on arable land was relatively easy, with comparable results between different sampling strategies.

Electric Field-aided Selective Activation for Indium-Gallium-Zinc-Oxide Thin Film Transistors

NASA Astrophysics Data System (ADS)

Lee, Heesoo; Chang, Ki Soo; Tak, Young Jun; Jung, Tae Soo; Park, Jeong Woo; Kim, Won-Gi; Chung, Jusung; Jeong, Chan Bae; Kim, Hyun Jae

2016-10-01

A new technique is proposed for the activation of low temperature amorphous InGaZnO thin film transistor (a-IGZO TFT) backplanes through application of a bias voltage and annealing at 130 °C simultaneously. In this ‘electrical activation’, the effects of annealing under bias are selectively focused in the channel region. Therefore, electrical activation can be an effective method for lower backplane processing temperatures from 280 °C to 130 °C. Devices fabricated with this method exhibit equivalent electrical properties to those of conventionally-fabricated samples. These results are analyzed electrically and thermodynamically using infrared microthermography. Various bias voltages are applied to the gate, source, and drain electrodes while samples are annealed at 130 °C for 1 hour. Without conventional high temperature annealing or electrical activation, current-voltage curves do not show transfer characteristics. However, electrically activated a-IGZO TFTs show superior electrical characteristics, comparable to the reference TFTs annealed at 280 °C for 1 hour. This effect is a result of the lower activation energy, and efficient transfer of electrical and thermal energy to a-IGZO TFTs. With this approach, superior low-temperature a-IGZO TFTs are fabricated successfully.

Electric Field-aided Selective Activation for Indium-Gallium-Zinc-Oxide Thin Film Transistors

PubMed Central

Lee, Heesoo; Chang, Ki Soo; Tak, Young Jun; Jung, Tae Soo; Park, Jeong Woo; Kim, Won-Gi; Chung, Jusung; Jeong, Chan Bae; Kim, Hyun Jae

2016-01-01

A new technique is proposed for the activation of low temperature amorphous InGaZnO thin film transistor (a-IGZO TFT) backplanes through application of a bias voltage and annealing at 130 °C simultaneously. In this ‘electrical activation’, the effects of annealing under bias are selectively focused in the channel region. Therefore, electrical activation can be an effective method for lower backplane processing temperatures from 280 °C to 130 °C. Devices fabricated with this method exhibit equivalent electrical properties to those of conventionally-fabricated samples. These results are analyzed electrically and thermodynamically using infrared microthermography. Various bias voltages are applied to the gate, source, and drain electrodes while samples are annealed at 130 °C for 1 hour. Without conventional high temperature annealing or electrical activation, current-voltage curves do not show transfer characteristics. However, electrically activated a-IGZO TFTs show superior electrical characteristics, comparable to the reference TFTs annealed at 280 °C for 1 hour. This effect is a result of the lower activation energy, and efficient transfer of electrical and thermal energy to a-IGZO TFTs. With this approach, superior low-temperature a-IGZO TFTs are fabricated successfully. PMID:27725695

redMaGiC: selecting luminous red galaxies from the DES Science Verification data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rozo, E.

We introduce redMaGiC, an automated algorithm for selecting Luminous Red Galaxies (LRGs). The algorithm was developed to minimize photometric redshift uncertainties in photometric large-scale structure studies. redMaGiC achieves this by self-training the color-cuts necessary to produce a luminosity-thresholded LRG sam- ple of constant comoving density. Additionally, we demonstrate that redMaGiC photo-zs are very nearly as accurate as the best machine-learning based methods, yet they require minimal spectroscopic training, do not suffer from extrapolation biases, and are very nearly Gaussian. We apply our algorithm to Dark Energy Survey (DES) Science Verification (SV) data to produce a redMaGiC catalog sampling the redshiftmore » range z ϵ [0.2,0.8]. Our fiducial sample has a comoving space density of 10 -3 (h -1Mpc) -3, and a median photo-z bias (z spec z photo) and scatter (σ z=(1 + z)) of 0.005 and 0.017 respectively.The corresponding 5σ outlier fraction is 1.4%. We also test our algorithm with Sloan Digital Sky Survey (SDSS) Data Release 8 (DR8) and Stripe 82 data, and discuss how spectroscopic training can be used to control photo-z biases at the 0.1% level.« less

THE XMM-NEWTON WIDE FIELD SURVEY IN THE COSMOS FIELD: REDSHIFT EVOLUTION OF AGN BIAS AND SUBDOMINANT ROLE OF MERGERS IN TRIGGERING MODERATE-LUMINOSITY AGNs AT REDSHIFTS UP TO 2.2

DOE Office of Scientific and Technical Information (OSTI.GOV)

Allevato, V.; Hasinger, G.; Salvato, M.

2011-08-01

We present a study of the redshift evolution of the projected correlation function of 593 X-ray selected active galactic nuclei (AGNs) with I{sub AB} < 23 and spectroscopic redshifts z < 4, extracted from the 0.5-2 keV X-ray mosaic of the 2.13 deg{sup 2} XMM- Cosmic Evolution Survey (COSMOS). We introduce a method to estimate the average bias of the AGN sample and the mass of AGN hosting halos, solving the sample variance using the halo model and taking into account the growth of the structure over time. We find evidence of a redshift evolution of the bias factor formore » the total population of XMM-COSMOS AGNs from b-bar (z-bar =0.92)=2.30{+-}0.11 to b-bar (z-bar =1.94)=4.37{+-}0.27 with an average mass of the hosting dark matter (DM) halos log M{sub 0}(h{sup -1} M{sub sun}) {approx} 13.12 {+-} 0.12 that remains constant at all z < 2. Splitting our sample into broad optical line AGNs (BL), AGNs without broad optical lines (NL), and X-ray unobscured and obscured AGNs, we observe an increase of the bias with redshift in the range z-bar = 0.7-2.25 and z-bar = 0.6-1.5 which corresponds to a constant halo mass of log M{sub 0}(h{sup -1} M{sub sun}) {approx} 13.28 {+-} 0.07 and log M{sub 0}(h{sup -1} M{sub sun}) {approx} 13.00 {+-} 0.06 for BL/X-ray unobscured AGNs and NL/X-ray obscured AGNs, respectively. The theoretical models, which assume a quasar phase triggered by major mergers, cannot reproduce the high bias factors and DM halo masses found for X-ray selected BL AGNs with L{sub BOL} {approx} 2 x 10{sup 45} erg s{sup -1}. Our work extends up to z {approx} 2.2 the z {approx}< 1 statement that, for moderate-luminosity X-ray selected BL AGNs, the contribution from major mergers is outnumbered by other processes, possibly secular ones such as tidal disruptions or disk instabilities.« less

Real-time photonic sampling with improved signal-to-noise and distortion ratio using polarization-dependent modulators

NASA Astrophysics Data System (ADS)

Liang, Dong; Zhang, Zhiyao; Liu, Yong; Li, Xiaojun; Jiang, Wei; Tan, Qinggui

2018-04-01

A real-time photonic sampling structure with effective nonlinearity suppression and excellent signal-to-noise ratio (SNR) performance is proposed. The key points of this scheme are the polarization-dependent modulators (P-DMZMs) and the sagnac loop structure. Thanks to the polarization sensitive characteristic of P-DMZMs, the differences between transfer functions of the fundamental signal and the distortion become visible. Meanwhile, the selection of specific biases in P-DMZMs is helpful to achieve a preferable linearized performance with a low noise level for real-time photonic sampling. Compared with the quadrature-biased scheme, the proposed scheme is capable of valid nonlinearity suppression and is able to provide a better SNR performance even in a large frequency range. The proposed scheme is proved to be effective and easily implemented for real time photonic applications.

Quality of volatile organic compound data from groundwater and surface water for the National Water-Quality Assessment Program, October 1996–December 2008

USGS Publications Warehouse

Bender, David A.; Zogorski, John S.; Mueller, David K.; Rose, Donna L.; Martin, Jeffrey D.; Brenner, Cassandra K.

2011-01-01

This report describes the quality of volatile organic compound (VOC) data collected from October 1996 to December 2008 from groundwater and surface-water sites for the U.S. Geological Survey's National Water-Quality Assessment (NAWQA) Program. The VOC data described were collected for three NAWQA site types: (1) domestic and public-supply wells, (2) monitoring wells, and (3) surface-water sites. Contamination bias, based on the 90-percent upper confidence limit (UCL) for the 90th percentile of concentrations in field blanks, was determined for VOC samples from the three site types. A way to express this bias is that there is 90-percent confidence that this amount of contamination would be exceeded in no more than 10 percent of all samples (including environmental samples) that were collected, processed, shipped, and analyzed in the same manner as the blank samples. This report also describes how important native water rinsing may be in decreasing carryover contamination, which could be affecting field blanks. The VOCs can be classified into four contamination categories on the basis of the 90-percent upper confidence limit (90-percent UCL) concentration distribution in field blanks. Contamination category 1 includes compounds that were not detected in any field blanks. Contamination category 2 includes VOCs that have a 90-percent UCL concentration distribution in field blanks that is about an order of magnitude lower than the concentration distribution of the environmental samples. Contamination category 3 includes VOCs that have a 90-percent UCL concentration distribution in field blanks that is within an order of magnitude of the distribution in environmental samples. Contamination category 4 includes VOCs that have a 90-percent UCL concentration distribution in field blanks that is at least an order of magnitude larger than the concentration distribution of the environmental samples. Fifty-four of the 87 VOCs analyzed in samples from domestic and public-supply wells were not detected in field blanks (contamination category 1), and 33 VOC were detected in field blanks. Ten of the 33 VOCs had a 90-percent UCL concentration distribution in field blanks that was at least an order of magnitude lower than the concentration distribution in environmental samples (contamination category 2). These 10 VOCs may have had some contamination bias associated with the environmental samples, but the potential contamination bias was negligible in comparison to the environmental data; therefore, the field blanks were assumed to be representative of the sources of contamination bias affecting the environmental samples for these 10 VOCs. Seven VOCs had a 90-percent UCL concentration distribution of the field blanks that was within an order of magnitude of the concentration distribution of the environmental samples (contamination category 3). Sixteen VOCs had a 90-percent UCL concentration distribution in the field blanks that was at least an order of magnitude greater than the concentration distribution of the environmental samples (contamination category 4). Field blanks for these 16 VOCs appear to be nonrepresentative of the sources of contamination bias affecting the environmental samples because of the larger concentration distributions (and sometimes higher frequency of detection) in field blanks than in environmental samples. Forty-three of the 87 VOCs analyzed in samples from monitoring wells were not detected in field blanks (contamination category 1), and 44 VOCs were detected in field blanks. Eight of the 44 VOCs had a 90-percent UCL concentration distribution in field blanks that was at least an order of magnitude lower than concentrations in environmental samples (contamination category 2). These eight VOCs may have had some contamination bias associated with the environmental samples, but the potential contamination bias was negligible in comparison to the environmental data; therefore, the field blanks were assumed to be representative. Seven VOCs had a 90-percent UCL concentration distribution in field blanks that was of the same order of magnitude as the concentration distribution of the environmental samples (contamination category 3). Twenty-nine VOCs had a 90-percent UCL concentration distribution in the field blanks that was an order of magnitude greater than the distribution of the environmental samples (contamination category 4). Field blanks for these 29 VOCs appear to be nonrepresentative of the sources of contamination bias to the environmental samples. Fifty-four of the 87 VOCs analyzed in surface-water samples were not detected in field blanks (category 1), and 33 VOC were detected in field blanks. Sixteen of the 33 VOCs had a 90-percent UCL concentration distribution in field blanks that was at least an order of magnitude lower than the concentration distribution in environmental samples (contamination category 2). These 16 VOCs may have had some contamination bias associated with the environmental samples, but the potential contamination bias was negligible in comparison to the environmental data; therefore, the field blanks were assumed to be representative. Ten VOCs had a 90-percent UCL concentration distribution in field blanks that was similar to the concentration distribution of environmental samples (contamination category 3). Seven VOCs had a 90-percent UCL concentration distribution in the field blanks that was greater than the concentration distribution in environmental samples (contamination category 4). Field-blank samples for these seven VOCs appear to be nonrepresentative of the sources of contamination bias to the environmental samples. The relation between the detection of a compound in field blanks and the detection in subsequent environmental samples appears to be minimal. The median minimum percent effectiveness of native water rinsing is about 79 percent for the 19 VOCs detected in more than 5 percent of field blanks from all three site types. The minimum percent effectiveness of native water rinsing (10 percent) was for toluene in surface-water samples, likely because of the large detection frequency of toluene in surface-water samples (about 79 percent) and in the associated field-blank samples (46.5 percent). The VOCs that were not detected in field blanks (contamination category 1) from the three site types can be considered free of contamination bias, and various interpretations for environmental samples, such as VOC detection frequency at multiple assessment levels and comparisons of concentrations to benchmarks, are not limited for these VOCs. A censoring level for making comparisons at different assessment levels among environmental samples could be applied to concentrations of 9 VOCs in samples from domestic and public-supply wells, 16 VOCs in samples from monitoring wells, and 9 VOCs in surface-water samples to account for potential low-level contamination bias associated with these selected VOCs. Bracketing the potential contamination by comparing the detection and concentration statistics with no censoring applied to the potential for contamination bias on the basis of the 90-percent UCL for the 90th-percentile concentrations in field blanks may be useful when comparisons to benchmarks are done in a study. The VOCs that were not detected in field blanks (contamination category 1) from the three site types can be considered free of contamination bias, and various interpretations for environmental samples, such as VOC detection frequency at multiple assessment levels and comparisons of concentrations to benchmarks, are not limited for these VOCs. A censoring level for making comparisons at different assessment levels among environmental samples could be applied to concentrations of 9 VOCs in samples from domestic and public-supply wells, 16 VOCs in samples from monitoring wells, and 9 VOCs in surface-water samples to account for potential low-level contamination bias associated with these selected VOCs. Bracketing the potential contamination by comparing the detection and concentration statistics with no censoring applied to the potential for contamination bias on the basis of the 90-percent UCL for the 90th-percentile concentrations in field blanks may be useful when comparisons to benchmarks are done in a study.

Sex-Specific Selection and Sex-Biased Gene Expression in Humans and Flies.

PubMed

Cheng, Changde; Kirkpatrick, Mark

2016-09-01

Sexual dimorphism results from sex-biased gene expression, which evolves when selection acts differently on males and females. While there is an intimate connection between sex-biased gene expression and sex-specific selection, few empirical studies have studied this relationship directly. Here we compare the two on a genome-wide scale in humans and flies. We find a distinctive "Twin Peaks" pattern in humans that relates the strength of sex-specific selection, quantified by genetic divergence between male and female adults at autosomal loci, to the degree of sex-biased expression. Genes with intermediate degrees of sex-biased expression show evidence of ongoing sex-specific selection, while genes with either little or completely sex-biased expression do not. This pattern apparently results from differential viability selection in males and females acting in the current generation. The Twin Peaks pattern is also found in Drosophila using a different measure of sex-specific selection acting on fertility. We develop a simple model that successfully recapitulates the Twin Peaks. Our results suggest that many genes with intermediate sex-biased expression experience ongoing sex-specific selection in humans and flies.

Multiple Imputation in Two-Stage Cluster Samples Using The Weighted Finite Population Bayesian Bootstrap.

PubMed

Zhou, Hanzhi; Elliott, Michael R; Raghunathan, Trivellore E

2016-06-01

Multistage sampling is often employed in survey samples for cost and convenience. However, accounting for clustering features when generating datasets for multiple imputation is a nontrivial task, particularly when, as is often the case, cluster sampling is accompanied by unequal probabilities of selection, necessitating case weights. Thus, multiple imputation often ignores complex sample designs and assumes simple random sampling when generating imputations, even though failing to account for complex sample design features is known to yield biased estimates and confidence intervals that have incorrect nominal coverage. In this article, we extend a recently developed, weighted, finite-population Bayesian bootstrap procedure to generate synthetic populations conditional on complex sample design data that can be treated as simple random samples at the imputation stage, obviating the need to directly model design features for imputation. We develop two forms of this method: one where the probabilities of selection are known at the first and second stages of the design, and the other, more common in public use files, where only the final weight based on the product of the two probabilities is known. We show that this method has advantages in terms of bias, mean square error, and coverage properties over methods where sample designs are ignored, with little loss in efficiency, even when compared with correct fully parametric models. An application is made using the National Automotive Sampling System Crashworthiness Data System, a multistage, unequal probability sample of U.S. passenger vehicle crashes, which suffers from a substantial amount of missing data in "Delta-V," a key crash severity measure.

Multiple Imputation in Two-Stage Cluster Samples Using The Weighted Finite Population Bayesian Bootstrap

PubMed Central

Zhou, Hanzhi; Elliott, Michael R.; Raghunathan, Trivellore E.

2017-01-01

Multistage sampling is often employed in survey samples for cost and convenience. However, accounting for clustering features when generating datasets for multiple imputation is a nontrivial task, particularly when, as is often the case, cluster sampling is accompanied by unequal probabilities of selection, necessitating case weights. Thus, multiple imputation often ignores complex sample designs and assumes simple random sampling when generating imputations, even though failing to account for complex sample design features is known to yield biased estimates and confidence intervals that have incorrect nominal coverage. In this article, we extend a recently developed, weighted, finite-population Bayesian bootstrap procedure to generate synthetic populations conditional on complex sample design data that can be treated as simple random samples at the imputation stage, obviating the need to directly model design features for imputation. We develop two forms of this method: one where the probabilities of selection are known at the first and second stages of the design, and the other, more common in public use files, where only the final weight based on the product of the two probabilities is known. We show that this method has advantages in terms of bias, mean square error, and coverage properties over methods where sample designs are ignored, with little loss in efficiency, even when compared with correct fully parametric models. An application is made using the National Automotive Sampling System Crashworthiness Data System, a multistage, unequal probability sample of U.S. passenger vehicle crashes, which suffers from a substantial amount of missing data in “Delta-V,” a key crash severity measure. PMID:29226161

Hierarchical modeling of cluster size in wildlife surveys

USGS Publications Warehouse

Royle, J. Andrew

2008-01-01

Clusters or groups of individuals are the fundamental unit of observation in many wildlife sampling problems, including aerial surveys of waterfowl, marine mammals, and ungulates. Explicit accounting of cluster size in models for estimating abundance is necessary because detection of individuals within clusters is not independent and detectability of clusters is likely to increase with cluster size. This induces a cluster size bias in which the average cluster size in the sample is larger than in the population at large. Thus, failure to account for the relationship between delectability and cluster size will tend to yield a positive bias in estimates of abundance or density. I describe a hierarchical modeling framework for accounting for cluster-size bias in animal sampling. The hierarchical model consists of models for the observation process conditional on the cluster size distribution and the cluster size distribution conditional on the total number of clusters. Optionally, a spatial model can be specified that describes variation in the total number of clusters per sample unit. Parameter estimation, model selection, and criticism may be carried out using conventional likelihood-based methods. An extension of the model is described for the situation where measurable covariates at the level of the sample unit are available. Several candidate models within the proposed class are evaluated for aerial survey data on mallard ducks (Anas platyrhynchos).

Age and sex selectivity in trapping mule deer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Garrott, R.A.; White, G.C.

1982-01-01

A mule deer (Odocoileus hemionus) trapping experiment is described using modified Clover traps in which changes in the placement of bait and height of the trap door modified the ratio of adult does to male and female fawns captured. The mechanisms responsible for the changes in age-sex capture ratios are discussed and indicate that modified Clover traps selectivity capture mule deer, thus introducing bias into population sampling. (JMT)

Performance validity testing in neuropsychology: a clinical guide, critical review, and update on a rapidly evolving literature.

PubMed

Lippa, Sara M

2018-04-01

Over the past two decades, there has been much research on measures of response bias and myriad measures have been validated in a variety of clinical and research samples. This critical review aims to guide clinicians through the use of performance validity tests (PVTs) from test selection and administration through test interpretation and feedback. Recommended cutoffs and relevant test operating characteristics are presented. Other important issues to consider during test selection, administration, interpretation, and feedback are discussed including order effects, coaching, impact on test data, and methods to combine measures and improve predictive power. When interpreting performance validity measures, neuropsychologists must use particular caution in cases of dementia, low intelligence, English as a second language/minority cultures, or low education. PVTs provide valuable information regarding response bias and, under the right circumstances, can provide excellent evidence of response bias. Only after consideration of the entire clinical picture, including validity test performance, can concrete determinations regarding the validity of test data be made.

Applying quantitative bias analysis to estimate the plausible effects of selection bias in a cluster randomised controlled trial: secondary analysis of the Primary care Osteoarthritis Screening Trial (POST).

PubMed

Barnett, L A; Lewis, M; Mallen, C D; Peat, G

2017-12-04

Selection bias is a concern when designing cluster randomised controlled trials (c-RCT). Despite addressing potential issues at the design stage, bias cannot always be eradicated from a trial design. The application of bias analysis presents an important step forward in evaluating whether trial findings are credible. The aim of this paper is to give an example of the technique to quantify potential selection bias in c-RCTs. This analysis uses data from the Primary care Osteoarthritis Screening Trial (POST). The primary aim of this trial was to test whether screening for anxiety and depression, and providing appropriate care for patients consulting their GP with osteoarthritis would improve clinical outcomes. Quantitative bias analysis is a seldom-used technique that can quantify types of bias present in studies. Due to lack of information on the selection probability, probabilistic bias analysis with a range of triangular distributions was also used, applied at all three follow-up time points; 3, 6, and 12 months post consultation. A simple bias analysis was also applied to the study. Worse pain outcomes were observed among intervention participants than control participants (crude odds ratio at 3, 6, and 12 months: 1.30 (95% CI 1.01, 1.67), 1.39 (1.07, 1.80), and 1.17 (95% CI 0.90, 1.53), respectively). Probabilistic bias analysis suggested that the observed effect became statistically non-significant if the selection probability ratio was between 1.2 and 1.4. Selection probability ratios of > 1.8 were needed to mask a statistically significant benefit of the intervention. The use of probabilistic bias analysis in this c-RCT suggested that worse outcomes observed in the intervention arm could plausibly be attributed to selection bias. A very large degree of selection of bias was needed to mask a beneficial effect of intervention making this interpretation less plausible.

The effect of mis-specification on mean and selection between the Weibull and lognormal models

NASA Astrophysics Data System (ADS)

Jia, Xiang; Nadarajah, Saralees; Guo, Bo

2018-02-01

The lognormal and Weibull models are commonly used to analyse data. Although selection procedures have been extensively studied, it is possible that the lognormal model could be selected when the true model is Weibull or vice versa. As the mean is important in applications, we focus on the effect of mis-specification on mean. The effect on lognormal mean is first considered if the lognormal sample is wrongly fitted by a Weibull model. The maximum likelihood estimate (MLE) and quasi-MLE (QMLE) of lognormal mean are obtained based on lognormal and Weibull models. Then, the impact is evaluated by computing ratio of biases and ratio of mean squared errors (MSEs) between MLE and QMLE. For completeness, the theoretical results are demonstrated by simulation studies. Next, the effect of the reverse mis-specification on Weibull mean is discussed. It is found that the ratio of biases and the ratio of MSEs are independent of the location and scale parameters of the lognormal and Weibull models. The influence could be ignored if some special conditions hold. Finally, a model selection method is proposed by comparing ratios concerning biases and MSEs. We also present a published data to illustrate the study in this paper.

Experimental Design in Clinical 'Omics Biomarker Discovery.

PubMed

Forshed, Jenny

2017-11-03

This tutorial highlights some issues in the experimental design of clinical 'omics biomarker discovery, how to avoid bias and get as true quantities as possible from biochemical analyses, and how to select samples to improve the chance of answering the clinical question at issue. This includes the importance of defining clinical aim and end point, knowing the variability in the results, randomization of samples, sample size, statistical power, and how to avoid confounding factors by including clinical data in the sample selection, that is, how to avoid unpleasant surprises at the point of statistical analysis. The aim of this Tutorial is to help translational clinical and preclinical biomarker candidate research and to improve the validity and potential of future biomarker candidate findings.

Sampling designs for HIV molecular epidemiology with application to Honduras.

PubMed

Shepherd, Bryan E; Rossini, Anthony J; Soto, Ramon Jeremias; De Rivera, Ivette Lorenzana; Mullins, James I

2005-11-01

Proper sampling is essential to characterize the molecular epidemiology of human immunodeficiency virus (HIV). HIV sampling frames are difficult to identify, so most studies use convenience samples. We discuss statistically valid and feasible sampling techniques that overcome some of the potential for bias due to convenience sampling and ensure better representation of the study population. We employ a sampling design called stratified cluster sampling. This first divides the population into geographical and/or social strata. Within each stratum, a population of clusters is chosen from groups, locations, or facilities where HIV-positive individuals might be found. Some clusters are randomly selected within strata and individuals are randomly selected within clusters. Variation and cost help determine the number of clusters and the number of individuals within clusters that are to be sampled. We illustrate the approach through a study designed to survey the heterogeneity of subtype B strains in Honduras.

A Ks-band-selected catalogue of objects in the ALHAMBRA survey

NASA Astrophysics Data System (ADS)

Nieves-Seoane, L.; Fernandez-Soto, A.; Arnalte-Mur, P.; Molino, A.; Stefanon, M.; Ferreras, I.; Ascaso, B.; Ballesteros, F. J.; Cristóbal-Hornillos, D.; López-Sanjuán, C.; Hurtado-Gil, Ll.; Márquez, I.; Masegosa, J.; Aguerri, J. A. L.; Alfaro, E.; Aparicio-Villegas, T.; Benítez, N.; Broadhurst, T.; Cabrera-Caño, J.; Castander, F. J.; Cepa, J.; Cerviño, M.; González Delgado, R. M.; Husillos, C.; Infante, L.; Martínez, V. J.; Moles, M.; Olmo, A. del; Perea, J.; Pović, M.; Prada, F.; Quintana, J. M.; Troncoso-Iribarren, P.; Viironen, K.

2017-02-01

The original ALHAMBRA catalogue contained over 400 000 galaxies selected using a synthetic F814W image, to the magnitude limit AB(F814W) ≈ 24.5. Given the photometric redshift depth of the ALHAMBRA multiband data ( = 0.86) and the approximately I-band selection, there is a noticeable bias against red objects at moderate redshift. We avoid this bias by creating a new catalogue selected in the Ks band. This newly obtained catalogue is certainly shallower in terms of apparent magnitude, but deeper in terms of redshift, with a significant population of red objects at z > 1. We select objects using the Ks band images, which reach an approximate AB magnitude limit Ks ≈ 22. We generate masks and derive completeness functions to characterize the sample. We have tested the quality of the photometry and photometric redshifts using both internal and external checks. Our final catalogue includes ≈95 000 sources down to Ks ≈ 22, with a significant tail towards high redshift. We have checked that there is a large sample of objects with spectral energy distributions that correspond to that of massive, passively evolving galaxies at z > 1, reaching as far as z ≈ 2.5. We have tested the possibility of combining our data with deep infrared observations at longer wavelengths, particularly Spitzer IRAC data.

Nature and Nurture Strike (Out) Again.

ERIC Educational Resources Information Center

Scarr, Sandra; Weinberg, Richard A.

1979-01-01

A reply to Plomin's critique and some criticisms of Munsinger's review of adopted child literature are presented. Selective bias in adoptee samples, implicit assumptions in models that lead to heritability estimates, and problems produced by lack of an accepted model of environmental transmission are also discussed. (Author/RD)

The Importance of Covariate Selection in Controlling for Selection Bias in Observational Studies

ERIC Educational Resources Information Center

Steiner, Peter M.; Cook, Thomas D.; Shadish, William R.; Clark, M. H.

2010-01-01

The assumption of strongly ignorable treatment assignment is required for eliminating selection bias in observational studies. To meet this assumption, researchers often rely on a strategy of selecting covariates that they think will control for selection bias. Theory indicates that the most important covariates are those highly correlated with…

Microcredit and domestic violence in Bangladesh: an exploration of selection bias influences.

PubMed

Bajracharya, Ashish; Amin, Sajeda

2013-10-01

This article explores the relationship between women's participation in microcredit groups and domestic violence in Bangladesh. Several recent studies have raised concern about microcredit programs by reporting higher levels of violence among women who are members. These results, however, may be attributable to selection bias because members might differ from nonmembers in ways that make them more susceptible to violence to begin with. Using a sample of currently married women from the 2007 Bangladesh Demographic Health Survey (BDHS) (N = 4,195), we use propensity score matching (PSM) as a way of exploring selection bias in this relationship. Results suggest that the previously seen strong positive association between membership and violence does not hold when an appropriate comparison group, generated using PSM, is used in the analyses. Additional analyses also suggest that levels of violence do not differ significantly between members and nonmembers and instead could depend on context-specific factors related to poverty. Members for whom a match is not found report considerably higher levels of violence relative to nonmembers in the unmatched group. The background characteristics of members and nonmembers who do not match suggest that they are more likely to be younger and from relatively well-to-do households.

The Clustering of High-redshift (2.9 ≤ z ≤ 5.1) Quasars in SDSS Stripe 82

NASA Astrophysics Data System (ADS)

Timlin, John D.; Ross, Nicholas P.; Richards, Gordon T.; Myers, Adam D.; Pellegrino, Andrew; Bauer, Franz E.; Lacy, Mark; Schneider, Donald P.; Wollack, Edward J.; Zakamska, Nadia L.

2018-05-01

We present a measurement of the two-point autocorrelation function of photometrically selected high-z quasars over ∼100 deg2 on the Sloan Digital Sky Survey Stripe 82 field. Selection is performed using three machine-learning algorithms in a six-dimensional optical/mid-infrared color space. Optical data from the Sloan Digital Sky Survey are combined with overlapping deep mid-infrared data from the Spitzer IRAC Equatorial Survey and the Spitzer-HETDEX Exploratory Large-Area survey. Our selection algorithms are trained on the colors of known high-z quasars. The selected quasar sample consists of 1378 objects and contains both spectroscopically confirmed quasars and photometrically selected quasar candidates. These objects span a redshift range of 2.9 ≤ z ≤ 5.1 and are generally fainter than i = 20.2, a regime that has lacked sufficient number density to perform autocorrelation function measurements of photometrically classified quasars. We compute the angular correlation function of these data, marginally detecting quasar clustering. We fit a single power law with an index of δ = 1.39 ± 0.618 and amplitude of θ 0 = 0.‧71 ± 0.‧546 . A dark matter model is fit to the angular correlation function to estimate the linear bias. At the average redshift of our survey (< z> =3.38), the bias is b = 6.78 ± 1.79. Using this bias, we calculate a characteristic dark matter halo mass of 1.70–9.83× {10}12{h}-1 {M}ȯ . Our bias estimate suggests that quasar feedback intermittently shuts down the accretion of gas onto the central supermassive black hole at early times. If confirmed, these results hint at a level of luminosity dependence in the clustering of quasars at high-z.

A simple bias correction in linear regression for quantitative trait association under two-tail extreme selection.

PubMed

Kwan, Johnny S H; Kung, Annie W C; Sham, Pak C

2011-09-01

Selective genotyping can increase power in quantitative trait association. One example of selective genotyping is two-tail extreme selection, but simple linear regression analysis gives a biased genetic effect estimate. Here, we present a simple correction for the bias.

Web based health surveys: Using a Two Step Heckman model to examine their potential for population health analysis.

PubMed

Morrissey, Karyn; Kinderman, Peter; Pontin, Eleanor; Tai, Sara; Schwannauer, Mathias

2016-08-01

In June 2011 the BBC Lab UK carried out a web-based survey on the causes of mental distress. The 'Stress Test' was launched on 'All in the Mind' a BBC Radio 4 programme and the test's URL was publicised on radio and TV broadcasts, and made available via BBC web pages and social media. Given the large amount of data created, over 32,800 participants, with corresponding diagnosis, demographic and socioeconomic characteristics; the dataset are potentially an important source of data for population based research on depression and anxiety. However, as respondents self-selected to participate in the online survey, the survey may comprise a non-random sample. It may be only individuals that listen to BBC Radio 4 and/or use their website that participated in the survey. In this instance using the Stress Test data for wider population based research may create sample selection bias. Focusing on the depression component of the Stress Test, this paper presents an easy-to-use method, the Two Step Probit Selection Model, to detect and statistically correct selection bias in the Stress Test. Using a Two Step Probit Selection Model; this paper did not find a statistically significant selection on unobserved factors for participants of the Stress Test. That is, survey participants who accessed and completed an online survey are not systematically different from non-participants on the variables of substantive interest. Copyright © 2016 Elsevier Ltd. All rights reserved.

Free energy computations by minimization of Kullback-Leibler divergence: An efficient adaptive biasing potential method for sparse representations

NASA Astrophysics Data System (ADS)

Bilionis, I.; Koutsourelakis, P. S.

2012-05-01

The present paper proposes an adaptive biasing potential technique for the computation of free energy landscapes. It is motivated by statistical learning arguments and unifies the tasks of biasing the molecular dynamics to escape free energy wells and estimating the free energy function, under the same objective of minimizing the Kullback-Leibler divergence between appropriately selected densities. It offers rigorous convergence diagnostics even though history dependent, non-Markovian dynamics are employed. It makes use of a greedy optimization scheme in order to obtain sparse representations of the free energy function which can be particularly useful in multidimensional cases. It employs embarrassingly parallelizable sampling schemes that are based on adaptive Sequential Monte Carlo and can be readily coupled with legacy molecular dynamics simulators. The sequential nature of the learning and sampling scheme enables the efficient calculation of free energy functions parametrized by the temperature. The characteristics and capabilities of the proposed method are demonstrated in three numerical examples.

Neither fixed nor random: weighted least squares meta-analysis.

PubMed

Stanley, T D; Doucouliagos, Hristos

2015-06-15

This study challenges two core conventional meta-analysis methods: fixed effect and random effects. We show how and explain why an unrestricted weighted least squares estimator is superior to conventional random-effects meta-analysis when there is publication (or small-sample) bias and better than a fixed-effect weighted average if there is heterogeneity. Statistical theory and simulations of effect sizes, log odds ratios and regression coefficients demonstrate that this unrestricted weighted least squares estimator provides satisfactory estimates and confidence intervals that are comparable to random effects when there is no publication (or small-sample) bias and identical to fixed-effect meta-analysis when there is no heterogeneity. When there is publication selection bias, the unrestricted weighted least squares approach dominates random effects; when there is excess heterogeneity, it is clearly superior to fixed-effect meta-analysis. In practical applications, an unrestricted weighted least squares weighted average will often provide superior estimates to both conventional fixed and random effects. Copyright © 2015 John Wiley & Sons, Ltd.

Sex-Specific Selection and Sex-Biased Gene Expression in Humans and Flies

PubMed Central

Kirkpatrick, Mark

2016-01-01

Sexual dimorphism results from sex-biased gene expression, which evolves when selection acts differently on males and females. While there is an intimate connection between sex-biased gene expression and sex-specific selection, few empirical studies have studied this relationship directly. Here we compare the two on a genome-wide scale in humans and flies. We find a distinctive “Twin Peaks” pattern in humans that relates the strength of sex-specific selection, quantified by genetic divergence between male and female adults at autosomal loci, to the degree of sex-biased expression. Genes with intermediate degrees of sex-biased expression show evidence of ongoing sex-specific selection, while genes with either little or completely sex-biased expression do not. This pattern apparently results from differential viability selection in males and females acting in the current generation. The Twin Peaks pattern is also found in Drosophila using a different measure of sex-specific selection acting on fertility. We develop a simple model that successfully recapitulates the Twin Peaks. Our results suggest that many genes with intermediate sex-biased expression experience ongoing sex-specific selection in humans and flies. PMID:27658217

[Biases in the study of prognostic factors].

PubMed

Delgado-Rodríguez, M

1999-01-01

The main objective is to detail the main biases in the study of prognostic factors. Confounding bias is illustrated with social class, a prognostic factor still discussed. Within selection bias several cases are commented: response bias, specially frequent when the patients of a clinical trial are used; the shortcomings in the formation of an inception cohort; the fallacy of Neyman (bias due to the duration of disease) when the study begins with a cross-sectional study; the selection bias in the treatment of survivors for the different treatment opportunity of those living longer; the bias due to the inclusion of heterogeneous diagnostic groups; and the selection bias due to differential information losses and the use of statistical multivariate procedures. Within the biases during follow-up, an empiric rule to value the impact of the number of losses is given. In information bias the Will Rogers' phenomenon and the usefulness of clinical databases are discussed. Lastly, a recommendation against the use of cutoff points yielded by bivariate analyses to select the variable to be included in multivariate analysis is given.

Clustering of quasars in SDSS-IV eBOSS: study of potential systematics and bias determination

DOE Office of Scientific and Technical Information (OSTI.GOV)

Laurent, Pierre; Goff, Jean-Marc Le; Burtin, Etienne

2017-07-01

We study the first year of the eBOSS quasar sample in the redshift range 0.9< z <2.2 which includes 68,772 homogeneously selected quasars. We show that the main source of systematics in the evaluation of the correlation function arises from inhomogeneities in the quasar target selection, particularly related to the extinction and depth of the imaging data used for targeting. We propose a weighting scheme that mitigates these systematics. We measure the quasar correlation function and provide the most accurate measurement to date of the quasar bias in this redshift range, b {sub Q} = 2.45 ± 0.05 at z-barmore » =1.55, together with its evolution with redshift. We use this information to determine the minimum mass of the halo hosting the quasars and the characteristic halo mass, which we find to be both independent of redshift within statistical error. Using a recently-measured quasar-luminosity-function we also determine the quasar duty cycle. The size of this first year sample is insufficient to detect any luminosity dependence to quasar clustering and this issue should be further studied with the final ∼500,000 eBOSS quasar sample.« less

Clustering of quasars in SDSS-IV eBOSS: study of potential systematics and bias determination

NASA Astrophysics Data System (ADS)

Laurent, Pierre; Eftekharzadeh, Sarah; Le Goff, Jean-Marc; Myers, Adam; Burtin, Etienne; White, Martin; Ross, Ashley J.; Tinker, Jeremy; Tojeiro, Rita; Bautista, Julian; Brinkmann, Jonathan; Comparat, Johan; Dawson, Kyle; du Mas des Bourboux, Hélion; Kneib, Jean-Paul; McGreer, Ian D.; Palanque-Delabrouille, Nathalie; Percival, Will J.; Prada, Francisco; Rossi, Graziano; Schneider, Donald P.; Weinberg, David; Yèche, Christophe; Zarrouk, Pauline; Zhao, Gong-Bo

2017-07-01

We study the first year of the eBOSS quasar sample in the redshift range 0.9

Getting DNA copy numbers without control samples

PubMed Central

2012-01-01

Background The selection of the reference to scale the data in a copy number analysis has paramount importance to achieve accurate estimates. Usually this reference is generated using control samples included in the study. However, these control samples are not always available and in these cases, an artificial reference must be created. A proper generation of this signal is crucial in terms of both noise and bias. We propose NSA (Normality Search Algorithm), a scaling method that works with and without control samples. It is based on the assumption that genomic regions enriched in SNPs with identical copy numbers in both alleles are likely to be normal. These normal regions are predicted for each sample individually and used to calculate the final reference signal. NSA can be applied to any CN data regardless the microarray technology and preprocessing method. It also finds an optimal weighting of the samples minimizing possible batch effects. Results Five human datasets (a subset of HapMap samples, Glioblastoma Multiforme (GBM), Ovarian, Prostate and Lung Cancer experiments) have been analyzed. It is shown that using only tumoral samples, NSA is able to remove the bias in the copy number estimation, to reduce the noise and therefore, to increase the ability to detect copy number aberrations (CNAs). These improvements allow NSA to also detect recurrent aberrations more accurately than other state of the art methods. Conclusions NSA provides a robust and accurate reference for scaling probe signals data to CN values without the need of control samples. It minimizes the problems of bias, noise and batch effects in the estimation of CNs. Therefore, NSA scaling approach helps to better detect recurrent CNAs than current methods. The automatic selection of references makes it useful to perform bulk analysis of many GEO or ArrayExpress experiments without the need of developing a parser to find the normal samples or possible batches within the data. The method is available in the open-source R package NSA, which is an add-on to the aroma.cn framework. http://www.aroma-project.org/addons. PMID:22898240

Getting DNA copy numbers without control samples.

PubMed

Ortiz-Estevez, Maria; Aramburu, Ander; Rubio, Angel

2012-08-16

The selection of the reference to scale the data in a copy number analysis has paramount importance to achieve accurate estimates. Usually this reference is generated using control samples included in the study. However, these control samples are not always available and in these cases, an artificial reference must be created. A proper generation of this signal is crucial in terms of both noise and bias.We propose NSA (Normality Search Algorithm), a scaling method that works with and without control samples. It is based on the assumption that genomic regions enriched in SNPs with identical copy numbers in both alleles are likely to be normal. These normal regions are predicted for each sample individually and used to calculate the final reference signal. NSA can be applied to any CN data regardless the microarray technology and preprocessing method. It also finds an optimal weighting of the samples minimizing possible batch effects. Five human datasets (a subset of HapMap samples, Glioblastoma Multiforme (GBM), Ovarian, Prostate and Lung Cancer experiments) have been analyzed. It is shown that using only tumoral samples, NSA is able to remove the bias in the copy number estimation, to reduce the noise and therefore, to increase the ability to detect copy number aberrations (CNAs). These improvements allow NSA to also detect recurrent aberrations more accurately than other state of the art methods. NSA provides a robust and accurate reference for scaling probe signals data to CN values without the need of control samples. It minimizes the problems of bias, noise and batch effects in the estimation of CNs. Therefore, NSA scaling approach helps to better detect recurrent CNAs than current methods. The automatic selection of references makes it useful to perform bulk analysis of many GEO or ArrayExpress experiments without the need of developing a parser to find the normal samples or possible batches within the data. The method is available in the open-source R package NSA, which is an add-on to the aroma.cn framework. http://www.aroma-project.org/addons.

Selection bias at the heterosexual HIV-1 transmission bottleneck

PubMed Central

Carlson, Jonathan M.; Schaefer, Malinda; Monaco, Daniela C.; Batorsky, Rebecca; Claiborne, Daniel T.; Prince, Jessica; Deymier, Martin J.; Ende, Zachary S.; Klatt, Nichole R.; DeZiel, Charles E.; Lin, Tien-Ho; Peng, Jian; Seese, Aaron M.; Shapiro, Roger; Frater, John; Ndung’u, Thumbi; Tang, Jianming; Goepfert, Paul; Gilmour, Jill; Price, Matt A.; Kilembe, William; Heckerman, David; Goulder, Philip J.R.; Allen, Todd M.; Allen, Susan; Hunter, Eric

2014-01-01

SUMMARY Introduction Heterosexual HIV-1 transmission is an inefficient process with rates reported at <1% per unprotected sexual exposure. When transmission occurs, systemic infection is typically established by a single genetic variant, taken from the swarm of genetically distinct viruses circulating in the donor. Whether that founder virus represents a chance event or was systematically favored is unclear. Our work has tested a central hypothesis that founder virus selection is biased toward certain genetic characteristics. Rationale If HIV-1 transmission involves selection for viruses with certain favorable characteristics, then such advantages should emerge as statistical biases when viewed across many viral loci in many transmitting partners. We therefore identified 137 Zambian heterosexual transmission pairs, for whom plasma samples were available for both the donor and recipient partner soon after transmission, and compared the viral sequences obtained from each partner to identify features that predicted whether the majority amino acid observed at any particular position in the donor was transmitted. We focused attention on two features: viral genetic characteristics that correlate with viral fitness, and clinical factors that influence transmission. Statistical modeling indicates that the former will be favored for transmission, while the latter will nullify this relative advantage. Results We observed a highly significant selection bias that favors the transmission of amino acids associated with increased fitness. These features included the frequency of the amino acid in the study cohort, the relative advantage of the amino acid with respect to the stability of the protein, and features related to immune escape and compensation. This selection bias was reduced in couples with high risk of transmission. In particular, significantly less selection bias was observed in women and in men with genital inflammation, compared to healthy men, suggesting a more permissive environment in the female than male genital tract. Consistent with this observation, viruses transmitted to women were characterized by lower predicted fitness than those in men. The presence of amino acids favored during transmission predicted which individual virus within a donor was transmitted to their partner, while chronically infected individuals with viral populations characterized by a predominance of these amino acids were more likely to transmit to their partners. Conclusion These data highlight the clear selection biases that benefit fitter viruses during transmission in the context of a stochastic process. That such biases exist, and are tempered by certain risk factors, suggests that transmission is frequently characterized by many abortive transmission events in which some target cells are nonproductively infected. Moreover, for efficient transmission, some changes that favored survival in the transmitting partner are frequently discarded, resulting in overall slower evolution of HIV-1 in the population. Paradoxically, by increasing the selection bias at the transmission bottleneck, reduction of susceptibility may increase the expected fitness of breakthrough viruses that establish infection and may therefore worsen the prognosis for the newly infected partner. Conversely, preventative or therapeutic approaches that weaken the virus may reduce overall transmission rates via a mechanism that is independent from the quantity of circulating virus, and may therefore provide long-term benefits even upon breakthrough infection. PMID:25013080

The Stanford Prison Experiment in Introductory Psychology Textbooks: A Content Analysis

ERIC Educational Resources Information Center

Bartels, Jared M.

2015-01-01

The present content analysis examines the coverage of theoretical and methodological problems with the Stanford prison experiment (SPE) in a sample of introductory psychology textbooks. Categories included the interpretation and replication of the study, variance in guard behavior, participant selection bias, the presence of demand characteristics…

Estimating Sampling Biases and Measurement Uncertainties of AIRS-AMSU-A Temperature and Water Vapor Observations Using MERRA Reanalysis

NASA Technical Reports Server (NTRS)

Hearty, Thomas J.; Savtchenko, Andrey K.; Tian, Baijun; Fetzer, Eric; Yung, Yuk L.; Theobald, Michael; Vollmer, Bruce; Fishbein, Evan; Won, Young-In

2014-01-01

We use MERRA (Modern Era Retrospective-Analysis for Research Applications) temperature and water vapor data to estimate the sampling biases of climatologies derived from the AIRS/AMSU-A (Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit-A) suite of instruments. We separate the total sampling bias into temporal and instrumental components. The temporal component is caused by the AIRS/AMSU-A orbit and swath that are not able to sample all of time and space. The instrumental component is caused by scenes that prevent successful retrievals. The temporal sampling biases are generally smaller than the instrumental sampling biases except in regions with large diurnal variations, such as the boundary layer, where the temporal sampling biases of temperature can be +/- 2 K and water vapor can be 10% wet. The instrumental sampling biases are the main contributor to the total sampling biases and are mainly caused by clouds. They are up to 2 K cold and greater than 30% dry over mid-latitude storm tracks and tropical deep convective cloudy regions and up to 20% wet over stratus regions. However, other factors such as surface emissivity and temperature can also influence the instrumental sampling bias over deserts where the biases can be up to 1 K cold and 10% wet. Some instrumental sampling biases can vary seasonally and/or diurnally. We also estimate the combined measurement uncertainties of temperature and water vapor from AIRS/AMSU-A and MERRA by comparing similarly sampled climatologies from both data sets. The measurement differences are often larger than the sampling biases and have longitudinal variations.

Different hunting strategies select for different weights in red deer

PubMed Central

Martínez, María; Rodríguez-Vigal, Carlos; Jones, Owen R; Coulson, Tim; Miguel, Alfonso San

2005-01-01

Much insight can be derived from records of shot animals. Most researchers using such data assume that their data represents a random sample of a particular demographic class. However, hunters typically select a non-random subset of the population and hunting is, therefore, not a random process. Here, with red deer (Cervus elaphus) hunting data from a ranch in Toledo, Spain, we demonstrate that data collection methods have a significant influence upon the apparent relationship between age and weight. We argue that a failure to correct for such methodological bias may have significant consequences for the interpretation of analyses involving weight or correlated traits such as breeding success, and urge researchers to explore methods to identify and correct for such bias in their data. PMID:17148205

Model selection with multiple regression on distance matrices leads to incorrect inferences.

PubMed

Franckowiak, Ryan P; Panasci, Michael; Jarvis, Karl J; Acuña-Rodriguez, Ian S; Landguth, Erin L; Fortin, Marie-Josée; Wagner, Helene H

2017-01-01

In landscape genetics, model selection procedures based on Information Theoretic and Bayesian principles have been used with multiple regression on distance matrices (MRM) to test the relationship between multiple vectors of pairwise genetic, geographic, and environmental distance. Using Monte Carlo simulations, we examined the ability of model selection criteria based on Akaike's information criterion (AIC), its small-sample correction (AICc), and the Bayesian information criterion (BIC) to reliably rank candidate models when applied with MRM while varying the sample size. The results showed a serious problem: all three criteria exhibit a systematic bias toward selecting unnecessarily complex models containing spurious random variables and erroneously suggest a high level of support for the incorrectly ranked best model. These problems effectively increased with increasing sample size. The failure of AIC, AICc, and BIC was likely driven by the inflated sample size and different sum-of-squares partitioned by MRM, and the resulting effect on delta values. Based on these findings, we strongly discourage the continued application of AIC, AICc, and BIC for model selection with MRM.

Pretest Measures of the Study Outcome and the Elimination of Selection Bias: Evidence from Three Within Study Comparisons.

PubMed

Hallberg, Kelly; Cook, Thomas D; Steiner, Peter M; Clark, M H

2018-04-01

This paper examines how pretest measures of a study outcome reduce selection bias in observational studies in education. The theoretical rationale for privileging pretests in bias control is that they are often highly correlated with the outcome, and in many contexts, they are also highly correlated with the selection process. To examine the pretest's role in bias reduction, we use the data from two within study comparisons and an especially strong quasi-experiment, each with an educational intervention that seeks to improve achievement. In each study, the pretest measures are consistently highly correlated with post-intervention measures of themselves, but the studies vary the correlation between the pretest and the process of selection into treatment. Across the three datasets with two outcomes each, there are three cases where this correlation is low and three where it is high. A single wave of pretest always reduces bias across the six instances examined, and it eliminates bias in three of them. Adding a second pretest wave eliminates bias in two more instances. However, the pattern of bias elimination does not follow the predicted pattern-that more bias reduction ensues as a function of how highly the pretest is correlated with selection. The findings show that bias is more complexly related to the pretest's correlation with selection than we hypothesized, and we seek to explain why.

Mendelian breeding units versus standard sampling strategies: Mitochondrial DNA variation in southwest Sardinia

PubMed Central

Sanna, Daria; Pala, Maria; Cossu, Piero; Dedola, Gian Luca; Melis, Sonia; Fresu, Giovanni; Morelli, Laura; Obinu, Domenica; Tonolo, Giancarlo; Secchi, Giannina; Triunfo, Riccardo; Lorenz, Joseph G.; Scheinfeldt, Laura; Torroni, Antonio; Robledo, Renato; Francalacci, Paolo

2011-01-01

We report a sampling strategy based on Mendelian Breeding Units (MBUs), representing an interbreeding group of individuals sharing a common gene pool. The identification of MBUs is crucial for case-control experimental design in association studies. The aim of this work was to evaluate the possible existence of bias in terms of genetic variability and haplogroup frequencies in the MBU sample, due to severe sample selection. In order to reach this goal, the MBU sampling strategy was compared to a standard selection of individuals according to their surname and place of birth. We analysed mitochondrial DNA variation (first hypervariable segment and coding region) in unrelated healthy subjects from two different areas of Sardinia: the area around the town of Cabras and the western Campidano area. No statistically significant differences were observed when the two sampling methods were compared, indicating that the stringent sample selection needed to establish a MBU does not alter original genetic variability and haplogroup distribution. Therefore, the MBU sampling strategy can be considered a useful tool in association studies of complex traits. PMID:21734814

Prevalence and factors related to dental caries among pre-school children of Saddar town, Karachi, Pakistan: a cross-sectional study.

PubMed

Dawani, Narendar; Nisar, Nighat; Khan, Nazeer; Syed, Shahbano; Tanweer, Navara

2012-12-27

Dental caries is highly prevalent and a significant public health problem among children throughout the world. Epidemiological data regarding prevalence of dental caries amongst Pakistani pre-school children is very limited. The objective of this study is to determine the frequency of dental caries among pre-school children of Saddar Town, Karachi, Pakistan and the factors related to caries. A cross-sectional study of 1000 preschool children was conducted in Saddar town, Karachi. Two-stage cluster sampling was used to select the sample. At first stage, eight clusters were selected randomly from total 11 clusters. In second stage, from the eight selected clusters, preschools were identified and children between 3- to 6-years age group were assessed for dental caries. Caries prevalence was 51% with a mean dmft score being 2.08 (±2.97) of which decayed teeth constituted 1.95. The mean dmft of males was 2.3 (±3.08) and of females was 1.90 (±2.90). The mean dmft of 3, 4, 5 and 6-year olds was 1.65, 2.11, 2.16 and 3.11 respectively. A significant association was found between dental caries and following variables: age group of 4-years (p-value < 0.029, RR = 1.248, 95% Bias corrected CI 0.029-0.437) and 5-years (p-value < 0.009, RR = 1.545, 95% Bias corrected CI 0.047-0.739), presence of dental plaque (p-value < 0.003, RR = 0.744, 95% Bias corrected CI (-0.433)-(-0.169)), poor oral hygiene (p-value < 0.000, RR = 0.661, 95% Bias corrected CI (-0.532)-(-0.284)), as well as consumption of non-sweetened milk (p-value < 0.049, RR = 1.232, 95% Bias corrected CI 0.061-0.367). Half of the preschoolers had dental caries coupled with a high prevalence of unmet dental treatment needs. Association between caries experience and age of child, consumption of non-sweetened milk, dental plaque and poor oral hygiene had been established.

Rational Learning and Information Sampling: On the "Naivety" Assumption in Sampling Explanations of Judgment Biases

ERIC Educational Resources Information Center

Le Mens, Gael; Denrell, Jerker

2011-01-01

Recent research has argued that several well-known judgment biases may be due to biases in the available information sample rather than to biased information processing. Most of these sample-based explanations assume that decision makers are "naive": They are not aware of the biases in the available information sample and do not correct for them.…

Alternative Approaches to Assessing Nonresponse Bias in Longitudinal Survey Estimates: An Application to Substance-Use Outcomes Among Young Adults in the United States

PubMed Central

West, Brady Thomas; McCabe, Sean Esteban

2017-01-01

Abstract We evaluated alternative approaches to assessing and correcting for nonresponse bias in a longitudinal survey. We considered the changes in substance-use outcomes over a 3-year period among young adults aged 18–24 years (n = 5,199) in the United States, analyzing data from the National Epidemiologic Survey on Alcohol and Related Conditions. This survey collected a variety of substance-use information from a nationally representative sample of US adults in 2 waves: 2001–2002 and 2004–2005. We first considered nonresponse rates in the second wave as a function of key substance-use outcomes in wave 1. We then evaluated 5 alternative approaches designed to correct for nonresponse bias under different attrition mechanisms, including weighting adjustments, multiple imputation, selection models, and pattern-mixture models. Nonignorable attrition in a longitudinal survey can lead to bias in estimates of change in certain health behaviors over time, and only selected procedures enable analysts to assess the sensitivity of their inferences to different assumptions about the extent of nonignorability. We compared estimates based on these 5 approaches, and we suggest a road map for assessing the risk of nonresponse bias in longitudinal studies. We conclude with directions for future research in this area given the results of our evaluations. PMID:28338839

Selectivity evaluation for two experimental gill-net configurations used to sample Lake Erie walleyes

USGS Publications Warehouse

Vandergoot, Christopher S.; Kocovsky, Patrick M.; Brenden, Travis O.; Liu, Weihai

2011-01-01

We used length frequencies of captured walleyes Sander vitreus to indirectly estimate and compare selectivity between two experimental gill-net configurations used to sample fish in Lake Erie: (1) a multifilament configuration currently used by the Ohio Department of Natural Resources (ODNR) with stretched-measure mesh sizes ranging from 51 to 127 mm and a constant filament diameter (0.37 mm); and (2) a monofilament configuration with mesh sizes ranging from 38 to 178 mm and varying filament diameter (range = 0.20–0.33 mm). Paired sampling with the two configurations revealed that the catch of walleyes smaller than 250 mm and larger than 600 mm was greater in the monofilament configuration than in the multifilament configuration, but the catch of 250–600-mm fish was greater in the multifilament configuration. Binormal selectivity functions yielded the best fit to observed walleye catches for both gill-net configurations based on model deviances. Incorporation of deviation terms in the binormal selectivity functions (i.e., to relax the assumption of geometric similarity) further improved the fit to observed catches. The final fitted selectivity functions produced results similar to those from the length-based catch comparisons: the monofilament configuration had greater selectivity for small and large walleyes and the multifilament configuration had greater selectivity for mid-sized walleyes. Computer simulations that incorporated the fitted binormal selectivity functions indicated that both nets were likely to result in some bias in age composition estimates and that the degree of bias would ultimately be determined by the underlying condition, mortality rate, and growth rate of the Lake Erie walleye population. Before the ODNR switches its survey gear, additional comparisons of the different gill-net configurations, such as fishing the net pairs across a greater range of depths and at more locations in the lake, should be conducted to maintain congruence in the fishery-independent survey time series.

Identical Profiles, Different Paths: Addressing Self-Selection Bias in Learning Community Cohorts

ERIC Educational Resources Information Center

Zobac, Stephanie; Spears, Julia; Barker, Gregory

2014-01-01

This article presents a method for addressing the self-selection bias of students who participate in learning communities (LCs). More specifically, this research utilizes equivalent comparison groups based on selected incoming characteristics of students, known as bootstraps, to account for self-selection bias. To address the differences in…

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mainzer, A.; Masiero, J.; Hand, E.

The NEOWISE data set offers the opportunity to study the variations in albedo for asteroid classification schemes based on visible and near-infrared observations for a large sample of minor planets. We have determined the albedos for nearly 1900 asteroids classified by the Tholen, Bus, and Bus-DeMeo taxonomic classification schemes. We find that the S-complex spans a broad range of bright albedos, partially overlapping the low albedo C-complex at small sizes. As expected, the X-complex covers a wide range of albedos. The multiwavelength infrared coverage provided by NEOWISE allows determination of the reflectivity at 3.4 and 4.6 {mu}m relative to themore » visible albedo. The direct computation of the reflectivity at 3.4 and 4.6 {mu}m enables a new means of comparing the various taxonomic classes. Although C, B, D, and T asteroids all have similarly low visible albedos, the D and T types can be distinguished from the C and B types by examining their relative reflectance at 3.4 and 4.6 {mu}m. All of the albedo distributions are strongly affected by selection biases against small, low albedo objects, as all objects selected for taxonomic classification were chosen according to their visible light brightness. Due to these strong selection biases, we are unable to determine whether or not there are correlations between size, albedo, and space weathering. We argue that the current set of classified asteroids makes any such correlations difficult to verify. A sample of taxonomically classified asteroids drawn without significant albedo bias is needed in order to perform such an analysis.« less

Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias.

PubMed

Fourcade, Yoan; Engler, Jan O; Rödder, Dennis; Secondi, Jean

2014-01-01

MAXENT is now a common species distribution modeling (SDM) tool used by conservation practitioners for predicting the distribution of a species from a set of records and environmental predictors. However, datasets of species occurrence used to train the model are often biased in the geographical space because of unequal sampling effort across the study area. This bias may be a source of strong inaccuracy in the resulting model and could lead to incorrect predictions. Although a number of sampling bias correction methods have been proposed, there is no consensual guideline to account for it. We compared here the performance of five methods of bias correction on three datasets of species occurrence: one "virtual" derived from a land cover map, and two actual datasets for a turtle (Chrysemys picta) and a salamander (Plethodon cylindraceus). We subjected these datasets to four types of sampling biases corresponding to potential types of empirical biases. We applied five correction methods to the biased samples and compared the outputs of distribution models to unbiased datasets to assess the overall correction performance of each method. The results revealed that the ability of methods to correct the initial sampling bias varied greatly depending on bias type, bias intensity and species. However, the simple systematic sampling of records consistently ranked among the best performing across the range of conditions tested, whereas other methods performed more poorly in most cases. The strong effect of initial conditions on correction performance highlights the need for further research to develop a step-by-step guideline to account for sampling bias. However, this method seems to be the most efficient in correcting sampling bias and should be advised in most cases.

Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias

PubMed Central

Fourcade, Yoan; Engler, Jan O.; Rödder, Dennis; Secondi, Jean

2014-01-01

MAXENT is now a common species distribution modeling (SDM) tool used by conservation practitioners for predicting the distribution of a species from a set of records and environmental predictors. However, datasets of species occurrence used to train the model are often biased in the geographical space because of unequal sampling effort across the study area. This bias may be a source of strong inaccuracy in the resulting model and could lead to incorrect predictions. Although a number of sampling bias correction methods have been proposed, there is no consensual guideline to account for it. We compared here the performance of five methods of bias correction on three datasets of species occurrence: one “virtual” derived from a land cover map, and two actual datasets for a turtle (Chrysemys picta) and a salamander (Plethodon cylindraceus). We subjected these datasets to four types of sampling biases corresponding to potential types of empirical biases. We applied five correction methods to the biased samples and compared the outputs of distribution models to unbiased datasets to assess the overall correction performance of each method. The results revealed that the ability of methods to correct the initial sampling bias varied greatly depending on bias type, bias intensity and species. However, the simple systematic sampling of records consistently ranked among the best performing across the range of conditions tested, whereas other methods performed more poorly in most cases. The strong effect of initial conditions on correction performance highlights the need for further research to develop a step-by-step guideline to account for sampling bias. However, this method seems to be the most efficient in correcting sampling bias and should be advised in most cases. PMID:24818607

Classification based upon gene expression data: bias and precision of error rates.

PubMed

Wood, Ian A; Visscher, Peter M; Mengersen, Kerrie L

2007-06-01

Gene expression data offer a large number of potentially useful predictors for the classification of tissue samples into classes, such as diseased and non-diseased. The predictive error rate of classifiers can be estimated using methods such as cross-validation. We have investigated issues of interpretation and potential bias in the reporting of error rate estimates. The issues considered here are optimization and selection biases, sampling effects, measures of misclassification rate, baseline error rates, two-level external cross-validation and a novel proposal for detection of bias using the permutation mean. Reporting an optimal estimated error rate incurs an optimization bias. Downward bias of 3-5% was found in an existing study of classification based on gene expression data and may be endemic in similar studies. Using a simulated non-informative dataset and two example datasets from existing studies, we show how bias can be detected through the use of label permutations and avoided using two-level external cross-validation. Some studies avoid optimization bias by using single-level cross-validation and a test set, but error rates can be more accurately estimated via two-level cross-validation. In addition to estimating the simple overall error rate, we recommend reporting class error rates plus where possible the conditional risk incorporating prior class probabilities and a misclassification cost matrix. We also describe baseline error rates derived from three trivial classifiers which ignore the predictors. R code which implements two-level external cross-validation with the PAMR package, experiment code, dataset details and additional figures are freely available for non-commercial use from http://www.maths.qut.edu.au/profiles/wood/permr.jsp

Comparison of Control Group Generating Methods.

PubMed

Szekér, Szabolcs; Fogarassy, György; Vathy-Fogarassy, Ágnes

2017-01-01

Retrospective studies suffer from drawbacks such as selection bias. As the selection of the control group has a significant impact on the evaluation of the results, it is very important to find the proper method to generate the most appropriate control group. In this paper we suggest two nearest neighbors based control group selection methods that aim to achieve good matching between the individuals of case and control groups. The effectiveness of the proposed methods is evaluated by runtime and accuracy tests and the results are compared to the classical stratified sampling method.

Attentional biases and memory for emotional stimuli in men and male rhesus monkeys.

PubMed

Lacreuse, Agnès; Schatz, Kelly; Strazzullo, Sarah; King, Hanna M; Ready, Rebecca

2013-11-01

We examined attentional biases for social and non-social emotional stimuli in young adult men and compared the results to those of male rhesus monkeys (Macaca mulatta) previously tested in a similar dot-probe task (King et al. in Psychoneuroendocrinology 37(3):396-409, 2012). Recognition memory for these stimuli was also analyzed in each species, using a recognition memory task in humans and a delayed non-matching-to-sample task in monkeys. We found that both humans and monkeys displayed a similar pattern of attentional biases toward threatening facial expressions of conspecifics. The bias was significant in monkeys and of marginal significance in humans. In addition, humans, but not monkeys, exhibited an attentional bias away from negative non-social images. Attentional biases for social and non-social threat differed significantly, with both species showing a pattern of vigilance toward negative social images and avoidance of negative non-social images. Positive stimuli did not elicit significant attentional biases for either species. In humans, emotional content facilitated the recognition of non-social images, but no effect of emotion was found for the recognition of social images. Recognition accuracy was not affected by emotion in monkeys, but response times were faster for negative relative to positive images. Altogether, these results suggest shared mechanisms of social attention in humans and monkeys, with both species showing a pattern of selective attention toward threatening faces of conspecifics. These data are consistent with the view that selective vigilance to social threat is the result of evolutionary constraints. Yet, selective attention to threat was weaker in humans than in monkeys, suggesting that regulatory mechanisms enable non-anxious humans to reduce sensitivity to social threat in this paradigm, likely through enhanced prefrontal control and reduced amygdala activation. In addition, the findings emphasize important differences in attentional biases to social versus non-social threat in both species. Differences in the impact of emotional stimuli on recognition memory between monkeys and humans will require further study, as methodological differences in the recognition tasks may have affected the results.

Pulsed discharge ionization source for miniature ion mobility spectrometers

DOEpatents

Xu, Jun; Ramsey, J. Michael; Whitten, William B.

2004-11-23

A method and apparatus is disclosed for flowing a sample gas and a reactant gas (38, 43) past a corona discharge electrode (26) situated at a first location in an ion drift chamber (24), applying a pulsed voltage waveform comprising a varying pulse component and a dc bias component to the corona discharge electrode (26) to cause a corona which in turn produces ions from the sample gas and the reactant gas, applying a dc bias to the ion drift chamber (24) to cause the ions to drift to a second location (25) in the ion drift chamber (24), detecting the ions at the second location (25) in the drift chamber (24), and timing the period for the ions to drift from the corona discharge electrode to the selected location in the drift chamber.

Sampling bias in an internet treatment trial for depression.

PubMed

Donkin, L; Hickie, I B; Christensen, H; Naismith, S L; Neal, B; Cockayne, N L; Glozier, N

2012-10-23

Internet psychological interventions are efficacious and may reduce traditional access barriers. No studies have evaluated whether any sampling bias exists in these trials that may limit the translation of the results of these trials into real-world application. We identified 7999 potentially eligible trial participants from a community-based health cohort study and invited them to participate in a randomized controlled trial of an online cognitive behavioural therapy programme for people with depression. We compared those who consented to being assessed for trial inclusion with nonconsenters on demographic, clinical and behavioural indicators captured in the health study. Any potentially biasing factors were then assessed for their association with depression outcome among trial participants to evaluate the existence of sampling bias. Of the 35 health survey variables explored, only 4 were independently associated with higher likelihood of consenting-female sex (odds ratio (OR) 1.11, 95% confidence interval (CI) 1.05-1.19), speaking English at home (OR 1.48, 95% CI 1.15-1.90) higher education (OR 1.67, 95% CI 1.46-1.92) and a prior diagnosis of depression (OR 1.37, 95% CI 1.22-1.55). The multivariate model accounted for limited variance (C-statistic 0.6) in explaining participation. These four factors were not significantly associated with either the primary trial outcome measure or any differential impact by intervention arm. This demonstrates that, among eligible trial participants, few factors were associated with the consent to participate. There was no indication that such self-selection biased the trial results or would limit the generalizability and translation into a public or clinical setting.

Quality-assurance design applied to an assessment of agricultural pesticides in ground water from carbonate bedrock aquifers in the Great Valley of eastern Pennsylvania

USGS Publications Warehouse

Breen, Kevin J.

2000-01-01

Assessments to determine whether agricultural pesticides are present in ground water are performed by the Commonwealth of Pennsylvania under the aquifer monitoring provisions of the State Pesticides and Ground Water Strategy. Pennsylvania's Department of Agriculture conducts the monitoring and collects samples; the Department of Environmental Protection (PaDEP) Laboratory analyzes the samples to measure pesticide concentration. To evaluate the quality of the measurements of pesticide concentration for a groundwater assessment, a quality-assurance design was developed and applied to a selected assessment area in Pennsylvania. This report describes the quality-assurance design, describes how and where the design was applied, describes procedures used to collect and analyze samples and to evaluate the results, and summarizes the quality assurance results along with the assessment results.The design was applied in an agricultural area of the Delaware River Basin in Berks, Lebanon, Lehigh, and Northampton Counties to evaluate the bias and variability in laboratory results for pesticides. The design—with random spatial and temporal components—included four data-quality objectives for bias and variability. The spatial design was primary and represented an area comprising 30 sampling cells. A quality-assurance sampling frequency of 20 percent of cells was selected to ensure a sample number of five or more for analysis. Quality-control samples included blanks, spikes, and replicates of laboratory water and spikes, replicates, and 2-lab splits of groundwater. Two analytical laboratories, the PaDEP Laboratory and a U.S. Geological Survey Laboratory, were part of the design. Bias and variability were evaluated by use of data collected from October 1997 through January 1998 for alachlor, atrazine, cyanazine, metolachlor, simazine, pendimethalin, metribuzin, and chlorpyrifos.Results of analyses of field blanks indicate that collection, processing, transport, and laboratory analysis procedures did not contaminate the samples; there were no false-positive results. Pesticides were detected in water when pesticides were spiked into (added to) samples. There were no false negatives for the eight pesticides in all spiked samples. Negative bias was characteristic of analytical results for the eight pesticides, and bias was generally in excess of 10 percent from the ‘true’ or expected concentration (34 of 39 analyses, or 87 percent of the ground-water results) for pesticide concentrations ranging from 0.31 to 0.51 mg/L (micrograms per liter). The magnitude of the negative bias for the eight pesticides, with the exception of cyanazine, would result in reported concentrations commonly 75-80 percent of the expected concentration in the water sample. The bias for cyanazine was negative and within 10 percent of the expected concentration. A comparison of spiked pesticide-concentration recoveries in laboratory water and ground water indicated no effect of the ground-water matrix, and matrix interference was not a source of the negative bias. Results for the laboratory-water spikes submitted in triplicate showed large variability for recoveries of atrazine, cyanazine, and pendimethalin. The relative standard deviation (RSD) was used as a measure of method variability over the course of the study for laboratory waters at a concentration of 0.4 mg/L. An RSD of about 11 percent (or about ?0.05 mg/L)characterizes the method results for alachlor, chlorpyrifos, metolachlor, metribuzin, and simazine. Atrazine and pendimethalin have RSD values of about 17 and 23 percent, respectively. Cyanazine showed the largest RSD at nearly 51 percent. The pesticides with low variability in laboratory-water spikes also had low variability in ground water.The assessment results showed that atrazinewas the most commonly detected pesticide in ground water in the assessment area. Atrazine was detected in water from 22 of the 28 wells sampled, and recovery results for atrazine were some of the worst (largest negative bias). Concentrations of the eight pesticides in ground water from wells were generally less than 0.3 µg/L. Only six individual measurements of the concentrations in water from six of the wells were at or above 0.3 µg/L, five for atrazine and one for metolachlor. There were eight additional detections of metolachlor and simazine at concentrations less than 0.1 µg/L. No well water contained more than one pesticide at concentra-tions at or above 0.3 µg/L. Evidence exists, how-ever, for a pattern of co-occurrence of metolachlor and simazine at low concentrations with higher concentrations of atrazine.Large variability in replicate samples and negative bias for pesticide recovery from spiked samples indicate the need to use data for pesticide recovery in the interpretation of measured pesti-cide concentrations in ground water. Data from samples spiked with known amounts of pesticides were a critical component of a quality-assurance design for the monitoring component of the Pesti-cides and Ground Water Strategy.Trigger concentrations, the concentrations that require action under the Pesticides and Ground Water Strategy, should be considered maximums for action. This consideration is needed because of the magnitude of negative bias.

Survival-related Selection Bias in Studies of Racial Health Disparities: The Importance of the Target Population and Study Design.

PubMed

Howe, Chanelle J; Robinson, Whitney R

2018-07-01

The impact of survival-related selection bias has not always been discussed in relevant studies of racial health disparities. Moreover, the analytic approaches most frequently employed in the epidemiologic literature to minimize selection bias are difficult to implement appropriately in racial disparities research. This difficulty stems from the fact that frequently employed analytic techniques require that common causes of survival and the outcome are accurately measured. Unfortunately, such common causes are often unmeasured or poorly measured in racial health disparities studies. In the absence of accurate measures of the aforementioned common causes, redefining the target population or changing the study design represents a useful approach for reducing the extent of survival-related selection bias. To help researchers recognize and minimize survival-related selection bias in racial health disparities studies, we illustrate the aforementioned selection bias and how redefining the target population or changing the study design can be useful.

Is protective equipment useful in preventing concussion? A systematic review of the literature.

PubMed

Benson, B W; Hamilton, G M; Meeuwisse, W H; McCrory, P; Dvorak, J

2009-05-01

To determine if there is evidence that equipment use reduces sport concussion risk and/or severity. 12 electronic databases were searched using a combination of Medical Subject Headings and text words to identify relevant articles. Specific inclusion and exclusion criteria were used to select studies for review. Data extracted included design, study population, exposure/outcome measures and results. The quality of evidence was assessed based on epidemiologic criteria regarding internal and external validity (ie, strength of design, sample size/power calculation, selection bias, misclassification bias, control of potential confounding and effect modification). In total, 51 studies were selected for review. A comparison between studies was difficult due to the variability in research designs, definition of concussion, mouthguard/helmet/headgear/face shield types, measurements used to assess exposure and outcomes, and variety of sports assessed. The majority of studies were observational, with 23 analytical epidemiologic designs related to the subject area. Selection bias was a concern in the reviewed studies, as was the lack of measurement and control for potentially confounding variables. There is evidence that helmet use reduces head injury risk in skiing, snowboarding and bicycling, but the effect on concussion risk is inconclusive. No strong evidence exists for the use of mouthguards or face shields to reduce concussion risk. Evidence is provided to suggest that full facial protection in ice hockey may reduce concussion severity, as measured by time loss from competition.

Measuring willingness to pay to improve municipal water in southeast Anatolia, Turkey

NASA Astrophysics Data System (ADS)

Bilgic, Abdulbaki

2010-12-01

Increasing demands for water and quality concerns have highlighted the importance of accounting for household perceptions before local municipalities rehabilitate existing water infrastructures and bring them into compliance. We compared different willingness-to-pay (WTP) estimates using household surveys in the southern Anatolian region of Turkey. Our study is the first of its kind in Turkey. Biases resulting from sample selection and the endogeneity of explanatory variables were corrected. When compared to a univariate probit model, correction of these biases was shown to result in statistically significant findings through moderate reductions in mean WTP.

Filtered cathodic arc deposition with ion-species-selective bias.

PubMed

Anders, André; Pasaja, Nitisak; Sansongsiri, Sakon

2007-06-01

A dual-cathode arc plasma source was combined with a computer-controlled bias amplifier to synchronize substrate bias with the pulsed production of plasma. In this way, bias can be applied in a material-selective way. The principle has been applied to the synthesis of metal-doped diamondlike carbon films, where the bias was applied and adjusted when the carbon plasma was condensing and the substrate was at ground when the metal was incorporated. In doing so, excessive sputtering by energetic metal ions can be avoided while the sp(3)sp(2) ratio can be adjusted. It is shown that the resistivity of the film can be tuned by this species-selective bias; Raman spectroscopy was used to confirm expected changes of the amorphous ta-C:Mo films. The species-selective bias principle could be extended to multiple material plasma sources and complex materials.

The Effects of Universal Pre-K on Cognitive Development

ERIC Educational Resources Information Center

Gormley, William T.; Gayer, Ted; Phillips, Deborah; Dawson, Brittany

2005-01-01

In this study of Oklahoma's universal pre-K program, the authors relied on a strict birthday eligibility criterion to compare "young" kindergarten children who just completed pre-K to "old" pre-K children just beginning pre-K. This regression-discontinuity design reduces the threat of selection bias. Their sample consisted of…

Young Adult Fantasy and the Search for Gender-Fair Genres.

ERIC Educational Resources Information Center

Forrest, Linda A.

1993-01-01

Examines gender bias in young adult literature collections and advocates selecting and promoting materials that help youth develop positive views of females and an awareness of the obstacles women face in overcoming sexual stereotypes. A sampling of 9 titles is described, and an annotated bibliography of 19 additional titles is included. (Contains…

How Broad Liberal Arts Training Produces Phd Economists: Carleton's Story

ERIC Educational Resources Information Center

Bourne, Jenny; Grawe, Nathan D.

2015-01-01

Several recent studies point to strong performance in economics PhD programs of graduates from liberal arts colleges. While every undergraduate program is unique and the likelihood of selection bias combines with small sample sizes to caution against drawing strong conclusions, the authors reflect on their experience at Carleton College to…

Sexual selection drives evolution and rapid turnover of male gene expression.

PubMed

Harrison, Peter W; Wright, Alison E; Zimmer, Fabian; Dean, Rebecca; Montgomery, Stephen H; Pointer, Marie A; Mank, Judith E

2015-04-07

The profound and pervasive differences in gene expression observed between males and females, and the unique evolutionary properties of these genes in many species, have led to the widespread assumption that they are the product of sexual selection and sexual conflict. However, we still lack a clear understanding of the connection between sexual selection and transcriptional dimorphism, often termed sex-biased gene expression. Moreover, the relative contribution of sexual selection vs. drift in shaping broad patterns of expression, divergence, and polymorphism remains unknown. To assess the role of sexual selection in shaping these patterns, we assembled transcriptomes from an avian clade representing the full range of sexual dimorphism and sexual selection. We use these species to test the links between sexual selection and sex-biased gene expression evolution in a comparative framework. Through ancestral reconstruction of sex bias, we demonstrate a rapid turnover of sex bias across this clade driven by sexual selection and show it to be primarily the result of expression changes in males. We use phylogenetically controlled comparative methods to demonstrate that phenotypic measures of sexual selection predict the proportion of male-biased but not female-biased gene expression. Although male-biased genes show elevated rates of coding sequence evolution, consistent with previous reports in a range of taxa, there is no association between sexual selection and rates of coding sequence evolution, suggesting that expression changes may be more important than coding sequence in sexual selection. Taken together, our results highlight the power of sexual selection to act on gene expression differences and shape genome evolution.

Is it better to select or to receive? Learning via active and passive hypothesis testing.

PubMed

Markant, Douglas B; Gureckis, Todd M

2014-02-01

People can test hypotheses through either selection or reception. In a selection task, the learner actively chooses observations to test his or her beliefs, whereas in reception tasks data are passively encountered. People routinely use both forms of testing in everyday life, but the critical psychological differences between selection and reception learning remain poorly understood. One hypothesis is that selection learning improves learning performance by enhancing generic cognitive processes related to motivation, attention, and engagement. Alternatively, we suggest that differences between these 2 learning modes derives from a hypothesis-dependent sampling bias that is introduced when a person collects data to test his or her own individual hypothesis. Drawing on influential models of sequential hypothesis-testing behavior, we show that such a bias (a) can lead to the collection of data that facilitates learning compared with reception learning and (b) can be more effective than observing the selections of another person. We then report a novel experiment based on a popular category learning paradigm that compares reception and selection learning. We additionally compare selection learners to a set of "yoked" participants who viewed the exact same sequence of observations under reception conditions. The results revealed systematic differences in performance that depended on the learner's role in collecting information and the abstract structure of the problem.

Implicit Racial/Ethnic Bias Among Health Care Professionals and Its Influence on Health Care Outcomes: A Systematic Review

PubMed Central

Hall, William J.; Lee, Kent M.; Merino, Yesenia M.; Thomas, Tainayah W.; Payne, B. Keith; Eng, Eugenia; Day, Steven H.; Coyne-Beasley, Tamera

2015-01-01

Background. In the United States, people of color face disparities in access to health care, the quality of care received, and health outcomes. The attitudes and behaviors of health care providers have been identified as one of many factors that contribute to health disparities. Implicit attitudes are thoughts and feelings that often exist outside of conscious awareness, and thus are difficult to consciously acknowledge and control. These attitudes are often automatically activated and can influence human behavior without conscious volition. Objectives. We investigated the extent to which implicit racial/ethnic bias exists among health care professionals and examined the relationships between health care professionals’ implicit attitudes about racial/ethnic groups and health care outcomes. Search Methods. To identify relevant studies, we searched 10 computerized bibliographic databases and used a reference harvesting technique. Selection Criteria. We assessed eligibility using double independent screening based on a priori inclusion criteria. We included studies if they sampled existing health care providers or those in training to become health care providers, measured and reported results on implicit racial/ethnic bias, and were written in English. Data Collection and Analysis. We included a total of 15 studies for review and then subjected them to double independent data extraction. Information extracted included the citation, purpose of the study, use of theory, study design, study site and location, sampling strategy, response rate, sample size and characteristics, measurement of relevant variables, analyses performed, and results and findings. We summarized study design characteristics, and categorized and then synthesized substantive findings. Main Results. Almost all studies used cross-sectional designs, convenience sampling, US participants, and the Implicit Association Test to assess implicit bias. Low to moderate levels of implicit racial/ethnic bias were found among health care professionals in all but 1 study. These implicit bias scores are similar to those in the general population. Levels of implicit bias against Black, Hispanic/Latino/Latina, and dark-skinned people were relatively similar across these groups. Although some associations between implicit bias and health care outcomes were nonsignificant, results also showed that implicit bias was significantly related to patient–provider interactions, treatment decisions, treatment adherence, and patient health outcomes. Implicit attitudes were more often significantly related to patient–provider interactions and health outcomes than treatment processes. Conclusions. Most health care providers appear to have implicit bias in terms of positive attitudes toward Whites and negative attitudes toward people of color. Future studies need to employ more rigorous methods to examine the relationships between implicit bias and health care outcomes. Interventions targeting implicit attitudes among health care professionals are needed because implicit bias may contribute to health disparities for people of color. PMID:26469668

Training effect of the exchange bias in sputter deposited Fe3O4 thin films with varying thickness

NASA Astrophysics Data System (ADS)

Muhammed Shameem, P. V.; Senthil Kumar, M.

2018-07-01

The training effect property of the exchange bias in the reactively sputtered polycrystalline Fe3O4 thin films of varying thicknesses in the range 25-200 nm are studied. Structural studies by X-ray diffraction, X-ray photoelectron spectroscopy and selected area electron diffraction confirm the formation of single phase Fe3O4. The scanning electron spectroscopy images show that the grains are uniformly distributed. All the samples show clear and consistent exchange bias training behaviour due to the dynamics of the spins at the interface of the ferrimagnetic core and the spin glass-like surface of the grains. The analysis of the training effect data of the exchange bias field HE measured at 2 K by using three different models show that the model based on the relaxation of the frozen and rotatable spin components at the interface gives the best description for all the samples. From this model, it is found that the reversible interface spins relax around 7 times faster than the frozen interface spins at 2 K for all the samples and that their relative relaxation rates are independent of the sample thickness. This constancy show that the relative relaxation rates of the interfacial frozen and rotatable spin components is a material dependent property. The frozen component of the interfacial spins of each sample is found to be dominated at the initial stage of the training. A direct equivalence between the HE and remanence asymmetry ME is observed. Above the spin freezing temperature, the training effect measurements at 75 K show that the HE decreases sharply with successive field cycling as compared to the measurements made at 2 K and the HE vanishes after first few cycles.

Bayesian Model Selection under Time Constraints

NASA Astrophysics Data System (ADS)

Hoege, M.; Nowak, W.; Illman, W. A.

2017-12-01

Bayesian model selection (BMS) provides a consistent framework for rating and comparing models in multi-model inference. In cases where models of vastly different complexity compete with each other, we also face vastly different computational runtimes of such models. For instance, time series of a quantity of interest can be simulated by an autoregressive process model that takes even less than a second for one run, or by a partial differential equations-based model with runtimes up to several hours or even days. The classical BMS is based on a quantity called Bayesian model evidence (BME). It determines the model weights in the selection process and resembles a trade-off between bias of a model and its complexity. However, in practice, the runtime of models is another weight relevant factor for model selection. Hence, we believe that it should be included, leading to an overall trade-off problem between bias, variance and computing effort. We approach this triple trade-off from the viewpoint of our ability to generate realizations of the models under a given computational budget. One way to obtain BME values is through sampling-based integration techniques. We argue with the fact that more expensive models can be sampled much less under time constraints than faster models (in straight proportion to their runtime). The computed evidence in favor of a more expensive model is statistically less significant than the evidence computed in favor of a faster model, since sampling-based strategies are always subject to statistical sampling error. We present a straightforward way to include this misbalance into the model weights that are the basis for model selection. Our approach follows directly from the idea of insufficient significance. It is based on a computationally cheap bootstrapping error estimate of model evidence and is easy to implement. The approach is illustrated in a small synthetic modeling study.

Dust-obscured star formation and the contribution of galaxies escaping UV/optical color selections at z ~ 2

NASA Astrophysics Data System (ADS)

Riguccini, L.; Le Floc'h, E.; Ilbert, O.; Aussel, H.; Salvato, M.; Capak, P.; McCracken, H.; Kartaltepe, J.; Sanders, D.; Scoville, N.

2011-10-01

Context. A substantial amount of the stellar mass growth across cosmic time occurred within dust-enshrouded environments. So far, identification of complete samples of distant star-forming galaxies from the short wavelength range has been strongly biased by the effect of dust extinction. Nevertheless, the exact amount of star-forming activity that took place in high-redshift dusty galaxies but that has currently been missed by optical surveys has barely been explored. Aims: Our goal is to determine the number of luminous star-forming galaxies at 1.5 ≲ z ≲ 3 that are potentially missed by the traditional color selection techniques because of dust extinction. We also aim at quantifying the contribution of these sources to the IR luminosity and cosmic star formation density at high redshift. Methods: We based our work on a sample of 24 μm sources brighter than 80 μJy and taken from the Spitzer survey of the COSMOS field. Almost all of these sources have accurate photometric redshifts. We applied to this mid-IR selected sample the BzK and BM/BX criteria, as well as the selections of the IRAC peakers and the Optically-Faint IR-bright (OFIR) galaxies. We analyzed the fraction of sources identified with these techniques. We also computed 8 μm rest-frame luminosity from the 24 μm fluxes of our sources, and considering the relationships between L8 μm and LPaα and between L8 μm and LIR, we derived ρIR and then ρSFR for our MIPS sources. Results: The BzK criterion offers an almost complete (~90%) identification of the 24 μm sources at 1.4 < z < 2.5. In contrast, the BM/BX criterion misses 50% of the MIPS sources. We attribute this bias to the effect of extinction, which reddens the typical colors of galaxies. The contribution of these two selections to the IR luminosity density produced by all the sources brighter than 80 μJy are on the same order. Moreover the criterion based on the presence of a stellar bump in their spectra (IRAC peakers) misses up to 40% of the IR luminosity density, while only 25% of the IR luminosity density at z ~ 2 is produced by OFIR galaxies characterized by extreme mid-IR to optical flux ratios. Conclusions: Color selections of distant star-forming galaxies must be used with care given the substantial bias they can suffer. In particular, the effect of dust extinction strongly affects the completeness of identifications at the bright end of the bolometric luminosity function, which implies large and uncertain extrapolations to account for the contribution of dusty galaxies missed by these selections. In the context of forthcoming facilities that will operate at long wavelengths (e.g., JWST, ALMA, SAFARI, EVLA, SKA), this emphasizes the importance of minimizing the extinction biases when probing the activity of star formation in the early Universe.

Assessing total nitrogen in surface-water samples--precision and bias of analytical and computational methods

USGS Publications Warehouse

Rus, David L.; Patton, Charles J.; Mueller, David K.; Crawford, Charles G.

2013-01-01

The characterization of total-nitrogen (TN) concentrations is an important component of many surface-water-quality programs. However, three widely used methods for the determination of total nitrogen—(1) derived from the alkaline-persulfate digestion of whole-water samples (TN-A); (2) calculated as the sum of total Kjeldahl nitrogen and dissolved nitrate plus nitrite (TN-K); and (3) calculated as the sum of dissolved nitrogen and particulate nitrogen (TN-C)—all include inherent limitations. A digestion process is intended to convert multiple species of nitrogen that are present in the sample into one measureable species, but this process may introduce bias. TN-A results can be negatively biased in the presence of suspended sediment, and TN-K data can be positively biased in the presence of elevated nitrate because some nitrate is reduced to ammonia and is therefore counted twice in the computation of total nitrogen. Furthermore, TN-C may not be subject to bias but is comparatively imprecise. In this study, the effects of suspended-sediment and nitrate concentrations on the performance of these TN methods were assessed using synthetic samples developed in a laboratory as well as a series of stream samples. A 2007 laboratory experiment measured TN-A and TN-K in nutrient-fortified solutions that had been mixed with varying amounts of sediment-reference materials. This experiment identified a connection between suspended sediment and negative bias in TN-A and detected positive bias in TN-K in the presence of elevated nitrate. A 2009–10 synoptic-field study used samples from 77 stream-sampling sites to confirm that these biases were present in the field samples and evaluated the precision and bias of TN methods. The precision of TN-C and TN-K depended on the precision and relative amounts of the TN-component species used in their respective TN computations. Particulate nitrogen had an average variability (as determined by the relative standard deviation) of 13 percent. However, because particulate nitrogen constituted only 14 percent, on average, of TN-C, the precision of the TN-C method approached that of the method for dissolved nitrogen (2.3 percent). On the other hand, total Kjeldahl nitrogen (having a variability of 7.6 percent) constituted an average of 40 percent of TN-K, suggesting that the reduced precision of the Kjeldahl digestion may affect precision of the TN-K estimates. For most samples, the precision of TN computed as TN-C would be better (lower variability) than the precision of TN-K. In general, TN-A precision (having a variability of 2.1 percent) was superior to TN-C and TN-K methods. The laboratory experiment indicated that negative bias in TN-A was present across the entire range of sediment concentration and increased as sediment concentration increased. This suggested that reagent limitation was not the predominant cause of observed bias in TN-A. Furthermore, analyses of particulate nitrogen present in digest residues provided an almost complete accounting for the nitrogen that was underestimated by alkaline-persulfate digestion. This experiment established that, for the reference materials at least, negative bias in TN-A was caused primarily by the sequestration of some particulate nitrogen that was refractory to the digestion process. TN-K biases varied between positive and negative values in the laboratory experiment. Positive bias in TN-K is likely the result of the unintended reduction of a small and variable amount of nitrate to ammonia during the Kjeldahl digestion process. Negative TN-K bias may be the result of the sequestration of a portion of particulate nitrogen during the digestion process. Negative bias in TN-A was present across the entire range of suspended-sediment concentration (1 to 14,700 milligrams per liter [mg/L]) in the synoptic-field study, with relative bias being nearly as great at sediment concentrations below 10 mg/L (median of -3.5 percent) as that observed at sediment concentrations up to 750 mg/L (median of -4.4 percent). This lent support to the laboratory-experiment finding that some particulate nitrogen is sequestered during the digestion process, and demonstrated that negative TN-A bias was present in samples with very low suspended-sediment concentrations. At sediment concentrations above 750 mg/L, the negative TN-A bias became more likely and larger (median of -13.2 percent), suggesting a secondary mechanism of bias, such as reagent limitation. From a geospatial perspective, trends in TN-A bias were not explained by selected basin characteristics. Though variable, TN-K bias generally was positive in the synoptic-field study (median of 3.1 percent), probably as a result of the reduction of nitrate. Three alternative approaches for assessing TN in surface water were evaluated for their impacts on existing and future sampling programs. Replacing TN-A with TN-C would remove the bias from subsequent data, but this approach also would introduce discontinuity in historical records. Replacing TN-K with TN-C would lead to the removal of positive bias in TN-K in the presence of elevated nitrate. However, in addition to the issues that may arise from a discontinuity in the data record, this approach may not be applicable to regulatory programs that require the use of total Kjeldahl nitrogen for stream assessment. By adding TN-C to existing TN-A or TN-K analyses, historical-data continuity would be preserved and the transitional period could be used to minimize the impact of bias on data analyses. This approach, however, imposes the greatest burdens on field operations and in terms of analytical costs. The variation in these impacts on different sampling programs will challenge U.S. Geological Survey scientists attempting to establish uniform standards for TN sample collection and analytical determinations.

Mechanism for and method of biasing magnetic sensor

DOEpatents

Kautz, David R.

2007-12-04

A magnetic sensor package having a biasing mechanism involving a coil-generated, resistor-controlled magnetic field for providing a desired biasing effect. In a preferred illustrated embodiment, the package broadly comprises a substrate; a magnetic sensor element; a biasing mechanism, including a coil and a first resistance element; an amplification mechanism; a filter capacitor element; and an encapsulant. The sensor is positioned within the coil. A current applied to the coil produces a biasing magnetic field. The biasing magnetic field is controlled by selecting a resistance value for the first resistance element which achieves the desired biasing effect. The first resistance element preferably includes a plurality of selectable resistors, the selection of one or more of which sets the resistance value.

Using Propensity Scores to Reduce Selection Bias in Mathematics Education Research

ERIC Educational Resources Information Center

Graham, Suzanne E.

2010-01-01

Selection bias is a problem for mathematics education researchers interested in using observational rather than experimental data to make causal inferences about the effects of different instructional methods in mathematics on student outcomes. Propensity score methods represent 1 approach to dealing with such selection bias. This article…

Performance of the disease risk score in a cohort study with policy-induced selection bias.

PubMed

Tadrous, Mina; Mamdani, Muhammad M; Juurlink, David N; Krahn, Murray D; Lévesque, Linda E; Cadarette, Suzanne M

2015-11-01

To examine the performance of the disease risk score (DRS) in a cohort study with evidence of policy-induced selection bias. We examined two cohorts of new users of bisphosphonates. Estimates for 1-year hip fracture rates between agents using DRS, exposure propensity scores and traditional multivariable analysis were compared. The results for the cohort with no evidence of policy-induced selection bias showed little variation across analyses (-4.1-2.0%). Analysis of the cohort with evidence of policy-induced selection bias showed greater variation (-13.5-8.1%), with the greatest difference seen with DRS analyses. Our findings suggest that caution may be warranted when using DRS methods in cohort studies with policy-induced selection bias, further research is needed.

Self-referent information processing in individuals with bipolar spectrum disorders.

PubMed

Molz Adams, Ashleigh; Shapero, Benjamin G; Pendergast, Laura H; Alloy, Lauren B; Abramson, Lyn Y

2014-01-01

Bipolar spectrum disorders (BSDs) are common and impairing, which has led to an examination of risk factors for their development and maintenance. Historically, research has examined cognitive vulnerabilities to BSDs derived largely from the unipolar depression literature. Specifically, theorists propose that dysfunctional information processing guided by negative self-schemata may be a risk factor for depression. However, few studies have examined whether BSD individuals also show self-referent processing biases. This study examined self-referent information processing differences between 66 individuals with and 58 individuals without a BSD in a young adult sample (age M=19.65, SD=1.74; 62% female; 47% Caucasian). Repeated measures multivariate analysis of variance (MANOVA) was conducted to examine multivariate effects of BSD diagnosis on 4 self-referent processing variables (self-referent judgments, response latency, behavioral predictions, and recall) in response to depression-related and nondepression-related stimuli. Bipolar individuals endorsed and recalled more negative and fewer positive self-referent adjectives, as well as made more negative and fewer positive behavioral predictions. Many of these information-processing biases were partially, but not fully, mediated by depressive symptoms. Our sample was not a clinical or treatment-seeking sample, so we cannot generalize our results to clinical BSD samples. No participants had a bipolar I disorder at baseline. This study provides further evidence that individuals with BSDs exhibit a negative self-referent information processing bias. This may mean that those with BSDs have selective attention and recall of negative information about themselves, highlighting the need for attention to cognitive biases in therapy. © 2013 Elsevier B.V. All rights reserved.

Self-referent information processing in individuals with bipolar spectrum disorders

PubMed Central

Molz Adams, Ashleigh; Shapero, Benjamin G.; Pendergast, Laura H.; Alloy, Lauren B.; Abramson, Lyn Y.

2014-01-01

Background Bipolar spectrum disorders (BSDs) are common and impairing, which has led to an examination of risk factors for their development and maintenance. Historically, research has examined cognitive vulnerabilities to BSDs derived largely from the unipolar depression literature. Specifically, theorists propose that dysfunctional information processing guided by negative self-schemata may be a risk factor for depression. However, few studies have examined whether BSD individuals also show self-referent processing biases. Methods This study examined self-referent information processing differences between 66 individuals with and 58 individuals without a BSD in a young adult sample (age M = 19.65, SD = 1.74; 62% female; 47% Caucasian). Repeated measures multivariate analysis of variance (MANOVA) was conducted to examine multivariate effects of BSD diagnosis on 4 self-referent processing variables (self-referent judgments, response latency, behavioral predictions, and recall) in response to depression-related and nondepression-related stimuli. Results Bipolar individuals endorsed and recalled more negative and fewer positive self-referent adjectives, as well as made more negative and fewer positive behavioral predictions. Many of these information-processing biases were partially, but not fully, mediated by depressive symptoms. Limitations Our sample was not a clinical or treatment-seeking sample, so we cannot generalize our results to clinical BSD samples. No participants had a bipolar I disorder at baseline. Conclusions This study provides further evidence that individuals with BSDs exhibit a negative self-referent information processing bias. This may mean that those with BSDs have selective attention and recall of negative information about themselves, highlighting the need for attention to cognitive biases in therapy. PMID:24074480

Inviting parents to take part in paediatric palliative care research: a mixed-methods examination of selection bias.

PubMed

Crocker, Joanna C; Beecham, Emma; Kelly, Paula; Dinsdale, Andrew P; Hemsley, June; Jones, Louise; Bluebond-Langner, Myra

2015-03-01

Recruitment to paediatric palliative care research is challenging, with high rates of non-invitation of eligible families by clinicians. The impact on sample characteristics is unknown. To investigate, using mixed methods, non-invitation of eligible families and ensuing selection bias in an interview study about parents' experiences of advance care planning (ACP). We examined differences between eligible families invited and not invited to participate by clinicians using (1) field notes of discussions with clinicians during the invitation phase and (2) anonymised information from the service's clinical database. Families were eligible for the ACP study if their child was receiving care from a UK-based tertiary palliative care service (Group A; N = 519) or had died 6-10 months previously having received care from the service (Group B; N = 73). Rates of non-invitation to the ACP study were high. A total of 28 (5.4%) Group A families and 21 (28.8%) Group B families (p < 0.0005) were invited. Family-clinician relationship appeared to be a key factor associated qualitatively with invitation in both groups. In Group A, out-of-hours contact with family was statistically associated with invitation (adjusted odds ratio 5.46 (95% confidence interval 2.13-14.00); p < 0.0005). Qualitative findings also indicated that clinicians' perceptions of families' wellbeing, circumstances, characteristics, engagement with clinicians and anticipated reaction to invitation influenced invitation. We found evidence of selective invitation practices that could bias research findings. Non-invitation and selection bias should be considered, assessed and reported in palliative care studies. © The Author(s) 2014.

Do less effective teachers choose professional development does it matter?

PubMed

Barrett, Nathan; Butler, J S; Toma, Eugenia F

2012-10-01

In an ongoing effort to improve teacher quality, most states require continuing education or professional development for their in-service teachers. Studies evaluating the effectiveness of various professional development programs have assumed a normal distribution of quality of teachers participating in the programs. Because participation in many professional development programs is either targeted or voluntary, this article suggests past evaluations of the effectiveness of professional development may be subject to selection bias and policy recommendations may be premature. This article presents an empirical framework for evaluating professional development programs where treatment is potentially nonrandom, and explicitly accounts for the teacher's prior effectiveness in the classroom as a factor that may influence participation in professional development. This article controls for the influence of selection bias on professional development outcomes by generating a matched sample based on propensity scores and then estimating the program's effect. In applying this framework to the professional development program examined in this article, less effective teachers are found to be more likely to participate in the program, and correcting for this selection leads to different conclusions regarding the program's effectiveness than when ignoring teacher selection patterns.

Untold stories: biases and selection effects in research with victims of trafficking for sexual exploitation.

PubMed

Brunovskis, Anette; Surtees, Rebecca

2010-01-01

Recent discussions of trafficking research have included calls for more innovative studies and new methodologies in order to move beyond the current trafficking narrative, which is often based on unrepresentative samples and overly simplified images. While new methods can potentially play a role in expanding the knowledge base on trafficking, this article argues that the solution is not entirely about applying new methods, but as much about using current methods to greater effect and with careful attention to their limitations and ethical constraints. Drawing on the authors' experience in researching trafficking issues in a number of projects over the past decade, the article outlines and exemplifies some of the methodological and ethical issues to be considered and accommodated when conducting research with trafficked persons -- including unrepresentative samples; access to respondents; selection biases by "gatekeepers" and self selection by potential respondents. Such considerations should inform not only how research is undertaken but also how this information is read and understood. Moreover, many of these considerations equally apply when considering the application of new methods within this field. The article maintains that a better understanding of how these issues come into play and inform trafficking research will translate into tools for conducting improved research in this field and, by implication, new perspectives on human trafficking.

New Insights on the White Dwarf Luminosity and Mass Functions from the LSS-GAC Survey

NASA Astrophysics Data System (ADS)

Rebassa-Mansergas, Alberto; Liu, Xiaowei; Cojocaru, Ruxandra; Torres, Santiago; García–Berro, Enrique; Yuan, Haibo; Huang, Yang; Xiang, Maosheng

2015-06-01

The white dwarf (WD) population observed in magnitude-limited surveys can be used to derive the luminosity function (LF) and mass function (MF), once the corresponding volume corrections are employed. However, the WD samples from which the observational LFs and MFs are built are the result of complicated target selection algorithms. Thus, it is difficult to quantify the effects of the observational biases on the observed functions. The LAMOST (Large sky Area Multi-Object fiber Spectroscopic Telescope) spectroscopic survey of the Galactic anti-center (LSS-GAC) has well-defined selection criteria. This is a noticeable advantage over previous surveys. Here we derive the WD LF and MF of the LSS-GAC, and use a Monte Carlo code to simulate the WD population in the Galactic anti-center. We apply the well-defined LSS-GAC selection criteria to the simulated populations, taking into account all observational biases, and perform the first meaningful comparison between the simulated WD LFs and MFs and the observed ones.

Selection bias in studies of human reproduction-longevity trade-offs.

PubMed

Helle, Samuli

2017-12-13

A shorter lifespan as a potential cost of high reproductive effort in humans has intrigued researchers for more than a century. However, the results have been inconclusive so far and despite strong theoretical expectations we do not currently have compelling evidence for the longevity costs of reproduction. Using Monte Carlo simulation, it is shown here that a common practice in human reproduction-longevity studies using historical data (the most relevant data sources for this question), the omission of women who died prior to menopausal age from the analysis, results in severe underestimation of the potential underlying trade-off between reproduction and lifespan. In other words, assuming that such a trade-off is expressed also during reproductive years, the strength of the trade-off between reproduction and lifespan is progressively weakened when women dying during reproductive ages are sequentially and non-randomly excluded from the analysis. In cases of small sample sizes (e.g. few hundreds of observations), this selection bias by reducing statistical power may even partly explain the null results commonly found in this field. Future studies in this field should thus apply statistical approaches that account for or avoid selection bias in order to recover reliable effect size estimates between reproduction and longevity. © 2017 The Author(s).

Moment and maximum likelihood estimators for Weibull distributions under length- and area-biased sampling

Treesearch

Jeffrey H. Gove

2003-01-01

Many of the most popular sampling schemes used in forestry are probability proportional to size methods. These methods are also referred to as size biased because sampling is actually from a weighted form of the underlying population distribution. Length- and area-biased sampling are special cases of size-biased sampling where the probability weighting comes from a...

Biased Brownian dynamics for rate constant calculation.

PubMed

Zou, G; Skeel, R D; Subramaniam, S

2000-08-01

An enhanced sampling method-biased Brownian dynamics-is developed for the calculation of diffusion-limited biomolecular association reaction rates with high energy or entropy barriers. Biased Brownian dynamics introduces a biasing force in addition to the electrostatic force between the reactants, and it associates a probability weight with each trajectory. A simulation loses weight when movement is along the biasing force and gains weight when movement is against the biasing force. The sampling of trajectories is then biased, but the sampling is unbiased when the trajectory outcomes are multiplied by their weights. With a suitable choice of the biasing force, more reacted trajectories are sampled. As a consequence, the variance of the estimate is reduced. In our test case, biased Brownian dynamics gives a sevenfold improvement in central processing unit (CPU) time with the choice of a simple centripetal biasing force.

Apparent directional selection by biased pleiotropic mutation.

PubMed

Tanaka, Yoshinari

2010-07-01

Pleiotropic effects of deleterious mutations are considered to be among the factors responsible for genetic constraints on evolution by long-term directional selection acting on a quantitative trait. If pleiotropic phenotypic effects are biased in a particular direction, mutations generate apparent directional selection, which refers to the covariance between fitness and the trait owing to a linear association between the number of mutations possessed by individuals and the genotypic values of the trait. The present analysis has shown how the equilibrium mean value of the trait is determined by a balance between directional selection and biased pleiotropic mutations. Assuming that genes act additively both on the trait and on fitness, the total variance-standardized directional selection gradient was decomposed into apparent and true components. Experimental data on mutation bias from the bristle traits of Drosophila and life history traits of Daphnia suggest that apparent selection explains a small but significant fraction of directional selection pressure that is observed in nature; the data suggest that changes induced in a trait by biased pleiotropic mutation (i.e., by apparent directional selection) are easily compensated for by (true) directional selection.

Evaluation of quality-control data collected by the U.S. Geological Survey for routine water-quality activities at the Idaho National Laboratory and vicinity, southeastern Idaho, 2002-08

USGS Publications Warehouse

Rattray, Gordon W.

2014-01-01

Quality-control (QC) samples were collected from 2002 through 2008 by the U.S. Geological Survey, in cooperation with the U.S. Department of Energy, to ensure data robustness by documenting the variability and bias of water-quality data collected at surface-water and groundwater sites at and near the Idaho National Laboratory. QC samples consisted of 139 replicates and 22 blanks (approximately 11 percent of the number of environmental samples collected). Measurements from replicates were used to estimate variability (from field and laboratory procedures and sample heterogeneity), as reproducibility and reliability, of water-quality measurements of radiochemical, inorganic, and organic constituents. Measurements from blanks were used to estimate the potential contamination bias of selected radiochemical and inorganic constituents in water-quality samples, with an emphasis on identifying any cross contamination of samples collected with portable sampling equipment. The reproducibility of water-quality measurements was estimated with calculations of normalized absolute difference for radiochemical constituents and relative standard deviation (RSD) for inorganic and organic constituents. The reliability of water-quality measurements was estimated with pooled RSDs for all constituents. Reproducibility was acceptable for all constituents except dissolved aluminum and total organic carbon. Pooled RSDs were equal to or less than 14 percent for all constituents except for total organic carbon, which had pooled RSDs of 70 percent for the low concentration range and 4.4 percent for the high concentration range. Source-solution and equipment blanks were measured for concentrations of tritium, strontium-90, cesium-137, sodium, chloride, sulfate, and dissolved chromium. Field blanks were measured for the concentration of iodide. No detectable concentrations were measured from the blanks except for strontium-90 in one source solution and one equipment blank collected in September and October 2004, respectively. The detectable concentrations of strontium-90 in the blanks probably were from a small source of strontium-90 contamination or large measurement variability, or both. Order statistics and the binomial probability distribution were used to estimate the magnitude and extent of any potential contamination bias of tritium, strontium-90, cesium-137, sodium, chloride, sulfate, dissolved chromium, and iodide in water-quality samples. These statistical methods indicated that, with (1) 87 percent confidence, contamination bias of cesium-137 and sodium in 60 percent of water-quality samples was less than the minimum detectable concentration or reporting level; (2) 92‒94 percent confidence, contamination bias of tritium, strontium-90, chloride, sulfate, and dissolved chromium in 70 percent of water-quality samples was less than the minimum detectable concentration or reporting level; and (3) 75 percent confidence, contamination bias of iodide in 50 percent of water-quality samples was less than the reporting level for iodide. These results support the conclusion that contamination bias of water-quality samples from sample processing, storage, shipping, and analysis was insignificant and that cross-contamination of perched groundwater samples collected with bailers during 2002–08 was insignificant.

Galaxy evolution by color-log(n) type since redshift unity in the Hubble Ultra Deep Field

NASA Astrophysics Data System (ADS)

Cameron, E.; Driver, S. P.

2009-01-01

Aims: We explore the use of the color-log(n) (where n is the global Sérsic index) plane as a tool for subdividing the galaxy population in a physically-motivated manner out to redshift unity. We thereby aim to quantify surface brightness evolution by color-log(n) type, accounting separately for the specific selection and measurement biases against each. Methods: We construct (u-r) color-log(n) diagrams for distant galaxies in the Hubble Ultra Deep Field (UDF) within a series of volume-limited samples to z=1.5. The color-log(n) distributions of these high redshift galaxies are compared against that measured for nearby galaxies in the Millennium Galaxy Catalogue (MGC), as well as to the results of visual morphological classification. Based on this analysis we divide our sample into three color-structure classes. Namely, “red, compact”, “blue, diffuse” and “blue, compact”. Luminosity-size diagrams are constructed for members of the two largest classes (“red, compact” and “blue, diffuse”), both in the UDF and the MGC. Artificial galaxy simulations (for systems with exponential and de Vaucouleurs profile shapes alternately) are used to identify “bias-free” regions of the luminosity-size plane in which galaxies are detected with high completeness, and their fluxes and sizes recovered with minimal surface brightness-dependent biases. Galaxy evolution is quantified via comparison of the low and high redshift luminosity-size relations within these “bias-free” regions. Results: We confirm the correlation between color-log(n) plane position and visual morphological type observed locally and in other high redshift studies in the color and/or structure domain. The combined effects of observational uncertainties, the morphological K-correction and cosmic variance preclude a robust statistical comparison of the shape of the MGC and UDF color-log(n) distributions. However, in the interval 0.75 < z <1.0 where the UDF i-band samples close to rest-frame B-band light (i.e., the morphological K-correction between our samples is negligible) we are able to present tentative evidence of bimodality, albiet for a very small sample size (17 galaxies). Our unique approach to quantifying selection and measurement biases in the luminosity-size plane highlights the need to consider errors in the recovery of both magnitudes and sizes, and their dependence on profile shape. Motivated by these results we divide our sample into the three color-structure classes mentioned above and quantify luminosity-size evolution by galaxy type. Specifically, we detect decreases in B-band, surface brightness of 1.57 ± 0.22 mag arcsec-2 and 1.65 ± 0.22 mag arcsec-2 for our “blue, diffuse” and “red, compact” classes respectively between redshift unity and the present day.

Vis-NIR spectrometric determination of Brix and sucrose in sugar production samples using kernel partial least squares with interval selection based on the successive projections algorithm.

PubMed

de Almeida, Valber Elias; de Araújo Gomes, Adriano; de Sousa Fernandes, David Douglas; Goicoechea, Héctor Casimiro; Galvão, Roberto Kawakami Harrop; Araújo, Mario Cesar Ugulino

2018-05-01

This paper proposes a new variable selection method for nonlinear multivariate calibration, combining the Successive Projections Algorithm for interval selection (iSPA) with the Kernel Partial Least Squares (Kernel-PLS) modelling technique. The proposed iSPA-Kernel-PLS algorithm is employed in a case study involving a Vis-NIR spectrometric dataset with complex nonlinear features. The analytical problem consists of determining Brix and sucrose content in samples from a sugar production system, on the basis of transflectance spectra. As compared to full-spectrum Kernel-PLS, the iSPA-Kernel-PLS models involve a smaller number of variables and display statistically significant superiority in terms of accuracy and/or bias in the predictions. Published by Elsevier B.V.

COSMOS: STOCHASTIC BIAS FROM MEASUREMENTS OF WEAK LENSING AND GALAXY CLUSTERING

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jullo, Eric; Rhodes, Jason; Kiessling, Alina

2012-05-01

In the theory of structure formation, galaxies are biased tracers of the underlying matter density field. The statistical relation between galaxy and matter density field is commonly referred to as galaxy bias. In this paper, we test the linear bias model with weak-lensing and galaxy clustering measurements in the 2 deg{sup 2} COSMOS field. We estimate the bias of galaxies between redshifts z = 0.2 and z = 1 and over correlation scales between R = 0.2 h{sup -1} Mpc and R = 15 h{sup -1} Mpc. We focus on three galaxy samples, selected in flux (simultaneous cuts I{sub 814W}more » < 26.5 and K{sub s} < 24) and in stellar mass (10{sup 9} < M{sub *} < 10{sup 10} h{sup -2} M{sub Sun} and 10{sup 10} < M{sub *} < 10{sup 11} h{sup -2} M{sub Sun }). At scales R > 2 h{sup -1} Mpc, our measurements support a model of bias increasing with redshift. The Tinker et al. fitting function provides a good fit to the data. We find the best-fit mass of the galaxy halos to be log (M{sub 200}/h{sup -1} M{sub Sun }) = 11.7{sup +0.6}{sub -1.3} and log (M{sub 200}/h{sup -1} M{sub Sun }) = 12.4{sup +0.2}{sub -2.9}, respectively, for the low and high stellar-mass samples. In the halo model framework, bias is scale dependent with a change of slope at the transition scale between the one and the two halo terms. We detect a scale dependence of bias with a turndown at scale R = 2.3 {+-} 1.5 h{sup -1} Mpc, in agreement with previous galaxy clustering studies. We find no significant amount of stochasticity, suggesting that a linear bias model is sufficient to describe our data. We use N-body simulations to quantify both the amount of cosmic variance and systematic errors in the measurement.« less

Efficient multidimensional free energy calculations for ab initio molecular dynamics using classical bias potentials

NASA Astrophysics Data System (ADS)

VandeVondele, Joost; Rothlisberger, Ursula

2000-09-01

We present a method for calculating multidimensional free energy surfaces within the limited time scale of a first-principles molecular dynamics scheme. The sampling efficiency is enhanced using selected terms of a classical force field as a bias potential. This simple procedure yields a very substantial increase in sampling accuracy while retaining the high quality of the underlying ab initio potential surface and can thus be used for a parameter free calculation of free energy surfaces. The success of the method is demonstrated by the applications to two gas phase molecules, ethane and peroxynitrous acid, as test case systems. A statistical analysis of the results shows that the entire free energy landscape is well converged within a 40 ps simulation at 500 K, even for a system with barriers as high as 15 kcal/mol.

Memory bias for threatening information in anxiety and anxiety disorders: a meta-analytic review.

PubMed

Mitte, Kristin

2008-11-01

Although some theories suggest that anxious individuals selectively remember threatening stimuli, findings remain contradictory despite a considerable amount of research. A quantitative integration of 165 studies with 9,046 participants (clinical and nonclinical samples) examined whether a memory bias exists and which moderator variables influence its magnitude. Implicit memory bias was investigated in lexical decision/stimulus identification and word-stem completion paradigms; explicit memory bias was investigated in recognition and recall paradigms. Overall, effect sizes showed no significant impact of anxiety on implicit memory and recognition. Analyses indicated a memory bias for recall, whose magnitude depended on experimental study procedures like the encoding procedure or retention interval. Anxiety influenced recollection of previous experiences; anxious individuals favored threat-related information. Across all paradigms, clinical status was not significantly linked to effect sizes, indicating no qualitative difference in information processing between anxiety patients and high-anxious persons. The large discrepancy between study effects in recall and recognition indicates that future research is needed to identify moderator variables for avoidant and preferred remembering.

Sex Attracts: Investigating Individual Differences in Attentional Bias to Sexual Stimuli

PubMed Central

Kagerer, Sabine; Wehrum, Sina; Klucken, Tim; Walter, Bertram; Vaitl, Dieter; Stark, Rudolf

2014-01-01

We investigated the impact of sexual stimuli and the influence of sexual motivation on the performance in a dot-probe task and a line-orientation task in a large sample of males and females. All pictures (neutral, erotic) were rated on the dimensions of valence, arousal, disgust, and sexual arousal. Additionally, questionnaires measuring sexual interest/desire/motivation were employed. The ratings of the sexual stimuli point to a successful picture selection because sexual arousal did not differ between the sexes. The stimuli were equally arousing for men and women. Higher scores in the employed questionnaires measuring sexual interest/desire/motivation led to higher sexual arousal ratings of the sex pictures. Attentional bias towards sex pictures was observed in both experimental tasks. The attentional biases measured by the dot-probe and the line-orientation task were moderately intercorrelated suggesting attentional bias as a possible marker for a sex-attention trait. Finally, only the sexual sensation seeking score correlated with the attentional biases of the two tasks. Future research is needed to increase the predictive power of these indirect measures of sexual interest. PMID:25238545

A Bayesian approach to truncated data sets: An application to Malmquist bias in Supernova Cosmology

NASA Astrophysics Data System (ADS)

March, Marisa Cristina

2018-01-01

A problem commonly encountered in statistical analysis of data is that of truncated data sets. A truncated data set is one in which a number of data points are completely missing from a sample, this is in contrast to a censored sample in which partial information is missing from some data points. In astrophysics this problem is commonly seen in a magnitude limited survey such that the survey is incomplete at fainter magnitudes, that is, certain faint objects are simply not observed. The effect of this `missing data' is manifested as Malmquist bias and can result in biases in parameter inference if it is not accounted for. In Frequentist methodologies the Malmquist bias is often corrected for by analysing many simulations and computing the appropriate correction factors. One problem with this methodology is that the corrections are model dependent. In this poster we derive a Bayesian methodology for accounting for truncated data sets in problems of parameter inference and model selection. We first show the methodology for a simple Gaussian linear model and then go on to show the method for accounting for a truncated data set in the case for cosmological parameter inference with a magnitude limited supernova Ia survey.

Emotional Issues and Peer Relations in Gifted Elementary Students: Regression Analysis of National Data

ERIC Educational Resources Information Center

Wiley, Kristofor R.

2013-01-01

Many of the social and emotional needs that have historically been associated with gifted students have been questioned on the basis of recent empirical evidence. Research on the topic, however, is often limited by sample size, selection bias, or definition. This study addressed these limitations by applying linear regression methodology to data…

Candidatus Liberibacter asiaticus (CLas)titer in field HLB-exposed commercial citrus cultivars

USDA-ARS?s Scientific Manuscript database

Eight Indian River groves with four or more diverse scions planted in close proximity were surveyed. Twenty trees of each scion in each grove were randomly selected to avoid bias and edge effects and an HLB diagnostic leaf sample was collected from each. CLas 16S rDNA primers were used in qPCR, a...

Intrinsic scatter of caustic masses and hydrostatic bias: An observational study

NASA Astrophysics Data System (ADS)

Andreon, S.; Trinchieri, G.; Moretti, A.; Wang, J.

2017-10-01

All estimates of cluster mass have some intrinsic scatter and perhaps some bias with true mass even in the absence of measurement errors for example caused by cluster triaxiality and large scale structure. Knowledge of the bias and scatter values is fundamental for both cluster cosmology and astrophysics. In this paper we show that the intrinsic scatter of a mass proxy can be constrained by measurements of the gas fraction because masses with higher values of intrinsic scatter with true mass produce more scattered gas fractions. Moreover, the relative bias of two mass estimates can be constrained by comparing the mean gas fraction at the same (nominal) cluster mass. Our observational study addresses the scatter between caustic (I.e., dynamically estimated) and true masses, and the relative bias of caustic and hydrostatic masses. For these purposes, we used the X-ray Unbiased Cluster Sample, a cluster sample selected independently from the intracluster medium content with reliable masses: 34 galaxy clusters in the nearby (0.050 < z < 0.135) Universe, mostly with 14 < log M500/M⊙ ≲ 14.5, and with caustic masses. We found a 35% scatter between caustic and true masses. Furthermore, we found that the relative bias between caustic and hydrostatic masses is small, 0.06 ± 0.05 dex, improving upon past measurements. The small scatter found confirms our previous measurements of a highly variable amount of feedback from cluster to cluster, which is the cause of the observed large variety of core-excised X-ray luminosities and gas masses.

Density estimation in wildlife surveys

USGS Publications Warehouse

Bart, Jonathan; Droege, Sam; Geissler, Paul E.; Peterjohn, Bruce G.; Ralph, C. John

2004-01-01

Several authors have recently discussed the problems with using index methods to estimate trends in population size. Some have expressed the view that index methods should virtually never be used. Others have responded by defending index methods and questioning whether better alternatives exist. We suggest that index methods are often a cost-effective component of valid wildlife monitoring but that double-sampling or another procedure that corrects for bias or establishes bounds on bias is essential. The common assertion that index methods require constant detection rates for trend estimation is mathematically incorrect; the requirement is no long-term trend in detection "ratios" (index result/parameter of interest), a requirement that is probably approximately met by many well-designed index surveys. We urge that more attention be given to defining bird density rigorously and in ways useful to managers. Once this is done, 4 sources of bias in density estimates may be distinguished: coverage, closure, surplus birds, and detection rates. Distance, double-observer, and removal methods do not reduce bias due to coverage, closure, or surplus birds. These methods may yield unbiased estimates of the number of birds present at the time of the survey, but only if their required assumptions are met, which we doubt occurs very often in practice. Double-sampling, in contrast, produces unbiased density estimates if the plots are randomly selected and estimates on the intensive surveys are unbiased. More work is needed, however, to determine the feasibility of double-sampling in different populations and habitats. We believe the tension that has developed over appropriate survey methods can best be resolved through increased appreciation of the mathematical aspects of indices, especially the effects of bias, and through studies in which candidate methods are evaluated against known numbers determined through intensive surveys.

Quality-assurance results for routine water analyses in U.S. Geological Survey laboratories, water year 1998

USGS Publications Warehouse

Ludtke, Amy S.; Woodworth, Mark T.; Marsh, Philip S.

2000-01-01

The U.S. Geological Survey operates a quality-assurance program based on the analyses of reference samples for two laboratories: the National Water Quality Laboratory and the Quality of Water Service Unit. Reference samples that contain selected inorganic, nutrient, and low-level constituents are prepared and submitted to the laboratory as disguised routine samples. The program goal is to estimate precision and bias for as many analytical methods offered by the participating laboratories as possible. Blind reference samples typically are submitted at a rate of 2 to 5 percent of the annual environmental-sample load for each constituent. The samples are distributed to the laboratories throughout the year. The reference samples are subject to the identical laboratory handling, processing, and analytical procedures as those applied to environmental samples and, therefore, have been used as an independent source to verify bias and precision of laboratory analytical methods and ambient water-quality measurements. The results are stored permanently in the National Water Information System and the Blind Sample Project's data base. During water year 1998, 95 analytical procedures were evaluated at the National Water Quality Laboratory and 63 analytical procedures were evaluated at the Quality of Water Service Unit. An overall evaluation of the inorganic and low-level constituent data for water year 1998 indicated 77 of 78 analytical procedures at the National Water Quality Laboratory met the criteria for precision. Silver (dissolved, inductively coupled plasma-mass spectrometry) was determined to be imprecise. Five of 78 analytical procedures showed bias throughout the range of reference samples: chromium (dissolved, inductively coupled plasma-atomic emission spectrometry), dissolved solids (dissolved, gravimetric), lithium (dissolved, inductively coupled plasma-atomic emission spectrometry), silver (dissolved, inductively coupled plasma-mass spectrometry), and zinc (dissolved, inductively coupled plasma-mass spectrometry). At the National Water Quality Laboratory during water year 1998, lack of precision was indicated for 2 of 17 nutrient procedures: ammonia as nitrogen (dissolved, colorimetric) and orthophosphate as phosphorus (dissolved, colorimetric). Bias was indicated throughout the reference sample range for ammonia as nitrogen (dissolved, colorimetric, low level) and nitrate plus nitrite as nitrogen (dissolved, colorimetric, low level). All analytical procedures tested at the Quality of Water Service Unit during water year 1998 met the criteria for precision. One of the 63 analytical procedures indicated a bias throughout the range of reference samples: aluminum (whole-water recoverable, inductively coupled plasma-atomic emission spectrometry, trace).

Quality-assurance results for routine water analysis in US Geological Survey laboratories, water year 1991

USGS Publications Warehouse

Maloney, T.J.; Ludtke, A.S.; Krizman, T.L.

1994-01-01

The US. Geological Survey operates a quality- assurance program based on the analyses of reference samples for the National Water Quality Laboratory in Arvada, Colorado, and the Quality of Water Service Unit in Ocala, Florida. Reference samples containing selected inorganic, nutrient, and low ionic-strength constituents are prepared and disguised as routine samples. The program goal is to determine precision and bias for as many analytical methods offered by the participating laboratories as possible. The samples typically are submitted at a rate of approximately 5 percent of the annual environmental sample load for each constituent. The samples are distributed to the laboratories throughout the year. Analytical data for these reference samples reflect the quality of environmental sample data produced by the laboratories because the samples are processed in the same manner for all steps from sample login through data release. The results are stored permanently in the National Water Data Storage and Retrieval System. During water year 1991, 86 analytical procedures were evaluated at the National Water Quality Laboratory and 37 analytical procedures were evaluated at the Quality of Water Service Unit. An overall evaluation of the inorganic (major ion and trace metal) constituent data for water year 1991 indicated analytical imprecision in the National Water Quality Laboratory for 5 of 67 analytical procedures: aluminum (whole-water recoverable, atomic emission spectrometric, direct-current plasma); calcium (atomic emission spectrometric, direct); fluoride (ion-exchange chromatographic); iron (whole-water recoverable, atomic absorption spectrometric, direct); and sulfate (ion-exchange chromatographic). The results for 11 of 67 analytical procedures had positive or negative bias during water year 1991. Analytical imprecision was indicated in the determination of two of the five National Water Quality Laboratory nutrient constituents: orthophosphate as phosphorus and phosphorus. A negative or positive bias condition was indicated in three of five nutrient constituents. There was acceptable precision and no indication of bias for the 14 low ionic-strength analytical procedures tested in the National Water Quality Laboratory program and for the 32 inorganic and 5 nutrient analytical procedures tested in the Quality of Water Service Unit during water year 1991.

Absolute magnitude calibration using trigonometric parallax - Incomplete, spectroscopic samples

NASA Technical Reports Server (NTRS)

Ratnatunga, Kavan U.; Casertano, Stefano

1991-01-01

A new numerical algorithm is used to calibrate the absolute magnitude of spectroscopically selected stars from their observed trigonometric parallax. This procedure, based on maximum-likelihood estimation, can retrieve unbiased estimates of the intrinsic absolute magnitude and its dispersion even from incomplete samples suffering from selection biases in apparent magnitude and color. It can also make full use of low accuracy and negative parallaxes and incorporate censorship on reported parallax values. Accurate error estimates are derived for each of the fitted parameters. The algorithm allows an a posteriori check of whether the fitted model gives a good representation of the observations. The procedure is described in general and applied to both real and simulated data.

Rational learning and information sampling: on the "naivety" assumption in sampling explanations of judgment biases.

PubMed

Le Mens, Gaël; Denrell, Jerker

2011-04-01

Recent research has argued that several well-known judgment biases may be due to biases in the available information sample rather than to biased information processing. Most of these sample-based explanations assume that decision makers are "naive": They are not aware of the biases in the available information sample and do not correct for them. Here, we show that this "naivety" assumption is not necessary. Systematically biased judgments can emerge even when decision makers process available information perfectly and are also aware of how the information sample has been generated. Specifically, we develop a rational analysis of Denrell's (2005) experience sampling model, and we prove that when information search is interested rather than disinterested, even rational information sampling and processing can give rise to systematic patterns of errors in judgments. Our results illustrate that a tendency to favor alternatives for which outcome information is more accessible can be consistent with rational behavior. The model offers a rational explanation for behaviors that had previously been attributed to cognitive and motivational biases, such as the in-group bias or the tendency to prefer popular alternatives. 2011 APA, all rights reserved

An experimental verification of laser-velocimeter sampling bias and its correction

NASA Technical Reports Server (NTRS)

Johnson, D. A.; Modarress, D.; Owen, F. K.

1982-01-01

The existence of 'sampling bias' in individual-realization laser velocimeter measurements is experimentally verified and shown to be independent of sample rate. The experiments were performed in a simple two-stream mixing shear flow with the standard for comparison being laser-velocimeter results obtained under continuous-wave conditions. It is also demonstrated that the errors resulting from sampling bias can be removed by a proper interpretation of the sampling statistics. In addition, data obtained in a shock-induced separated flow and in the near-wake of airfoils are presented, both bias-corrected and uncorrected, to illustrate the effects of sampling bias in the extreme.

Alcohol cognitive bias modification training for problem drinkers over the web.

PubMed

Wiers, Reinout W; Houben, Katrijn; Fadardi, Javad S; van Beek, Paul; Rhemtulla, Mijke; Cox, W Miles

2015-01-01

Following successful outcomes of cognitive bias modification (CBM) programs for alcoholism in clinical and community samples, the present study investigated whether different varieties of CBM (attention control training and approach-bias re-training) could be delivered successfully in a fully automated web-based way and whether these interventions would help self-selected problem drinkers to reduce their drinking. Participants were recruited through online advertising, which resulted in 697 interested participants, of whom 615 were screened in. Of the 314 who initiated training, 136 completed a pretest, four sessions of computerized training and a posttest. Participants were randomly assigned to one of four experimental conditions (attention control or one of three varieties of approach-bias re-training) or a sham-training control condition. The general pattern of findings was that participants in all conditions (including participants in the control-training condition) reduced their drinking. It is suggested that integrating CBM with online cognitive and motivational interventions could improve results. Copyright © 2014 Elsevier Ltd. All rights reserved.

Looking on the bright side: biased attention and the human serotonin transporter gene.

PubMed

Fox, Elaine; Ridgewell, Anna; Ashwin, Chris

2009-05-22

Humans differ in terms of biased attention for emotional stimuli and these biases can confer differential resilience and vulnerability to emotional disorders. Selective processing of positive emotional information, for example, is associated with enhanced sociability and well-being while a bias for negative material is associated with neuroticism and anxiety. A tendency to selectively avoid negative material might also be associated with mental health and well-being. The neurobiological mechanisms underlying these cognitive phenotypes are currently unknown. Here we show for the first time that allelic variation in the promotor region of the serotonin transporter gene (5-HTTLPR) is associated with differential biases for positive and negative affective pictures. Individuals homozygous for the long allele (LL) showed a marked bias to selectively process positive affective material alongside selective avoidance of negative affective material. This potentially protective pattern was absent among individuals carrying the short allele (S or SL). Thus, allelic variation on a common genetic polymorphism was associated with the tendency to selectively process positive or negative information. The current study is important in demonstrating a genotype-related alteration in a well-established processing bias, which is a known risk factor in determining both resilience and vulnerability to emotional disorders.

Age-ordered shirt numbering reduces the selection bias associated with the relative age effect.

PubMed

Mann, David L; van Ginneken, Pleun J M A

2017-04-01

When placed into age groups for junior sporting competition, the relative differences in age between children leads to a bias in who is evaluated as being talented. While the impact of this relative age effect (RAE) is clear, until now there has been no evidence to show how to reduce it. The aim of this study was to determine whether the selection bias associated with the RAE could be reduced. Talent scouts from an elite football club watched junior games and ranked players on the basis of their potential. Scouts were allocated to one of three groups provided with contrasting information about the age of the players: (1) no age information, (2) players' birthdates or (3) knowledge that the numbers on the playing shirts corresponded to the relative age of the players. Results revealed a significant selection bias for the scouts in the no-age information group, and that bias remained when scouts knew the players' dates-of-birth. Strikingly though, the selection bias was eliminated when scouts watched the games knowing the shirt numbers corresponded to the relative ages of the players. The selection bias associated with the RAE can be reduced if information about age is presented appropriately.

Quality and provider choice: a multinomial logit-least-squares model with selectivity.

PubMed Central

Haas-Wilson, D; Savoca, E

1990-01-01

A Federal Trade Commission survey of contact lens wearers is used to estimate a multinomial logit-least-squares model of the joint determination of provider choice and quality of care in the contact lens industry. The effect of personal and industry characteristics on a consumer's choice among three types of providers--opticians, ophthalmologists, and optometrists--is estimated via multinomial logit. The regression model of the quality of care has two features that distinguish it from previous work in the area. First, it uses an outcome rather than a structural or process measure of quality. Quality is measured as an index of the presence of seven potentially pathological eye conditions caused by poorly fitted lenses. Second, the model controls for possible selection bias that may arise from the fact that the sample observations on quality are generated by consumers' nonrandom choices of providers. The multinomial logit estimates of provider choice indicate that professional regulations limiting the commercial practices of optometrists shift demand for contact lens services away from optometrists toward ophthalmologists. Further, consumers are more likely to have their lenses fitted by opticians in states that require the licensing of opticians. The regression analysis of variations in quality across provider types shows a strong positive selection bias in the estimate of the quality of care received by consumers of ophthalmologists' services. Failure to control for this selection bias results in an overestimate of the quality of care provided by ophthalmologists. PMID:2312308

Calibration model maintenance in melamine resin production: Integrating drift detection, smart sample selection and model adaptation.

PubMed

Nikzad-Langerodi, Ramin; Lughofer, Edwin; Cernuda, Carlos; Reischer, Thomas; Kantner, Wolfgang; Pawliczek, Marcin; Brandstetter, Markus

2018-07-12

The physico-chemical properties of Melamine Formaldehyde (MF) based thermosets are largely influenced by the degree of polymerization (DP) in the underlying resin. On-line supervision of the turbidity point by means of vibrational spectroscopy has recently emerged as a promising technique to monitor the DP of MF resins. However, spectroscopic determination of the DP relies on chemometric models, which are usually sensitive to drifts caused by instrumental and/or sample-associated changes occurring over time. In order to detect the time point when drifts start causing prediction bias, we here explore a universal drift detector based on a faded version of the Page-Hinkley (PH) statistic, which we test in three data streams from an industrial MF resin production process. We employ committee disagreement (CD), computed as the variance of model predictions from an ensemble of partial least squares (PLS) models, as a measure for sample-wise prediction uncertainty and use the PH statistic to detect changes in this quantity. We further explore supervised and unsupervised strategies for (semi-)automatic model adaptation upon detection of a drift. For the former, manual reference measurements are requested whenever statistical thresholds on Hotelling's T 2 and/or Q-Residuals are violated. Models are subsequently re-calibrated using weighted partial least squares in order to increase the influence of newer samples, which increases the flexibility when adapting to new (drifted) states. Unsupervised model adaptation is carried out exploiting the dual antecedent-consequent structure of a recently developed fuzzy systems variant of PLS termed FLEXFIS-PLS. In particular, antecedent parts are updated while maintaining the internal structure of the local linear predictors (i.e. the consequents). We found improved drift detection capability of the CD compared to Hotelling's T 2 and Q-Residuals when used in combination with the proposed PH test. Furthermore, we found that active selection of samples by active learning (AL) used for subsequent model adaptation is advantageous compared to passive (random) selection in case that a drift leads to persistent prediction bias allowing more rapid adaptation at lower reference measurement rates. Fully unsupervised adaptation using FLEXFIS-PLS could improve predictive accuracy significantly for light drifts but was not able to fully compensate for prediction bias in case of significant lack of fit w.r.t. the latent variable space. Copyright © 2018 Elsevier B.V. All rights reserved.

Toward a clarification of the taxonomy of "bias" in epidemiology textbooks.

PubMed

Schwartz, Sharon; Campbell, Ulka B; Gatto, Nicolle M; Gordon, Kirsha

2015-03-01

Epidemiology textbooks typically divide biases into 3 general categories-confounding, selection bias, and information bias. Despite the ubiquity of this categorization, authors often use these terms to mean different things. This hinders communication among epidemiologists and confuses students who are just learning about the field. To understand the sources of this problem, we reviewed current general epidemiology textbooks to examine how the authors defined and categorized biases. We found that much of the confusion arises from different definitions of "validity" and from a mixing of 3 overlapping organizational features in defining and differentiating among confounding, selection bias, and information bias: consequence, the result of the problem; cause, the processes that give rise to the problem; and cure, how these biases can be addressed once they occur. By contrast, a consistent taxonomy would provide (1) a clear and consistent definition of what unites confounding, selection bias, and information bias and (2) a clear articulation and consistent application of the feature that distinguishes these categories. Based on a distillation of these textbook discussions, we provide an example of a taxonomy that we think meets these criteria.

Prevalence and factors related to dental caries among pre-school children of Saddar town, Karachi, Pakistan: a cross-sectional study

PubMed Central

2012-01-01

Background Dental caries is highly prevalent and a significant public health problem among children throughout the world. Epidemiological data regarding prevalence of dental caries amongst Pakistani pre-school children is very limited. The objective of this study is to determine the frequency of dental caries among pre-school children of Saddar Town, Karachi, Pakistan and the factors related to caries. Methods A cross-sectional study of 1000 preschool children was conducted in Saddar town, Karachi. Two-stage cluster sampling was used to select the sample. At first stage, eight clusters were selected randomly from total 11 clusters. In second stage, from the eight selected clusters, preschools were identified and children between 3- to 6-years age group were assessed for dental caries. Results Caries prevalence was 51% with a mean dmft score being 2.08 (±2.97) of which decayed teeth constituted 1.95. The mean dmft of males was 2.3 (±3.08) and of females was 1.90 (±2.90). The mean dmft of 3, 4, 5 and 6- year olds was 1.65, 2.11, 2.16 and 3.11 respectively. A significant association was found between dental caries and following variables: age group of 4-years (p-value ² 0.029, RR = 1.248, 95% Bias corrected CI 0.029-0.437) and 5-years (p-value ² 0.009, RR = 1.545, 95% Bias corrected CI 0.047-0.739), presence of dental plaque (p-value ² 0.003, RR = 0.744, 95% Bias corrected CI (−0.433)-(−0.169)), poor oral hygiene (p-value ² 0.000, RR = 0.661, 95% Bias corrected CI (−0.532)-(−0.284)), as well as consumption of non-sweetened milk (p-value ² 0.049, RR = 1.232, 95% Bias corrected CI 0.061-0.367). Conclusion Half of the preschoolers had dental caries coupled with a high prevalence of unmet dental treatment needs. Association between caries experience and age of child, consumption of non-sweetened milk, dental plaque and poor oral hygiene had been established. PMID:23270546

Cross-correlation redshift calibration without spectroscopic calibration samples in DES Science Verification Data

NASA Astrophysics Data System (ADS)

Davis, C.; Rozo, E.; Roodman, A.; Alarcon, A.; Cawthon, R.; Gatti, M.; Lin, H.; Miquel, R.; Rykoff, E. S.; Troxel, M. A.; Vielzeuf, P.; Abbott, T. M. C.; Abdalla, F. B.; Allam, S.; Annis, J.; Bechtol, K.; Benoit-Lévy, A.; Bertin, E.; Brooks, D.; Buckley-Geer, E.; Burke, D. L.; Carnero Rosell, A.; Carrasco Kind, M.; Carretero, J.; Castander, F. J.; Crocce, M.; Cunha, C. E.; D'Andrea, C. B.; da Costa, L. N.; Desai, S.; Diehl, H. T.; Doel, P.; Drlica-Wagner, A.; Fausti Neto, A.; Flaugher, B.; Fosalba, P.; Frieman, J.; García-Bellido, J.; Gaztanaga, E.; Gerdes, D. W.; Giannantonio, T.; Gruen, D.; Gruendl, R. A.; Gutierrez, G.; Honscheid, K.; Jain, B.; James, D. J.; Jeltema, T.; Krause, E.; Kuehn, K.; Kuhlmann, S.; Kuropatkin, N.; Lahav, O.; Li, T. S.; Lima, M.; March, M.; Marshall, J. L.; Martini, P.; Melchior, P.; Ogando, R. L. C.; Plazas, A. A.; Romer, A. K.; Sanchez, E.; Scarpine, V.; Schindler, R.; Schubnell, M.; Sevilla-Noarbe, I.; Smith, M.; Soares-Santos, M.; Sobreira, F.; Suchyta, E.; Swanson, M. E. C.; Tarle, G.; Thomas, D.; Vikram, V.; Walker, A. R.; Wechsler, R. H.

2018-06-01

Galaxy cross-correlations with high-fidelity redshift samples hold the potential to precisely calibrate systematic photometric redshift uncertainties arising from the unavailability of complete and representative training and validation samples of galaxies. However, application of this technique in the Dark Energy Survey (DES) is hampered by the relatively low number density, small area, and modest redshift overlap between photometric and spectroscopic samples. We propose instead using photometric catalogues with reliable photometric redshifts for photo-z calibration via cross-correlations. We verify the viability of our proposal using redMaPPer clusters from the Sloan Digital Sky Survey (SDSS) to successfully recover the redshift distribution of SDSS spectroscopic galaxies. We demonstrate how to combine photo-z with cross-correlation data to calibrate photometric redshift biases while marginalizing over possible clustering bias evolution in either the calibration or unknown photometric samples. We apply our method to DES Science Verification (DES SV) data in order to constrain the photometric redshift distribution of a galaxy sample selected for weak lensing studies, constraining the mean of the tomographic redshift distributions to a statistical uncertainty of Δz ˜ ±0.01. We forecast that our proposal can, in principle, control photometric redshift uncertainties in DES weak lensing experiments at a level near the intrinsic statistical noise of the experiment over the range of redshifts where redMaPPer clusters are available. Our results provide strong motivation to launch a programme to fully characterize the systematic errors from bias evolution and photo-z shapes in our calibration procedure.

Cross-correlation redshift calibration without spectroscopic calibration samples in DES Science Verification Data

DOE PAGES

Davis, C.; Rozo, E.; Roodman, A.; ...

2018-03-26

Galaxy cross-correlations with high-fidelity redshift samples hold the potential to precisely calibrate systematic photometric redshift uncertainties arising from the unavailability of complete and representative training and validation samples of galaxies. However, application of this technique in the Dark Energy Survey (DES) is hampered by the relatively low number density, small area, and modest redshift overlap between photometric and spectroscopic samples. We propose instead using photometric catalogs with reliable photometric redshifts for photo-z calibration via cross-correlations. We verify the viability of our proposal using redMaPPer clusters from the Sloan Digital Sky Survey (SDSS) to successfully recover the redshift distribution of SDSS spectroscopic galaxies. We demonstrate how to combine photo-z with cross-correlation data to calibrate photometric redshift biases while marginalizing over possible clustering bias evolution in either the calibration or unknown photometric samples. We apply our method to DES Science Verification (DES SV) data in order to constrain the photometric redshift distribution of a galaxy sample selected for weak lensing studies, constraining the mean of the tomographic redshift distributions to a statistical uncertainty ofmore » $$\\Delta z \\sim \\pm 0.01$$. We forecast that our proposal can in principle control photometric redshift uncertainties in DES weak lensing experiments at a level near the intrinsic statistical noise of the experiment over the range of redshifts where redMaPPer clusters are available. Here, our results provide strong motivation to launch a program to fully characterize the systematic errors from bias evolution and photo-z shapes in our calibration procedure.« less

Cross-correlation redshift calibration without spectroscopic calibration samples in DES Science Verification Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Davis, C.; Rozo, E.; Roodman, A.

Galaxy cross-correlations with high-fidelity redshift samples hold the potential to precisely calibrate systematic photometric redshift uncertainties arising from the unavailability of complete and representative training and validation samples of galaxies. However, application of this technique in the Dark Energy Survey (DES) is hampered by the relatively low number density, small area, and modest redshift overlap between photometric and spectroscopic samples. We propose instead using photometric catalogs with reliable photometric redshifts for photo-z calibration via cross-correlations. We verify the viability of our proposal using redMaPPer clusters from the Sloan Digital Sky Survey (SDSS) to successfully recover the redshift distribution of SDSS spectroscopic galaxies. We demonstrate how to combine photo-z with cross-correlation data to calibrate photometric redshift biases while marginalizing over possible clustering bias evolution in either the calibration or unknown photometric samples. We apply our method to DES Science Verification (DES SV) data in order to constrain the photometric redshift distribution of a galaxy sample selected for weak lensing studies, constraining the mean of the tomographic redshift distributions to a statistical uncertainty ofmore » $$\\Delta z \\sim \\pm 0.01$$. We forecast that our proposal can in principle control photometric redshift uncertainties in DES weak lensing experiments at a level near the intrinsic statistical noise of the experiment over the range of redshifts where redMaPPer clusters are available. Here, our results provide strong motivation to launch a program to fully characterize the systematic errors from bias evolution and photo-z shapes in our calibration procedure.« less

Quality-assurance data for routine water analysis in the National Water-Quality Laboratory of the US Geological Survey for water year 1988

USGS Publications Warehouse

Lucey, K.J.

1989-01-01

The US Geological Survey maintains a quality assurance program based on the analysis of reference samples for its National Water Quality Laboratory located in Denver, Colorado. Reference samples containing selected inorganic, nutrient, and precipitation (low-level concentration) constituents are prepared at the Survey 's Water Quality Services Unit in Ocala, Florida, disguised as routine samples, and sent daily or weekly, as appropriate, to the laboratory through other Survey offices. The results are stored permanently in the National Water Data Storage and Retrieval System (WATSTORE), the Survey 's database for all water data. These data are analyzed statistically for precision and bias. An overall evaluation of the inorganic major ion and trace metal constituent data for water year 1988 indicated a lack of precision in the National Water Quality Laboratory for the determination of 8 out of 58 constituents: calcium (inductively coupled plasma emission spectrometry), fluoride, iron (atomic absorption spectrometry), iron (total recoverable), magnesium (atomic absorption spectrometry), manganese (total recoverable), potassium, and sodium (inductively coupled plasma emission spectrometry). The results for 31 constituents had positive or negative bias during water year 1988. A lack of precision was indicated in the determination of three of the six nutrient constituents: nitrate plus nitrite nitrogen as nitrogen, nitrite nitrogen as nitrogen, and orthophosphate as phosphorus. A biased condition was indicated in the determination of ammonia nitrogen as nitrogen, ammonia plus organic nitrogen as nitrogen, and nitrate plus nitrite nitrogen as nitrogen. There was acceptable precision in the determination of all 10 constituents contained in precipitation samples. Results for ammonia nitrogen as nitrogen, sodium, and fluoride indicated a biased condition. (Author 's abstract)

Assessment of Sample Preparation Bias in Mass Spectrometry-Based Proteomics.

PubMed

Klont, Frank; Bras, Linda; Wolters, Justina C; Ongay, Sara; Bischoff, Rainer; Halmos, Gyorgy B; Horvatovich, Péter

2018-04-17

For mass spectrometry-based proteomics, the selected sample preparation strategy is a key determinant for information that will be obtained. However, the corresponding selection is often not based on a fit-for-purpose evaluation. Here we report a comparison of in-gel (IGD), in-solution (ISD), on-filter (OFD), and on-pellet digestion (OPD) workflows on the basis of targeted (QconCAT-multiple reaction monitoring (MRM) method for mitochondrial proteins) and discovery proteomics (data-dependent acquisition, DDA) analyses using three different human head and neck tissues (i.e., nasal polyps, parotid gland, and palatine tonsils). Our study reveals differences between the sample preparation methods, for example, with respect to protein and peptide losses, quantification variability, protocol-induced methionine oxidation, and asparagine/glutamine deamidation as well as identification of cysteine-containing peptides. However, none of the methods performed best for all types of tissues, which argues against the existence of a universal sample preparation method for proteome analysis.

Systematic review of the methodological quality of controlled trials evaluating Chinese herbal medicine in patients with rheumatoid arthritis

PubMed Central

Pan, Xin; Lopez-Olivo, Maria A; Song, Juhee; Pratt, Gregory; Suarez-Almazor, Maria E

2017-01-01

Objectives We appraised the methodological and reporting quality of randomised controlled clinical trials (RCTs) evaluating the efficacy and safety of Chinese herbal medicine (CHM) in patients with rheumatoid arthritis (RA). Design For this systematic review, electronic databases were searched from inception until June 2015. The search was limited to humans and non-case report studies, but was not limited by language, year of publication or type of publication. Two independent reviewers selected RCTs, evaluating CHM in RA (herbals and decoctions). Descriptive statistics were used to report on risk of bias and their adherence to reporting standards. Multivariable logistic regression analysis was performed to determine study characteristics associated with high or unclear risk of bias. Results Out of 2342 unique citations, we selected 119 RCTs including 18 919 patients: 10 108 patients received CHM alone and 6550 received one of 11 treatment combinations. A high risk of bias was observed across all domains: 21% had a high risk for selection bias (11% from sequence generation and 30% from allocation concealment), 85% for performance bias, 89% for detection bias, 4% for attrition bias and 40% for reporting bias. In multivariable analysis, fewer authors were associated with selection bias (allocation concealment), performance bias and attrition bias, and earlier year of publication and funding source not reported or disclosed were associated with selection bias (sequence generation). Studies published in non-English language were associated with reporting bias. Poor adherence to recommended reporting standards (<60% of the studies not providing sufficient information) was observed in 11 of the 23 sections evaluated. Limitations Study quality and data extraction were performed by one reviewer and cross-checked by a second reviewer. Translation to English was performed by one reviewer in 85% of the included studies. Conclusions Studies evaluating CHM often fail to meet expected methodological criteria, and high-quality evidence is lacking. PMID:28249848

Target selection biases from recent experience transfer across effectors.

PubMed

Moher, Jeff; Song, Joo-Hyun

2016-02-01

Target selection is often biased by an observer's recent experiences. However, not much is known about whether these selection biases influence behavior across different effectors. For example, does looking at a red object make it easier to subsequently reach towards another red object? In the current study, we asked observers to find the uniquely colored target object on each trial. Randomly intermixed pre-trial cues indicated the mode of action: either an eye movement or a visually guided reach movement to the target. In Experiment 1, we found that priming of popout, reflected in faster responses following repetition of the target color on consecutive trials, occurred regardless of whether the effector was repeated from the previous trial or not. In Experiment 2, we examined whether an inhibitory selection bias away from a feature could transfer across effectors. While priming of popout reflects both enhancement of the repeated target features and suppression of the repeated distractor features, the distractor previewing effect isolates a purely inhibitory component of target selection in which a previewed color is presented in a homogenous display and subsequently inhibited. Much like priming of popout, intertrial suppression biases in the distractor previewing effect transferred across effectors. Together, these results suggest that biases for target selection driven by recent trial history transfer across effectors. This indicates that representations in memory that bias attention towards or away from specific features are largely independent from their associated actions.

Batch Effect Confounding Leads to Strong Bias in Performance Estimates Obtained by Cross-Validation

PubMed Central

Delorenzi, Mauro

2014-01-01

Background With the large amount of biological data that is currently publicly available, many investigators combine multiple data sets to increase the sample size and potentially also the power of their analyses. However, technical differences (“batch effects”) as well as differences in sample composition between the data sets may significantly affect the ability to draw generalizable conclusions from such studies. Focus The current study focuses on the construction of classifiers, and the use of cross-validation to estimate their performance. In particular, we investigate the impact of batch effects and differences in sample composition between batches on the accuracy of the classification performance estimate obtained via cross-validation. The focus on estimation bias is a main difference compared to previous studies, which have mostly focused on the predictive performance and how it relates to the presence of batch effects. Data We work on simulated data sets. To have realistic intensity distributions, we use real gene expression data as the basis for our simulation. Random samples from this expression matrix are selected and assigned to group 1 (e.g., ‘control’) or group 2 (e.g., ‘treated’). We introduce batch effects and select some features to be differentially expressed between the two groups. We consider several scenarios for our study, most importantly different levels of confounding between groups and batch effects. Methods We focus on well-known classifiers: logistic regression, Support Vector Machines (SVM), k-nearest neighbors (kNN) and Random Forests (RF). Feature selection is performed with the Wilcoxon test or the lasso. Parameter tuning and feature selection, as well as the estimation of the prediction performance of each classifier, is performed within a nested cross-validation scheme. The estimated classification performance is then compared to what is obtained when applying the classifier to independent data. PMID:24967636

Topology in two dimensions. II - The Abell and ACO cluster catalogues

NASA Astrophysics Data System (ADS)

Plionis, Manolis; Valdarnini, Riccardo; Coles, Peter

1992-09-01

We apply a method for quantifying the topology of projected galaxy clustering to the Abell and ACO catalogues of rich clusters. We use numerical simulations to quantify the statistical bias involved in using high peaks to define the large-scale structure, and we use the results obtained to correct our observational determinations for this known selection effect and also for possible errors introduced by boundary effects. We find that the Abell cluster sample is consistent with clusters being identified with high peaks of a Gaussian random field, but that the ACO shows a slight meatball shift away from the Gaussian behavior over and above that expected purely from the high-peak selection. The most conservative explanation of this effect is that it is caused by some artefact of the procedure used to select the clusters in the two samples.

Kinship and Nonrelative Foster Care: The Effect of Placement Type on Child Well-Being

ERIC Educational Resources Information Center

Font, Sarah A.

2014-01-01

This study uses a national sample of 1,215 children, ages 6-17, who spent some time in formal kinship or nonrelative foster care to identify the effect of placement type on academic achievement, behavior, and health. Several identification strategies are used to reduce selection bias, including ordinary least squares, change score models,…

Profiling the mobile-only population in Australia: insights from the Australian National Health Survey.

PubMed

Baffour, Bernard; Haynes, Michele; Dinsdale, Shane; Western, Mark; Pennay, Darren

2016-10-01

The Australian population that relies on mobile phones exclusively has increased from 5% in 2005 to 29% in 2014. Failing to include this mobile-only population leads to a potential bias in estimates from landline-based telephone surveys. This paper considers the impacts on selected health prevalence estimates with and without the mobile-only population. Using data from the Australian Health Survey - which, for the first time, included a question on telephone status - we examined demographic, geographic and health differences between the landline-accessible and mobile-only population. These groups were also compared to the full population, controlling for the sampling design and differential non-response patterns in the observed sample through weighting and benchmarking. The landline-accessible population differs from the mobile-only population for selected health measures resulting in biased prevalence estimates for smoking, alcohol risk and private health insurance coverage in the full population. The differences remain even after adjusting for age and gender. Using landline telephones only for conducting population health surveys will have an impact on prevalence rate estimates of health risk factors due to the differing profiles of the mobile-only population from the landline-accessible population. © 2016 Public Health Association of Australia.

Selective attention and drug related attention bias in methadone maintenance patients.

PubMed

Nejati, Majid; Nejati, Vahid; Mohammadi, Mohammad Reza

2011-01-01

One of the main problems of the drug abusers is drug related attention bias, which causes craving, and as a result drive the drug abusers to take narcotics. Methadone is used as a maintenance treatment for drug abusers. The purpose of this study is evaluation of the effect of Methadone maintenance therapy (MMT) on selective attention and drug related attention bias. This study investigated drug cue-related attention bias and selective attention in 16 methadone-maintained patients before and 45 days after methadone therapy period. Stroop color-word test and addiction Stroop test were used as measurement methods. Results show less reaction time and higher accuracy in Color-Word Stroop Test after MMT and less delay for addict related word in addiction Stroop test. It is concluded that methadone can improve selective attention capability and reduce drug related attention bias.

Parameter recovery, bias and standard errors in the linear ballistic accumulator model.

PubMed

Visser, Ingmar; Poessé, Rens

2017-05-01

The linear ballistic accumulator (LBA) model (Brown & Heathcote, , Cogn. Psychol., 57, 153) is increasingly popular in modelling response times from experimental data. An R package, glba, has been developed to fit the LBA model using maximum likelihood estimation which is validated by means of a parameter recovery study. At sufficient sample sizes parameter recovery is good, whereas at smaller sample sizes there can be large bias in parameters. In a second simulation study, two methods for computing parameter standard errors are compared. The Hessian-based method is found to be adequate and is (much) faster than the alternative bootstrap method. The use of parameter standard errors in model selection and inference is illustrated in an example using data from an implicit learning experiment (Visser et al., , Mem. Cogn., 35, 1502). It is shown that typical implicit learning effects are captured by different parameters of the LBA model. © 2017 The British Psychological Society.

The Intervention Selection Bias: An Underrecognized Confound in Intervention Research

ERIC Educational Resources Information Center

Larzelere, Robert E.; Kuhn, Brett R.; Johnson, Byron

2004-01-01

Selection bias can be the most important threat to internal validity in intervention research, but is often insufficiently recognized and controlled. The bias is illustrated in research on parental interventions (punishment, homework assistance); medical interventions (hospitalization); and psychological interventions for suicide risk, sex…

Magnetic braking in Solar-type close binaries

NASA Astrophysics Data System (ADS)

Maceroni, C.; Rucinski, S. M.

In tidally locked binaries the angular momentum loss by magnetic braking affects the orbital period. While this effect is too small to be detected in individual systems, its signature can be seen in shape of the orbital period distribution of suitable samples. As a consequence information on the braking mechanisms can be obtained - at least in principle - from the analysis of the distributions, the main problems being the selection of a large and homogeneous sample of binaries and the appropriate treatment of the observational biases. New large databases of variable stars are becoming available as by-products of microlensing projects, which have the advantage of joining, for the first time, sample richness and homogeneity. We report the main results of the analysis of the eclipsing binaries in OGLE-I catalog, that contains several thousands variables detected in a pencil-beam search volume towards the Baade's Window. By means of an automatic filtering algorithm we extracted a sample of 74 detached, equal-mass, main-sequence binary stars with short orbital periods (i.e., in the range 0.19 < P < 8 days) and derived from the presently observed period distribution, after correction for selection effects, the expected slope of the braking law. The results suggest an AML braking law very close to the "saturated" one, with a very weak dependence on the period. However we are still far from constraining the precise value of the slope, because of the important role played by the observational bias.

Nonmedical influences on medical decision making: an experimental technique using videotapes, factorial design, and survey sampling.

PubMed Central

Feldman, H A; McKinlay, J B; Potter, D A; Freund, K M; Burns, R B; Moskowitz, M A; Kasten, L E

1997-01-01

OBJECTIVE: To study nonmedical influences on the doctor-patient interaction. A technique using simulated patients and "real" doctors is described. DATA SOURCES: A random sample of physicians, stratified on such characteristics as demographics, specialty, or experience, and selected from commercial and professional listings. STUDY DESIGN: A medical appointment is depicted on videotape by professional actors. The patient's presenting complaint (e.g., chest pain) allows a range of valid interpretation. Several alternative versions are taped, featuring the same script with patient-actors of different age, sex, race, or other characteristics. Fractional factorial design is used to select a balanced subset of patient characteristics, reducing costs without biasing the outcome. DATA COLLECTION: Each physician is shown one version of the videotape appointment and is asked to describe how he or she would diagnose or treat such a patient. PRINCIPAL FINDINGS: Two studies using this technique have been completed to date, one involving chest pain and dyspnea and the other involving breast cancer. The factorial design provided sufficient power, despite limited sample size, to demonstrate with statistical significance various influences of the experimental and stratification variables, including the patient's gender and age and the physician's experience. Persistent recruitment produced a high response rate, minimizing selection bias and enhancing validity. CONCLUSION: These techniques permit us to determine, with a degree of control unattainable in observational studies, whether medical decisions as described by actual physicians and drawn from a demographic or professional group of interest, are influenced by a prescribed set of nonmedical factors. PMID:9240285

Gender Bias in Leader Selection.

ERIC Educational Resources Information Center

Teaching of Psychology, 1995

1995-01-01

Describes a classroom exercise showing students how stereotypes can result in sex-biased leader selection. Finds that task-oriented competitive instructions produce a disproportionate number of selected male leaders. Includes procedures for replicating and evaluating the exercise. (CFR)

HELIOSEISMOLOGY OF PRE-EMERGING ACTIVE REGIONS. I. OVERVIEW, DATA, AND TARGET SELECTION CRITERIA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Leka, K. D.; Barnes, G.; Birch, A. C.

2013-01-10

This first paper in a series describes the design of a study testing whether pre-appearance signatures of solar magnetic active regions were detectable using various tools of local helioseismology. The ultimate goal is to understand flux-emergence mechanisms by setting observational constraints on pre-appearance subsurface changes, for comparison with results from simulation efforts. This first paper provides details of the data selection and preparation of the samples, each containing over 100 members, of two populations: regions on the Sun that produced a numbered NOAA active region, and a 'control' sample of areas that did not. The seismology is performed on datamore » from the GONG network; accompanying magnetic data from SOHO/MDI are used for co-temporal analysis of the surface magnetic field. Samples are drawn from 2001-2007, and each target is analyzed for 27.7 hr prior to an objectively determined time of emergence. The results of two analysis approaches are published separately: one based on averages of the seismology- and magnetic-derived signals over the samples, another based on Discriminant Analysis of these signals, for a statistical test of detectable differences between the two populations. We include here descriptions of a new potential-field calculation approach and the algorithm for matching sample distributions over multiple variables. We describe known sources of bias and the approaches used to mitigate them. We also describe unexpected bias sources uncovered during the course of the study and include a discussion of refinements that should be included in future work on this topic.« less

Joint constraints on galaxy bias and σ{sub 8} through the N-pdf of the galaxy number density

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arnalte-Mur, Pablo; Martínez, Vicent J.; Vielva, Patricio

We present a full description of the N-probability density function of the galaxy number density fluctuations. This N-pdf is given in terms, on the one hand, of the cold dark matter correlations and, on the other hand, of the galaxy bias parameter. The method relies on the assumption commonly adopted that the dark matter density fluctuations follow a local non-linear transformation of the initial energy density perturbations. The N-pdf of the galaxy number density fluctuations allows for an optimal estimation of the bias parameter (e.g., via maximum-likelihood estimation, or Bayesian inference if there exists any a priori information on themore » bias parameter), and of those parameters defining the dark matter correlations, in particular its amplitude (σ{sub 8}). It also provides the proper framework to perform model selection between two competitive hypotheses. The parameters estimation capabilities of the N-pdf are proved by SDSS-like simulations (both, ideal log-normal simulations and mocks obtained from Las Damas simulations), showing that our estimator is unbiased. We apply our formalism to the 7th release of the SDSS main sample (for a volume-limited subset with absolute magnitudes M{sub r} ≤ −20). We obtain b-circumflex  = 1.193 ± 0.074 and σ-bar{sub 8} = 0.862 ± 0.080, for galaxy number density fluctuations in cells of the size of 30h{sup −1}Mpc. Different model selection criteria show that galaxy biasing is clearly favoured.« less

Randomized controlled trials of simulation-based interventions in Emergency Medicine: a methodological review.

PubMed

Chauvin, Anthony; Truchot, Jennifer; Bafeta, Aida; Pateron, Dominique; Plaisance, Patrick; Yordanov, Youri

2018-04-01

The number of trials assessing Simulation-Based Medical Education (SBME) interventions has rapidly expanded. Many studies show that potential flaws in design, conduct and reporting of randomized controlled trials (RCTs) can bias their results. We conducted a methodological review of RCTs assessing a SBME in Emergency Medicine (EM) and examined their methodological characteristics. We searched MEDLINE via PubMed for RCT that assessed a simulation intervention in EM, published in 6 general and internal medicine and in the top 10 EM journals. The Cochrane Collaboration risk of Bias tool was used to assess risk of bias, intervention reporting was evaluated based on the "template for intervention description and replication" checklist, and methodological quality was evaluated by the Medical Education Research Study Quality Instrument. Reports selection and data extraction was done by 2 independents researchers. From 1394 RCTs screened, 68 trials assessed a SBME intervention. They represent one quarter of our sample. Cardiopulmonary resuscitation (CPR) is the most frequent topic (81%). Random sequence generation and allocation concealment were performed correctly in 66 and 49% of trials. Blinding of participants and assessors was performed correctly in 19 and 68%. Risk of attrition bias was low in three-quarters of the studies (n = 51). Risk of selective reporting bias was unclear in nearly all studies. The mean MERQSI score was of 13.4/18.4% of the reports provided a description allowing the intervention replication. Trials assessing simulation represent one quarter of RCTs in EM. Their quality remains unclear, and reproducing the interventions appears challenging due to reporting issues.

Unicompartmental Knee Arthroplasty: Does a Selection Bias Exist?

PubMed

Howell, Robert E; Lombardi, Adolph V; Crilly, Ryan; Opolot, Shem; Berend, Keith R

2015-10-01

Unicompartmental knee arthroplasty (UKA) is a minimally invasive option reported to allow a more rapid recovery and better patient outcomes. However, whether these outcomes are related to selection bias has not been fully investigated. This study examines whether a bias existed in selection of UKA candidates. We compared outcomes of patients who were scheduled for UKA but had the plan changed intraoperatively to total knee arthroplasty (TKA) to two randomly selected contemporaneous control groups: 1) patients planned as UKA who received UKA and 2) patients planned as TKA who received TKA. Our results not only showed a selection bias existed, but also showed patients converted to TKA intraoperatively had similar clinical results to patients receiving UKAs and better results than patients originally scheduled for TKA. Copyright © 2015 Elsevier Inc. All rights reserved.

A robust method using propensity score stratification for correcting verification bias for binary tests

PubMed Central

He, Hua; McDermott, Michael P.

2012-01-01

Sensitivity and specificity are common measures of the accuracy of a diagnostic test. The usual estimators of these quantities are unbiased if data on the diagnostic test result and the true disease status are obtained from all subjects in an appropriately selected sample. In some studies, verification of the true disease status is performed only for a subset of subjects, possibly depending on the result of the diagnostic test and other characteristics of the subjects. Estimators of sensitivity and specificity based on this subset of subjects are typically biased; this is known as verification bias. Methods have been proposed to correct verification bias under the assumption that the missing data on disease status are missing at random (MAR), that is, the probability of missingness depends on the true (missing) disease status only through the test result and observed covariate information. When some of the covariates are continuous, or the number of covariates is relatively large, the existing methods require parametric models for the probability of disease or the probability of verification (given the test result and covariates), and hence are subject to model misspecification. We propose a new method for correcting verification bias based on the propensity score, defined as the predicted probability of verification given the test result and observed covariates. This is estimated separately for those with positive and negative test results. The new method classifies the verified sample into several subsamples that have homogeneous propensity scores and allows correction for verification bias. Simulation studies demonstrate that the new estimators are more robust to model misspecification than existing methods, but still perform well when the models for the probability of disease and probability of verification are correctly specified. PMID:21856650

Interpopulation Comparison of Sex-Biased Mortality and Sexual Size Dimorphism in Sea-Run Masu Salmon, Oncorhynchus masou.

PubMed

Tamate, Tsuyoshi

2015-08-01

Evolutionary ecologists often expect that natural and sexual selection result in systematic co-occurrence patterns of sex-biased mortality and sexual size dimorphism (SSD) within animal species. However, whether such patterns actually occur in wild animals is poorly examined. The following expectation, the larger sex suffers higher mortality, was primarily tested here for apparently native sea-run masu salmon (Oncorhynchus masou) in three populations in Hokkaido, Japan. Field surveys on sex ratios, body sizes, and ages of smolts and returning adults revealed that two of the three populations exhibited an expected pattern, a female-biased marine mortality and SSD, but one population demonstrated an unexpected co-occurrence of male-biased marine mortality and female-biased SSD. These female-biased SSDs were attributed to faster marine growth of females because of no sex difference in smolt body size. It has been previously suggested that breeding selection favoring large size generally act more strongly in females than in males in Japanese anadromous masu, as there is a weak sexual selection on adult males but universally intensive natural selection on adult females. Thus, this hypothesis explains female-biased SSDs well in all study populations. Interpopulation variation in sex-biased mortality found here might result from differences in marine predation and/or fishing pressures, given that selection driving female-biased SSD makes females forage more aggressively than males during the marine phase. Taken together, these results raise the possibility that evolutionary forces have shaped adaptive sex-specific foraging strategies under relationships between growth and mortality, resulting in co-occurrence patterns of sex-biased mortality and SSD within animal species.

Location and color biases have different influences on selective attention.

PubMed

Fecteau, Jillian H; Korjoukov, Ilia; Roelfsema, Pieter R

2009-05-01

Are locations or colors more effective cues in biasing attention? We addressed this question with a visual search task that featured an associative priming manipulation. The observers indicated which target appeared in a search array. Unknown to them, one target appeared at the same location more often and a second target appeared in the same color more often. Both location and color biases facilitated performance, but location biases benefited the selection of all targets, whereas color biases only benefited the associated target letter. The generalized benefit of location biases suggests that locations are more effective cues to attention.

Silicon solar cell process development, fabrication and analysis

NASA Technical Reports Server (NTRS)

Iles, P. A.; Leung, D. C.

1982-01-01

For UCP Si, randomly selected wafers and wafers cut from two specific ingots were studied. For the randomly selected wafers, a moderate gettering diffusion had little effect. Moreover, an efficiency up to 14% AMI was achieved with advanced processes. For the two specific UCP ingots, ingot #5848-13C displayed severe impurity effects as shown by lower 3sc in the middle of the ingot and low CFF in the top of the ingot. Also the middle portions of this ingot responded to a series of progressively more severe gettering diffusion. Unexplained was the fact that severely gettered samples of this ingot displayed a negative light biased effect on the minority carrier diffusion length while the nongettered or moderately gettered ones had the more conventional positive light biased effect on diffusion length. On the other hand, ingot C-4-21A did not have the problem of ingot 5848-13C and behaved like to the randomly selected wafers. The top half of the ingot was shown to be slightly superior to the bottom half, but moderate gettering helped to narrow the gap.

Stochastic sampling of quadrature grids for the evaluation of vibrational expectation values

NASA Astrophysics Data System (ADS)

López Ríos, Pablo; Monserrat, Bartomeu; Needs, Richard J.

2018-02-01

The thermal lines method for the evaluation of vibrational expectation values of electronic observables [B. Monserrat, Phys. Rev. B 93, 014302 (2016), 10.1103/PhysRevB.93.014302] was recently proposed as a physically motivated approximation offering balance between the accuracy of direct Monte Carlo integration and the low computational cost of using local quadratic approximations. In this paper we reformulate thermal lines as a stochastic implementation of quadrature-grid integration, analyze the analytical form of its bias, and extend the method to multiple-point quadrature grids applicable to any factorizable harmonic or anharmonic nuclear wave function. The bias incurred by thermal lines is found to depend on the local form of the expectation value, and we demonstrate that the use of finer quadrature grids along selected modes can eliminate this bias, while still offering an ˜30 % lower computational cost than direct Monte Carlo integration in our tests.

Accounting for imperfect detection and survey bias in statistical analysis of presence-only data

USGS Publications Warehouse

Dorazio, Robert M.

2014-01-01

Using mathematical proof and simulation-based comparisons, I demonstrate that biases induced by errors in detection or biased selection of survey locations can be reduced or eliminated by using the hierarchical model to analyse presence-only data in conjunction with counts observed in planned surveys. I show that a relatively small number of high-quality data (from planned surveys) can be used to leverage the information in presence-only observations, which usually have broad spatial coverage but may not be informative of both occurrence and detectability of individuals. Because a variety of sampling protocols can be used in planned surveys, this approach to the analysis of presence-only data is widely applicable. In addition, since the point-process model is formulated at the level of an individual, it can be extended to account for biological interactions between individuals and temporal changes in their spatial distributions.

Cognitive interference and a food-related memory bias in binge eating disorder.

PubMed

Svaldi, Jennifer; Schmitz, Florian; Trentowska, Monika; Tuschen-Caffier, Brunna; Berking, Matthias; Naumann, Eva

2014-01-01

The present study was concerned with cognitive interference and a specific memory bias for eating-related stimuli in binge eating disorder (BED). Further objectives were to find out under which circumstances such effects would occur, and whether they are related with each other and with reported severity of BED symptoms. A group of women diagnosed with BED and a matched sample of overweight controls completed two paradigms, an n-back task with lures and a recent-probes task. The BED group generally experienced more interference in the n-back task. Additionally, they revealed selectively increased interference for food items in the recent-probes task. Findings can be reconciled with the view that control functions are generally impaired in BED, and that there is an additional bias for eating-related stimuli, both of which were related with reported severity of BED symptoms. Copyright © 2013 Elsevier Ltd. All rights reserved.

Inverse probability weighting and doubly robust methods in correcting the effects of non-response in the reimbursed medication and self-reported turnout estimates in the ATH survey.

PubMed

Härkänen, Tommi; Kaikkonen, Risto; Virtala, Esa; Koskinen, Seppo

2014-11-06

To assess the nonresponse rates in a questionnaire survey with respect to administrative register data, and to correct the bias statistically. The Finnish Regional Health and Well-being Study (ATH) in 2010 was based on a national sample and several regional samples. Missing data analysis was based on socio-demographic register data covering the whole sample. Inverse probability weighting (IPW) and doubly robust (DR) methods were estimated using the logistic regression model, which was selected using the Bayesian information criteria. The crude, weighted and true self-reported turnout in the 2008 municipal election and prevalences of entitlements to specially reimbursed medication, and the crude and weighted body mass index (BMI) means were compared. The IPW method appeared to remove a relatively large proportion of the bias compared to the crude prevalence estimates of the turnout and the entitlements to specially reimbursed medication. Several demographic factors were shown to be associated with missing data, but few interactions were found. Our results suggest that the IPW method can improve the accuracy of results of a population survey, and the model selection provides insight into the structure of missing data. However, health-related missing data mechanisms are beyond the scope of statistical methods, which mainly rely on socio-demographic information to correct the results.

How bandwidth selection algorithms impact exploratory data analysis using kernel density estimation.

PubMed

Harpole, Jared K; Woods, Carol M; Rodebaugh, Thomas L; Levinson, Cheri A; Lenze, Eric J

2014-09-01

Exploratory data analysis (EDA) can reveal important features of underlying distributions, and these features often have an impact on inferences and conclusions drawn from data. Graphical analysis is central to EDA, and graphical representations of distributions often benefit from smoothing. A viable method of estimating and graphing the underlying density in EDA is kernel density estimation (KDE). This article provides an introduction to KDE and examines alternative methods for specifying the smoothing bandwidth in terms of their ability to recover the true density. We also illustrate the comparison and use of KDE methods with 2 empirical examples. Simulations were carried out in which we compared 8 bandwidth selection methods (Sheather-Jones plug-in [SJDP], normal rule of thumb, Silverman's rule of thumb, least squares cross-validation, biased cross-validation, and 3 adaptive kernel estimators) using 5 true density shapes (standard normal, positively skewed, bimodal, skewed bimodal, and standard lognormal) and 9 sample sizes (15, 25, 50, 75, 100, 250, 500, 1,000, 2,000). Results indicate that, overall, SJDP outperformed all methods. However, for smaller sample sizes (25 to 100) either biased cross-validation or Silverman's rule of thumb was recommended, and for larger sample sizes the adaptive kernel estimator with SJDP was recommended. Information is provided about implementing the recommendations in the R computing language. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Quantifying Selection Bias in National Institute of Health Stroke Scale Data Documented in an Acute Stroke Registry.

PubMed

Thompson, Michael P; Luo, Zhehui; Gardiner, Joseph; Burke, James F; Nickles, Adrienne; Reeves, Mathew J

2016-05-01

As a measure of stroke severity, the National Institutes of Health Stroke Scale (NIHSS) is an important predictor of patient- and hospital-level outcomes, yet is often undocumented. The purpose of this study is to quantify and correct for potential selection bias in observed NIHSS data. Data were obtained from the Michigan Stroke Registry and included 10 262 patients with ischemic stroke aged ≥65 years discharged from 23 hospitals from 2009 to 2012, of which 74.6% of patients had documented NIHSS. We estimated models predicting NIHSS documentation and NIHSS score and used the Heckman selection model to estimate a correlation coefficient (ρ) between the 2 model error terms, which quantifies the degree of selection bias in the documentation of NIHSS. The Heckman model found modest, but significant, selection bias (ρ=0.19; 95% confidence interval: 0.09, 0.29; P<0.001), indicating that because NIHSS score increased (ie, strokes were more severe), the probability of documentation also increased. We also estimated a selection bias-corrected population mean NIHSS score of 4.8, which was substantially lower than the observed mean NIHSS score of 7.4. Evidence of selection bias was also identified using hospital-level analysis, where increased NIHSS documentation was correlated with lower mean NIHSS scores (r=-0.39; P<0.001). We demonstrate modest, but important, selection bias in documented NIHSS data, which are missing more often in patients with less severe stroke. The population mean NIHSS score was overestimated by >2 points, which could significantly alter the risk profile of hospitals treating patients with ischemic stroke and subsequent hospital risk-adjusted outcomes. © 2016 American Heart Association, Inc.

Identifying Items to Assess Methodological Quality in Physical Therapy Trials: A Factor Analysis

PubMed Central

Cummings, Greta G.; Fuentes, Jorge; Saltaji, Humam; Ha, Christine; Chisholm, Annabritt; Pasichnyk, Dion; Rogers, Todd

2014-01-01

Background Numerous tools and individual items have been proposed to assess the methodological quality of randomized controlled trials (RCTs). The frequency of use of these items varies according to health area, which suggests a lack of agreement regarding their relevance to trial quality or risk of bias. Objective The objectives of this study were: (1) to identify the underlying component structure of items and (2) to determine relevant items to evaluate the quality and risk of bias of trials in physical therapy by using an exploratory factor analysis (EFA). Design A methodological research design was used, and an EFA was performed. Methods Randomized controlled trials used for this study were randomly selected from searches of the Cochrane Database of Systematic Reviews. Two reviewers used 45 items gathered from 7 different quality tools to assess the methodological quality of the RCTs. An exploratory factor analysis was conducted using the principal axis factoring (PAF) method followed by varimax rotation. Results Principal axis factoring identified 34 items loaded on 9 common factors: (1) selection bias; (2) performance and detection bias; (3) eligibility, intervention details, and description of outcome measures; (4) psychometric properties of the main outcome; (5) contamination and adherence to treatment; (6) attrition bias; (7) data analysis; (8) sample size; and (9) control and placebo adequacy. Limitation Because of the exploratory nature of the results, a confirmatory factor analysis is needed to validate this model. Conclusions To the authors' knowledge, this is the first factor analysis to explore the underlying component items used to evaluate the methodological quality or risk of bias of RCTs in physical therapy. The items and factors represent a starting point for evaluating the methodological quality and risk of bias in physical therapy trials. Empirical evidence of the association among these items with treatment effects and a confirmatory factor analysis of these results are needed to validate these items. PMID:24786942

Identifying items to assess methodological quality in physical therapy trials: a factor analysis.

PubMed

Armijo-Olivo, Susan; Cummings, Greta G; Fuentes, Jorge; Saltaji, Humam; Ha, Christine; Chisholm, Annabritt; Pasichnyk, Dion; Rogers, Todd

2014-09-01

Numerous tools and individual items have been proposed to assess the methodological quality of randomized controlled trials (RCTs). The frequency of use of these items varies according to health area, which suggests a lack of agreement regarding their relevance to trial quality or risk of bias. The objectives of this study were: (1) to identify the underlying component structure of items and (2) to determine relevant items to evaluate the quality and risk of bias of trials in physical therapy by using an exploratory factor analysis (EFA). A methodological research design was used, and an EFA was performed. Randomized controlled trials used for this study were randomly selected from searches of the Cochrane Database of Systematic Reviews. Two reviewers used 45 items gathered from 7 different quality tools to assess the methodological quality of the RCTs. An exploratory factor analysis was conducted using the principal axis factoring (PAF) method followed by varimax rotation. Principal axis factoring identified 34 items loaded on 9 common factors: (1) selection bias; (2) performance and detection bias; (3) eligibility, intervention details, and description of outcome measures; (4) psychometric properties of the main outcome; (5) contamination and adherence to treatment; (6) attrition bias; (7) data analysis; (8) sample size; and (9) control and placebo adequacy. Because of the exploratory nature of the results, a confirmatory factor analysis is needed to validate this model. To the authors' knowledge, this is the first factor analysis to explore the underlying component items used to evaluate the methodological quality or risk of bias of RCTs in physical therapy. The items and factors represent a starting point for evaluating the methodological quality and risk of bias in physical therapy trials. Empirical evidence of the association among these items with treatment effects and a confirmatory factor analysis of these results are needed to validate these items. © 2014 American Physical Therapy Association.

Unbiased feature selection in learning random forests for high-dimensional data.

PubMed

Nguyen, Thanh-Tung; Huang, Joshua Zhexue; Nguyen, Thuy Thi

2015-01-01

Random forests (RFs) have been widely used as a powerful classification method. However, with the randomization in both bagging samples and feature selection, the trees in the forest tend to select uninformative features for node splitting. This makes RFs have poor accuracy when working with high-dimensional data. Besides that, RFs have bias in the feature selection process where multivalued features are favored. Aiming at debiasing feature selection in RFs, we propose a new RF algorithm, called xRF, to select good features in learning RFs for high-dimensional data. We first remove the uninformative features using p-value assessment, and the subset of unbiased features is then selected based on some statistical measures. This feature subset is then partitioned into two subsets. A feature weighting sampling technique is used to sample features from these two subsets for building trees. This approach enables one to generate more accurate trees, while allowing one to reduce dimensionality and the amount of data needed for learning RFs. An extensive set of experiments has been conducted on 47 high-dimensional real-world datasets including image datasets. The experimental results have shown that RFs with the proposed approach outperformed the existing random forests in increasing the accuracy and the AUC measures.

The Complete Local-Volume Groups Sample (CLoGS): Early results from X-ray and radio observations

NASA Astrophysics Data System (ADS)

Vrtilek, Jan M.; O'Sullivan, Ewan; David, Laurence P.; Giacintucci, Simona; Kolokythas, Konstantinos

2017-08-01

Although the group environment is the dominant locus of galaxy evolution (in contrast to rich clusters, which contain only a few percent of galaxies), there has been a lack of reliable, representative group samples in the local Universe. In particular, X-ray selected samples are strongly biased in favor of the X-ray bright, centrally-concentrated cool-core systems. In response, we have designed the Complete Local-Volume Groups Sample (CLoGS), an optically-selected statistically-complete sample of 53 groups within 80 Mpc which is intended to overcome the limitations of X-ray selected samples and serve as a representative survey of groups in the local Universe. We have supplemented X-ray data from Chandra and XMM (70% complete to date, using both archival and new observations, with a 26-group high richness subsample 100% complete) with GMRT radio continuum observations (at 235 and 610 MHz, complete for the entire sample). CLoGS includes groups with a wide variety of properties in terms of galaxy population, hot gas content, and AGN power. We here describe early results from the survey, including the range of AGN activity observed in the dominant galaxies, the relative fraction of cool-core and non-cool-core groups in our sample, and the degree of disturbance observed in the IGM.

What’s New? Children Prefer Novelty in Referent Selection

PubMed Central

Horst, Jessica S.; Samuelson, Larissa K.; Kucker, Sarah C.; McMurray, Bob

2010-01-01

Determining the referent of a novel name is a critical task for young language learners. The majority of studies on children’s referent selection focus on manipulating the sources of information (linguistic, contextual and pragmatic) that children can use to solve the referent mapping problem. Here, we take a step back and explore how children’s endogenous biases towards novelty and their own familiarity with novel objects influence their performance in such a task. We familiarized 2-year-old children with previously novel objects. Then, on novel name referent selection trials children were asked to select the referent from three novel objects: two previously seen and one completely novel object. Children demonstrated a clear bias to select the most novel object. A second experiment controls for pragmatic responding and replicates this finding. We conclude, therefore, that children’s referent selection is biased by previous exposure and children’s endogenous bias to novelty. PMID:21092945

Biased selection within the social health insurance market in Colombia.

PubMed

Castano, Ramon; Zambrano, Andres

2006-12-01

Reducing the impact of insurance market failures with regulations such as community-rated premiums, standardized benefit packages and open enrolment, yield limited effect because they create room for selection bias. The Colombian social health insurance system started a market approach in 1993 expecting to improve performance of preexisting monopolistic insurance funds by exposing them to competition by new entrants. This paper tests the hypothesis that market failures would lead to biased selection favoring new entrants. Two household surveys are analyzed using Self-Reported Health Status and the presence of chronic conditions as prospective indicators of individual risk. Biased selection is found to take place, leading to adverse selection among incumbents, and favorable selection among new entrants. This pattern is absent in 1997 but is evident in 2003. Given that the two incumbents analyzed are public organizations, the fiscal implications of the findings in terms of government bailouts, are analyzed.

Impact of waning acquired immunity and asymptomatic infections on case-control studies for enteric pathogens.

PubMed

Havelaar, Arie H; Swart, Arno

2016-12-01

Case-control studies of outbreaks and of sporadic cases of infectious diseases may provide a biased estimate of the infection rate ratio, due to selecting controls that are not at risk of disease. We use a dynamic mathematical model to explore biases introduced in results drawn from case-control studies of enteric pathogens by waning and boosting of immunity, and by asymptomatic infections, using Campylobacter jejuni as an example. Individuals in the population are either susceptible (at risk of infection and disease), fully protected (not at risk of either) or partially protected (at risk of infection but not of disease). The force of infection is a function of the exposure frequency and the exposure dose. We show that the observed disease odds ratios are indeed strongly biased towards the null, i.e. much lower than the infection rate ratio, and furthermore even not proportional to it. The bias could theoretically be controlled by sampling controls only from the reservoir of susceptible individuals. The population at risk is in a dynamic equilibrium, and cannot be identified as those who are not and have never experienced disease. Individual-level samples to measure protective immunity would be required, complicating the design, cost and execution of case-control studies. Copyright © 2016 The Author(s). Published by Elsevier B.V. All rights reserved.

Does stereotype threat influence performance of girls in stereotyped domains? A meta-analysis.

PubMed

Flore, Paulette C; Wicherts, Jelte M

2015-02-01

Although the effect of stereotype threat concerning women and mathematics has been subject to various systematic reviews, none of them have been performed on the sub-population of children and adolescents. In this meta-analysis we estimated the effects of stereotype threat on performance of girls on math, science and spatial skills (MSSS) tests. Moreover, we studied publication bias and four moderators: test difficulty, presence of boys, gender equality within countries, and the type of control group that was used in the studies. We selected study samples when the study included girls, samples had a mean age below 18years, the design was (quasi-)experimental, the stereotype threat manipulation was administered between-subjects, and the dependent variable was a MSSS test related to a gender stereotype favoring boys. To analyze the 47 effect sizes, we used random effects and mixed effects models. The estimated mean effect size equaled -0.22 and significantly differed from 0. None of the moderator variables was significant; however, there were several signs for the presence of publication bias. We conclude that publication bias might seriously distort the literature on the effects of stereotype threat among schoolgirls. We propose a large replication study to provide a less biased effect size estimate. Copyright © 2014 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Assessing the Representativeness of Population-Sampled Health Surveys Through Linkage to Administrative Data on Alcohol-Related Outcomes

PubMed Central

Gorman, Emma; Leyland, Alastair H.; McCartney, Gerry; White, Ian R.; Katikireddi, Srinivasa Vittal; Rutherford, Lisa; Graham, Lesley; Gray, Linsay

2014-01-01

Health surveys are an important resource for monitoring population health, but selective nonresponse may impede valid inference. This study aimed to assess nonresponse bias in a population-sampled health survey in Scotland, with a focus on alcohol-related outcomes. Nonresponse bias was assessed by examining whether rates of alcohol-related harm (i.e., hospitalization or death) and all-cause mortality among respondents to the Scottish Health Surveys (from 1995 to 2010) were equivalent to those in the general population, and whether the extent of any bias varied according to sociodemographic attributes or over time. Data from consenting respondents (aged 20–64 years) to 6 Scottish Health Surveys were confidentially linked to death and hospitalization records and compared with general population counterparts. Directly age-standardized incidence rates of alcohol-related harm and all-cause mortality were lower among Scottish Health Survey respondents compared with the general population. For all years combined, the survey-to-population rate ratios were 0.69 (95% confidence interval: 0.61, 0.76) for the incidence of alcohol-related harm and 0.89 (95% confidence interval: 0.83, 0.96) for all-cause mortality. Bias was more pronounced among persons residing in more deprived areas; limited evidence was found for regional or temporal variation. This suggests that corresponding underestimation of population rates of alcohol consumption is likely to be socially patterned. PMID:25227767

Galaxy bias from galaxy-galaxy lensing in the DES Science Verification Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Prat, J.; et al.

We present a measurement of galaxy-galaxy lensing around a magnitude-limited (more » $$i_{AB} < 22.5$$) sample of galaxies selected from the Dark Energy Survey Science Verification (DES-SV) data. We split these lenses into three photometric-redshift bins from 0.2 to 0.8, and determine the product of the galaxy bias $b$ and cross-correlation coefficient between the galaxy and dark matter overdensity fields $r$ in each bin, using scales above 4 Mpc/$h$ comoving, where we find the linear bias model to be valid given our current uncertainties. We compare our galaxy bias results from galaxy-galaxy lensing with those obtained from galaxy clustering (Crocce et al. 2016) and CMB lensing (Giannantonio et al. 2016) for the same sample of galaxies, and find our measurements to be in good agreement with those in Crocce et al. (2016), while, in the lowest redshift bin ($$z\\sim0.3$$), they show some tension with the findings in Giannantonio et al. (2016). Our results are found to be rather insensitive to a large range of systematic effects. We measure $$b\\cdot r$$ to be $$0.87\\pm 0.11$$, $$1.12 \\pm 0.16$$ and $$1.24\\pm 0.23$$, respectively for the three redshift bins of width $$\\Delta z = 0.2$$ in the range $0.2« less

Detecting Bias in Selection for Higher Education: Three Different Methods

ERIC Educational Resources Information Center

Kennet-Cohen, Tamar; Turvall, Elliot; Oren, Carmel

2014-01-01

This study examined selection bias in Israeli university admissions with respect to test language and gender, using three approaches for the detection of such bias: Cleary's model of differential prediction, boundary conditions for differential prediction and difference between "d's" (the Constant Ratio Model). The university admissions…

Meta-Regression Approximations to Reduce Publication Selection Bias

ERIC Educational Resources Information Center

Stanley, T. D.; Doucouliagos, Hristos

2014-01-01

Publication selection bias is a serious challenge to the integrity of all empirical sciences. We derive meta-regression approximations to reduce this bias. Our approach employs Taylor polynomial approximations to the conditional mean of a truncated distribution. A quadratic approximation without a linear term, precision-effect estimate with…

The Bologna complete sample of nearby radio sources. II. Phase referenced observations of faint nuclear sources

NASA Astrophysics Data System (ADS)

Liuzzo, E.; Giovannini, G.; Giroletti, M.; Taylor, G. B.

2009-10-01

Aims: To study statistical properties of different classes of sources, it is necessary to observe a sample that is free of selection effects. To do this, we initiated a project to observe a complete sample of radio galaxies selected from the B2 Catalogue of Radio Sources and the Third Cambridge Revised Catalogue (3CR), with no selection constraint on the nuclear properties. We named this sample “the Bologna Complete Sample” (BCS). Methods: We present new VLBI observations at 5 and 1.6 GHz for 33 sources drawn from a sample not biased toward orientation. By combining these data with those in the literature, information on the parsec-scale morphology is available for a total of 76 of 94 radio sources with a range in radio power and kiloparsec-scale morphologies. Results: The fraction of two-sided sources at milliarcsecond resolution is high (30%), compared to the fraction found in VLBI surveys selected at centimeter wavelengths, as expected from the predictions of unified models. The parsec-scale jets are generally found to be straight and to line up with the kiloparsec-scale jets. A few peculiar sources are discussed in detail. Tables 1-4 are only available in electronic form at http://www.aanda.org

A novel approach to non-biased systematic random sampling: a stereologic estimate of Purkinje cells in the human cerebellum.

PubMed

Agashiwala, Rajiv M; Louis, Elan D; Hof, Patrick R; Perl, Daniel P

2008-10-21

Non-biased systematic sampling using the principles of stereology provides accurate quantitative estimates of objects within neuroanatomic structures. However, the basic principles of stereology are not optimally suited for counting objects that selectively exist within a limited but complex and convoluted portion of the sample, such as occurs when counting cerebellar Purkinje cells. In an effort to quantify Purkinje cells in association with certain neurodegenerative disorders, we developed a new method for stereologic sampling of the cerebellar cortex, involving calculating the volume of the cerebellar tissues, identifying and isolating the Purkinje cell layer and using this information to extrapolate non-biased systematic sampling data to estimate the total number of Purkinje cells in the tissues. Using this approach, we counted Purkinje cells in the right cerebella of four human male control specimens, aged 41, 67, 70 and 84 years, and estimated the total Purkinje cell number for the four entire cerebella to be 27.03, 19.74, 20.44 and 22.03 million cells, respectively. The precision of the method is seen when comparing the density of the cells within the tissue: 266,274, 173,166, 167,603 and 183,575 cells/cm3, respectively. Prior literature documents Purkinje cell counts ranging from 14.8 to 30.5 million cells. These data demonstrate the accuracy of our approach. Our novel approach, which offers an improvement over previous methodologies, is of value for quantitative work of this nature. This approach could be applied to morphometric studies of other similarly complex tissues as well.

A novel approach to non-biased systematic random sampling: A stereologic estimate of Purkinje cells in the human cerebellum

PubMed Central

Agashiwala, Rajiv M.; Louis, Elan D.; Hof, Patrick R.; Perl, Daniel P.

2010-01-01

Non-biased systematic sampling using the principles of stereology provides accurate quantitative estimates of objects within neuroanatomic structures. However, the basic principles of stereology are not optimally suited for counting objects that selectively exist within a limited but complex and convoluted portion of the sample, such as occurs when counting cerebellar Purkinje cells. In an effort to quantify Purkinje cells in association with certain neurodegenerative disorders, we developed a new method for stereologic sampling of the cerebellar cortex, involving calculating the volume of the cerebellar tissues, identifying and isolating the Purkinje cell layer and using this information to extrapolate non-biased systematic sampling data to estimate the total number of Purkinje cells in the tissues. Using this approach, we counted Purkinje cells in the right cerebella of four human male control specimens, aged 41, 67, 70 and 84 years, and estimated the total Purkinje cell number for the four entire cerebella to be 27.03, 19.74, 20.44 and 22.03 million cells, respectively. The precision of the method is seen when comparing the density of the cells within the tissue: 266,274, 173,166, 167,603 and 183,575 cells/cm3, respectively. Prior literature documents Purkinje cell counts ranging from 14.8 to 30.5 million cells. These data demonstrate the accuracy of our approach. Our novel approach, which offers an improvement over previous methodologies, is of value for quantitative work of this nature. This approach could be applied to morphometric studies of other similarly complex tissues as well. PMID:18725208

Only pick the right grains: Modelling the bias due to subjective grain-size interval selection for chronometric and fingerprinting approaches.

NASA Astrophysics Data System (ADS)

Dietze, Michael; Fuchs, Margret; Kreutzer, Sebastian

2016-04-01

Many modern approaches of radiometric dating or geochemical fingerprinting rely on sampling sedimentary deposits. A key assumption of most concepts is that the extracted grain-size fraction of the sampled sediment adequately represents the actual process to be dated or the source area to be fingerprinted. However, these assumptions are not always well constrained. Rather, they have to align with arbitrary, method-determined size intervals, such as "coarse grain" or "fine grain" with partly even different definitions. Such arbitrary intervals violate principal process-based concepts of sediment transport and can thus introduce significant bias to the analysis outcome (i.e., a deviation of the measured from the true value). We present a flexible numerical framework (numOlum) for the statistical programming language R that allows quantifying the bias due to any given analysis size interval for different types of sediment deposits. This framework is applied to synthetic samples from the realms of luminescence dating and geochemical fingerprinting, i.e. a virtual reworked loess section. We show independent validation data from artificially dosed and subsequently mixed grain-size proportions and we present a statistical approach (end-member modelling analysis, EMMA) that allows accounting for the effect of measuring the compound dosimetric history or geochemical composition of a sample. EMMA separates polymodal grain-size distributions into the underlying transport process-related distributions and their contribution to each sample. These underlying distributions can then be used to adjust grain-size preparation intervals to minimise the incorporation of "undesired" grain-size fractions.

SU-E-I-46: Sample-Size Dependence of Model Observers for Estimating Low-Contrast Detection Performance From CT Images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Reiser, I; Lu, Z

2014-06-01

Purpose: Recently, task-based assessment of diagnostic CT systems has attracted much attention. Detection task performance can be estimated using human observers, or mathematical observer models. While most models are well established, considerable bias can be introduced when performance is estimated from a limited number of image samples. Thus, the purpose of this work was to assess the effect of sample size on bias and uncertainty of two channelized Hotelling observers and a template-matching observer. Methods: The image data used for this study consisted of 100 signal-present and 100 signal-absent regions-of-interest, which were extracted from CT slices. The experimental conditions includedmore » two signal sizes and five different x-ray beam current settings (mAs). Human observer performance for these images was determined in 2-alternative forced choice experiments. These data were provided by the Mayo clinic in Rochester, MN. Detection performance was estimated from three observer models, including channelized Hotelling observers (CHO) with Gabor or Laguerre-Gauss (LG) channels, and a template-matching observer (TM). Different sample sizes were generated by randomly selecting a subset of image pairs, (N=20,40,60,80). Observer performance was quantified as proportion of correct responses (PC). Bias was quantified as the relative difference of PC for 20 and 80 image pairs. Results: For n=100, all observer models predicted human performance across mAs and signal sizes. Bias was 23% for CHO (Gabor), 7% for CHO (LG), and 3% for TM. The relative standard deviation, σ(PC)/PC at N=20 was highest for the TM observer (11%) and lowest for the CHO (Gabor) observer (5%). Conclusion: In order to make image quality assessment feasible in the clinical practice, a statistically efficient observer model, that can predict performance from few samples, is needed. Our results identified two observer models that may be suited for this task.« less

Vertebrate codon bias indicates a highly GC-rich ancestral genome.

PubMed

Nabiyouni, Maryam; Prakash, Ashwin; Fedorov, Alexei

2013-04-25

Two factors are thought to have contributed to the origin of codon usage bias in eukaryotes: 1) genome-wide mutational forces that shape overall GC-content and create context-dependent nucleotide bias, and 2) positive selection for codons that maximize efficient and accurate translation. Particularly in vertebrates, these two explanations contradict each other and cloud the origin of codon bias in the taxon. On the one hand, mutational forces fail to explain GC-richness (~60%) of third codon positions, given the GC-poor overall genomic composition among vertebrates (~40%). On the other hand, positive selection cannot easily explain strict regularities in codon preferences. Large-scale bioinformatic assessment, of nucleotide composition of coding and non-coding sequences in vertebrates and other taxa, suggests a simple possible resolution for this contradiction. Specifically, we propose that the last common vertebrate ancestor had a GC-rich genome (~65% GC). The data suggest that whole-genome mutational bias is the major driving force for generating codon bias. As the bias becomes prominent, it begins to affect translation and can result in positive selection for optimal codons. The positive selection can, in turn, significantly modulate codon preferences. Copyright © 2013 Elsevier B.V. All rights reserved.

Correction factors for self-selection when evaluating screening programmes.

PubMed

Spix, Claudia; Berthold, Frank; Hero, Barbara; Michaelis, Jörg; Schilling, Freimut H

2016-03-01

In screening programmes there is recognized bias introduced through participant self-selection (the healthy screenee bias). Methods used to evaluate screening programmes include Intention-to-screen, per-protocol, and the "post hoc" approach in which, after introducing screening for everyone, the only evaluation option is participants versus non-participants. All methods are prone to bias through self-selection. We present an overview of approaches to correct for this bias. We considered four methods to quantify and correct for self-selection bias. Simple calculations revealed that these corrections are actually all identical, and can be converted into each other. Based on this, correction factors for further situations and measures were derived. The application of these correction factors requires a number of assumptions. Using as an example the German Neuroblastoma Screening Study, no relevant reduction in mortality or stage 4 incidence due to screening was observed. The largest bias (in favour of screening) was observed when comparing participants with non-participants. Correcting for bias is particularly necessary when using the post hoc evaluation approach, however, in this situation not all required data are available. External data or further assumptions may be required for estimation. © The Author(s) 2015.

A test of the critical assumption of the sensory bias model for the evolution of female mating preference using neural networks.

PubMed

Fuller, Rebecca C

2009-07-01

The sensory bias model for the evolution of mating preferences states that mating preferences evolve as correlated responses to selection on nonmating behaviors sharing a common sensory system. The critical assumption is that pleiotropy creates genetic correlations that affect the response to selection. I simulated selection on populations of neural networks to test this. First, I selected for various combinations of foraging and mating preferences. Sensory bias predicts that populations with preferences for like-colored objects (red food and red mates) should evolve more readily than preferences for differently colored objects (red food and blue mates). Here, I found no evidence for sensory bias. The responses to selection on foraging and mating preferences were independent of one another. Second, I selected on foraging preferences alone and asked whether there were correlated responses for increased mating preferences for like-colored mates. Here, I found modest evidence for sensory bias. Selection for a particular foraging preference resulted in increased mating preference for similarly colored mates. However, the correlated responses were small and inconsistent. Selection on foraging preferences alone may affect initial levels of mating preferences, but these correlations did not constrain the joint evolution of foraging and mating preferences in these simulations.

Bias Assessment of General Chemistry Analytes using Commutable Samples.

PubMed

Koerbin, Gus; Tate, Jillian R; Ryan, Julie; Jones, Graham Rd; Sikaris, Ken A; Kanowski, David; Reed, Maxine; Gill, Janice; Koumantakis, George; Yen, Tina; St John, Andrew; Hickman, Peter E; Simpson, Aaron; Graham, Peter

2014-11-01

Harmonisation of reference intervals for routine general chemistry analytes has been a goal for many years. Analytical bias may prevent this harmonisation. To determine if analytical bias is present when comparing methods, the use of commutable samples, or samples that have the same properties as the clinical samples routinely analysed, should be used as reference samples to eliminate the possibility of matrix effect. The use of commutable samples has improved the identification of unacceptable analytical performance in the Netherlands and Spain. The International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) has undertaken a pilot study using commutable samples in an attempt to determine not only country specific reference intervals but to make them comparable between countries. Australia and New Zealand, through the Australasian Association of Clinical Biochemists (AACB), have also undertaken an assessment of analytical bias using commutable samples and determined that of the 27 general chemistry analytes studied, 19 showed sufficiently small between method biases as to not prevent harmonisation of reference intervals. Application of evidence based approaches including the determination of analytical bias using commutable material is necessary when seeking to harmonise reference intervals.

Assessing Compliance-Effect Bias in the Two Stage Least Squares Estimator

ERIC Educational Resources Information Center

Reardon, Sean; Unlu, Fatih; Zhu, Pei; Bloom, Howard

2011-01-01

The proposed paper studies the bias in the two-stage least squares, or 2SLS, estimator that is caused by the compliance-effect covariance (hereafter, the compliance-effect bias). It starts by deriving the formula for the bias in an infinite sample (i.e., in the absence of finite sample bias) under different circumstances. Specifically, it…

The Local Universe as Seen in the Far-Infrared and Far-Ultraviolet: A Global Point of View of the Local Recent Star Formation

NASA Astrophysics Data System (ADS)

Buat, V.; Takeuchi, T. T.; Iglesias-Páramo, J.; Xu, C. K.; Burgarella, D.; Boselli, A.; Barlow, T.; Bianchi, L.; Donas, J.; Forster, K.; Friedman, P. G.; Heckman, T. M.; Lee, Y.-W.; Madore, B. F.; Martin, D. C.; Milliard, B.; Morissey, P.; Neff, S.; Rich, M.; Schiminovich, D.; Seibert, M.; Small, T.; Szalay, A. S.; Welsh, B.; Wyder, T.; Yi, S. K.

2007-12-01

We select far-infrared (FIR: 60 μm) and far-ultraviolet (FUV: 530 Å) samples of nearby galaxies in order to discuss the biases encountered by monochromatic surveys (FIR or FUV). Very different volumes are sampled by each selection, and much care is taken to apply volume corrections to all the analyses. The distributions of the bolometric luminosity of young stars are compared for both samples: they are found to be consistent with each other for galaxies of intermediate luminosities, but some differences are found for high (>5×1010 Lsolar) luminosities. The shallowness of the IRAS survey prevents us from securing a comparison at low luminosities (<2×109 Lsolar). The ratio of the total infrared (TIR) luminosity to the FUV luminosity is found to increase with the bolometric luminosity in a similar way for both samples up to 5×1010 Lsolar. Brighter galaxies are found to have a different behavior according to their selection: the LTIR/LFUV ratio of the FUV-selected galaxies brighter than 5×1010 Lsolar reaches a plateau, whereas LTIR/LFUV continues to increase with the luminosity of bright galaxies selected in FIR. The volume-averaged specific star formation rate (SFR per unit galaxy stellar mass, SSFR) is found to decrease toward massive galaxies within each selection. The mean values of the SSFR are found to be larger than those measured for optical and NIR-selected samples over the whole mass range for the FIR selection, and for masses larger than 1010 Msolar for the FUV selection. Luminous and massive galaxies selected in FIR appear as active as galaxies with similar characteristics detected at z~0.7.

AEGIS: Demographics of X-ray and Optically Selected Active Galactic Nuclei

NASA Astrophysics Data System (ADS)

Yan, Renbin; Ho, Luis C.; Newman, Jeffrey A.; Coil, Alison L.; Willmer, Christopher N. A.; Laird, Elise S.; Georgakakis, Antonis; Aird, James; Barmby, Pauline; Bundy, Kevin; Cooper, Michael C.; Davis, Marc; Faber, S. M.; Fang, Taotao; Griffith, Roger L.; Koekemoer, Anton M.; Koo, David C.; Nandra, Kirpal; Park, Shinae Q.; Sarajedini, Vicki L.; Weiner, Benjamin J.; Willner, S. P.

2011-02-01

We develop a new diagnostic method to classify galaxies into active galactic nucleus (AGN) hosts, star-forming galaxies, and absorption-dominated galaxies by combining the [O III]/Hβ ratio with rest-frame U - B color. This can be used to robustly select AGNs in galaxy samples at intermediate redshifts (z < 1). We compare the result of this optical AGN selection with X-ray selection using a sample of 3150 galaxies with 0.3 < z < 0.8 and I AB < 22, selected from the DEEP2 Galaxy Redshift Survey and the All-wavelength Extended Groth Strip International Survey. Among the 146 X-ray sources in this sample, 58% are classified optically as emission-line AGNs, the rest as star-forming galaxies or absorption-dominated galaxies. The latter are also known as "X-ray bright, optically normal galaxies" (XBONGs). Analysis of the relationship between optical emission lines and X-ray properties shows that the completeness of optical AGN selection suffers from dependence on the star formation rate and the quality of observed spectra. It also shows that XBONGs do not appear to be a physically distinct population from other X-ray detected, emission-line AGNs. On the other hand, X-ray AGN selection also has strong bias. About 2/3 of all emission-line AGNs at L bol > 1044 erg s-1 in our sample are not detected in our 200 ks Chandra images, most likely due to moderate or heavy absorption by gas near the AGN. The 2-7 keV detection rate of Seyfert 2s at z ~ 0.6 suggests that their column density distribution and Compton-thick fraction are similar to that of local Seyferts. Multiple sample selection techniques are needed to obtain as complete a sample as possible.

Systematic review of the methodological quality of controlled trials evaluating Chinese herbal medicine in patients with rheumatoid arthritis.

PubMed

Pan, Xin; Lopez-Olivo, Maria A; Song, Juhee; Pratt, Gregory; Suarez-Almazor, Maria E

2017-03-01

We appraised the methodological and reporting quality of randomised controlled clinical trials (RCTs) evaluating the efficacy and safety of Chinese herbal medicine (CHM) in patients with rheumatoid arthritis (RA). For this systematic review, electronic databases were searched from inception until June 2015. The search was limited to humans and non-case report studies, but was not limited by language, year of publication or type of publication. Two independent reviewers selected RCTs, evaluating CHM in RA (herbals and decoctions). Descriptive statistics were used to report on risk of bias and their adherence to reporting standards. Multivariable logistic regression analysis was performed to determine study characteristics associated with high or unclear risk of bias. Out of 2342 unique citations, we selected 119 RCTs including 18 919 patients: 10 108 patients received CHM alone and 6550 received one of 11 treatment combinations. A high risk of bias was observed across all domains: 21% had a high risk for selection bias (11% from sequence generation and 30% from allocation concealment), 85% for performance bias, 89% for detection bias, 4% for attrition bias and 40% for reporting bias. In multivariable analysis, fewer authors were associated with selection bias (allocation concealment), performance bias and attrition bias, and earlier year of publication and funding source not reported or disclosed were associated with selection bias (sequence generation). Studies published in non-English language were associated with reporting bias. Poor adherence to recommended reporting standards (<60% of the studies not providing sufficient information) was observed in 11 of the 23 sections evaluated. Study quality and data extraction were performed by one reviewer and cross-checked by a second reviewer. Translation to English was performed by one reviewer in 85% of the included studies. Studies evaluating CHM often fail to meet expected methodological criteria, and high-quality evidence is lacking. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Test Bias in the Intermediate Mental Alertness, Mechanical Comprehension, Blox and High Level Figure Classification Tests. An NTB/HSRC Report.

ERIC Educational Resources Information Center

Holburn, P. T.

Research is reported on four tests commonly used in South Africa to select apprentices, the Intermediate Mental Alertness Test, the High Level Figure Classification Test, the Blox Test, and the Mechanical Comprehension Test. Samples were as follows: (1) 206 Asian, 208 Black, 102 Coloured, and 99 White mostly male applicants for sugar industry…

Reflections on Heckman and Pinto’s Causal Analysis After Haavelmo

DTIC Science & Technology

2013-11-01

Econometric Analysis , Cambridge University Press, 477–490, 1995. Halpern, J. (1998). Axiomatizing causal reasoning. In Uncertainty in Artificial...Models, Structural Models and Econometric Policy Evaluation. Elsevier B.V., Amsterdam, 4779–4874. Heckman, J. J. (1979). Sample selection bias as a...Reflections on Heckman and Pinto’s “Causal Analysis After Haavelmo” Judea Pearl University of California, Los Angeles Computer Science Department Los

Electroacupuncture for Tinnitus: A Systematic Review

PubMed Central

Liu, Yang; Zhong, Juan; Jiang, Luyun; Liu, Ying; Chen, Qing; Xie, Yan; Zhang, Qinxiu

2016-01-01

Background Treatment effects of electroacupuncture for patients with subjective tinnitus has yet to be clarified. Objectives To assess the effect of electroacupuncutre for alleviating the symptoms of subjective tinnitus. Methods Extensive literature searches were carried out in three English and four Chinese databases (PubMed, EMBASE, Cochrane Library, CNKI, Wanfang Chinese Digital Periodical and Conference Database, VIP, and ChiCTR).The date of the most recent search was 1 June 2014. Randomized controlled trials (RCTs) or quasi-RCTs were included. The titles, abstracts, and keywords of all records were reviewed by two authors independently. The data were collected and extracted by three authors. The risk of bias in the trials was assessed in accordance with the Cochrane Handbook, version 5.1.0. (http://www.handbook.cochrane.org). Eighty-nine studies were retrieved. After discarding 84 articles, five studies with 322 participants were identified. Assessment of the methodological quality of the studies identified weaknesses in all five studies. All studies were judged as having a high risk of selection and performance bias. The attrition bias was high in four studies. Incompleteness bias was low in all studies. Reporting bias was unclear in all studies. Because of the limited number of trials included and the various types of interventions and outcomes, we were unable to conduct pooled analyses. Conclusions Due to the poor methodological quality of the primary studies and the small sample sizes, no convincing evidence that electroacupuncture is beneficial for treating tinnitus could be found. There is an urgent need for more high-quality trials with large sample sizes for the investigation of electroacupuncture treatment for tinnitus. PMID:26938213

Negatively-Biased Credulity and the Cultural Evolution of Beliefs

PubMed Central

Fessler, Daniel M. T.; Pisor, Anne C.; Navarrete, Carlos David

2014-01-01

The functions of cultural beliefs are often opaque to those who hold them. Accordingly, to benefit from cultural evolution’s ability to solve complex adaptive problems, learners must be credulous. However, credulity entails costs, including susceptibility to exploitation, and effort wasted due to false beliefs. One determinant of the optimal level of credulity is the ratio between the costs of two types of errors: erroneous incredulity (failing to believe information that is true) and erroneous credulity (believing information that is false). This ratio can be expected to be asymmetric when information concerns hazards, as the costs of erroneous incredulity will, on average, exceed the costs of erroneous credulity; no equivalent asymmetry characterizes information concerning benefits. Natural selection can therefore be expected to have crafted learners’ minds so as to be more credulous toward information concerning hazards. This negatively-biased credulity extends general negativity bias, the adaptive tendency for negative events to be more salient than positive events. Together, these biases constitute attractors that should shape cultural evolution via the aggregated effects of learners’ differential retention and transmission of information. In two studies in the U.S., we demonstrate the existence of negatively-biased credulity, and show that it is most pronounced in those who believe the world to be dangerous, individuals who may constitute important nodes in cultural transmission networks. We then document the predicted imbalance in cultural content using a sample of urban legends collected from the Internet and a sample of supernatural beliefs obtained from ethnographies of a representative collection of the world’s cultures, showing that beliefs about hazards predominate in both. PMID:24736596

Negatively-biased credulity and the cultural evolution of beliefs.

PubMed

Fessler, Daniel M T; Pisor, Anne C; Navarrete, Carlos David

2014-01-01

The functions of cultural beliefs are often opaque to those who hold them. Accordingly, to benefit from cultural evolution's ability to solve complex adaptive problems, learners must be credulous. However, credulity entails costs, including susceptibility to exploitation, and effort wasted due to false beliefs. One determinant of the optimal level of credulity is the ratio between the costs of two types of errors: erroneous incredulity (failing to believe information that is true) and erroneous credulity (believing information that is false). This ratio can be expected to be asymmetric when information concerns hazards, as the costs of erroneous incredulity will, on average, exceed the costs of erroneous credulity; no equivalent asymmetry characterizes information concerning benefits. Natural selection can therefore be expected to have crafted learners' minds so as to be more credulous toward information concerning hazards. This negatively-biased credulity extends general negativity bias, the adaptive tendency for negative events to be more salient than positive events. Together, these biases constitute attractors that should shape cultural evolution via the aggregated effects of learners' differential retention and transmission of information. In two studies in the U.S., we demonstrate the existence of negatively-biased credulity, and show that it is most pronounced in those who believe the world to be dangerous, individuals who may constitute important nodes in cultural transmission networks. We then document the predicted imbalance in cultural content using a sample of urban legends collected from the Internet and a sample of supernatural beliefs obtained from ethnographies of a representative collection of the world's cultures, showing that beliefs about hazards predominate in both.

Cultural differences in athlete attributions for success and failure: the sports pages revisited.

PubMed

Aldridge, Lynley J; Islam, Mir Rabiul

2012-01-01

Self-serving biases in attribution, while found with relative consistency in research with Western samples, have rarely been found in Japanese samples typically recruited for research. However, research conducted with Japanese participants to date has tended to use forced-choice and/or reactive paradigms, with school or university students, focusing mainly on academic performance or arbitrary and/or researcher-selected tasks. This archival study explored whether self-serving attributional biases would be shown in the real-life attributions for sporting performance made by elite Olympic athletes from Japan and Australia. Attributions (N = 216) were extracted from the sports pages of Japanese and Australian newspapers and rated by Australian judges for locus and controllability. It was hypothesized that Australian, but not Japanese, athletes would show self-serving biases such that they attributed wins to causes more internal and controllable than the causes to which they attributed losses. Contrary to predictions, self-serving biases were shown to at least some extent by athletes of both nationalities. Both Australian and Japanese men attributed wins to causes more internal than those to which they attributed losses. Women, however, attributed wins and losses to causes that did not differ significantly in terms of locus. All athletes tended to attribute wins to causes that were more controllable than the causes to which losses were attributed. Results are inconsistent with a large body of research suggesting that Japanese do not show self-serving biases in attribution, and are discussed in the light of differences in methodology, context, and participants that may have contributed to these effects.

Syzygies, Pluricanonical Maps, and the Birational Geometry of Varieties of Maximal Albanese Dimension

NASA Astrophysics Data System (ADS)

Tesfagiorgis, Kibrewossen B.

Satellite Precipitation Estimates (SPEs) may be the only available source of information for operational hydrologic and flash flood prediction due to spatial limitations of radar and gauge products in mountainous regions. The present work develops an approach to seamlessly blend satellite, available radar, climatological and gauge precipitation products to fill gaps in ground-based radar precipitation field. To mix different precipitation products, the error of any of the products relative to each other should be removed. For bias correction, the study uses a new ensemble-based method which aims to estimate spatially varying multiplicative biases in SPEs using a radar-gauge precipitation product. Bias factors were calculated for a randomly selected sample of rainy pixels in the study area. Spatial fields of estimated bias were generated taking into account spatial variation and random errors in the sampled values. In addition to biases, sometimes there is also spatial error between the radar and satellite precipitation estimates; one of them has to be geometrically corrected with reference to the other. A set of corresponding raining points between SPE and radar products are selected to apply linear registration using a regularized least square technique to minimize the dislocation error in SPEs with respect to available radar products. A weighted Successive Correction Method (SCM) is used to make the merging between error corrected satellite and radar precipitation estimates. In addition to SCM, we use a combination of SCM and Bayesian spatial method for merging the rain gauges and climatological precipitation sources with radar and SPEs. We demonstrated the method using two satellite-based, CPC Morphing (CMORPH) and Hydro-Estimator (HE), two radar-gauge based, Stage-II and ST-IV, a climatological product PRISM and rain gauge dataset for several rain events from 2006 to 2008 over different geographical locations of the United States. Results show that: (a) the method of ensembles helped reduce biases in SPEs significantly; (b) the SCM method in combination with the Bayesian spatial model produced a precipitation product in good agreement with independent measurements .The study implies that using the available radar pixels surrounding the gap area, rain gauge, PRISM and satellite products, a radar like product is achievable over radar gap areas that benefits the operational meteorology and hydrology community.

Validating Analytical Protocols to Determine Selected Pesticides and PCBs Using Routine Samples.

PubMed

Pindado Jiménez, Oscar; García Alonso, Susana; Pérez Pastor, Rosa María

2017-01-01

This study aims at providing recommendations concerning the validation of analytical protocols by using routine samples. It is intended to provide a case-study on how to validate the analytical methods in different environmental matrices. In order to analyze the selected compounds (pesticides and polychlorinated biphenyls) in two different environmental matrices, the current work has performed and validated two analytical procedures by GC-MS. A description is given of the validation of the two protocols by the analysis of more than 30 samples of water and sediments collected along nine months. The present work also scopes the uncertainty associated with both analytical protocols. In detail, uncertainty of water sample was performed through a conventional approach. However, for the sediments matrices, the estimation of proportional/constant bias is also included due to its inhomogeneity. Results for the sediment matrix are reliable, showing a range 25-35% of analytical variability associated with intermediate conditions. The analytical methodology for the water matrix determines the selected compounds with acceptable recoveries and the combined uncertainty ranges between 20 and 30%. Analyzing routine samples is rarely applied to assess trueness of novel analytical methods and up to now this methodology was not focused on organochlorine compounds in environmental matrices.

Meta-regression approximations to reduce publication selection bias.

PubMed

Stanley, T D; Doucouliagos, Hristos

2014-03-01

Publication selection bias is a serious challenge to the integrity of all empirical sciences. We derive meta-regression approximations to reduce this bias. Our approach employs Taylor polynomial approximations to the conditional mean of a truncated distribution. A quadratic approximation without a linear term, precision-effect estimate with standard error (PEESE), is shown to have the smallest bias and mean squared error in most cases and to outperform conventional meta-analysis estimators, often by a great deal. Monte Carlo simulations also demonstrate how a new hybrid estimator that conditionally combines PEESE and the Egger regression intercept can provide a practical solution to publication selection bias. PEESE is easily expanded to accommodate systematic heterogeneity along with complex and differential publication selection bias that is related to moderator variables. By providing an intuitive reason for these approximations, we can also explain why the Egger regression works so well and when it does not. These meta-regression methods are applied to several policy-relevant areas of research including antidepressant effectiveness, the value of a statistical life, the minimum wage, and nicotine replacement therapy. Copyright © 2013 John Wiley & Sons, Ltd.

Consistently Showing Your Best Side? Intra-individual Consistency in #Selfie Pose Orientation

PubMed Central

Lindell, Annukka K.

2017-01-01

Painted and photographic portraits of others show an asymmetric bias: people favor their left cheek. Both experimental and database studies confirm that the left cheek bias extends to selfies. To date all such selfie studies have been cross-sectional; whether individual selfie-takers tend to consistently favor the same pose orientation, or switch between multiple poses, remains to be determined. The present study thus examined intra-individual consistency in selfie pose orientations. Two hundred selfie-taking participants (100 male and 100 female) were identified by searching #selfie on Instagram. The most recent 10 single-subject selfies for the each of the participants were selected and coded for type of selfie (normal; mirror) and pose orientation (left, midline, right), resulting in a sample of 2000 selfies. Results indicated that selfie-takers do tend to consistently adopt a preferred pose orientation (α = 0.72), with more participants showing an overall left cheek bias (41%) than would be expected by chance (overall right cheek bias = 31.5%; overall midline bias = 19.5%; no overall bias = 8%). Logistic regression modellng, controlling for the repeated measure of participant identity, indicated that sex did not affect pose orientation. However, selfie type proved a significant predictor when comparing left and right cheek poses, with a stronger left cheek bias for mirror than normal selfies. Overall, these novel findings indicate that selfie-takers show intra-individual consistency in pose orientation, and in addition, replicate the previously reported left cheek bias for selfies and other types of portrait, confirming that the left cheek bias also presents within individuals’ selfie corpora. PMID:28270790

The late Neandertal supraorbital fossils from Vindija Cave, Croatia: a biased sample?

PubMed

Ahern, James C M; Lee, Sang-Hee; Hawks, John D

2002-09-01

The late Neandertal sample from Vindija (Croatia) has been described as transitional between the earlier Central European Neandertals from Krapina (Croatia) and modern humans. However, the morphological differences indicating this transition may rather be the result of different sex and/or age compositions between the samples. This study tests the hypothesis that the metric differences between the Krapina and Vindija supraorbital samples are due to sampling bias. We focus upon the supraorbital region because past studies have posited this region as particularly indicative of the Vindija sample's transitional nature. Furthermore, the supraorbital region varies significantly with both age and sex. We analyzed four chords and two derived indices of supraorbital torus form as defined by Smith & Ranyard (1980, Am. J. phys. Anthrop.93, pp. 589-610). For each variable, we analyzed relative sample bias of the Krapina and Vindija samples using three sampling methods. In order to test the hypothesis that the Vindija sample contains an over-representation of females and/or young while the Krapina sample is normal or also female/young biased, we determined the probability of drawing a sample of the same size as and with a mean equal to or less than Vindija's from a Krapina-based population. In order to test the hypothesis that the Vindija sample is female/young biased while the Krapina sample is male/old biased, we determined the probability of drawing a sample of the same size as and with a mean equal or less than Vindija's from a generated population whose mean is halfway between Krapina's and Vindija's. Finally, in order to test the hypothesis that the Vindija sample is normal while the Krapina sample contains an over-representation of males and/or old, we determined the probability of drawing a sample of the same size as and with a mean equal to or greater than Krapina's from a Vindija-based population. Unless we assume that the Vindija sample is female/young and the Krapina sample is male/old biased, our results falsify the hypothesis that the metric differences between the Krapina and Vindija samples are due to sample bias.

Racial, gender, and socioeconomic status bias in senior medical student clinical decision-making: a national survey.

PubMed

Williams, Robert L; Romney, Crystal; Kano, Miria; Wright, Randy; Skipper, Betty; Getrich, Christina M; Sussman, Andrew L; Zyzanski, Stephen J

2015-06-01

Research suggests stereotyping by clinicians as one contributor to racial and gender-based health disparities. It is necessary to understand the origins of such biases before interventions can be developed to eliminate them. As a first step toward this understanding, we tested for the presence of bias in senior medical students. The purpose of the study was to determine whether bias based on race, gender, or socioeconomic status influenced clinical decision-making among medical students. We surveyed seniors at 84 medical schools, who were required to choose between two clinically equivalent management options for a set of cardiac patient vignettes. We examined variations in student recommendations based on patient race, gender, and socioeconomic status. The study included senior medical students. We investigated the percentage of students selecting cardiac procedural options for vignette patients, analyzed by patient race, gender, and socioeconomic status. Among 4,603 returned surveys, we found no evidence in the overall sample supporting racial or gender bias in student clinical decision-making. Students were slightly more likely to recommend cardiac procedural options for black (43.9 %) vs. white (42 %, p = .03) patients; there was no difference by patient gender. Patient socioeconomic status was the strongest predictor of student recommendations, with patients described as having the highest socioeconomic status most likely to receive procedural care recommendations (50.3 % vs. 43.2 % for those in the lowest socioeconomic status group, p < .001). Analysis by subgroup, however, showed significant regional geographic variation in the influence of patient race and gender on decision-making. Multilevel analysis showed that white female patients were least likely to receive procedural recommendations. In the sample as a whole, we found no evidence of racial or gender bias in student clinical decision-making. However, we did find evidence of bias with regard to the influence of patient socioeconomic status, geographic variations, and the influence of interactions between patient race and gender on student recommendations.

Effects of sample size on estimates of population growth rates calculated with matrix models.

PubMed

Fiske, Ian J; Bruna, Emilio M; Bolker, Benjamin M

2008-08-28

Matrix models are widely used to study the dynamics and demography of populations. An important but overlooked issue is how the number of individuals sampled influences estimates of the population growth rate (lambda) calculated with matrix models. Even unbiased estimates of vital rates do not ensure unbiased estimates of lambda-Jensen's Inequality implies that even when the estimates of the vital rates are accurate, small sample sizes lead to biased estimates of lambda due to increased sampling variance. We investigated if sampling variability and the distribution of sampling effort among size classes lead to biases in estimates of lambda. Using data from a long-term field study of plant demography, we simulated the effects of sampling variance by drawing vital rates and calculating lambda for increasingly larger populations drawn from a total population of 3842 plants. We then compared these estimates of lambda with those based on the entire population and calculated the resulting bias. Finally, we conducted a review of the literature to determine the sample sizes typically used when parameterizing matrix models used to study plant demography. We found significant bias at small sample sizes when survival was low (survival = 0.5), and that sampling with a more-realistic inverse J-shaped population structure exacerbated this bias. However our simulations also demonstrate that these biases rapidly become negligible with increasing sample sizes or as survival increases. For many of the sample sizes used in demographic studies, matrix models are probably robust to the biases resulting from sampling variance of vital rates. However, this conclusion may depend on the structure of populations or the distribution of sampling effort in ways that are unexplored. We suggest more intensive sampling of populations when individual survival is low and greater sampling of stages with high elasticities.

Image subsampling and point scoring approaches for large-scale marine benthic monitoring programs

NASA Astrophysics Data System (ADS)

Perkins, Nicholas R.; Foster, Scott D.; Hill, Nicole A.; Barrett, Neville S.

2016-07-01

Benthic imagery is an effective tool for quantitative description of ecologically and economically important benthic habitats and biota. The recent development of autonomous underwater vehicles (AUVs) allows surveying of spatial scales that were previously unfeasible. However, an AUV collects a large number of images, the scoring of which is time and labour intensive. There is a need to optimise the way that subsamples of imagery are chosen and scored to gain meaningful inferences for ecological monitoring studies. We examine the trade-off between the number of images selected within transects and the number of random points scored within images on the percent cover of target biota, the typical output of such monitoring programs. We also investigate the efficacy of various image selection approaches, such as systematic or random, on the bias and precision of cover estimates. We use simulated biotas that have varying size, abundance and distributional patterns. We find that a relatively small sampling effort is required to minimise bias. An increased precision for groups that are likely to be the focus of monitoring programs is best gained through increasing the number of images sampled rather than the number of points scored within images. For rare species, sampling using point count approaches is unlikely to provide sufficient precision, and alternative sampling approaches may need to be employed. The approach by which images are selected (simple random sampling, regularly spaced etc.) had no discernible effect on mean and variance estimates, regardless of the distributional pattern of biota. Field validation of our findings is provided through Monte Carlo resampling analysis of a previously scored benthic survey from temperate waters. We show that point count sampling approaches are capable of providing relatively precise cover estimates for candidate groups that are not overly rare. The amount of sampling required, in terms of both the number of images and number of points, varies with the abundance, size and distributional pattern of target biota. Therefore, we advocate either the incorporation of prior knowledge or the use of baseline surveys to establish key properties of intended target biota in the initial stages of monitoring programs.

Addressing care-seeking as well as insurance-seeking selection biases in estimating the impact of health insurance on out-of-pocket expenditure.

PubMed

Ali, Shehzad; Cookson, Richard; Dusheiko, Mark

2017-03-01

Health Insurance (HI) programmes in low-income countries aim to reduce the burden of out-of-pocket (OOP) health care expenditure. However, if the decisions to purchase insurance and to seek care when ill are correlated with the expected health care expenditure, the use of naïve regression models may produce biased estimates of the impact of insurance membership on OOP expenditure. Whilst many studies in the literature have accounted for the endogeneity of the insurance decision, the potential selection bias due to the care-seeking decision has not been taken into account. We extend the Heckman selection model to account simultaneously for both care-seeking and insurance-seeking selection biases in the health care expenditure regression model. The proposed model is illustrated in the context of a Vietnamese HI programme using data from a household survey of 1,192 individuals conducted in 1999. Results were compared with those of alternative econometric models making no or partial allowance for selection bias. In this illustrative example, the impact of insurance membership on reducing OOP expenditures was underestimated by 21 percentage points when selection biases were not taken into account. We believe this is an important methodological contribution that will be relevant to future empirical work. Copyright © 2016 Elsevier Ltd. All rights reserved.

Alcohol-related biases in selective attention and action tendency make distinct contributions to dysregulated drinking behaviour.

PubMed

Sharbanee, Jason M; Stritzke, Werner G K; Wiers, Reinout W; MacLeod, Colin

2013-10-01

To assess whether alcohol-related biases in selective-attention and action tendency uniquely or concurrently predict the ability to regulate alcohol consumption. Two groups of undergraduate social drinkers (total n = 55) who differed in their ability to regulate their alcohol consumption completed a novel Selective-Attention/Action-Tendency Task (SA/ATT), which assessed separately alcohol-related biases in selective attention and action tendency. University of Western Australia, Australia. Dysregulated drinking was operationalized as a self-reported high level of alcohol consumption on the Alcohol Consumption Questionnaire, and a high desire to reduce consumption on the Brief Readiness to Change Algorithm. Selective attention and action tendency were assessed using the SA/ATT, working memory was assessed using the operation-span task and participant characteristics were assessed using the Alcohol Use Disorders Identification Test (AUDIT) and Stages of Change Readiness and Treatment Eagerness Scale (SOCRATES). Results indicated that (i) there was no significant association between alcohol-related biases in selective attention and action tendency, r = 0.16, P = 0.274, and (ii) biases towards alcohol, in both selective attention, β = 1.01, odds ratio = 2.74, P = 0.022, and action tendency, β = 1.24, odds ratio = 3.45, P = 0.015, predicted independent variance in dysregulated-drinker status. Biases in selective attention and action tendency appear to be distinct mechanisms that contribute independently to difficulty regulating alcohol consumption. Treatment components that could be combined to target both mechanisms could enhance treatment outcomes for alcohol-use disorders. © 2013 Society for the Study of Addiction.

Selection and response bias as determinants of priming of pop-out search: Revelations from diffusion modeling.

PubMed

Burnham, Bryan R

2018-05-03

During visual search, both top-down factors and bottom-up properties contribute to the guidance of visual attention, but selection history can influence attention independent of bottom-up and top-down factors. For example, priming of pop-out (PoP) is the finding that search for a singleton target is faster when the target and distractor features repeat than when those features trade roles between trials. Studies have suggested that such priming (selection history) effects on pop-out search manifest either early, by biasing the selection of the preceding target feature, or later in processing, by facilitating response and target retrieval processes. The present study was designed to examine the influence of selection history on pop-out search by introducing a speed-accuracy trade-off manipulation in a pop-out search task. Ratcliff diffusion modeling (RDM) was used to examine how selection history influenced both attentional bias and response execution processes. The results support the hypothesis that selection history biases attention toward the preceding target's features on the current trial and also influences selection of the response to the target.

Field methods in medical record abstraction: assessing the properties of comparative effectiveness estimates.

PubMed

Cook, Elizabeth A; Schneider, Kathleen M; Robinson, Jennifer; Wilwert, June; Chrischilles, Elizabeth; Pendergast, Jane; Brooks, John

2014-09-15

Comparative effectiveness studies using Medicare claims data are vulnerable to treatment selection biases and supplemental data from a sample of patients has been recommended for examining the magnitude of this bias. Previous research using nationwide Medicare claims data has typically relied on the Medicare Current Beneficiary Survey (MCBS) for supplemental data. Because many important clinical variables for our specific research question are not available in the MCBS, we collected medical record data from a subsample of patients to assess the validity of assumptions and to aid in the interpretation of our estimates. This paper seeks to describe and document the process used to collect and validate this supplemental information. Medicare claims data files for all patients with fee-for-service Medicare benefits who had an acute myocardial infarction (AMI) in 2007 or 2008 were obtained. Medical records were obtained and abstracted for a stratified subsample of 1,601 of these patients, using strata defined by claims-based measures of physician prescribing practices and drug treatment combinations. The abstraction tool was developed collaboratively by study clinicians and researchers, leveraging important elements from previously validated tools. Records for 2,707 AMI patients were requested from the admitting hospitals and 1,751 were received for an overall response rate of 65%; 1,601 cases were abstracted by trained personnel at a contracted firm. Data were collected with overall 96% inter-abstractor agreement across all variables. Some non-response bias was detected at the patient and facility level. Although Medicare claims data are a potentially powerful resource for conducting comparative effectiveness analyses, observational databases are vulnerable to treatment selection biases. This study demonstrates that it is feasible to abstract medical records for Medicare patients nationwide and collect high quality data, to design the sampling purposively to address specific research questions, and to more thoroughly evaluate the appropriateness of care delivered to AMI patients.

[Epidemiology of atherogenic dyslipidemia in an urban area of the city of Barcelona].

PubMed

Caballero Sarmiento, Rafael

2014-01-01

We performed a descriptive cross-sectional epidemiological study data on lipid profile and blood glucose of sample collected in 2021 consecutive and anonymous patients. We calculated the prevalence of atherogenic dyslipidemia by sex, according to several cutoff HDL cholesterol in women, and in the whole sample, and its association with diabetes. There is in the study selection bias, as it is performed in patients attending in a Primary Care Laboratory and not in a sample of the general population. Prevalence epidemiological data are therefore approximate and provisional. Copyright © 2013 Elsevier España, S.L. y SEA. All rights reserved.

Methodological considerations in using complex survey data: an applied example with the Head Start Family and Child Experiences Survey.

PubMed

Hahs-Vaughn, Debbie L; McWayne, Christine M; Bulotsky-Shearer, Rebecca J; Wen, Xiaoli; Faria, Ann-Marie

2011-06-01

Complex survey data are collected by means other than simple random samples. This creates two analytical issues: nonindependence and unequal selection probability. Failing to address these issues results in underestimated standard errors and biased parameter estimates. Using data from the nationally representative Head Start Family and Child Experiences Survey (FACES; 1997 and 2000 cohorts), three diverse multilevel models are presented that illustrate differences in results depending on addressing or ignoring the complex sampling issues. Limitations of using complex survey data are reported, along with recommendations for reporting complex sample results. © The Author(s) 2011

Gear and seasonal bias associated with abundance and size structure estimates for lentic freshwater fishes

USGS Publications Warehouse

Fischer, Jesse R.; Quist, Michael C.

2014-01-01

All freshwater fish sampling methods are biased toward particular species, sizes, and sexes and are further influenced by season, habitat, and fish behavior changes over time. However, little is known about gear-specific biases for many common fish species because few multiple-gear comparison studies exist that have incorporated seasonal dynamics. We sampled six lakes and impoundments representing a diversity of trophic and physical conditions in Iowa, USA, using multiple gear types (i.e., standard modified fyke net, mini-modified fyke net, sinking experimental gill net, bag seine, benthic trawl, boat-mounted electrofisher used diurnally and nocturnally) to determine the influence of sampling methodology and season on fisheries assessments. Specifically, we describe the influence of season on catch per unit effort, proportional size distribution, and the number of samples required to obtain 125 stock-length individuals for 12 species of recreational and ecological importance. Mean catch per unit effort generally peaked in the spring and fall as a result of increased sampling effectiveness in shallow areas and seasonal changes in habitat use (e.g., movement offshore during summer). Mean proportional size distribution decreased from spring to fall for white bass Morone chrysops, largemouth bass Micropterus salmoides, bluegill Lepomis macrochirus, and black crappie Pomoxis nigromaculatus, suggesting selectivity for large and presumably sexually mature individuals in the spring and summer. Overall, the mean number of samples required to sample 125 stock-length individuals was minimized in the fall with sinking experimental gill nets, a boat-mounted electrofisher used at night, and standard modified nets for 11 of the 12 species evaluated. Our results provide fisheries scientists with relative comparisons between several recommended standard sampling methods and illustrate the effects of seasonal variation on estimates of population indices that will be critical to the future development of standardized sampling methods for freshwater fish in lentic ecosystems.

Exploring Selective Exposure and Confirmation Bias as Processes Underlying Employee Work Happiness: An Intervention Study.

PubMed

Williams, Paige; Kern, Margaret L; Waters, Lea

2016-01-01

Employee psychological capital (PsyCap), perceptions of organizational virtue (OV), and work happiness have been shown to be associated within and over time. This study examines selective exposure and confirmation bias as potential processes underlying PsyCap, OV, and work happiness associations. As part of a quasi-experimental study design, school staff (N = 69) completed surveys at three time points. After the first assessment, some staff (n = 51) completed a positive psychology training intervention. Results of descriptive statistics, correlation, and regression analyses on the intervention group provide some support for selective exposure and confirmation bias as explanatory mechanisms. In focusing on the processes through which employee attitudes may influence work happiness this study advances theoretical understanding, specifically of selective exposure and confirmation bias in a field study context.

Apparatus for transporting hazardous materials

DOEpatents

Osterman, Robert A.; Cox, Robert

1992-01-01

An apparatus and method are provided for selectively receiving, transporting, and releasing one or more radioactive or other hazardous samples for analysis on a differential thermal analysis (DTA) apparatus. The apparatus includes a portable sample transporting apparatus for storing and transporting the samples and includes a support assembly for supporting the transporting apparatus when a sample is transferred to the DTA apparatus. The transporting apparatus includes a storage member which includes a plurality of storage chambers arrayed circumferentially with respect to a central axis. An adjustable top door is located on the top side of the storage member, and the top door includes a channel capable of being selectively placed in registration with the respective storage chambers thereby permitting the samples to selectively enter the respective storage chambers. The top door, when closed, isolates the respective samples within the storage chambers. A plurality of spring-biased bottom doors are located on the bottom sides of the respective storage chambers. The bottom doors isolate the samples in the respective storage chambers when the bottom doors are in the closed position. The bottom doors permit the samples to leave the respective storage chambers from the bottom side when the respective bottom doors are in respective open positions. The bottom doors permit the samples to be loaded into the respective storage chambers after the analysis for storage and transport to a permanent storage location.

GENERALITY OF THE MATCHING LAW AS A DESCRIPTOR OF SHOT SELECTION IN BASKETBALL

PubMed Central

Alferink, Larry A; Critchfield, Thomas S; Hitt, Jennifer L; Higgins, William J

2009-01-01

Based on a small sample of highly successful teams, past studies suggested that shot selection (two- vs. three-point field goals) in basketball corresponds to predictions of the generalized matching law. We examined the generality of this finding by evaluating shot selection of college (Study 1) and professional (Study 3) players. The matching law accounted for the majority of variance in shot selection, with undermatching and a bias for taking three-point shots. Shot-selection matching varied systematically for players who (a) were members of successful versus unsuccessful teams, (b) competed at different levels of collegiate play, and (c) served as regulars versus substitutes (Study 2). These findings suggest that the matching law is a robust descriptor of basketball shot selection, although the mechanism that produces matching is unknown. PMID:20190921

Sex Bias in Research Design.

ERIC Educational Resources Information Center

Grady, Kathleen E.

1981-01-01

Presents feminist criticisms of selected aspects of research methods in psychology. Reviews data relevant to sex bias in topic selection, subject selection and single-sex designs, operationalization of variables, testing for sex differences, and interpretation of results. Suggestions for achieving more "sex fair" research methods are discussed.…

Artificial selection on ant female caste ratio uncovers a link between female-biased sex ratios and infection by Wolbachia endosymbionts.

PubMed

Pontieri, L; Schmidt, A M; Singh, R; Pedersen, J S; Linksvayer, T A

2017-02-01

Social insect sex and caste ratios are well-studied targets of evolutionary conflicts, but the heritable factors affecting these traits remain unknown. To elucidate these factors, we carried out a short-term artificial selection study on female caste ratio in the ant Monomorium pharaonis. Across three generations of bidirectional selection, we observed no response for caste ratio, but sex ratios rapidly became more female-biased in the two replicate high selection lines and less female-biased in the two replicate low selection lines. We hypothesized that this rapid divergence for sex ratio was caused by changes in the frequency of infection by the heritable bacterial endosymbiont Wolbachia, because the initial breeding stock varied for Wolbachia infection, and Wolbachia is known to cause female-biased sex ratios in other insects. Consistent with this hypothesis, the proportions of Wolbachia-infected colonies in the selection lines changed rapidly, mirroring the sex ratio changes. Moreover, the estimated effect of Wolbachia on sex ratio (~13% female bias) was similar in colonies before and during artificial selection, indicating that this Wolbachia effect is likely independent of the effects of artificial selection on other heritable factors. Our study provides evidence for the first case of endosymbiont sex ratio manipulation in a social insect. © 2016 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2016 European Society For Evolutionary Biology.

Guidelines for reporting methodological challenges and evaluating potential bias in dementia research

PubMed Central

Weuve, Jennifer; Proust-Lima, Cécile; Power, Melinda C.; Gross, Alden L.; Hofer, Scott M.; Thiébaut, Rodolphe; Chêne, Geneviève; Glymour, M. Maria; Dufouil, Carole

2015-01-01

Clinical and population research on dementia and related neurologic conditions, including Alzheimer’s disease, faces several unique methodological challenges. Progress to identify preventive and therapeutic strategies rests on valid and rigorous analytic approaches, but the research literature reflects little consensus on “best practices.” We present findings from a large scientific working group on research methods for clinical and population studies of dementia, which identified five categories of methodological challenges as follows: (1) attrition/sample selection, including selective survival; (2) measurement, including uncertainty in diagnostic criteria, measurement error in neuropsychological assessments, and practice or retest effects; (3) specification of longitudinal models when participants are followed for months, years, or even decades; (4) time-varying measurements; and (5) high-dimensional data. We explain why each challenge is important in dementia research and how it could compromise the translation of research findings into effective prevention or care strategies. We advance a checklist of potential sources of bias that should be routinely addressed when reporting dementia research. PMID:26397878

Quantifying the impact of selection bias caused by nonparticipation in a case-control study of mobile phone use.

PubMed

Vrijheid, Martine; Richardson, Lesley; Armstrong, Bruce K; Auvinen, Anssi; Berg, Gabriele; Carroll, Matthew; Chetrit, Angela; Deltour, Isabelle; Feychting, Maria; Giles, Graham G; Hours, Martine; Iavarone, Ivano; Lagorio, Susanna; Lönn, Stefan; McBride, Mary; Parent, Marie-Elise; Sadetzki, Siegal; Salminen, Tina; Sanchez, Marie; Schlehofer, Birgitte; Schüz, Joachim; Siemiatycki, Jack; Tynes, Tore; Woodward, Alistair; Yamaguchi, Naohito; Cardis, Elisabeth

2009-01-01

To quantitatively assess the impact of selection bias caused by nonparticipation in a multinational case-control study of mobile phone use and brain tumor. Non-response questionnaires (NRQ) were completed by a sub-set of nonparticipants. Selection bias factors were calculated based on the prevalence of mobile phone use reported by nonparticipants with NRQ data, and on scenarios of hypothetical exposure prevalence for other nonparticipants. Regular mobile phone use was reported less frequently by controls and cases who completed the NRQ (controls, 56%; cases, 50%) than by those who completed the full interview (controls, 69%; cases, 66%). This relationship was consistent across study centers, sex, and age groups. Lower education and more recent start of mobile phone use were associated with refusal to participate. Bias factors varied between 0.87 and 0.92 in the most plausible scenarios. Refusal to participate in brain tumor case-control studies seems to be related to less prevalent use of mobile phones, and this could result in a downward bias of around 10% in odds ratios for regular mobile phone use. The use of simple selection bias estimation methods in case-control studies can give important insights into the extent of any bias, even when nonparticipant information is incomplete.

Prevalence of HIV among Aboriginal and Torres Strait Islander Australians: a systematic review and meta-analysis.

PubMed

Graham, Simon; O'Connor, Catherine C; Morgan, Stephen; Chamberlain, Catherine; Hocking, Jane

2017-06-01

Background Aboriginal and Torres Strait Islanders (Aboriginal) are Australia's first peoples. Between 2006 and 2015, HIV notifications increased among Aboriginal people; however, among non-Aboriginal people, notifications remained relatively stable. This systematic review and meta-analysis aims to examine the prevalence of HIV among Aboriginal people overall and by subgroups. In November 2015, a search of PubMed and Web of Science, grey literature and abstracts from conferences was conducted. A study was included if it reported the number of Aboriginal people tested and those who tested positive for HIV. The following variables were extracted: gender; Aboriginal status; population group (men who have sex with men, people who inject drugs, adults, youth in detention and pregnant females) and geographical location. An assessment of between study heterogeneity (I 2 test) and within study bias (selection, measurement and sample size) was also conducted. Seven studies were included; all were cross-sectional study designs. The overall sample size was 3772 and the prevalence of HIV was 0.1% (I 2 =38.3%, P=0.136). Five studies included convenient samples of people attending Australian Needle and Syringe Program Centres, clinics, hospitals and a youth detention centre, increasing the potential of selection bias. Four studies had a sample size, thus decreasing the ability to report pooled estimates. The prevalence of HIV among Aboriginal people in Australia is low. Community-based programs that include both prevention messages for those at risk of infection and culturally appropriate clinical management and support for Aboriginal people living with HIV are needed to prevent HIV increasing among Aboriginal people.

Neural Network and Nearest Neighbor Algorithms for Enhancing Sampling of Molecular Dynamics.

PubMed

Galvelis, Raimondas; Sugita, Yuji

2017-06-13

The free energy calculations of complex chemical and biological systems with molecular dynamics (MD) are inefficient due to multiple local minima separated by high-energy barriers. The minima can be escaped using an enhanced sampling method such as metadynamics, which apply bias (i.e., importance sampling) along a set of collective variables (CV), but the maximum number of CVs (or dimensions) is severely limited. We propose a high-dimensional bias potential method (NN2B) based on two machine learning algorithms: the nearest neighbor density estimator (NNDE) and the artificial neural network (ANN) for the bias potential approximation. The bias potential is constructed iteratively from short biased MD simulations accounting for correlation among CVs. Our method is capable of achieving ergodic sampling and calculating free energy of polypeptides with up to 8-dimensional bias potential.

A Monte-Carlo simulation analysis for evaluating the severity distribution functions (SDFs) calibration methodology and determining the minimum sample-size requirements.

PubMed

Shirazi, Mohammadali; Reddy Geedipally, Srinivas; Lord, Dominique

2017-01-01

Severity distribution functions (SDFs) are used in highway safety to estimate the severity of crashes and conduct different types of safety evaluations and analyses. Developing a new SDF is a difficult task and demands significant time and resources. To simplify the process, the Highway Safety Manual (HSM) has started to document SDF models for different types of facilities. As such, SDF models have recently been introduced for freeway and ramps in HSM addendum. However, since these functions or models are fitted and validated using data from a few selected number of states, they are required to be calibrated to the local conditions when applied to a new jurisdiction. The HSM provides a methodology to calibrate the models through a scalar calibration factor. However, the proposed methodology to calibrate SDFs was never validated through research. Furthermore, there are no concrete guidelines to select a reliable sample size. Using extensive simulation, this paper documents an analysis that examined the bias between the 'true' and 'estimated' calibration factors. It was indicated that as the value of the true calibration factor deviates further away from '1', more bias is observed between the 'true' and 'estimated' calibration factors. In addition, simulation studies were performed to determine the calibration sample size for various conditions. It was found that, as the average of the coefficient of variation (CV) of the 'KAB' and 'C' crashes increases, the analyst needs to collect a larger sample size to calibrate SDF models. Taking this observation into account, sample-size guidelines are proposed based on the average CV of crash severities that are used for the calibration process. Copyright © 2016 Elsevier Ltd. All rights reserved.

Comparison of Relative Bias, Precision, and Efficiency of Sampling Methods for Natural Enemies of Soybean Aphid (Hemiptera: Aphididae).

PubMed

Bannerman, J A; Costamagna, A C; McCornack, B P; Ragsdale, D W

2015-06-01

Generalist natural enemies play an important role in controlling soybean aphid, Aphis glycines (Hemiptera: Aphididae), in North America. Several sampling methods are used to monitor natural enemy populations in soybean, but there has been little work investigating their relative bias, precision, and efficiency. We compare five sampling methods: quadrats, whole-plant counts, sweep-netting, walking transects, and yellow sticky cards to determine the most practical methods for sampling the three most prominent species, which included Harmonia axyridis (Pallas), Coccinella septempunctata L. (Coleoptera: Coccinellidae), and Orius insidiosus (Say) (Hemiptera: Anthocoridae). We show an important time by sampling method interaction indicated by diverging community similarities within and between sampling methods as the growing season progressed. Similarly, correlations between sampling methods for the three most abundant species over multiple time periods indicated differences in relative bias between sampling methods and suggests that bias is not consistent throughout the growing season, particularly for sticky cards and whole-plant samples. Furthermore, we show that sticky cards produce strongly biased capture rates relative to the other four sampling methods. Precision and efficiency differed between sampling methods and sticky cards produced the most precise (but highly biased) results for adult natural enemies, while walking transects and whole-plant counts were the most efficient methods for detecting coccinellids and O. insidiosus, respectively. Based on bias, precision, and efficiency considerations, the most practical sampling methods for monitoring in soybean include walking transects for coccinellid detection and whole-plant counts for detection of small predators like O. insidiosus. Sweep-netting and quadrat samples are also useful for some applications, when efficiency is not paramount. © The Authors 2015. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

A method to correct sampling ghosts in historic near-infrared Fourier transform spectrometer (FTS) measurements

NASA Astrophysics Data System (ADS)

Dohe, S.; Sherlock, V.; Hase, F.; Gisi, M.; Robinson, J.; Sepúlveda, E.; Schneider, M.; Blumenstock, T.

2013-08-01

The Total Carbon Column Observing Network (TCCON) has been established to provide ground-based remote sensing measurements of the column-averaged dry air mole fractions (DMF) of key greenhouse gases. To ensure network-wide consistency, biases between Fourier transform spectrometers at different sites have to be well controlled. Errors in interferogram sampling can introduce significant biases in retrievals. In this study we investigate a two-step scheme to correct these errors. In the first step the laser sampling error (LSE) is estimated by determining the sampling shift which minimises the magnitude of the signal intensity in selected, fully absorbed regions of the solar spectrum. The LSE is estimated for every day with measurements which meet certain selection criteria to derive the site-specific time series of the LSEs. In the second step, this sequence of LSEs is used to resample all the interferograms acquired at the site, and hence correct the sampling errors. Measurements acquired at the Izaña and Lauder TCCON sites are used to demonstrate the method. At both sites the sampling error histories show changes in LSE due to instrument interventions (e.g. realignment). Estimated LSEs are in good agreement with sampling errors inferred from the ratio of primary and ghost spectral signatures in optically bandpass-limited tungsten lamp spectra acquired at Lauder. The original time series of Xair and XCO2 (XY: column-averaged DMF of the target gas Y) at both sites show discrepancies of 0.2-0.5% due to changes in the LSE associated with instrument interventions or changes in the measurement sample rate. After resampling, discrepancies are reduced to 0.1% or less at Lauder and 0.2% at Izaña. In the latter case, coincident changes in interferometer alignment may also have contributed to the residual difference. In the future the proposed method will be used to correct historical spectra at all TCCON sites.

Avoiding treatment bias of REDD+ monitoring by sampling with partial replacement.

PubMed

Köhl, Michael; Scott, Charles T; Lister, Andrew J; Demon, Inez; Plugge, Daniel

2015-12-01

Implementing REDD+ renders the development of a measurement, reporting and verification (MRV) system necessary to monitor carbon stock changes. MRV systems generally apply a combination of remote sensing techniques and in-situ field assessments. In-situ assessments can be based on 1) permanent plots, which are assessed on all successive occasions, 2) temporary plots, which are assessed only once, and 3) a combination of both. The current study focuses on in-situ assessments and addresses the effect of treatment bias, which is introduced by managing permanent sampling plots differently than the surrounding forests. Temporary plots are not subject to treatment bias, but are associated with large sampling errors and low cost-efficiency. Sampling with partial replacement (SPR) utilizes both permanent and temporary plots. We apply a scenario analysis with different intensities of deforestation and forest degradation to show that SPR combines cost-efficiency with the handling of treatment bias. Without treatment bias permanent plots generally provide lower sampling errors for change estimates than SPR and temporary plots, but do not provide reliable estimates, if treatment bias occurs, SPR allows for change estimates that are comparable to those provided by permanent plots, offers the flexibility to adjust sample sizes in the course of time, and allows to compare data on permanent versus temporary plots for detecting treatment bias. Equivalence of biomass or carbon stock estimates between permanent and temporary plots serves as an indication for the absence of treatment bias while differences suggest that there is evidence for treatment bias. SPR is a flexible tool for estimating emission factors from successive measurements. It does not entirely depend on sample plots that are installed at the first occasion but allows for the adjustment of sample sizes and placement of new plots at any occasion. This ensures that in-situ samples provide representative estimates over time. SPR offers the possibility to increase sampling intensity in areas with high degradation intensities or to establish new plots in areas where permanent plots are lost due to deforestation. SPR is also an ideal approach to mitigate concerns about treatment bias.

Characterizing sampling and quality screening biases in infrared and microwave limb sounding

NASA Astrophysics Data System (ADS)

Millán, Luis F.; Livesey, Nathaniel J.; Santee, Michelle L.; von Clarmann, Thomas

2018-03-01

This study investigates orbital sampling biases and evaluates the additional impact caused by data quality screening for the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) and the Aura Microwave Limb Sounder (MLS). MIPAS acts as a proxy for typical infrared limb emission sounders, while MLS acts as a proxy for microwave limb sounders. These biases were calculated for temperature and several trace gases by interpolating model fields to real sampling patterns and, additionally, screening those locations as directed by their corresponding quality criteria. Both instruments have dense uniform sampling patterns typical of limb emission sounders, producing almost identical sampling biases. However, there is a substantial difference between the number of locations discarded. MIPAS, as a mid-infrared instrument, is very sensitive to clouds, and measurements affected by them are thus rejected from the analysis. For example, in the tropics, the MIPAS yield is strongly affected by clouds, while MLS is mostly unaffected. The results show that upper-tropospheric sampling biases in zonally averaged data, for both instruments, can be up to 10 to 30 %, depending on the species, and up to 3 K for temperature. For MIPAS, the sampling reduction due to quality screening worsens the biases, leading to values as large as 30 to 100 % for the trace gases and expanding the 3 K bias region for temperature. This type of sampling bias is largely induced by the geophysical origins of the screening (e.g. clouds). Further, analysis of long-term time series reveals that these additional quality screening biases may affect the ability to accurately detect upper-tropospheric long-term changes using such data. In contrast, MLS data quality screening removes sufficiently few points that no additional bias is introduced, although its penetration is limited to the upper troposphere, while MIPAS may cover well into the mid-troposphere in cloud-free scenarios. We emphasize that the results of this study refer only to the representativeness of the respective data, not to their intrinsic quality.

Bayesian selective response-adaptive design using the historical control.

PubMed

Kim, Mi-Ok; Harun, Nusrat; Liu, Chunyan; Khoury, Jane C; Broderick, Joseph P

2018-06-13

High quality historical control data, if incorporated, may reduce sample size, trial cost, and duration. A too optimistic use of the data, however, may result in bias under prior-data conflict. Motivated by well-publicized two-arm comparative trials in stroke, we propose a Bayesian design that both adaptively incorporates historical control data and selectively adapt the treatment allocation ratios within an ongoing trial responsively to the relative treatment effects. The proposed design differs from existing designs that borrow from historical controls. As opposed to reducing the number of subjects assigned to the control arm blindly, this design does so adaptively to the relative treatment effects only if evaluation of cumulated current trial data combined with the historical control suggests the superiority of the intervention arm. We used the effective historical sample size approach to quantify borrowed information on the control arm and modified the treatment allocation rules of the doubly adaptive biased coin design to incorporate the quantity. The modified allocation rules were then implemented under the Bayesian framework with commensurate priors addressing prior-data conflict. Trials were also more frequently concluded earlier in line with the underlying truth, reducing trial cost, and duration and yielded parameter estimates with smaller standard errors. © 2018 The Authors. Statistics in Medicine Published by John Wiley & Sons, Ltd.

Moving Beyond Salmon Bias: Mexican Return Migration and Health Selection

PubMed Central

Diaz, Christina J.; Koning, Stephanie M.; Martinez-Donate, Ana P.

2017-01-01

Despite having lower levels of education and limited access to health care services, Mexican immigrants report better health outcomes than U.S.-born individuals. Research suggests that the Mexican health advantage may be partially attributable to selective return migration among less healthy migrants—often referred to as “salmon bias.” Our study takes advantage of a rare opportunity to observe the health status of Mexican-origin males as they cross the Mexican border. To assess whether unhealthy migrants are disproportionately represented among those who return, we use data from two California-based studies: the California Health Interview Survey; and the Migrante Study, a survey that samples Mexican migrants entering and leaving the United States through Tijuana. We pool these data sources to look for evidence of health-related return migration. Results provide mixed support for salmon bias. Although migrants who report health limitations and frequent stress are more likely to return, we find little evidence that chronic conditions and self-reported health are associated with higher probabilities of return. Results also provide some indication that limited health care access increases the likelihood of return among the least healthy. This study provides new theoretical considerations of return migration and further elucidates the relationship between health and migration decisions. PMID:27848222

Moving Beyond Salmon Bias: Mexican Return Migration and Health Selection.

PubMed

Diaz, Christina J; Koning, Stephanie M; Martinez-Donate, Ana P

2016-12-01

Despite having lower levels of education and limited access to health care services, Mexican immigrants report better health outcomes than U.S.-born individuals. Research suggests that the Mexican health advantage may be partially attributable to selective return migration among less healthy migrants-often referred to as "salmon bias." Our study takes advantage of a rare opportunity to observe the health status of Mexican-origin males as they cross the Mexican border. To assess whether unhealthy migrants are disproportionately represented among those who return, we use data from two California-based studies: the California Health Interview Survey; and the Migrante Study, a survey that samples Mexican migrants entering and leaving the United States through Tijuana. We pool these data sources to look for evidence of health-related return migration. Results provide mixed support for salmon bias. Although migrants who report health limitations and frequent stress are more likely to return, we find little evidence that chronic conditions and self-reported health are associated with higher probabilities of return. Results also provide some indication that limited health care access increases the likelihood of return among the least healthy. This study provides new theoretical considerations of return migration and further elucidates the relationship between health and migration decisions.

Efficiently estimating salmon escapement uncertainty using systematically sampled data

USGS Publications Warehouse

Reynolds, Joel H.; Woody, Carol Ann; Gove, Nancy E.; Fair, Lowell F.

2007-01-01

Fish escapement is generally monitored using nonreplicated systematic sampling designs (e.g., via visual counts from towers or hydroacoustic counts). These sampling designs support a variety of methods for estimating the variance of the total escapement. Unfortunately, all the methods give biased results, with the magnitude of the bias being determined by the underlying process patterns. Fish escapement commonly exhibits positive autocorrelation and nonlinear patterns, such as diurnal and seasonal patterns. For these patterns, poor choice of variance estimator can needlessly increase the uncertainty managers have to deal with in sustaining fish populations. We illustrate the effect of sampling design and variance estimator choice on variance estimates of total escapement for anadromous salmonids from systematic samples of fish passage. Using simulated tower counts of sockeye salmon Oncorhynchus nerka escapement on the Kvichak River, Alaska, five variance estimators for nonreplicated systematic samples were compared to determine the least biased. Using the least biased variance estimator, four confidence interval estimators were compared for expected coverage and mean interval width. Finally, five systematic sampling designs were compared to determine the design giving the smallest average variance estimate for total annual escapement. For nonreplicated systematic samples of fish escapement, all variance estimators were positively biased. Compared to the other estimators, the least biased estimator reduced bias by, on average, from 12% to 98%. All confidence intervals gave effectively identical results. Replicated systematic sampling designs consistently provided the smallest average estimated variance among those compared.

Regression dilution bias: tools for correction methods and sample size calculation.

PubMed

Berglund, Lars

2012-08-01

Random errors in measurement of a risk factor will introduce downward bias of an estimated association to a disease or a disease marker. This phenomenon is called regression dilution bias. A bias correction may be made with data from a validity study or a reliability study. In this article we give a non-technical description of designs of reliability studies with emphasis on selection of individuals for a repeated measurement, assumptions of measurement error models, and correction methods for the slope in a simple linear regression model where the dependent variable is a continuous variable. Also, we describe situations where correction for regression dilution bias is not appropriate. The methods are illustrated with the association between insulin sensitivity measured with the euglycaemic insulin clamp technique and fasting insulin, where measurement of the latter variable carries noticeable random error. We provide software tools for estimation of a corrected slope in a simple linear regression model assuming data for a continuous dependent variable and a continuous risk factor from a main study and an additional measurement of the risk factor in a reliability study. Also, we supply programs for estimation of the number of individuals needed in the reliability study and for choice of its design. Our conclusion is that correction for regression dilution bias is seldom applied in epidemiological studies. This may cause important effects of risk factors with large measurement errors to be neglected.

Exploring and accounting for publication bias in mental health: a brief overview of methods.

PubMed

Mavridis, Dimitris; Salanti, Georgia

2014-02-01

OBJECTIVE Publication bias undermines the integrity of published research. The aim of this paper is to present a synopsis of methods for exploring and accounting for publication bias. METHODS We discussed the main features of the following methods to assess publication bias: funnel plot analysis; trim-and-fill methods; regression techniques and selection models. We applied these methods to a well-known example of antidepressants trials that compared trials submitted to the Food and Drug Administration (FDA) for regulatory approval. RESULTS The funnel plot-related methods (visual inspection, trim-and-fill, regression models) revealed an association between effect size and SE. Contours of statistical significance showed that asymmetry in the funnel plot is probably due to publication bias. Selection model found a significant correlation between effect size and propensity for publication. CONCLUSIONS Researchers should always consider the possible impact of publication bias. Funnel plot-related methods should be seen as a means of examining for small-study effects and not be directly equated with publication bias. Possible causes for funnel plot asymmetry should be explored. Contours of statistical significance may help disentangle whether asymmetry in a funnel plot is caused by publication bias or not. Selection models, although underused, could be useful resource when publication bias and heterogeneity are suspected because they address directly the problem of publication bias and not that of small-study effects.

Tools for assessing risk of reporting biases in studies and syntheses of studies: a systematic review

PubMed Central

Page, Matthew J; McKenzie, Joanne E; Higgins, Julian P T

2018-01-01

Background Several scales, checklists and domain-based tools for assessing risk of reporting biases exist, but it is unclear how much they vary in content and guidance. We conducted a systematic review of the content and measurement properties of such tools. Methods We searched for potentially relevant articles in Ovid MEDLINE, Ovid Embase, Ovid PsycINFO and Google Scholar from inception to February 2017. One author screened all titles, abstracts and full text articles, and collected data on tool characteristics. Results We identified 18 tools that include an assessment of the risk of reporting bias. Tools varied in regard to the type of reporting bias assessed (eg, bias due to selective publication, bias due to selective non-reporting), and the level of assessment (eg, for the study as a whole, a particular result within a study or a particular synthesis of studies). Various criteria are used across tools to designate a synthesis as being at ‘high’ risk of bias due to selective publication (eg, evidence of funnel plot asymmetry, use of non-comprehensive searches). However, the relative weight assigned to each criterion in the overall judgement is unclear for most of these tools. Tools for assessing risk of bias due to selective non-reporting guide users to assess a study, or an outcome within a study, as ‘high’ risk of bias if no results are reported for an outcome. However, assessing the corresponding risk of bias in a synthesis that is missing the non-reported outcomes is outside the scope of most of these tools. Inter-rater agreement estimates were available for five tools. Conclusion There are several limitations of existing tools for assessing risk of reporting biases, in terms of their scope, guidance for reaching risk of bias judgements and measurement properties. Development and evaluation of a new, comprehensive tool could help overcome present limitations. PMID:29540417

Estimation of genetic parameters and response to selection for a continuous trait subject to culling before testing.

PubMed

Arnason, T; Albertsdóttir, E; Fikse, W F; Eriksson, S; Sigurdsson, A

2012-02-01

The consequences of assuming a zero environmental covariance between a binary trait 'test-status' and a continuous trait on the estimates of genetic parameters by restricted maximum likelihood and Gibbs sampling and on response from genetic selection when the true environmental covariance deviates from zero were studied. Data were simulated for two traits (one that culling was based on and a continuous trait) using the following true parameters, on the underlying scale: h² = 0.4; r(A) = 0.5; r(E) = 0.5, 0.0 or -0.5. The selection on the continuous trait was applied to five subsequent generations where 25 sires and 500 dams produced 1500 offspring per generation. Mass selection was applied in the analysis of the effect on estimation of genetic parameters. Estimated breeding values were used in the study of the effect of genetic selection on response and accuracy. The culling frequency was either 0.5 or 0.8 within each generation. Each of 10 replicates included 7500 records on 'test-status' and 9600 animals in the pedigree file. Results from bivariate analysis showed unbiased estimates of variance components and genetic parameters when true r(E) = 0.0. For r(E) = 0.5, variance components (13-19% bias) and especially (50-80%) were underestimated for the continuous trait, while heritability estimates were unbiased. For r(E) = -0.5, heritability estimates of test-status were unbiased, while genetic variance and heritability of the continuous trait together with were overestimated (25-50%). The bias was larger for the higher culling frequency. Culling always reduced genetic progress from selection, but the genetic progress was found to be robust to the use of wrong parameter values of the true environmental correlation between test-status and the continuous trait. Use of a bivariate linear-linear model reduced bias in genetic evaluations, when data were subject to culling. © 2011 Blackwell Verlag GmbH.

Empirical Validation of a Procedure to Correct Position and Stimulus Biases in Matching-to-Sample

ERIC Educational Resources Information Center

Kangas, Brian D.; Branch, Marc N.

2008-01-01

The development of position and stimulus biases often occurs during initial training on matching-to-sample tasks. Furthermore, without intervention, these biases can be maintained via intermittent reinforcement provided by matching-to-sample contingencies. The present study evaluated the effectiveness of a correction procedure designed to…

Four Reasons to Question the Accuracy of a Biotic Index; the Risk of Metric Bias and the Scope to Improve Accuracy

PubMed Central

Monaghan, Kieran A.

2016-01-01

Natural ecological variability and analytical design can bias the derived value of a biotic index through the variable influence of indicator body-size, abundance, richness, and ascribed tolerance scores. Descriptive statistics highlight this risk for 26 aquatic indicator systems; detailed analysis is provided for contrasting weighted-average indices applying the example of the BMWP, which has the best supporting data. Differences in body size between taxa from respective tolerance classes is a common feature of indicator systems; in some it represents a trend ranging from comparatively small pollution tolerant to larger intolerant organisms. Under this scenario, the propensity to collect a greater proportion of smaller organisms is associated with negative bias however, positive bias may occur when equipment (e.g. mesh-size) selectively samples larger organisms. Biotic indices are often derived from systems where indicator taxa are unevenly distributed along the gradient of tolerance classes. Such skews in indicator richness can distort index values in the direction of taxonomically rich indicator classes with the subsequent degree of bias related to the treatment of abundance data. The misclassification of indicator taxa causes bias that varies with the magnitude of the misclassification, the relative abundance of misclassified taxa and the treatment of abundance data. These artifacts of assessment design can compromise the ability to monitor biological quality. The statistical treatment of abundance data and the manipulation of indicator assignment and class richness can be used to improve index accuracy. While advances in methods of data collection (i.e. DNA barcoding) may facilitate improvement, the scope to reduce systematic bias is ultimately limited to a strategy of optimal compromise. The shortfall in accuracy must be addressed by statistical pragmatism. At any particular site, the net bias is a probabilistic function of the sample data, resulting in an error variance around an average deviation. Following standardized protocols and assigning precise reference conditions, the error variance of their comparative ratio (test-site:reference) can be measured and used to estimate the accuracy of the resultant assessment. PMID:27392036

Bias in Testing: A Presentation of Selected Methods.

ERIC Educational Resources Information Center

Merz, William R.; Rudner, Lawrence M.

A variety of terms related to test bias or test fairness have been used in a variety of ways, but in this document the "fair use of tests" is defined as equitable selection procedures by means of intact tests, and "test item bias" refers to the study of separate items with respect to the tests of which they are a part. Seven…

High–frequency cluster radio galaxies: Luminosity functions and implications for SZE–selected cluster samples

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gupta, Nikhel; Saro, A.; Mohr, J. J.

We study the overdensity of point sources in the direction of X-ray-selected galaxy clusters from the meta-catalogue of X-ray-detected clusters of galaxies (MCXC; < z > = 0.14) at South Pole Telescope (SPT) and Sydney University Molonglo Sky Survey (SUMSS) frequencies. Flux densities at 95, 150 and 220 GHz are extracted from the 2500 deg 2 SPT-SZ survey maps at the locations of SUMSS sources, producing a multifrequency catalogue of radio galaxies. In the direction of massive galaxy clusters, the radio galaxy flux densities at 95 and 150 GHz are biased low by the cluster Sunyaev–Zel’dovich Effect (SZE) signal, whichmore » is negative at these frequencies. We employ a cluster SZE model to remove the expected flux bias and then study these corrected source catalogues. We find that the high-frequency radio galaxies are centrally concentrated within the clusters and that their luminosity functions (LFs) exhibit amplitudes that are characteristically an order of magnitude lower than the cluster LF at 843 MHz. We use the 150 GHz LF to estimate the impact of cluster radio galaxies on an SPT-SZ like survey. The radio galaxy flux typically produces a small bias on the SZE signal and has negligible impact on the observed scatter in the SZE mass–observable relation. If we assume there is no redshift evolution in the radio galaxy LF then 1.8 ± 0.7 per cent of the clusters with detection significance ξ ≥ 4.5 would be lost from the sample. As a result, allowing for redshift evolution of the form (1 + z) 2.5 increases the incompleteness to 5.6 ± 1.0 per cent. Improved constraints on the evolution of the cluster radio galaxy LF require a larger cluster sample extending to higher redshift.« less

High–frequency cluster radio galaxies: Luminosity functions and implications for SZE–selected cluster samples

DOE PAGES

Gupta, Nikhel; Saro, A.; Mohr, J. J.; ...

2017-01-15

We study the overdensity of point sources in the direction of X-ray-selected galaxy clusters from the meta-catalogue of X-ray-detected clusters of galaxies (MCXC; < z > = 0.14) at South Pole Telescope (SPT) and Sydney University Molonglo Sky Survey (SUMSS) frequencies. Flux densities at 95, 150 and 220 GHz are extracted from the 2500 deg 2 SPT-SZ survey maps at the locations of SUMSS sources, producing a multifrequency catalogue of radio galaxies. In the direction of massive galaxy clusters, the radio galaxy flux densities at 95 and 150 GHz are biased low by the cluster Sunyaev–Zel’dovich Effect (SZE) signal, whichmore » is negative at these frequencies. We employ a cluster SZE model to remove the expected flux bias and then study these corrected source catalogues. We find that the high-frequency radio galaxies are centrally concentrated within the clusters and that their luminosity functions (LFs) exhibit amplitudes that are characteristically an order of magnitude lower than the cluster LF at 843 MHz. We use the 150 GHz LF to estimate the impact of cluster radio galaxies on an SPT-SZ like survey. The radio galaxy flux typically produces a small bias on the SZE signal and has negligible impact on the observed scatter in the SZE mass–observable relation. If we assume there is no redshift evolution in the radio galaxy LF then 1.8 ± 0.7 per cent of the clusters with detection significance ξ ≥ 4.5 would be lost from the sample. As a result, allowing for redshift evolution of the form (1 + z) 2.5 increases the incompleteness to 5.6 ± 1.0 per cent. Improved constraints on the evolution of the cluster radio galaxy LF require a larger cluster sample extending to higher redshift.« less

Quasi-experimental study designs series-paper 6: risk of bias assessment.

PubMed

Waddington, Hugh; Aloe, Ariel M; Becker, Betsy Jane; Djimeu, Eric W; Hombrados, Jorge Garcia; Tugwell, Peter; Wells, George; Reeves, Barney

2017-09-01

Rigorous and transparent bias assessment is a core component of high-quality systematic reviews. We assess modifications to existing risk of bias approaches to incorporate rigorous quasi-experimental approaches with selection on unobservables. These are nonrandomized studies using design-based approaches to control for unobservable sources of confounding such as difference studies, instrumental variables, interrupted time series, natural experiments, and regression-discontinuity designs. We review existing risk of bias tools. Drawing on these tools, we present domains of bias and suggest directions for evaluation questions. The review suggests that existing risk of bias tools provide, to different degrees, incomplete transparent criteria to assess the validity of these designs. The paper then presents an approach to evaluating the internal validity of quasi-experiments with selection on unobservables. We conclude that tools for nonrandomized studies of interventions need to be further developed to incorporate evaluation questions for quasi-experiments with selection on unobservables. Copyright © 2017 Elsevier Inc. All rights reserved.

Influence of parameter settings in voxel-based morphometry 8. Using DARTEL and region-of-interest on reproducibility in gray matter volumetry.

PubMed

Goto, M; Abe, O; Aoki, S; Hayashi, N; Miyati, T; Takao, H; Matsuda, H; Yamashita, F; Iwatsubo, T; Mori, H; Kunimatsu, A; Ino, K; Yano, K; Ohtomo, K

2015-01-01

To investigate whether reproducibility of gray matter volumetry is influenced by parameter settings for VBM 8 using Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) with region-of-interest (ROI) analyses. We prepared three-dimensional T1-weighted magnetic resonance images (3D-T1WIs) of 21 healthy subjects. All subjects were imaged with each of five MRI systems. Voxel-based morphometry 8 (VBM 8) and WFU PickAtlas software were used for gray matter volumetry. The bilateral ROI labels used were those provided as default settings with the software: Frontal Lobe, Hippocampus, Occipital Lobe, Orbital Gyrus, Parietal Lobe, Putamen, and Temporal Lobe. All 3D-T1WIs were segmented to gray matter with six parameters of VBM 8, with each parameter having between three and eight selectable levels. Reproducibility was evaluated as the standard deviation (mm³) of measured values for the five MRI systems. Reproducibility was influenced by 'Bias regularization (BiasR)', 'Bias FWHM', and 'De-noising filter' settings, but not by 'MRF weighting', 'Sampling distance', or 'Warping regularization' settings. Reproducibility in BiasR was influenced by ROI. Superior reproducibility was observed in Frontal Lobe with the BiasR1 setting, and in Hippocampus, Parietal Lobe, and Putamen with the BiasR3*, BiasR1, and BiasR5 settings, respectively. Reproducibility of gray matter volumetry was influenced by parameter settings in VBM 8 using DARTEL and ROI. In multi-center studies, the use of appropriate settings in VBM 8 with DARTEL results in reduced scanner effect.

The impact of time-window bias on the assessment of the long-term effect of medication adherence: the case of secondary prevention after myocardial infarction.

PubMed

Di Martino, Mirko; Kirchmayer, Ursula; Agabiti, Nera; Bauleo, Lisa; Fusco, Danilo; Perucci, Carlo Alberto; Davoli, Marina

2015-06-10

Time-window bias was described in case-control studies and led to a biased estimate of drug effect. No studies have measured the impact of this bias on the assessment of the effect of medication adherence on health outcomes. Our goals were to estimate the association between adherence to drug therapies after myocardial infarction (MI) and the incidence of a new MI, and to quantify the error that would have been produced by a time-window bias. This is a population-based study. Data were obtained from the Regional Health Information Systems of the Lazio Region in Central Italy (around 5 million inhabitants). Patients discharged after MI in 2006-2007 were enrolled in the cohort and followed through 2009. The study outcome was reinfarction: either mortality, or hospital admission for MI, whichever occurred first. A nested case-control study was performed. Controls were selected using both time-dependent and time-independent sampling. Adherence to antiplatelets, β-blockers, ACE inhibitors/angiotensin receptor blockers (ACEI/ARBs) and statins was calculated using the proportion of days covered (PDC). A total of 6880 patients were enrolled in the cohort. Using time-dependent sampling, a protective effect was detected for all study drugs. Conversely, using time-independent sampling, the beneficial effect was attenuated, as in the case of antiplatelet agents and statins, or completely masked, as in the case of ACEI/ARBs and β-blockers. For ACEI/ARBs, the time-dependent approach produced ORs of 0.83 (95% CI 0.57 to 1.21) and 0.72 (0.55 to 0.95), respectively, for '0.5 < PDC ≤ 0.75' and 'PDC>0.75' versus '0 ≤ PDC ≤ 0.5'. Using the time-independent approach, the ORs were 0.96 (0.65 to 1.43) and 1.00 (0.76 to 1.33), respectively. A time-independent definition of a time-dependent exposure introduces a bias when the length of follow-up varies with the outcome. The persistence of time-related biases in peer-reviewed papers strongly suggests the need for increased awareness of this methodological pitfall. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Exploring Selective Exposure and Confirmation Bias as Processes Underlying Employee Work Happiness: An Intervention Study

PubMed Central

Williams, Paige; Kern, Margaret L.; Waters, Lea

2016-01-01

Employee psychological capital (PsyCap), perceptions of organizational virtue (OV), and work happiness have been shown to be associated within and over time. This study examines selective exposure and confirmation bias as potential processes underlying PsyCap, OV, and work happiness associations. As part of a quasi-experimental study design, school staff (N = 69) completed surveys at three time points. After the first assessment, some staff (n = 51) completed a positive psychology training intervention. Results of descriptive statistics, correlation, and regression analyses on the intervention group provide some support for selective exposure and confirmation bias as explanatory mechanisms. In focusing on the processes through which employee attitudes may influence work happiness this study advances theoretical understanding, specifically of selective exposure and confirmation bias in a field study context. PMID:27378978

A quality-assurance assessment for constituents reported by the national atmospheric deposition program and the national trends network

NASA Astrophysics Data System (ADS)

See, Randolph B.; Schroder, LeRoy J.; Willoughby, Timothy C.

A continuing quality-assurance program has been operated by the U.S. Geological Survey to evaluate any bias introduced by routine handling, shipping, and laboratory analyses of wet-deposition samples collected in the National Atmospheric Deposition Program (NADP) and National Trends Network (NTN). Blind-audit samples having a variety of constituent concentrations and values were selected. Only blind-audit samples with constituent concentrations and values less than the 95th-percentile concentration for natural wet-deposition samples were included in the analysis. Of the major ions, there was a significant increase of Ca 2+, Mg 2+, Na 2+, K +, SO 42- and Cl -1 in samples handled according to standard protocols and shipped in NADP/NTN sample-collection buckets. For 1979-1987, graphs of smoothed data showing the estimated contamination in blind-audit samples indicate a decrease in the median concentration and ranges of Ca 2+, Mg 2+ and SO 42- contamination of blind-audit samples shipped in sample-collection buckets. Part of the contamination detected in blind-audit samples can be attributed to contact with the sample-collection bucket and lid; however, additional sources also seem to contaminate the blind-audit sample. Apparent decreases in the magnitude and range of sample contamination may be caused by differences in sample-collection bucket- and lid-washing procedures by the NADP/NTN Central Analytical Laboratory. Although the degree of bias is minimal for most constituents, summaries of the NADP/NTN data base may contain overestimates of Ca 2+, Mg 2+, Na -, K + and SO 42- and Cl - concentrations, and underestimates of H + concentrations.

A quality-assurance assessment for constituents reported by the National Atmospheric Deposition Program and the National Trends Network

USGS Publications Warehouse

See, R.B.; Schroder, L.J.; Willoughby, T.C.

1989-01-01

A continuing quality-assurance program has been operated by the U.S. Geographical Survey to evaluate any bias introduced by routine handling, shipping, and laboratory analyses of wet-deposition samples collected in the National Atmospheric Deposition Program (NADP) and National Trends Network (NTN). Blind-audit samples having a variety of constituent concentrations and values were selected. Only blind-audit samples with constituent concentrations and values less than the 95th-percentile concentration for natural wet-deposition samples were included in the analysis. Of the major ions, there was a significant increase of Ca2+, Mg2+, K+ SO42+ and Cl- in samples handled according to standard protocols and shipped in NADP/NTN sample-collection buckets. For 1979-1987, graphs of smoothed data showing the estimated contaminations in blind-audit samples indicate a decrease in the median concentration and ranges of Ca2+, Mg2+ and SO42- contamination of blind-audit samples shipped in sample-collection buckets. Part of the contamination detected in blind-audit samples can be attributed to contact with the sample-collection bucket and lid; however, additional sources also seem to contaminate the blind-audit sample. Apparent decreases in the magnitude and range of sample contamination may be caused by differences in sample-collection bucket- and lid-washing procedures by the NADP/NTN Central Analytical Laboratory. Although the degree of bias is minimal for most constituents, summaries of the NADP/NTN data base may contain overestimates of Ca2+, Mg2+, Na-, K+, SO42- and Cl- concentrations, and underestimates of H+ concentrations.

The APOGEE-2 Survey of the Orion Star-forming Complex. I. Target Selection and Validation with Early Observations

NASA Astrophysics Data System (ADS)

Cottle, J.’Neil; Covey, Kevin R.; Suárez, Genaro; Román-Zúñiga, Carlos; Schlafly, Edward; Downes, Juan Jose; Ybarra, Jason E.; Hernandez, Jesus; Stassun, Keivan; Stringfellow, Guy S.; Getman, Konstantin; Feigelson, Eric; Borissova, Jura; Kim, J. Serena; Roman-Lopes, A.; Da Rio, Nicola; De Lee, Nathan; Frinchaboy, Peter M.; Kounkel, Marina; Majewski, Steven R.; Mennickent, Ronald E.; Nidever, David L.; Nitschelm, Christian; Pan, Kaike; Shetrone, Matthew; Zasowski, Gail; Chambers, Ken; Magnier, Eugene; Valenti, Jeff

2018-06-01

The Orion Star-forming Complex (OSFC) is a central target for the APOGEE-2 Young Cluster Survey. Existing membership catalogs span limited portions of the OSFC, reflecting the difficulty of selecting targets homogeneously across this extended, highly structured region. We have used data from wide-field photometric surveys to produce a less biased parent sample of young stellar objects (YSOs) with infrared (IR) excesses indicative of warm circumstellar material or photometric variability at optical wavelengths across the full 420 square degree extent of the OSFC. When restricted to YSO candidates with H < 12.4, to ensure S/N ∼ 100 for a six-visit source, this uniformly selected sample includes 1307 IR excess sources selected using criteria vetted by Koenig & Liesawitz (2014) and 990 optical variables identified in the Pan-STARRS1 3π survey: 319 sources exhibit both optical variability and evidence of circumstellar disks through IR excess. Objects from this uniformly selected sample received the highest priority for targeting, but required fewer than half of the fibers on each APOGEE-2 plate. We filled the remaining fibers with previously confirmed and new color–magnitude selected candidate OSFC members. Radial velocity measurements from APOGEE-1 and new APOGEE-2 observations taken in the survey’s first year indicate that ∼90% of the uniformly selected targets have radial velocities consistent with Orion membership. The APOGEE-2 Orion survey will include >1100 bona fide YSOs whose uniform selection function will provide a robust sample for comparative analyses of the stellar populations and properties across all sub-regions of Orion.

Schematic for efficient computation of GC, GC3, and AT3 bias spectra of genome

PubMed Central

Rizvi, Ahsan Z; Venu Gopal, T; Bhattacharya, C

2012-01-01

Selection of synonymous codons for an amino acid is biased in protein translation process. This biased selection causes repetition of synonymous codons in structural parts of genome that stands for high N/3 peaks in DNA spectrum. Period-3 spectral property is utilized here to produce a 3-phase network model based on polyphase filterbank concepts for derivation of codon bias spectra (CBS). Modification of parameters in this model can produce GC, GC3, and AT3 bias spectra. Complete schematic in LabVIEW platform is presented here for efficient and parallel computation of GC, GC3, and AT3 bias spectra of genomes alongwith results of CBS patterns. We have performed the correlation coefficient analysis of GC, GC3, and AT3 bias spectra with codon bias patterns of CBS for biological and statistical significance of this model. PMID:22368390

Schematic for efficient computation of GC, GC3, and AT3 bias spectra of genome.

PubMed

Rizvi, Ahsan Z; Venu Gopal, T; Bhattacharya, C

2012-01-01

Selection of synonymous codons for an amino acid is biased in protein translation process. This biased selection causes repetition of synonymous codons in structural parts of genome that stands for high N/3 peaks in DNA spectrum. Period-3 spectral property is utilized here to produce a 3-phase network model based on polyphase filterbank concepts for derivation of codon bias spectra (CBS). Modification of parameters in this model can produce GC, GC3, and AT3 bias spectra. Complete schematic in LabVIEW platform is presented here for efficient and parallel computation of GC, GC3, and AT3 bias spectra of genomes alongwith results of CBS patterns. We have performed the correlation coefficient analysis of GC, GC3, and AT3 bias spectra with codon bias patterns of CBS for biological and statistical significance of this model.

Evaluating the Validity of a Two-stage Sample in a Birth Cohort Established from Administrative Databases.

PubMed

El-Zein, Mariam; Conus, Florence; Benedetti, Andrea; Parent, Marie-Elise; Rousseau, Marie-Claude

2016-01-01

When using administrative databases for epidemiologic research, a subsample of subjects can be interviewed, eliciting information on undocumented confounders. This article presents a thorough investigation of the validity of a two-stage sample encompassing an assessment of nonparticipation and quantification of the extent of bias. Established through record linkage of administrative databases, the Québec Birth Cohort on Immunity and Health (n = 81,496) aims to study the association between Bacillus Calmette-Guérin vaccination and asthma. Among 76,623 subjects classified in four Bacillus Calmette-Guérin-asthma strata, a two-stage sampling strategy with a balanced design was used to randomly select individuals for interviews. We compared stratum-specific sociodemographic characteristics and healthcare utilization of stage 2 participants (n = 1,643) with those of eligible nonparticipants (n = 74,980) and nonrespondents (n = 3,157). We used logistic regression to determine whether participation varied across strata according to these characteristics. The effect of nonparticipation was described by the relative odds ratio (ROR = ORparticipants/ORsource population) for the association between sociodemographic characteristics and asthma. Parental age at childbirth, area of residence, family income, and healthcare utilization were comparable between groups. Participants were slightly more likely to be women and have a mother born in Québec. Participation did not vary across strata by sex, parental birthplace, or material and social deprivation. Estimates were not biased by nonparticipation; most RORs were below one and bias never exceeded 20%. Our analyses evaluate and provide a detailed demonstration of the validity of a two-stage sample for researchers assembling similar research infrastructures.

The performance of atmospheric pressure gas chromatography-tandem mass spectrometry compared to gas chromatography-high resolution mass spectrometry for the analysis of polychlorinated dioxins and polychlorinated biphenyls in food and feed samples.

PubMed

Ten Dam, Guillaume; Pussente, Igor Cabreira; Scholl, Georges; Eppe, Gauthier; Schaechtele, Alexander; van Leeuwen, Stefan

2016-12-16

Recently, gas chromatography tandem mass spectrometry (GC-MS/MS) has been added in European Union (EU) legislation as an alternative to magnetic sector high resolution mass spectrometry (HRMS) for the analysis of dioxins and dioxin like polychlorinated biphenyls (dl-PCB) in food and feed. In this study the performance of APGC-MS/MS compared to GC-HRMS is investigated and compared with EU legislation. The study includes the legislative parameters, relative intermediate precision standard deviation (S Rw ,rel), trueness, sensitivity, linear range and ion ratio tolerance. In addition, over 200 real samples of large variety and spanning several orders of magnitude in concentration were analyzed by both techniques and the selectivity was evaluated by comparing chromatograms. The S Rw ,rel and trueness were evaluated using (in-house) reference samples and fulfill to EU legislation, though the S Rw ,rel was better with GC-HRMS. The sensitivity was considerably better than of GC-HRMS while the linear range was similar. Ion ratios were mostly within the tolerable range of ±15%. A (temporary unresolved) systematic deviation in ion ratio was observed for several congeners, yet this did not lead to exceeding of the maximum ion ratio limits. The APGC-MS/MS results for the non-dioxin-like-PCBs (ndl-PCBs) were negatively biased, particularly for PCB138 and 153 in contaminated samples. The selectivity of APGC-MS/MS was lower for several matrices. Particularly for contaminated samples, interfering peaks were observed in the APGC chromatograms of the native compounds (dioxins) and labeled internal standards (PCBs). These can lead to biased results and ultimately to false positive samples. It was concluded that the determination of dioxins and PCBs using APGC-MS/MS meets the requirements set by the European Commission. However, due to generally better selectivity and S Rw ,rel, GC-HRMS is the preferred method for monitoring purposes. Copyright © 2016 Elsevier B.V. All rights reserved.

Evaluation of Bias-Variance Trade-Off for Commonly Used Post-Summarizing Normalization Procedures in Large-Scale Gene Expression Studies

PubMed Central

Qiu, Xing; Hu, Rui; Wu, Zhixin

2014-01-01

Normalization procedures are widely used in high-throughput genomic data analyses to remove various technological noise and variations. They are known to have profound impact to the subsequent gene differential expression analysis. Although there has been some research in evaluating different normalization procedures, few attempts have been made to systematically evaluate the gene detection performances of normalization procedures from the bias-variance trade-off point of view, especially with strong gene differentiation effects and large sample size. In this paper, we conduct a thorough study to evaluate the effects of normalization procedures combined with several commonly used statistical tests and MTPs under different configurations of effect size and sample size. We conduct theoretical evaluation based on a random effect model, as well as simulation and biological data analyses to verify the results. Based on our findings, we provide some practical guidance for selecting a suitable normalization procedure under different scenarios. PMID:24941114

A multi-source precipitation approach to fill gaps over a radar precipitation field

NASA Astrophysics Data System (ADS)

Tesfagiorgis, K. B.; Mahani, S. E.; Khanbilvardi, R.

2012-12-01

Satellite Precipitation Estimates (SPEs) may be the only available source of information for operational hydrologic and flash flood prediction due to spatial limitations of radar and gauge products. The present work develops an approach to seamlessly blend satellite, radar, climatological and gauge precipitation products to fill gaps over ground-based radar precipitation fields. To mix different precipitation products, the bias of any of the products relative to each other should be removed. For bias correction, the study used an ensemble-based method which aims to estimate spatially varying multiplicative biases in SPEs using a radar rainfall product. Bias factors were calculated for a randomly selected sample of rainy pixels in the study area. Spatial fields of estimated bias were generated taking into account spatial variation and random errors in the sampled values. A weighted Successive Correction Method (SCM) is proposed to make the merging between error corrected satellite and radar rainfall estimates. In addition to SCM, we use a Bayesian spatial method for merging the gap free radar with rain gauges, climatological rainfall sources and SPEs. We demonstrate the method using SPE Hydro-Estimator (HE), radar- based Stage-II, a climatological product PRISM and rain gauge dataset for several rain events from 2006 to 2008 over three different geographical locations of the United States. Results show that: the SCM method in combination with the Bayesian spatial model produced a precipitation product in good agreement with independent measurements. The study implies that using the available radar pixels surrounding the gap area, rain gauge, PRISM and satellite products, a radar like product is achievable over radar gap areas that benefits the scientific community.

Birth order and sibship size: evaluation of the role of selection bias in a case-control study of non-Hodgkin's lymphoma.

PubMed

Mensah, F K; Willett, E V; Simpson, J; Smith, A G; Roman, E

2007-09-15

Substantial heterogeneity has been observed among case-control studies investigating associations between non-Hodgkin's lymphoma and familial characteristics, such as birth order and sibship size. The potential role of selection bias in explaining such heterogeneity is considered within this study. Selection bias according to familial characteristics and socioeconomic status is investigated within a United Kingdom-based case-control study of non-Hodgkin's lymphoma diagnosed during 1998-2001. Reported distributions of birth order and maternal age are each compared with expected reference distributions derived using national birth statistics from the United Kingdom. A method is detailed in which yearly data are used to derive expected distributions, taking account of variability in birth statistics over time. Census data are used to reweight both the case and control study populations such that they are comparable with the general population with regard to socioeconomic status. The authors found little support for an association between non-Hodgkin's lymphoma and birth order or family size and little evidence for an influence of selection bias. However, the findings suggest that between-study heterogeneity could be explained by selection biases that influence the demographic characteristics of participants.

Quantification of six herbicide metabolites in human urine.

PubMed

Norrgran, Jessica; Bravo, Roberto; Bishop, Amanda M; Restrepo, Paula; Whitehead, Ralph D; Needham, Larry L; Barr, Dana B

2006-01-18

We developed a sensitive, selective and precise method for measuring herbicide metabolites in human urine. Our method uses automated liquid delivery of internal standards and acetate buffer and a mixed polarity polymeric phase solid phase extraction of a 2 mL urine sample. The concentrated eluate is analyzed using high-performance liquid chromatography-tandem mass spectrometry. Isotope dilution calibration is used for quantification of all analytes. The limits of detection of our method range from 0.036 to 0.075 ng/mL. The within- and between-day variation in pooled quality control samples range from 2.5 to 9.0% and from 3.2 to 16%, respectively, for all analytes at concentrations ranging from 0.6 to 12 ng/mL. Precision was similar with samples fortified with 0.1 and 0.25 ng/mL that were analyzed in each run. We validated our selective method against a less selective method used previously in our laboratory by analyzing human specimens using both methods. The methods produced results that were in agreement, with no significant bias observed.

X-Ray Morphological Analysis of the Planck ESZ Clusters

NASA Astrophysics Data System (ADS)

Lovisari, Lorenzo; Forman, William R.; Jones, Christine; Ettori, Stefano; Andrade-Santos, Felipe; Arnaud, Monique; Démoclès, Jessica; Pratt, Gabriel W.; Randall, Scott; Kraft, Ralph

2017-09-01

X-ray observations show that galaxy clusters have a very large range of morphologies. The most disturbed systems, which are good to study how clusters form and grow and to test physical models, may potentially complicate cosmological studies because the cluster mass determination becomes more challenging. Thus, we need to understand the cluster properties of our samples to reduce possible biases. This is complicated by the fact that different experiments may detect different cluster populations. For example, Sunyaev-Zeldovich (SZ) selected cluster samples have been found to include a greater fraction of disturbed systems than X-ray selected samples. In this paper we determine eight morphological parameters for the Planck Early Sunyaev-Zeldovich (ESZ) objects observed with XMM-Newton. We found that two parameters, concentration and centroid shift, are the best to distinguish between relaxed and disturbed systems. For each parameter we provide the values that allow selecting the most relaxed or most disturbed objects from a sample. We found that there is no mass dependence on the cluster dynamical state. By comparing our results with what was obtained with REXCESS clusters, we also confirm that the ESZ clusters indeed tend to be more disturbed, as found by previous studies.

X-Ray Morphological Analysis of the Planck ESZ Clusters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lovisari, Lorenzo; Forman, William R.; Jones, Christine

2017-09-01

X-ray observations show that galaxy clusters have a very large range of morphologies. The most disturbed systems, which are good to study how clusters form and grow and to test physical models, may potentially complicate cosmological studies because the cluster mass determination becomes more challenging. Thus, we need to understand the cluster properties of our samples to reduce possible biases. This is complicated by the fact that different experiments may detect different cluster populations. For example, Sunyaev–Zeldovich (SZ) selected cluster samples have been found to include a greater fraction of disturbed systems than X-ray selected samples. In this paper wemore » determine eight morphological parameters for the Planck Early Sunyaev–Zeldovich (ESZ) objects observed with XMM-Newton . We found that two parameters, concentration and centroid shift, are the best to distinguish between relaxed and disturbed systems. For each parameter we provide the values that allow selecting the most relaxed or most disturbed objects from a sample. We found that there is no mass dependence on the cluster dynamical state. By comparing our results with what was obtained with REXCESS clusters, we also confirm that the ESZ clusters indeed tend to be more disturbed, as found by previous studies.« less

Rapid loss of behavioral plasticity and immunocompetence under intense sexual selection.

PubMed

van Lieshout, Emile; McNamara, Kathryn B; Simmons, Leigh W

2014-09-01

Phenotypic plasticity allows animals to maximize fitness by conditionally expressing the phenotype best adapted to their environment. Although evidence for such adjustment in reproductive tactics is common, little is known about how phenotypic plasticity evolves in response to sexual selection. We examined the effect of sexual selection intensity on phenotypic plasticity in mating behavior using the beetle Callosobruchus maculatus. Male genital spines harm females during mating and females exhibit copulatory kicking, an apparent resistance trait aimed to dislodge mating males. After exposing individuals from male- and female-biased experimental evolution lines to male- and female-biased sociosexual environments, we examined behavioral plasticity in matings with standard partners. While females from female-biased lines kicked sooner after exposure to male-biased sociosexual contexts, in male-biased lines this plasticity was lost. Ejaculate size did not diverge in response to selection history, but males from both treatments exhibited plasticity consistent with sperm competition intensity models, reducing size as the number of competitors increased. Analysis of immunocompetence revealed reduced immunity in both sexes in male-biased lines, pointing to increased reproductive costs under high sexual selection. These results highlight how male and female reproductive strategies are shaped by interactions between phenotypically plastic and genetic mechanisms of sexual trait expression. © 2014 The Author(s). Evolution © 2014 The Society for the Study of Evolution.

Hydrocarbon gas in sediment of the Southern Pacific Ocean

USGS Publications Warehouse

Kvenvolden, K.A.

1988-01-01

Methane, ethane, ethene, propane, and propene are common hydrocarbon gases in near-surface sediment from offshore areas in the southern Pacific Ocean near Papua New Guinea, the Solomon Islands, Vanuatu, Tonga, New Zealand, and Antarctica. Sea floor sites for sampling of sediment were selected on the basis of anomalies in marine seismic records, and the samples were intentionally biased toward finding possible thermogenic hydrocarbon gases. In none of the areas, however, were thermogenic hydrocarbons clearly identified. The hydrocarbon gases that were found appear to be mainly the products of in situ microbial processes. ?? 1988 Springer-Verlag New York Inc.

Emergent biological properties of arrestin pathway-selective biased agonism.

PubMed

Appleton, Kathryn M; Luttrell, Louis M

2013-06-01

Our growing appreciation of the pluridimensionality of G protein-coupled receptor (GPCR) signaling, combined with the phenomenon of orthosteric ligand "bias", has created the possibility of drugs that selectively modulate different aspects of GPCR function for therapeutic benefit. When viewed from the short-term perspective, e.g. changes in receptor conformation, effector coupling or second messenger generation, biased ligands appear to activate a subset of the response profile produced by a conventional agonist. Yet when examined in vivo, the limited data available suggest that biased ligand effects can diverge from their conventional counterparts in ways that cannot be predicted from their in vitro efficacy profile. What is currently missing, at least with respect to G protein and arrestin pathway-selective ligands, is a rational framework for relating the in vitro efficacy of a "biased" agonist to its in vivo actions that will enable drug screening programs to identify ligands with the desired biological effects.

A Spectroscopic Follow-up Program of Very Massive Galaxies at 3 < z < 4: Confirmation of Spectroscopic Redshifts, and a High Fraction of Powerful AGNs

NASA Astrophysics Data System (ADS)

Marsan, Z. Cemile; Marchesini, Danilo; Brammer, Gabriel B.; Geier, Stefan; Kado-Fong, Erin; Labbé, Ivo; Muzzin, Adam; Stefanon, Mauro

2017-06-01

We present the analysis and results of a spectroscopic follow-up program of a mass-selected sample of six galaxies at 3< z< 4 using data from Keck-NIRPSEC and VLT-Xshooter. We confirm the z> 3 redshifts for half of the sample through the detection of strong nebular emission lines, and improve the z phot accuracy for the remainder of the sample through the combination of photometry and spectra. The modeling of the emission-line-corrected spectral energy distributions (SEDs) adopting improved redshifts confirms the very large stellar masses of the sample ({M}* ˜ 1.5{--}4× {10}11{M}⊙ ) in the first 2 Gyr of cosmic history, with a diverse range in stellar ages, star-formation rates, and dust content. From the analysis of emission-line luminosities and widths, and far-infrared (FIR) fluxes, we confirm that ≳ 80 % of the sample are hosts to luminous hidden active galactic nuclei (AGNs), with bolometric luminosities of ˜1044-46 erg s-1. We find that the MIPS 24 μm photometry is largely contaminated by AGN continuum, rendering the SFRs derived using only 24 μm photometry to be severely overestimated. By including the emission from the AGN in the modeling of the UV-to-FIR SEDs, we confirm that the presence of the AGN does not considerably bias the stellar masses (< 0.3 dex at 1σ). We show evidence for a rapid increase of the AGN fraction from ˜30% to ˜60%-100% over the 1 Gyr between z˜ 2 and z˜ 3. Although we cannot exclude some enhancement of the AGN fraction for our sample due to selection effects, the small measured [O III] contamination to the observed K-band fluxes suggests that our sample is not significantly biased toward massive galaxies hosting AGNs.

Precision and bias of selected analytes reported by the National Atmospheric Deposition Program and National Trends Network, 1983; and January 1980 through September 1984

USGS Publications Warehouse

Schroder, L.J.; Bricker, A.W.; Willoughby, T.C.

1985-01-01

Blind-audit samples with known analyte concentrations have been prepared by the U.S. Geological Survey and distributed to the National Atmospheric Deposition Program 's Central Analytical Laboratory. The difference between the National Atmospheric Deposition Program and National Trends Network reported analyte concentrations and known analyte concentrations have been calculated, and the bias has been determined. Calcium, magnesium , sodium, and chloride were biased at the 99-percent confidence limit; potassium and sulfate were unbiased at the 99-percent confidence limit, for 1983 results. Relative-percent differences between the measured and known analyte concentration for calcium , magnesium, sodium, potassium, chloride, and sulfate have been calculated for 1983. The median relative percent difference for calcium was 17.0; magnesium was 6.4; sodium was 10.8; potassium was 6.4; chloride was 17.2; and sulfate was -5.3. These relative percent differences should be used to correct the 1983 data before user-analysis of the data. Variances have been calculated for calcium, magnesium, sodium, potassium, chloride, and sulfate determinations. These variances should be applicable to natural-sample analyte concentrations reported by the National Atmospheric Deposition Program and National Trends Network for calendar year 1983. (USGS)

Connecting massive galaxies to dark matter haloes in BOSS - I. Is galaxy colour a stochastic process in high-mass haloes?

NASA Astrophysics Data System (ADS)

Saito, Shun; Leauthaud, Alexie; Hearin, Andrew P.; Bundy, Kevin; Zentner, Andrew R.; Behroozi, Peter S.; Reid, Beth A.; Sinha, Manodeep; Coupon, Jean; Tinker, Jeremy L.; White, Martin; Schneider, Donald P.

2016-08-01

We use subhalo abundance matching (SHAM) to model the stellar mass function (SMF) and clustering of the Baryon Oscillation Spectroscopic Survey (BOSS) `CMASS' sample at z ˜ 0.5. We introduce a novel method which accounts for the stellar mass incompleteness of CMASS as a function of redshift, and produce CMASS mock catalogues which include selection effects, reproduce the overall SMF, the projected two-point correlation function wp, the CMASS dn/dz, and are made publicly available. We study the effects of assembly bias above collapse mass in the context of `age matching' and show that these effects are markedly different compared to the ones explored by Hearin et al. at lower stellar masses. We construct two models, one in which galaxy colour is stochastic (`AbM' model) as well as a model which contains assembly bias effects (`AgM' model). By confronting the redshift dependent clustering of CMASS with the predictions from our model, we argue that that galaxy colours are not a stochastic process in high-mass haloes. Our results suggest that the colours of galaxies in high-mass haloes are determined by other halo properties besides halo peak velocity and that assembly bias effects play an important role in determining the clustering properties of this sample.

Immediate movement history influences reach-to-grasp action selection in children and adults.

PubMed

Kent, Samuel W; Wilson, Andrew D; Plumb, Mandy S; Williams, Justin H G; Mon-Williams, Mark

2009-01-01

Action selection is subject to many biases. Immediate movement history is one such bias seen in young infants. Is this bias strong enough to affect adult behavior? Adult participants reached and grasped a cylinder positioned to require either pronation or supination of the hand. Successive cylinder positions changed either randomly or systematically between trials. Random positioning led to optimized economy of movement. In contrast, systematic changes in position biased action selection toward previously selected actions at the expense of movement economy. Thus, one switches to a new movement only when the savings outweigh the costs of the switch. Immediate movement history had an even larger influence on children aged 7-15 years. This suggests that switching costs are greater in children, which is consistent with their reduced grasping experience. The presence of this effect in adults suggests that immediate movement history exerts a more widespread and pervasive influence on patterns of action selection than researchers had previously recognized.

SETI target selection.

PubMed

Latham, D W; Soderblom, D R

1995-01-01

The NASA High Resolution Microwave Survey consists of two complementary elements: a Sky Survey of the entire sky to a moderate level of sensitivity; and a Targeted Search of nearby stars, one at a time, to a much deeper level of sensitivity. In this paper we propose strategies for target selection. We have two goals: to improve the chances of successful detection of signals from technical civilizations that inhabit planets around solar-type stars, and to minimize the chances of missing signals from unexpected sites. For the main Targeted Search survey of approximately 1000 nearby solar-type stars, we argue that the selection criteria should be heavily biased by what we know about the origin and evolution of life here on Earth. We propose that observations of stars with stellar companions orbiting near the habitable zone should be de-emphasized, because such companions would prevent the formation of habitable planets. We also propose that observations of stars younger than about three billion years should be de-emphasized in favor of older stars, because our own technical civilization took longer than three billion years to evolve here on Earth. To provide the information needed for the preparation of specific target lists, we have undertaken an inventory of a large sample of solar-type stars out to a distance of 60 pc, with the goal of characterizing the relevant astrophysical properties of these stars, especially their ages and companionship. To complement the main survey, we propose that a modest sample of the nearest stars should be observed without any selection biases whatsoever. Finally, we argue that efforts to identify stars with planetary systems should be expanded. If found, such systems should receive intensive scrutiny.

Can we estimate molluscan abundance and biomass on the continental shelf?

NASA Astrophysics Data System (ADS)

Powell, Eric N.; Mann, Roger; Ashton-Alcox, Kathryn A.; Kuykendall, Kelsey M.; Chase Long, M.

2017-11-01

Few empirical studies have focused on the effect of sample density on the estimate of abundance of the dominant carbonate-producing fauna of the continental shelf. Here, we present such a study and consider the implications of suboptimal sampling design on estimates of abundance and size-frequency distribution. We focus on a principal carbonate producer of the U.S. Atlantic continental shelf, the Atlantic surfclam, Spisula solidissima. To evaluate the degree to which the results are typical, we analyze a dataset for the principal carbonate producer of Mid-Atlantic estuaries, the Eastern oyster Crassostrea virginica, obtained from Delaware Bay. These two species occupy different habitats and display different lifestyles, yet demonstrate similar challenges to survey design and similar trends with sampling density. The median of a series of simulated survey mean abundances, the central tendency obtained over a large number of surveys of the same area, always underestimated true abundance at low sample densities. More dramatic were the trends in the probability of a biased outcome. As sample density declined, the probability of a survey availability event, defined as a survey yielding indices >125% or <75% of the true population abundance, increased and that increase was disproportionately biased towards underestimates. For these cases where a single sample accessed about 0.001-0.004% of the domain, 8-15 random samples were required to reduce the probability of a survey availability event below 40%. The problem of differential bias, in which the probabilities of a biased-high and a biased-low survey index were distinctly unequal, was resolved with fewer samples than the problem of overall bias. These trends suggest that the influence of sampling density on survey design comes with a series of incremental challenges. At woefully inadequate sampling density, the probability of a biased-low survey index will substantially exceed the probability of a biased-high index. The survey time series on the average will return an estimate of the stock that underestimates true stock abundance. If sampling intensity is increased, the frequency of biased indices balances between high and low values. Incrementing sample number from this point steadily reduces the likelihood of a biased survey; however, the number of samples necessary to drive the probability of survey availability events to a preferred level of infrequency may be daunting. Moreover, certain size classes will be disproportionately susceptible to such events and the impact on size frequency will be species specific, depending on the relative dispersion of the size classes.

Selection Bias in College Admissions Test Scores

ERIC Educational Resources Information Center

Clark, Melissa; Rothstein, Jesse; Schanzenbach, Diane Whitmore

2009-01-01

Data from college admissions tests can provide a valuable measure of student achievement, but the non-representativeness of test-takers is an important concern. We examine selectivity bias in both state-level and school-level SAT and ACT averages. The degree of selectivity may differ importantly across and within schools, and across and within…

Sampling for Global Epidemic Models and the Topology of an International Airport Network

PubMed Central

Bobashev, Georgiy; Morris, Robert J.; Goedecke, D. Michael

2008-01-01

Mathematical models that describe the global spread of infectious diseases such as influenza, severe acute respiratory syndrome (SARS), and tuberculosis (TB) often consider a sample of international airports as a network supporting disease spread. However, there is no consensus on how many cities should be selected or on how to select those cities. Using airport flight data that commercial airlines reported to the Official Airline Guide (OAG) in 2000, we have examined the network characteristics of network samples obtained under different selection rules. In addition, we have examined different size samples based on largest flight volume and largest metropolitan populations. We have shown that although the bias in network characteristics increases with the reduction of the sample size, a relatively small number of areas that includes the largest airports, the largest cities, the most-connected cities, and the most central cities is enough to describe the dynamics of the global spread of influenza. The analysis suggests that a relatively small number of cities (around 200 or 300 out of almost 3000) can capture enough network information to adequately describe the global spread of a disease such as influenza. Weak traffic flows between small airports can contribute to noise and mask other means of spread such as the ground transportation. PMID:18776932

Sources and preparation of data for assessing trends in concentrations of pesticides in streams of the United States, 1992–2010

USGS Publications Warehouse

Martin, Jeffrey D.; Eberle, Michael; Nakagaki, Naomi

2011-01-01

This report updates a previously published water-quality dataset of 44 commonly used pesticides and 8 pesticide degradates suitable for a national assessment of trends in pesticide concentrations in streams of the United States. Water-quality samples collected from January 1992 through September 2010 at stream-water sites of the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) Program and the National Stream Quality Accounting Network (NASQAN) were compiled, reviewed, selected, and prepared for trend analysis. The principal steps in data review for trend analysis were to (1) identify analytical schedule, (2) verify sample-level coding, (3) exclude inappropriate samples or results, (4) review pesticide detections per sample, (5) review high pesticide concentrations, and (6) review the spatial and temporal extent of NAWQA pesticide data and selection of analytical methods for trend analysis. The principal steps in data preparation for trend analysis were to (1) select stream-water sites for trend analysis, (2) round concentrations to a consistent level of precision for the concentration range, (3) identify routine reporting levels used to report nondetections unaffected by matrix interference, (4) reassign the concentration value for routine nondetections to the maximum value of the long-term method detection level (maxLT-MDL), (5) adjust concentrations to compensate for temporal changes in bias of recovery of the gas chromatography/mass spectrometry (GCMS) analytical method, and (6) identify samples considered inappropriate for trend analysis. Samples analyzed at the USGS National Water Quality Laboratory (NWQL) by the GCMS analytical method were the most extensive in time and space and, consequently, were selected for trend analysis. Stream-water sites with 3 or more water years of data with six or more samples per year were selected for pesticide trend analysis. The selection criteria described in the report produced a dataset of 21,988 pesticide samples at 212 stream-water sites. Only 21,144 pesticide samples, however, are considered appropriate for trend analysis.

Phase-space overlap measures. I. Fail-safe bias detection in free energies calculated by molecular simulation

NASA Astrophysics Data System (ADS)

Wu, Di; Kofke, David A.

2005-08-01

We consider ways to quantify the overlap of the parts of phase space important to two systems, labeled A and B. Of interest is how much of the A-important phase space lies in that important to B, and how much of B lies in A. Two measures are proposed. The first considers four total-energy distributions, formed from all combinations made by tabulating either the A-system or the B-system energy when sampling either the A or B system. Measures for A in B and B in A are given by two overlap integrals defined on pairs of these distributions. The second measure is based on information theory, and defines two relative entropies which are conveniently expressed in terms of the dissipated work for free-energy perturbation (FEP) calculations in the A →B and B →A directions, respectively. Phase-space overlap is an important consideration in the performance of free-energy calculations. To demonstrate this connection, we examine bias in FEP calculations applied to a system of independent particles in a harmonic potential. Systems are selected to represent a range of overlap situations, including extreme subset, subset, partial overlap, and nonoverlap. The magnitude and symmetry of the bias (A →B vs B →A) are shown to correlate well with the overlap, and consequently with the overlap measures. The relative entropies are used to scale the amount of sampling to obtain a universal bias curve. This result leads to develop a simple heuristic that can be applied to determine whether a work-based free-energy measurement is free of bias. The heuristic is based in part on the measured free energy, but we argue that it is fail-safe inasmuch as any bias in the measurement will not promote a false indication of accuracy.

Sampling for area estimation: A comparison of full-frame sampling with the sample segment approach. [Kansas

NASA Technical Reports Server (NTRS)

Hixson, M. M.; Bauer, M. E.; Davis, B. J.

1979-01-01

The effect of sampling on the accuracy (precision and bias) of crop area estimates made from classifications of LANDSAT MSS data was investigated. Full-frame classifications of wheat and non-wheat for eighty counties in Kansas were repetitively sampled to simulate alternative sampling plants. Four sampling schemes involving different numbers of samples and different size sampling units were evaluated. The precision of the wheat area estimates increased as the segment size decreased and the number of segments was increased. Although the average bias associated with the various sampling schemes was not significantly different, the maximum absolute bias was directly related to sampling unit size.

A two-stage cluster sampling method using gridded population data, a GIS, and Google Earth(TM) imagery in a population-based mortality survey in Iraq.

PubMed

Galway, Lp; Bell, Nathaniel; Sae, Al Shatari; Hagopian, Amy; Burnham, Gilbert; Flaxman, Abraham; Weiss, Wiliam M; Rajaratnam, Julie; Takaro, Tim K

2012-04-27

Mortality estimates can measure and monitor the impacts of conflict on a population, guide humanitarian efforts, and help to better understand the public health impacts of conflict. Vital statistics registration and surveillance systems are rarely functional in conflict settings, posing a challenge of estimating mortality using retrospective population-based surveys. We present a two-stage cluster sampling method for application in population-based mortality surveys. The sampling method utilizes gridded population data and a geographic information system (GIS) to select clusters in the first sampling stage and Google Earth TM imagery and sampling grids to select households in the second sampling stage. The sampling method is implemented in a household mortality study in Iraq in 2011. Factors affecting feasibility and methodological quality are described. Sampling is a challenge in retrospective population-based mortality studies and alternatives that improve on the conventional approaches are needed. The sampling strategy presented here was designed to generate a representative sample of the Iraqi population while reducing the potential for bias and considering the context specific challenges of the study setting. This sampling strategy, or variations on it, are adaptable and should be considered and tested in other conflict settings.

A two-stage cluster sampling method using gridded population data, a GIS, and Google EarthTM imagery in a population-based mortality survey in Iraq

PubMed Central

2012-01-01

Background Mortality estimates can measure and monitor the impacts of conflict on a population, guide humanitarian efforts, and help to better understand the public health impacts of conflict. Vital statistics registration and surveillance systems are rarely functional in conflict settings, posing a challenge of estimating mortality using retrospective population-based surveys. Results We present a two-stage cluster sampling method for application in population-based mortality surveys. The sampling method utilizes gridded population data and a geographic information system (GIS) to select clusters in the first sampling stage and Google Earth TM imagery and sampling grids to select households in the second sampling stage. The sampling method is implemented in a household mortality study in Iraq in 2011. Factors affecting feasibility and methodological quality are described. Conclusion Sampling is a challenge in retrospective population-based mortality studies and alternatives that improve on the conventional approaches are needed. The sampling strategy presented here was designed to generate a representative sample of the Iraqi population while reducing the potential for bias and considering the context specific challenges of the study setting. This sampling strategy, or variations on it, are adaptable and should be considered and tested in other conflict settings. PMID:22540266

Aquifer environment selects for microbial species cohorts in sediment and groundwater

PubMed Central

Hug, Laura A; Thomas, Brian C; Brown, Christopher T; Frischkorn, Kyle R; Williams, Kenneth H; Tringe, Susannah G; Banfield, Jillian F

2015-01-01

Little is known about the biogeography or stability of sediment-associated microbial community membership because these environments are biologically complex and generally difficult to sample. High-throughput-sequencing methods provide new opportunities to simultaneously genomically sample and track microbial community members across a large number of sampling sites or times, with higher taxonomic resolution than is associated with 16 S ribosomal RNA gene surveys, and without the disadvantages of primer bias and gene copy number uncertainty. We characterized a sediment community at 5 m depth in an aquifer adjacent to the Colorado River and tracked its most abundant 133 organisms across 36 different sediment and groundwater samples. We sampled sites separated by centimeters, meters and tens of meters, collected on seven occasions over 6 years. Analysis of 1.4 terabase pairs of DNA sequence showed that these 133 organisms were more consistently detected in saturated sediments than in samples from the vadose zone, from distant locations or from groundwater filtrates. Abundance profiles across aquifer locations and from different sampling times identified organism cohorts that comprised subsets of the 133 organisms that were consistently associated. The data suggest that cohorts are partly selected for by shared environmental adaptation. PMID:25647349

Optimization and validation of moving average quality control procedures using bias detection curves and moving average validation charts.

PubMed

van Rossum, Huub H; Kemperman, Hans

2017-02-01

To date, no practical tools are available to obtain optimal settings for moving average (MA) as a continuous analytical quality control instrument. Also, there is no knowledge of the true bias detection properties of applied MA. We describe the use of bias detection curves for MA optimization and MA validation charts for validation of MA. MA optimization was performed on a data set of previously obtained consecutive assay results. Bias introduction and MA bias detection were simulated for multiple MA procedures (combination of truncation limits, calculation algorithms and control limits) and performed for various biases. Bias detection curves were generated by plotting the median number of test results needed for bias detection against the simulated introduced bias. In MA validation charts the minimum, median, and maximum numbers of assay results required for MA bias detection are shown for various bias. Their use was demonstrated for sodium, potassium, and albumin. Bias detection curves allowed optimization of MA settings by graphical comparison of bias detection properties of multiple MA. The optimal MA was selected based on the bias detection characteristics obtained. MA validation charts were generated for selected optimal MA and provided insight into the range of results required for MA bias detection. Bias detection curves and MA validation charts are useful tools for optimization and validation of MA procedures.

Modelling multiple sources of dissemination bias in meta-analysis.

PubMed

Bowden, Jack; Jackson, Dan; Thompson, Simon G

2010-03-30

Asymmetry in the funnel plot for a meta-analysis suggests the presence of dissemination bias. This may be caused by publication bias through the decisions of journal editors, by selective reporting of research results by authors or by a combination of both. Typically, study results that are statistically significant or have larger estimated effect sizes are more likely to appear in the published literature, hence giving a biased picture of the evidence-base. Previous statistical approaches for addressing dissemination bias have assumed only a single selection mechanism. Here we consider a more realistic scenario in which multiple dissemination processes, involving both the publishing authors and journals, are operating. In practical applications, the methods can be used to provide sensitivity analyses for the potential effects of multiple dissemination biases operating in meta-analysis.

Cool Core Bias in Sunyaev-Zel’dovich Galaxy Cluster Surveys

DOE PAGES

Lin, Henry W.; McDonald, Michael; Benson, Bradford; ...

2015-03-18

Sunyaev-Zeldovich (SZ) surveys find massive clusters of galaxies by measuring the inverse Compton scattering of cosmic microwave background off of intra-cluster gas. The cluster selection function from such surveys is expected to be nearly independent of redshift and cluster astrophysics. In this work, we estimate the effect on the observed SZ signal of centrally-peaked gas density profiles (cool cores) and radio emission from the brightest cluster galaxy (BCG) by creating mock observations of a sample of clusters that span the observed range of classical cooling rates and radio luminosities. For each cluster, we make simulated SZ observations by the Southmore » Pole Telescope and characterize the cluster selection function, but note that our results are broadly applicable to other SZ surveys. We find that the inclusion of a cool core can cause a change in the measured SPT significance of a cluster between 0.01%–10% at z > 0.3, increasing with cuspiness of the cool core and angular size on the sky of the cluster (i.e., decreasing redshift, increasing mass). We provide quantitative estimates of the bias in the SZ signal as a function of a gas density cuspiness parameter, redshift, mass, and the 1.4 GHz radio luminosity of the central AGN. Based on this work, we estimate that, for the Phoenix cluster (one of the strongest cool cores known), the presence of a cool core is biasing the SZ significance high by ~6%. The ubiquity of radio galaxies at the centers of cool core clusters will offset the cool core bias to varying degrees« less

Implications of weight-based stigma and self-bias on quality of life among individuals with Schizophrenia

PubMed Central

Barber, Jessica; Palmese, Laura; Reutenauer, Erin L.; Grilo, Carlos; Tek, Cenk

2011-01-01

Obesity has been associated with significant stigma and weight-related self-bias in community and clinical studies, but these issues have not been studied among individuals with schizophrenia. A consecutive series of 70 obese individuals with schizophrenia or schizoaffective disorder underwent assessment for perceptions of weight-based stigmatization, self-directed weight-bias, negative affect, medication compliance, and quality of life. Levels of weight-based stigmatization and self-bias were compared to levels reported for non-psychiatric overweight/obese samples. Weight measures were unrelated to stigma, self-bias, affect, and quality of life. Weight-based stigmatization was lower than published levels for non-psychiatric samples, whereas levels of weight-based self-bias did not differ. After controlling for negative affect, weight-based self-bias predicted an additional 11% of the variance in the quality of life measure. Individuals with schizophrenia and schizoaffective disorder reported weight-based self-bias to the same extent as non-psychiatric samples despite reporting less weight stigma. Weight-based self-bias was associated with poorer quality of life after controlling for negative affect. PMID:21716053

Recent progresses in outcome-dependent sampling with failure time data.

PubMed

Ding, Jieli; Lu, Tsui-Shan; Cai, Jianwen; Zhou, Haibo

2017-01-01

An outcome-dependent sampling (ODS) design is a retrospective sampling scheme where one observes the primary exposure variables with a probability that depends on the observed value of the outcome variable. When the outcome of interest is failure time, the observed data are often censored. By allowing the selection of the supplemental samples depends on whether the event of interest happens or not and oversampling subjects from the most informative regions, ODS design for the time-to-event data can reduce the cost of the study and improve the efficiency. We review recent progresses and advances in research on ODS designs with failure time data. This includes researches on ODS related designs like case-cohort design, generalized case-cohort design, stratified case-cohort design, general failure-time ODS design, length-biased sampling design and interval sampling design.

Recent progresses in outcome-dependent sampling with failure time data

PubMed Central

Ding, Jieli; Lu, Tsui-Shan; Cai, Jianwen; Zhou, Haibo

2016-01-01

An outcome-dependent sampling (ODS) design is a retrospective sampling scheme where one observes the primary exposure variables with a probability that depends on the observed value of the outcome variable. When the outcome of interest is failure time, the observed data are often censored. By allowing the selection of the supplemental samples depends on whether the event of interest happens or not and oversampling subjects from the most informative regions, ODS design for the time-to-event data can reduce the cost of the study and improve the efficiency. We review recent progresses and advances in research on ODS designs with failure time data. This includes researches on ODS related designs like case–cohort design, generalized case–cohort design, stratified case–cohort design, general failure-time ODS design, length-biased sampling design and interval sampling design. PMID:26759313

Electric shielding films for biased TEM samples and their application to in situ electron holography.

PubMed

Nomura, Yuki; Yamamoto, Kazuo; Hirayama, Tsukasa; Saitoh, Koh

2018-06-01

We developed a novel sample preparation method for transmission electron microscopy (TEM) to suppress superfluous electric fields leaked from biased TEM samples. In this method, a thin TEM sample is first coated with an insulating amorphous aluminum oxide (AlOx) film with a thickness of about 20 nm. Then, the sample is coated with a conductive amorphous carbon film with a thickness of about 10 nm, and the film is grounded. This technique was applied to a model sample of a metal electrode/Li-ion-conductive-solid-electrolyte/metal electrode for biasing electron holography. We found that AlOx film with a thickness of 10 nm has a large withstand voltage of about 8 V and that double layers of AlOx and carbon act as a 'nano-shield' to suppress 99% of the electric fields outside of the sample. We also found an asymmetry potential distribution between high and low potential electrodes in biased solid-electrolyte, indicating different accumulation behaviors of lithium-ions (Li+) and lithium-ion vacancies (VLi-) in the biased solid-electrolyte.

Now or Later? An fMRI study of the effects of endogenous opioid blockade on a decision-making network

PubMed Central

Boettiger, Charlotte A.; Kelley, Elizabeth A.; Mitchell, Jennifer M.; D’Esposito, Mark; Fields, Howard L.

2009-01-01

Previously, we found that distinct brain areas predict individual selection bias in decisions between small immediate (“Now”) and larger delayed rewards (“Later”). Furthermore, such selection bias can be manipulated by endogenous opioid blockade. To test whether blocking endogenous opioids with Naltrexone (NTX) alters brain activity during decision-making in areas predicting individual bias, we compared fMRI BOLD signal correlated with Now versus Later decision-making after acute administration of NTX (50 mg) or placebo. We tested abstinent alcoholics and control subjects in a double-blind two-session design. We defined regions of interest (ROI) centered on activation peaks predicting Now versus Later selection bias. NTX administration significantly increased BOLD signal during decision-making in the right lateral orbital gyrus ROI, an area where enhanced activity during decision-making predicts Later bias. Exploratory analyses identified additional loci where BOLD signal during decision-making was enhanced (left orbitofrontal cortex, left inferior temporal gyrus, and cerebellum) or reduced (right superior temporal pole) by NTX. Additional analyses identified sites, including the right lateral orbital gyrus, in which NTX effects on BOLD signal predicted NTX effects on selection bias. These data agree with opioid receptor expression in human frontal and temporal cortices, and suggest possible mechanisms of NTX’s therapeutic effects. PMID:19258022

Are mutagenic non D-loop direct repeat motifs in mitochondrial DNA under a negative selection pressure?

PubMed Central

Lakshmanan, Lakshmi Narayanan; Gruber, Jan; Halliwell, Barry; Gunawan, Rudiyanto

2015-01-01

Non D-loop direct repeats (DRs) in mitochondrial DNA (mtDNA) have been commonly implicated in the mutagenesis of mtDNA deletions associated with neuromuscular disease and ageing. Further, these DRs have been hypothesized to put a constraint on the lifespan of mammals and are under a negative selection pressure. Using a compendium of 294 mammalian mtDNA, we re-examined the relationship between species lifespan and the mutagenicity of such DRs. Contradicting the prevailing hypotheses, we found no significant evidence that long-lived mammals possess fewer mutagenic DRs than short-lived mammals. By comparing DR counts in human mtDNA with those in selectively randomized sequences, we also showed that the number of DRs in human mtDNA is primarily determined by global mtDNA properties, such as the bias in synonymous codon usage (SCU) and nucleotide composition. We found that SCU bias in mtDNA positively correlates with DR counts, where repeated usage of a subset of codons leads to more frequent DR occurrences. While bias in SCU and nucleotide composition has been attributed to nucleotide mutational bias, mammalian mtDNA still exhibit higher SCU bias and DR counts than expected from such mutational bias, suggesting a lack of negative selection against non D-loop DRs. PMID:25855815

Bias Selectable Dual Band AlGaN Ultra-violet Detectors

NASA Technical Reports Server (NTRS)

Yan, Feng; Miko, Laddawan; Franz, David; Guan, Bing; Stahle, Carl M.

2007-01-01

Bias selectable dual band AlGaN ultra-violet (UV) detectors, which can separate UV-A and UV-B using one detector in the same pixel by bias switching, have been designed, fabricated and characterized. A two-terminal n-p-n photo-transistor-like structure was used. When a forward bias is applied between the top electrode and the bottom electrode, the detectors can successfully detect W-A and reject UV-B. Under reverse bias, they can detect UV-B and reject UV-A. The proof of concept design shows that it is feasible to fabricate high performance dual-band UV detectors based on the current AlGaN material growth and fabrication technologies.

EFS: an ensemble feature selection tool implemented as R-package and web-application.

PubMed

Neumann, Ursula; Genze, Nikita; Heider, Dominik

2017-01-01

Feature selection methods aim at identifying a subset of features that improve the prediction performance of subsequent classification models and thereby also simplify their interpretability. Preceding studies demonstrated that single feature selection methods can have specific biases, whereas an ensemble feature selection has the advantage to alleviate and compensate for these biases. The software EFS (Ensemble Feature Selection) makes use of multiple feature selection methods and combines their normalized outputs to a quantitative ensemble importance. Currently, eight different feature selection methods have been integrated in EFS, which can be used separately or combined in an ensemble. EFS identifies relevant features while compensating specific biases of single methods due to an ensemble approach. Thereby, EFS can improve the prediction accuracy and interpretability in subsequent binary classification models. EFS can be downloaded as an R-package from CRAN or used via a web application at http://EFS.heiderlab.de.

Model selection bias and Freedman's paradox

USGS Publications Warehouse

Lukacs, P.M.; Burnham, K.P.; Anderson, D.R.

2010-01-01

In situations where limited knowledge of a system exists and the ratio of data points to variables is small, variable selection methods can often be misleading. Freedman (Am Stat 37:152-155, 1983) demonstrated how common it is to select completely unrelated variables as highly "significant" when the number of data points is similar in magnitude to the number of variables. A new type of model averaging estimator based on model selection with Akaike's AIC is used with linear regression to investigate the problems of likely inclusion of spurious effects and model selection bias, the bias introduced while using the data to select a single seemingly "best" model from a (often large) set of models employing many predictor variables. The new model averaging estimator helps reduce these problems and provides confidence interval coverage at the nominal level while traditional stepwise selection has poor inferential properties. ?? The Institute of Statistical Mathematics, Tokyo 2009.

Real-time image annotation by manifold-based biased Fisher discriminant analysis

NASA Astrophysics Data System (ADS)

Ji, Rongrong; Yao, Hongxun; Wang, Jicheng; Sun, Xiaoshuai; Liu, Xianming

2008-01-01

Automatic Linguistic Annotation is a promising solution to bridge the semantic gap in content-based image retrieval. However, two crucial issues are not well addressed in state-of-art annotation algorithms: 1. The Small Sample Size (3S) problem in keyword classifier/model learning; 2. Most of annotation algorithms can not extend to real-time online usage due to their low computational efficiencies. This paper presents a novel Manifold-based Biased Fisher Discriminant Analysis (MBFDA) algorithm to address these two issues by transductive semantic learning and keyword filtering. To address the 3S problem, Co-Training based Manifold learning is adopted for keyword model construction. To achieve real-time annotation, a Bias Fisher Discriminant Analysis (BFDA) based semantic feature reduction algorithm is presented for keyword confidence discrimination and semantic feature reduction. Different from all existing annotation methods, MBFDA views image annotation from a novel Eigen semantic feature (which corresponds to keywords) selection aspect. As demonstrated in experiments, our manifold-based biased Fisher discriminant analysis annotation algorithm outperforms classical and state-of-art annotation methods (1.K-NN Expansion; 2.One-to-All SVM; 3.PWC-SVM) in both computational time and annotation accuracy with a large margin.

Measurement effects of seasonal and monthly variability on pedometer-determined data.

PubMed

Kang, Minsoo; Bassett, David R; Barreira, Tiago V; Tudor-Locke, Catrine; Ainsworth, Barbara E

2012-03-01

The seasonal and monthly variability of pedometer-determined physical activity and its effects on accurate measurement have not been examined. The purpose of the study was to reduce measurement error in step-count data by controlling a) the length of the measurement period and b) the season or month of the year in which sampling was conducted. Twenty-three middle-aged adults were instructed to wear a Yamax SW-200 pedometer over 365 consecutive days. The step-count measurement periods of various lengths (eg, 2, 3, 4, 5, 6, 7 days, etc.) were randomly selected 10 times for each season and month. To determine accurate estimates of yearly step-count measurement, mean absolute percentage error (MAPE) and bias were calculated. The year-round average was considered as a criterion measure. A smaller MAPE and bias represent a better estimate. Differences in MAPE and bias among seasons were trivial; however, they varied among different months. The months in which seasonal changes occur presented the highest MAPE and bias. Targeting the data collection during certain months (eg, May) may reduce pedometer measurement error and provide more accurate estimates of year-round averages.

Can(not) take my eyes off it: attention bias for food in overweight participants.

PubMed

Werthmann, Jessica; Roefs, Anne; Nederkoorn, Chantal; Mogg, Karin; Bradley, Brendan P; Jansen, Anita

2011-09-01

The aim of the current study was to investigate attention biases for food cues, craving, and overeating in overweight and healthy-weight participants. Specifically, it was tested whether attention allocation processes toward high-fat foods differ between overweight and normal weight individuals and whether selective attention biases for food cues are related to craving and food intake. Eye movements were recorded as a direct index of attention allocation in a sample of 22 overweight/obese and 29 healthy-weight female students during a visual probe task with food pictures. In addition, self-reported craving and actual food intake during a bogus "taste-test" were assessed. Overweight participants showed an approach-avoidance pattern of attention allocation toward high-fat food. Overweight participants directed their first gaze more often toward food pictures than healthy-weight individuals, but subsequently showed reduced maintenance of attention on these pictures. For overweight participants, craving was related to initial orientation toward food. Moreover, overweight participants consumed significantly more snack food than healthy-weight participants. Results emphasize the importance of identifying different attention bias components in overweight individuals with regard to craving and subsequent overeating.

How many dinosaur species were there? Fossil bias and true richness estimated using a Poisson sampling model

PubMed Central

Starrfelt, Jostein; Liow, Lee Hsiang

2016-01-01

The fossil record is a rich source of information about biological diversity in the past. However, the fossil record is not only incomplete but has also inherent biases due to geological, physical, chemical and biological factors. Our knowledge of past life is also biased because of differences in academic and amateur interests and sampling efforts. As a result, not all individuals or species that lived in the past are equally likely to be discovered at any point in time or space. To reconstruct temporal dynamics of diversity using the fossil record, biased sampling must be explicitly taken into account. Here, we introduce an approach that uses the variation in the number of times each species is observed in the fossil record to estimate both sampling bias and true richness. We term our technique TRiPS (True Richness estimated using a Poisson Sampling model) and explore its robustness to violation of its assumptions via simulations. We then venture to estimate sampling bias and absolute species richness of dinosaurs in the geological stages of the Mesozoic. Using TRiPS, we estimate that 1936 (1543–2468) species of dinosaurs roamed the Earth during the Mesozoic. We also present improved estimates of species richness trajectories of the three major dinosaur clades: the sauropodomorphs, ornithischians and theropods, casting doubt on the Jurassic–Cretaceous extinction event and demonstrating that all dinosaur groups are subject to considerable sampling bias throughout the Mesozoic. PMID:26977060

How many dinosaur species were there? Fossil bias and true richness estimated using a Poisson sampling model.

PubMed

Starrfelt, Jostein; Liow, Lee Hsiang

2016-04-05

The fossil record is a rich source of information about biological diversity in the past. However, the fossil record is not only incomplete but has also inherent biases due to geological, physical, chemical and biological factors. Our knowledge of past life is also biased because of differences in academic and amateur interests and sampling efforts. As a result, not all individuals or species that lived in the past are equally likely to be discovered at any point in time or space. To reconstruct temporal dynamics of diversity using the fossil record, biased sampling must be explicitly taken into account. Here, we introduce an approach that uses the variation in the number of times each species is observed in the fossil record to estimate both sampling bias and true richness. We term our technique TRiPS (True Richness estimated using a Poisson Sampling model) and explore its robustness to violation of its assumptions via simulations. We then venture to estimate sampling bias and absolute species richness of dinosaurs in the geological stages of the Mesozoic. Using TRiPS, we estimate that 1936 (1543-2468) species of dinosaurs roamed the Earth during the Mesozoic. We also present improved estimates of species richness trajectories of the three major dinosaur clades: the sauropodomorphs, ornithischians and theropods, casting doubt on the Jurassic-Cretaceous extinction event and demonstrating that all dinosaur groups are subject to considerable sampling bias throughout the Mesozoic. © 2016 The Authors.

Non-specific filtering of beta-distributed data.

PubMed

Wang, Xinhui; Laird, Peter W; Hinoue, Toshinori; Groshen, Susan; Siegmund, Kimberly D

2014-06-19

Non-specific feature selection is a dimension reduction procedure performed prior to cluster analysis of high dimensional molecular data. Not all measured features are expected to show biological variation, so only the most varying are selected for analysis. In DNA methylation studies, DNA methylation is measured as a proportion, bounded between 0 and 1, with variance a function of the mean. Filtering on standard deviation biases the selection of probes to those with mean values near 0.5. We explore the effect this has on clustering, and develop alternate filter methods that utilize a variance stabilizing transformation for Beta distributed data and do not share this bias. We compared results for 11 different non-specific filters on eight Infinium HumanMethylation data sets, selected to span a variety of biological conditions. We found that for data sets having a small fraction of samples showing abnormal methylation of a subset of normally unmethylated CpGs, a characteristic of the CpG island methylator phenotype in cancer, a novel filter statistic that utilized a variance-stabilizing transformation for Beta distributed data outperformed the common filter of using standard deviation of the DNA methylation proportion, or its log-transformed M-value, in its ability to detect the cancer subtype in a cluster analysis. However, the standard deviation filter always performed among the best for distinguishing subgroups of normal tissue. The novel filter and standard deviation filter tended to favour features in different genome contexts; for the same data set, the novel filter always selected more features from CpG island promoters and the standard deviation filter always selected more features from non-CpG island intergenic regions. Interestingly, despite selecting largely non-overlapping sets of features, the two filters did find sample subsets that overlapped for some real data sets. We found two different filter statistics that tended to prioritize features with different characteristics, each performed well for identifying clusters of cancer and non-cancer tissue, and identifying a cancer CpG island hypermethylation phenotype. Since cluster analysis is for discovery, we would suggest trying both filters on any new data sets, evaluating the overlap of features selected and clusters discovered.

Limitations of studies on school-based nutrition education interventions for obesity in China: a systematic review and meta-analysis.

PubMed

Kong, Kaimeng; Liu, Jie; Tao, Yexuan

2016-01-01

School-based nutrition education has been widely implemented in recent years to fight the increasing prevalence of childhood obesity in China. A comprehensive literature search was performed using six databases to identify studies of school-based nutrition education interventions in China. The methodological quality and the risk of bias of selected literature were evaluated. Stratified analysis was performed to identify whether different methodologies influenced the estimated effect of the intervention. Seventeen articles were included in the analysis. Several of the included studies had inadequate intervention duration, inappropriate randomization methods, selection bias, unbalanced baseline characteristics between control and intervention groups, and absent sample size calculation. Overall, the studies showed no significant impact of nutrition education on obesity (OR=0.76; 95% CI=0.55-1.05; p=0.09). This can be compared with an OR of 0.68 for interventions aimed at preventing malnutrition and an OR of 0.49 for interventions aimed at preventing iron-deficiency anemia. When studies with unbalanced baseline characteristics between groups and selection bias in the study subjects were excluded, the impact of nutrition education on obesity was significant (OR=0.73; 95% CI=0.55-0.98; p=0.003). An analysis stratified according to the duration of intervention revealed that the intervention was effective only when it lasted for more than 2 years (OR=0.49, 95% CI=0.42-0.58; p<0.001). Studies of school-based nutrition education programs in China have some important limitations that might affect the estimated effectiveness of the intervention.

Selection Bias in College Admissions Test Scores. NBER Working Paper No. 14265

ERIC Educational Resources Information Center

Clark, Melissa; Rothstein, Jesse; Schanzenbach, Diane Whitmore

2008-01-01

Data from college admissions tests can provide a valuable measure of student achievement, but the non-representativeness of test-takers is an important concern. We examine selectivity bias in both state-level and school-level SAT and ACT averages. The degree of selectivity may differ importantly across and within schools, and across and within…

Design and methods in a survey of living conditions in the Arctic - the SLiCA study.

PubMed

Eliassen, Bent-Martin; Melhus, Marita; Kruse, Jack; Poppel, Birger; Broderstad, Ann Ragnhild

2012-03-19

The main objective of this study is to describe the methods and design of the survey of living conditions in the Arctic (SLiCA), relevant participation rates and the distribution of participants, as applicable to the survey data in Alaska, Greenland and Norway. This article briefly addresses possible selection bias in the data and also the ways to tackle it in future studies. Population-based cross-sectional survey. Indigenous individuals aged 16 years and older, living in Greenland, Alaska and in traditional settlement areas in Norway, were invited to participate. Random sampling methods were applied in Alaska and Greenland, while non-probability sampling methods were applied in Norway. Data were collected in 3 periods: in Alaska, from January 2002 to February 2003; in Greenland, from December 2003 to August 2006; and in Norway, in 2003 and from June 2006 to June 2008. The principal method in SLiCA was standardised face-to-face interviews using a questionnaire. A total of 663, 1,197 and 445 individuals were interviewed in Alaska, Greenland and Norway, respectively. Very high overall participation rates of 83% were obtained in Greenland and Alaska, while a more conventional rate of 57% was achieved in Norway. A predominance of female respondents was obtained in Alaska. Overall, the Sami cohort is older than the cohorts from Greenland and Alaska. Preliminary assessments suggest that selection bias in the Sami sample is plausible but not a major threat. Few or no threats to validity are detected in the data from Alaska and Greenland. Despite different sampling and recruitment methods, and sociocultural differences, a unique database has been generated, which shall be used to explore relationships between health and other living conditions variables.

Automating data analysis for two-dimensional gas chromatography/time-of-flight mass spectrometry non-targeted analysis of comparative samples.

PubMed

Titaley, Ivan A; Ogba, O Maduka; Chibwe, Leah; Hoh, Eunha; Cheong, Paul H-Y; Simonich, Staci L Massey

2018-03-16

Non-targeted analysis of environmental samples, using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC/ToF-MS), poses significant data analysis challenges due to the large number of possible analytes. Non-targeted data analysis of complex mixtures is prone to human bias and is laborious, particularly for comparative environmental samples such as contaminated soil pre- and post-bioremediation. To address this research bottleneck, we developed OCTpy, a Python™ script that acts as a data reduction filter to automate GC × GC/ToF-MS data analysis from LECO ® ChromaTOF ® software and facilitates selection of analytes of interest based on peak area comparison between comparative samples. We used data from polycyclic aromatic hydrocarbon (PAH) contaminated soil, pre- and post-bioremediation, to assess the effectiveness of OCTpy in facilitating the selection of analytes that have formed or degraded following treatment. Using datasets from the soil extracts pre- and post-bioremediation, OCTpy selected, on average, 18% of the initial suggested analytes generated by the LECO ® ChromaTOF ® software Statistical Compare feature. Based on this list, 63-100% of the candidate analytes identified by a highly trained individual were also selected by OCTpy. This process was accomplished in several minutes per sample, whereas manual data analysis took several hours per sample. OCTpy automates the analysis of complex mixtures of comparative samples, reduces the potential for human error during heavy data handling and decreases data analysis time by at least tenfold. Copyright © 2018 Elsevier B.V. All rights reserved.

Tools for assessing risk of reporting biases in studies and syntheses of studies: a systematic review.

PubMed

Page, Matthew J; McKenzie, Joanne E; Higgins, Julian P T

2018-03-14

Several scales, checklists and domain-based tools for assessing risk of reporting biases exist, but it is unclear how much they vary in content and guidance. We conducted a systematic review of the content and measurement properties of such tools. We searched for potentially relevant articles in Ovid MEDLINE, Ovid Embase, Ovid PsycINFO and Google Scholar from inception to February 2017. One author screened all titles, abstracts and full text articles, and collected data on tool characteristics. We identified 18 tools that include an assessment of the risk of reporting bias. Tools varied in regard to the type of reporting bias assessed (eg, bias due to selective publication, bias due to selective non-reporting), and the level of assessment (eg, for the study as a whole, a particular result within a study or a particular synthesis of studies). Various criteria are used across tools to designate a synthesis as being at 'high' risk of bias due to selective publication (eg, evidence of funnel plot asymmetry, use of non-comprehensive searches). However, the relative weight assigned to each criterion in the overall judgement is unclear for most of these tools. Tools for assessing risk of bias due to selective non-reporting guide users to assess a study, or an outcome within a study, as 'high' risk of bias if no results are reported for an outcome. However, assessing the corresponding risk of bias in a synthesis that is missing the non-reported outcomes is outside the scope of most of these tools. Inter-rater agreement estimates were available for five tools. There are several limitations of existing tools for assessing risk of reporting biases, in terms of their scope, guidance for reaching risk of bias judgements and measurement properties. Development and evaluation of a new, comprehensive tool could help overcome present limitations. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Reward-related attentional bias and adolescent substance use: a prognostic relationship?

PubMed

van Hemel-Ruiter, Madelon E; de Jong, Peter J; Ostafin, Brian D; Oldehinkel, Albertine J

2015-01-01

Current cognitive-motivational addiction theories propose that prioritizing appetitive, reward-related information (attentional bias) plays a vital role in substance abuse behavior. Previous cross-sectional research has shown that adolescent substance use is related to reward-related attentional biases. The present study was designed to extend these findings by testing whether these reward biases have predictive value for adolescent substance use at three-year follow-up. Participants (N = 657, mean age = 16.2 yrs at baseline) were a sub-sample of Tracking Adolescents' Individual Lives Survey (TRAILS), a large longitudinal community cohort study. We used a spatial orienting task as a behavioral index of appetitive-related attentional processes at baseline and a substance use questionnaire at both baseline and three years follow-up. Bivariate correlational analyses showed that enhanced attentional engagement with cues that predicted potential reward and nonpunishment was positively associated with substance use (alcohol, tobacco, and cannabis) three years later. However, reward bias was not predictive of changes in substance use. A post-hoc analysis in a selection of adolescents who started using illicit drugs (other than cannabis) in the follow-up period demonstrated that stronger baseline attentional engagement toward cues of nonpunishment was related to a higher level of illicit drug use three years later. The finding that reward bias was not predictive for the increase in substance use in adolescents who already started using substances at baseline, but did show prognostic value in adolescents who initiated drug use in between baseline and follow-up suggests that appetitive bias might be especially important in the initiation stages of adolescent substance use.

Reward-Related Attentional Bias and Adolescent Substance Use: A Prognostic Relationship?

PubMed Central

van Hemel-Ruiter, Madelon E.; de Jong, Peter J.; Ostafin, Brian D.; Oldehinkel, Albertine J.

2015-01-01

Current cognitive-motivational addiction theories propose that prioritizing appetitive, reward-related information (attentional bias) plays a vital role in substance abuse behavior. Previous cross-sectional research has shown that adolescent substance use is related to reward-related attentional biases. The present study was designed to extend these findings by testing whether these reward biases have predictive value for adolescent substance use at three-year follow-up. Participants (N = 657, mean age = 16.2 yrs at baseline) were a sub-sample of Tracking Adolescents’ Individual Lives Survey (TRAILS), a large longitudinal community cohort study. We used a spatial orienting task as a behavioral index of appetitive-related attentional processes at baseline and a substance use questionnaire at both baseline and three years follow-up. Bivariate correlational analyses showed that enhanced attentional engagement with cues that predicted potential reward and nonpunishment was positively associated with substance use (alcohol, tobacco, and cannabis) three years later. However, reward bias was not predictive of changes in substance use. A post-hoc analysis in a selection of adolescents who started using illicit drugs (other than cannabis) in the follow-up period demonstrated that stronger baseline attentional engagement toward cues of nonpunishment was related to a higher level of illicit drug use three years later. The finding that reward bias was not predictive for the increase in substance use in adolescents who already started using substances at baseline, but did show prognostic value in adolescents who initiated drug use in between baseline and follow-up suggests that appetitive bias might be especially important in the initiation stages of adolescent substance use. PMID:25816295

How did women count? A note on gender-specific age heaping differences in the sixteenth to nineteenth centuries.

PubMed

Földvári, Peter; Van Leeuwen, Bas; Van Leeuwen-Li, Jieli

2012-01-01

The role of human capital in economic growth is now largely uncontested. One indicator of human capital frequently used for the pre-1900 period is age heaping, which has been increasingly used to measure gender-specific differences. In this note, we find that in some historical samples, married women heap significantly less than unmarried women. This is still true after correcting for possible selection effects. A possible explanation is that a percentage of women adapted their ages to that of their husbands, hence biasing the Whipple index. We find the same effect to a lesser extent for men. Since this bias differs over time and across countries, a consistent comparison of female age heaping should be made by focusing on unmarried women.

A bayesian hierarchical model for classification with selection of functional predictors.

PubMed

Zhu, Hongxiao; Vannucci, Marina; Cox, Dennis D

2010-06-01

In functional data classification, functional observations are often contaminated by various systematic effects, such as random batch effects caused by device artifacts, or fixed effects caused by sample-related factors. These effects may lead to classification bias and thus should not be neglected. Another issue of concern is the selection of functions when predictors consist of multiple functions, some of which may be redundant. The above issues arise in a real data application where we use fluorescence spectroscopy to detect cervical precancer. In this article, we propose a Bayesian hierarchical model that takes into account random batch effects and selects effective functions among multiple functional predictors. Fixed effects or predictors in nonfunctional form are also included in the model. The dimension of the functional data is reduced through orthonormal basis expansion or functional principal components. For posterior sampling, we use a hybrid Metropolis-Hastings/Gibbs sampler, which suffers slow mixing. An evolutionary Monte Carlo algorithm is applied to improve the mixing. Simulation and real data application show that the proposed model provides accurate selection of functional predictors as well as good classification.

Prospective identification of parasitic sequences in phage display screens

PubMed Central

Matochko, Wadim L.; Cory Li, S.; Tang, Sindy K.Y.; Derda, Ratmir

2014-01-01

Phage display empowered the development of proteins with new function and ligands for clinically relevant targets. In this report, we use next-generation sequencing to analyze phage-displayed libraries and uncover a strong bias induced by amplification preferences of phage in bacteria. This bias favors fast-growing sequences that collectively constitute <0.01% of the available diversity. Specifically, a library of 109 random 7-mer peptides (Ph.D.-7) includes a few thousand sequences that grow quickly (the ‘parasites’), which are the sequences that are typically identified in phage display screens published to date. A similar collapse was observed in other libraries. Using Illumina and Ion Torrent sequencing and multiple biological replicates of amplification of Ph.D.-7 library, we identified a focused population of 770 ‘parasites’. In all, 197 sequences from this population have been identified in literature reports that used Ph.D.-7 library. Many of these enriched sequences have confirmed function (e.g. target binding capacity). The bias in the literature, thus, can be viewed as a selection with two different selection pressures: (i) target-binding selection, and (ii) amplification-induced selection. Enrichment of parasitic sequences could be minimized if amplification bias is removed. Here, we demonstrate that emulsion amplification in libraries of ∼106 diverse clones prevents the biased selection of parasitic clones. PMID:24217917

Estimating causal contrasts involving intermediate variables in the presence of selection bias.

PubMed

Valeri, Linda; Coull, Brent A

2016-11-20

An important goal across the biomedical and social sciences is the quantification of the role of intermediate factors in explaining how an exposure exerts an effect on an outcome. Selection bias has the potential to severely undermine the validity of inferences on direct and indirect causal effects in observational as well as in randomized studies. The phenomenon of selection may arise through several mechanisms, and we here focus on instances of missing data. We study the sign and magnitude of selection bias in the estimates of direct and indirect effects when data on any of the factors involved in the analysis is either missing at random or not missing at random. Under some simplifying assumptions, the bias formulae can lead to nonparametric sensitivity analyses. These sensitivity analyses can be applied to causal effects on the risk difference and risk-ratio scales irrespectively of the estimation approach employed. To incorporate parametric assumptions, we also develop a sensitivity analysis for selection bias in mediation analysis in the spirit of the expectation-maximization algorithm. The approaches are applied to data from a health disparities study investigating the role of stage at diagnosis on racial disparities in colorectal cancer survival. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Methodological characteristics and treatment effect sizes in oral health randomised controlled trials: Is there a relationship? Protocol for a meta-epidemiological study.

PubMed

Saltaji, Humam; Armijo-Olivo, Susan; Cummings, Greta G; Amin, Maryam; Flores-Mir, Carlos

2014-02-25

It is fundamental that randomised controlled trials (RCTs) are properly conducted in order to reach well-supported conclusions. However, there is emerging evidence that RCTs are subject to biases which can overestimate or underestimate the true treatment effect, due to flaws in the study design characteristics of such trials. The extent to which this holds true in oral health RCTs, which have some unique design characteristics compared to RCTs in other health fields, is unclear. As such, we aim to examine the empirical evidence quantifying the extent of bias associated with methodological and non-methodological characteristics in oral health RCTs. We plan to perform a meta-epidemiological study, where a sample size of 60 meta-analyses (MAs) including approximately 600 RCTs will be selected. The MAs will be randomly obtained from the Oral Health Database of Systematic Reviews using a random number table; and will be considered for inclusion if they include a minimum of five RCTs, and examine a therapeutic intervention related to one of the recognised dental specialties. RCTs identified in selected MAs will be subsequently included if their study design includes a comparison between an intervention group and a placebo group or another intervention group. Data will be extracted from selected trials included in MAs based on a number of methodological and non-methodological characteristics. Moreover, the risk of bias will be assessed using the Cochrane Risk of Bias tool. Effect size estimates and measures of variability for the main outcome will be extracted from each RCT included in selected MAs, and a two-level analysis will be conducted using a meta-meta-analytic approach with a random effects model to allow for intra-MA and inter-MA heterogeneity. The intended audiences of the findings will include dental clinicians, oral health researchers, policymakers and graduate students. The aforementioned will be introduced to the findings through workshops, seminars, round table discussions and targeted individual meetings. Other opportunities for knowledge transfer will be pursued such as key dental conferences. Finally, the results will be published as a scientific report in a dental peer-reviewed journal.

Personality, Cognitive Style, Motivation, and Aptitude Predict Systematic Trends in Analytic Forecasting Behavior.

PubMed

Poore, Joshua C; Forlines, Clifton L; Miller, Sarah M; Regan, John R; Irvine, John M

2014-12-01

The decision sciences are increasingly challenged to advance methods for modeling analysts, accounting for both analytic strengths and weaknesses, to improve inferences taken from increasingly large and complex sources of data. We examine whether psychometric measures-personality, cognitive style, motivated cognition-predict analytic performance and whether psychometric measures are competitive with aptitude measures (i.e., SAT scores) as analyst sample selection criteria. A heterogeneous, national sample of 927 participants completed an extensive battery of psychometric measures and aptitude tests and was asked 129 geopolitical forecasting questions over the course of 1 year. Factor analysis reveals four dimensions among psychometric measures; dimensions characterized by differently motivated "top-down" cognitive styles predicted distinctive patterns in aptitude and forecasting behavior. These dimensions were not better predictors of forecasting accuracy than aptitude measures. However, multiple regression and mediation analysis reveals that these dimensions influenced forecasting accuracy primarily through bias in forecasting confidence. We also found that these facets were competitive with aptitude tests as forecast sampling criteria designed to mitigate biases in forecasting confidence while maximizing accuracy. These findings inform the understanding of individual difference dimensions at the intersection of analytic aptitude and demonstrate that they wield predictive power in applied, analytic domains.

Personality, Cognitive Style, Motivation, and Aptitude Predict Systematic Trends in Analytic Forecasting Behavior

PubMed Central

Forlines, Clifton L.; Miller, Sarah M.; Regan, John R.; Irvine, John M.

2014-01-01

The decision sciences are increasingly challenged to advance methods for modeling analysts, accounting for both analytic strengths and weaknesses, to improve inferences taken from increasingly large and complex sources of data. We examine whether psychometric measures—personality, cognitive style, motivated cognition—predict analytic performance and whether psychometric measures are competitive with aptitude measures (i.e., SAT scores) as analyst sample selection criteria. A heterogeneous, national sample of 927 participants completed an extensive battery of psychometric measures and aptitude tests and was asked 129 geopolitical forecasting questions over the course of 1 year. Factor analysis reveals four dimensions among psychometric measures; dimensions characterized by differently motivated “top-down” cognitive styles predicted distinctive patterns in aptitude and forecasting behavior. These dimensions were not better predictors of forecasting accuracy than aptitude measures. However, multiple regression and mediation analysis reveals that these dimensions influenced forecasting accuracy primarily through bias in forecasting confidence. We also found that these facets were competitive with aptitude tests as forecast sampling criteria designed to mitigate biases in forecasting confidence while maximizing accuracy. These findings inform the understanding of individual difference dimensions at the intersection of analytic aptitude and demonstrate that they wield predictive power in applied, analytic domains. PMID:25983670

Health Surveys Using Mobile Phones in Developing Countries: Automated Active Strata Monitoring and Other Statistical Considerations for Improving Precision and Reducing Biases

PubMed Central

Blynn, Emily; Ahmed, Saifuddin; Gibson, Dustin; Pariyo, George; Hyder, Adnan A

2017-01-01

In low- and middle-income countries (LMICs), historically, household surveys have been carried out by face-to-face interviews to collect survey data related to risk factors for noncommunicable diseases. The proliferation of mobile phone ownership and the access it provides in these countries offers a new opportunity to remotely conduct surveys with increased efficiency and reduced cost. However, the near-ubiquitous ownership of phones, high population mobility, and low cost require a re-examination of statistical recommendations for mobile phone surveys (MPS), especially when surveys are automated. As with landline surveys, random digit dialing remains the most appropriate approach to develop an ideal survey-sampling frame. Once the survey is complete, poststratification weights are generally applied to reduce estimate bias and to adjust for selectivity due to mobile ownership. Since weights increase design effects and reduce sampling efficiency, we introduce the concept of automated active strata monitoring to improve representativeness of the sample distribution to that of the source population. Although some statistical challenges remain, MPS represent a promising emerging means for population-level data collection in LMICs. PMID:28476726

Accounting for Selection Bias in Studies of Acute Cardiac Events.

PubMed

Banack, Hailey R; Harper, Sam; Kaufman, Jay S

2018-06-01

In cardiovascular research, pre-hospital mortality represents an important potential source of selection bias. Inverse probability of censoring weights are a method to account for this source of bias. The objective of this article is to examine and correct for the influence of selection bias due to pre-hospital mortality on the relationship between cardiovascular risk factors and all-cause mortality after an acute cardiac event. The relationship between the number of cardiovascular disease (CVD) risk factors (0-5; smoking status, diabetes, hypertension, dyslipidemia, and obesity) and all-cause mortality was examined using data from the Atherosclerosis Risk in Communities (ARIC) study. To illustrate the magnitude of selection bias, estimates from an unweighted generalized linear model with a log link and binomial distribution were compared with estimates from an inverse probability of censoring weighted model. In unweighted multivariable analyses the estimated risk ratio for mortality ranged from 1.09 (95% confidence interval [CI], 0.98-1.21) for 1 CVD risk factor to 1.95 (95% CI, 1.41-2.68) for 5 CVD risk factors. In the inverse probability of censoring weights weighted analyses, the risk ratios ranged from 1.14 (95% CI, 0.94-1.39) to 4.23 (95% CI, 2.69-6.66). Estimates from the inverse probability of censoring weighted model were substantially greater than unweighted, adjusted estimates across all risk factor categories. This shows the magnitude of selection bias due to pre-hospital mortality and effect on estimates of the effect of CVD risk factors on mortality. Moreover, the results highlight the utility of using this method to address a common form of bias in cardiovascular research. Copyright © 2018 Canadian Cardiovascular Society. Published by Elsevier Inc. All rights reserved.

The hospital anxiety and depression scale--dimensionality, reliability and construct validity among cognitively intact nursing home patients.

PubMed

Haugan, Gørill; Drageset, Jorunn

2014-08-01

Depression and anxiety are particularly common among individuals living in long-term care facilities. Therefore, access to a valid and reliable measure of anxiety and depression among nursing home patients is highly warranted. To investigate the dimensionality, reliability and construct validity of the Hospital Anxiety and Depression scale (HADS) in a cognitively intact nursing home population. Cross-sectional data were collected from two samples; 429 cognitively intact nursing home patients participated, representing 74 different Norwegian nursing homes. Confirmative factor analyses and correlations with selected constructs were used. The two-factor model provided a good fit in Sample1, revealing a poorer fit in Sample2. Good-acceptable measurement reliability was demonstrated, and construct validity was supported. Using listwise deletion the sample sizes were 227 and 187, for Sample1 and Sample2, respectively. Greater sample sizes would have strengthen the statistical power in the tests. The researchers visited the participants to help fill in the questionnaires; this might have introduced some bias into the respondents׳ reporting. The 14 HADS items were part of greater questionnaires. Thus, frail, older NH patients might have tired during the interview causing a possible bias. Low reliability for depression was disclosed, mainly resulting from three items appearing to be inappropriate indicators for depression in this population. Further research is needed exploring which items might perform as more reliably indicators for depression among nursing home patients. Copyright © 2014 Elsevier B.V. All rights reserved.

Adjustment of pesticide concentrations for temporal changes in analytical recovery, 1992–2010

USGS Publications Warehouse

Martin, Jeffrey D.; Eberle, Michael

2011-01-01

Recovery is the proportion of a target analyte that is quantified by an analytical method and is a primary indicator of the analytical bias of a measurement. Recovery is measured by analysis of quality-control (QC) water samples that have known amounts of target analytes added ("spiked" QC samples). For pesticides, recovery is the measured amount of pesticide in the spiked QC sample expressed as a percentage of the amount spiked, ideally 100 percent. Temporal changes in recovery have the potential to adversely affect time-trend analysis of pesticide concentrations by introducing trends in apparent environmental concentrations that are caused by trends in performance of the analytical method rather than by trends in pesticide use or other environmental conditions. This report presents data and models related to the recovery of 44 pesticides and 8 pesticide degradates (hereafter referred to as "pesticides") that were selected for a national analysis of time trends in pesticide concentrations in streams. Water samples were analyzed for these pesticides from 1992 through 2010 by gas chromatography/mass spectrometry. Recovery was measured by analysis of pesticide-spiked QC water samples. Models of recovery, based on robust, locally weighted scatterplot smooths (lowess smooths) of matrix spikes, were developed separately for groundwater and stream-water samples. The models of recovery can be used to adjust concentrations of pesticides measured in groundwater or stream-water samples to 100 percent recovery to compensate for temporal changes in the performance (bias) of the analytical method.

New Evaluation of the Electronically Activated Recorder (EAR): Obtrusiveness, Compliance, and Participant Self-selection Effects.

PubMed

Manson, Joseph H; Robbins, Megan L

2017-01-01

The Electronically Activated Recorder (EAR) is a method for collecting periodic brief audio snippets of participants' daily lives using a portable recording device. The EAR can potentially intrude into people's privacy, alter their natural behavior, and introduce self-selection biases greater than in other types of social science methods. Previous research (Mehl and Holleran, 2007, hereafter M&H) has shown that participant non-compliance with, and perceived obtrusiveness of, an EAR protocol are both low. However, these questions have not been addressed in jurisdictions that require the consent of all parties to recording conversations. This EAR study required participants to wear a button bearing a microphone icon and the words "This conversation may be recorded" to comply with California's all-party consent law. Results revealed self-reported obtrusiveness and non-compliance were actually lower in the present study than in the M&H study. Behaviorally assessed non-compliance did not differ between the two studies. Participants in the present study talked more about being in the study than participants in the M&H study, but such talk still comprised <2% of sampled conversations. Another potential problem with the EAR, participant self-selection bias, was addressed by comparing the EAR volunteers' HEXACO personality dimensions to a non-volunteer sample drawn from the same student population. EAR volunteers were significantly and moderately higher in Conscientiousness, and lower in Emotionality, than non-volunteers. In conclusion, the EAR method can be successfully implemented in at least one all-party consent state (California). Interested researchers are encouraged to review this procedure with their own legal counsel.

Reducing the number of reconstructions needed for estimating channelized observer performance

NASA Astrophysics Data System (ADS)

Pineda, Angel R.; Miedema, Hope; Brenner, Melissa; Altaf, Sana

2018-03-01

A challenge for task-based optimization is the time required for each reconstructed image in applications where reconstructions are time consuming. Our goal is to reduce the number of reconstructions needed to estimate the area under the receiver operating characteristic curve (AUC) of the infinitely-trained optimal channelized linear observer. We explore the use of classifiers which either do not invert the channel covariance matrix or do feature selection. We also study the assumption that multiple low contrast signals in the same image of a non-linear reconstruction do not significantly change the estimate of the AUC. We compared the AUC of several classifiers (Hotelling, logistic regression, logistic regression using Firth bias reduction and the least absolute shrinkage and selection operator (LASSO)) with a small number of observations both for normal simulated data and images from a total variation reconstruction in magnetic resonance imaging (MRI). We used 10 Laguerre-Gauss channels and the Mann-Whitney estimator for AUC. For this data, our results show that at small sample sizes feature selection using the LASSO technique can decrease bias of the AUC estimation with increased variance and that for large sample sizes the difference between these classifiers is small. We also compared the use of multiple signals in a single reconstructed image to reduce the number of reconstructions in a total variation reconstruction for accelerated imaging in MRI. We found that AUC estimation using multiple low contrast signals in the same image resulted in similar AUC estimates as doing a single reconstruction per signal leading to a 13x reduction in the number of reconstructions needed.

Strong spurious transcription likely contributes to DNA insert bias in typical metagenomic clone libraries.

PubMed

Lam, Kathy N; Charles, Trevor C

2015-01-01

Clone libraries provide researchers with a powerful resource to study nucleic acid from diverse sources. Metagenomic clone libraries in particular have aided in studies of microbial biodiversity and function, and allowed the mining of novel enzymes. Libraries are often constructed by cloning large inserts into cosmid or fosmid vectors. Recently, there have been reports of GC bias in fosmid metagenomic libraries, and it was speculated to be a result of fragmentation and loss of AT-rich sequences during cloning. However, evidence in the literature suggests that transcriptional activity or gene product toxicity may play a role. To explore possible mechanisms responsible for sequence bias in clone libraries, we constructed a cosmid library from a human microbiome sample and sequenced DNA from different steps during library construction: crude extract DNA, size-selected DNA, and cosmid library DNA. We confirmed a GC bias in the final cosmid library, and we provide evidence that the bias is not due to fragmentation and loss of AT-rich sequences but is likely occurring after DNA is introduced into Escherichia coli. To investigate the influence of strong constitutive transcription, we searched the sequence data for promoters and found that rpoD/σ(70) promoter sequences were underrepresented in the cosmid library. Furthermore, when we examined the genomes of taxa that were differentially abundant in the cosmid library relative to the original sample, we found the bias to be more correlated with the number of rpoD/σ(70) consensus sequences in the genome than with simple GC content. The GC bias of metagenomic libraries does not appear to be due to DNA fragmentation. Rather, analysis of promoter sequences provides support for the hypothesis that strong constitutive transcription from sequences recognized as rpoD/σ(70) consensus-like in E. coli may lead to instability, causing loss of the plasmid or loss of the insert DNA that gives rise to the transcription. Despite widespread use of E. coli to propagate foreign DNA in metagenomic libraries, the effects of in vivo transcriptional activity on clone stability are not well understood. Further work is required to tease apart the effects of transcription from those of gene product toxicity.

Bias against foreign-born or foreign-trained doctors: experimental evidence.

PubMed

Louis, Winnifred R; Lalonde, Richard N; Esses, Victoria M

2010-12-01

Bias against foreign-born or -trained medical students and doctors is not well understood, despite its documented impact on recruitment, integration and retention. This research experimentally examines the interaction of location of medical education and nationality in evaluations of doctors' competence and trustworthiness. A convenience sample of prospective patients evaluated fictitious candidates for a position as a doctor in community practice at a new local health clinic. All applicants were described as having the same personality profile, legal qualifications to practise, a multi-degree education and relevant work experience. The location of medical education (the candidate's home country or the UK) and national background (Australia or Pakistan) of the applicants were independently experimentally manipulated. Consistent with previous research on skills discounting and bias, foreign-born candidates were evaluated less favourably than native-born candidates, despite their comparable education level, work experience and personality. However, overseas medical education obtained in the First World both boosted evaluations (of competence and trustworthiness) and attenuated bias based on nationality. The present findings demonstrate the selective discounting of foreign-born doctors' credentials. The data show an interaction of location of medical education and birth nationality in bias against foreign doctors. On an applied level, the data document that the benefits of medical education obtained in the First World can extend beyond its direct outcomes (high-quality training and institutional recognition) to the indirect benefit of the attenuation of patient bias based on nationality. © Blackwell Publishing Ltd 2010.

Sampling bias in blending validation and a different approach to homogeneity assessment.

PubMed

Kraemer, J; Svensson, J R; Melgaard, H

1999-02-01

Sampling of batches studied for validation is reported. A thief particularly suited for granules, rather than cohesive powders, was used in the study. It is shown, as has been demonstrated in the past, that traditional 1x to 3x thief sampling of a blend is biased, and that the bias decreases as the sample size increases. It is shown that taking 50 samples of tablets after blending and testing this subpopulation for normality is a discriminating manner of testing for homogeneity. As a criterion, it is better than sampling at mixer or drum stage would be even if an unbiased sampling device were available.

The Difference-in-Difference Method: Assessing the Selection Bias in the Effects of Neighborhood Environment on Health

PubMed Central

Grafova, Irina; Freedman, Vicki; Lurie, Nicole; Kumar, Rizie; Rogowski, Jeannette

2013-01-01

This paper uses the difference-in-difference estimation approach to explore the self-selection bias in estimating the effect of neighborhood economic environment on self-assessed health among older adults. The results indicate that there is evidence of downward bias in the conventional estimates of the effect of neighborhood economic disadvantage on self-reported health, representing a lower bound of the true effect. PMID:23623818

Characterization of the porcine epidemic diarrhea virus codon usage bias.

PubMed

Chen, Ye; Shi, Yuzhen; Deng, Hongjuan; Gu, Ting; Xu, Jian; Ou, Jinxin; Jiang, Zhiguo; Jiao, Yiren; Zou, Tan; Wang, Chong

2014-12-01

Porcine epidemic diarrhea virus (PEDV) has been responsible for several recent outbreaks of porcine epidemic diarrhea (PED) and has caused great economic loss in the swine-raising industry. Considering the significance of PEDV, a systemic analysis was performed to study its codon usage patterns. The relative synonymous codon usage value of each codon revealed that codon usage bias exists and that PEDV tends to use codons that end in T. The mean ENC value of 47.91 indicates that the codon usage bias is low. However, we still wanted to identify the cause of this codon usage bias. A correlation analysis between the codon compositions (A3s, T3s, G3s, C3s, and GC3s), the ENC values, and the nucleotide contents (A%, T%, G%, C%, and GC%) indicated that mutational bias plays role in shaping the PEDV codon usage bias. This was further confirmed by a principal component analysis between the codon compositions and the axis values. Using the Gravy, Aroma, and CAI values, a role of natural selection in the PEDV codon usage pattern was also identified. Neutral analysis indicated that natural selection pressure plays a more important role than mutational bias in codon usage bias. Natural selection also plays an increasingly significant role during PEDV evolution. Additionally, gene function and geographic distribution also influence the codon usage bias to a degree. Copyright © 2014 Elsevier B.V. All rights reserved.

A new standard of visual data representation for imaging mass spectrometry.

PubMed

O'Rourke, Matthew B; Padula, Matthew P

2017-03-01

MALDI imaging MS (IMS) is principally used for cancer diagnostics. In our own experience with publishing IMS data, we have been requested to modify our protocols with respect to the areas of the tissue that are imaged in order to comply with the wider literature. In light of this, we have determined that current methodologies lack effective controls and can potentially introduce bias by only imaging specific areas of the targeted tissue EXPERIMENTAL DESIGN: A previously imaged sample was selected and then cropped in different ways to show the potential effect of only imaging targeted areas. By using a model sample, we were able to effectively show how selective imaging of samples can misinterpret tissue features and by changing the areas that are acquired, according to our new standard, an effective internal control can be introduced. Current IMS sampling convention relies on the assumption that sample preparation has been performed correctly. This prevents users from checking whether molecules have moved beyond borders of the tissue due to delocalization and consequentially products of improper sample preparation could be interpreted as biological features that are of critical importance when encountered in a visual diagnostic. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

An Improved Correction for Range Restricted Correlations Under Extreme, Monotonic Quadratic Nonlinearity and Heteroscedasticity.

PubMed

Culpepper, Steven Andrew

2016-06-01

Standardized tests are frequently used for selection decisions, and the validation of test scores remains an important area of research. This paper builds upon prior literature about the effect of nonlinearity and heteroscedasticity on the accuracy of standard formulas for correcting correlations in restricted samples. Existing formulas for direct range restriction require three assumptions: (1) the criterion variable is missing at random; (2) a linear relationship between independent and dependent variables; and (3) constant error variance or homoscedasticity. The results in this paper demonstrate that the standard approach for correcting restricted correlations is severely biased in cases of extreme monotone quadratic nonlinearity and heteroscedasticity. This paper offers at least three significant contributions to the existing literature. First, a method from the econometrics literature is adapted to provide more accurate estimates of unrestricted correlations. Second, derivations establish bounds on the degree of bias attributed to quadratic functions under the assumption of a monotonic relationship between test scores and criterion measurements. New results are presented on the bias associated with using the standard range restriction correction formula, and the results show that the standard correction formula yields estimates of unrestricted correlations that deviate by as much as 0.2 for high to moderate selectivity. Third, Monte Carlo simulation results demonstrate that the new procedure for correcting restricted correlations provides more accurate estimates in the presence of quadratic and heteroscedastic test score and criterion relationships.

Measuring the hydrostatic mass bias in galaxy clusters by combining Sunyaev-Zel'dovich and CMB lensing data

NASA Astrophysics Data System (ADS)

Hurier, G.; Angulo, R. E.

2018-02-01

The cosmological parameters preferred by the cosmic microwave background (CMB) primary anisotropies predict many more galaxy clusters than those that have been detected via the thermal Sunyaev-Zeldovich (tSZ) effect. This discrepancy has attracted considerable attention since it might be evidence of physics beyond the simplest ΛCDM model. However, an accurate and robust calibration of the mass-observable relation for clusters is necessary for the comparison, which has been proven difficult to obtain so far. Here, we present new constraints on the mass-pressure relation by combining tSZ and CMB lensing measurements of optically selected clusters. Consequently, our galaxy cluster sample is independent of the data employed to derive cosmological constrains. We estimate an average hydrostatic mass bias of b = 0.26 ± 0.07, with no significant mass or redshift evolution. This value greatly reduces the discrepancy between the predictions of ΛCDM and the observed abundance of tSZ clusters but agrees with recent estimates from tSZ clustering. On the other hand, our value for b is higher than the predictions from hydrodynamical simulations. This suggests mechanisms that drive large departures from hydrostatic equilibrium and that are not included in the latest simulations, and/or unaccounted systematic errors such as biases in the cluster catalogue that are due to the optical selection.

A novel method for expediting the development of patient-reported outcome measures and an evaluation across several populations

PubMed Central

Garrard, Lili; Price, Larry R.; Bott, Marjorie J.; Gajewski, Byron J.

2016-01-01

Item response theory (IRT) models provide an appropriate alternative to the classical ordinal confirmatory factor analysis (CFA) during the development of patient-reported outcome measures (PROMs). Current literature has identified the assessment of IRT model fit as both challenging and underdeveloped (Sinharay & Johnson, 2003; Sinharay, Johnson, & Stern, 2006). This study evaluates the performance of Ordinal Bayesian Instrument Development (OBID), a Bayesian IRT model with a probit link function approach, through applications in two breast cancer-related instrument development studies. The primary focus is to investigate an appropriate method for comparing Bayesian IRT models in PROMs development. An exact Bayesian leave-one-out cross-validation (LOO-CV) approach (Vehtari & Lampinen, 2002) is implemented to assess prior selection for the item discrimination parameter in the IRT model and subject content experts’ bias (in a statistical sense and not to be confused with psychometric bias as in differential item functioning) toward the estimation of item-to-domain correlations. Results support the utilization of content subject experts’ information in establishing evidence for construct validity when sample size is small. However, the incorporation of subject experts’ content information in the OBID approach can be sensitive to the level of expertise of the recruited experts. More stringent efforts need to be invested in the appropriate selection of subject experts to efficiently use the OBID approach and reduce potential bias during PROMs development. PMID:27667878

A novel method for expediting the development of patient-reported outcome measures and an evaluation across several populations.

PubMed

Garrard, Lili; Price, Larry R; Bott, Marjorie J; Gajewski, Byron J

2016-10-01

Item response theory (IRT) models provide an appropriate alternative to the classical ordinal confirmatory factor analysis (CFA) during the development of patient-reported outcome measures (PROMs). Current literature has identified the assessment of IRT model fit as both challenging and underdeveloped (Sinharay & Johnson, 2003; Sinharay, Johnson, & Stern, 2006). This study evaluates the performance of Ordinal Bayesian Instrument Development (OBID), a Bayesian IRT model with a probit link function approach, through applications in two breast cancer-related instrument development studies. The primary focus is to investigate an appropriate method for comparing Bayesian IRT models in PROMs development. An exact Bayesian leave-one-out cross-validation (LOO-CV) approach (Vehtari & Lampinen, 2002) is implemented to assess prior selection for the item discrimination parameter in the IRT model and subject content experts' bias (in a statistical sense and not to be confused with psychometric bias as in differential item functioning) toward the estimation of item-to-domain correlations. Results support the utilization of content subject experts' information in establishing evidence for construct validity when sample size is small. However, the incorporation of subject experts' content information in the OBID approach can be sensitive to the level of expertise of the recruited experts. More stringent efforts need to be invested in the appropriate selection of subject experts to efficiently use the OBID approach and reduce potential bias during PROMs development.

Velocity segregation and systematic biases in velocity dispersion estimates with the SPT-GMOS spectroscopic survey

DOE PAGES

Bayliss, Matthew. B.; Zengo, Kyle; Ruel, Jonathan; ...

2017-03-07

The velocity distribution of galaxies in clusters is not universal; rather, galaxies are segregated according to their spectral type and relative luminosity. We examine the velocity distributions of different populations of galaxies within 89 Sunyaev Zel'dovich (SZ) selected galaxy clusters spanningmore » $ 0.28 < z < 1.08$. Our sample is primarily draw from the SPT-GMOS spectroscopic survey, supplemented by additional published spectroscopy, resulting in a final spectroscopic sample of 4148 galaxy spectra---2868 cluster members. The velocity dispersion of star-forming cluster galaxies is $$17\\pm4$$% greater than that of passive cluster galaxies, and the velocity dispersion of bright ($$m < m^{*}-0.5$$) cluster galaxies is $$11\\pm4$$% lower than the velocity dispersion of our total member population. We find good agreement with simulations regarding the shape of the relationship between the measured velocity dispersion and the fraction of passive vs. star-forming galaxies used to measure it, but we find a small offset between this relationship as measured in data and simulations in which suggests that our dispersions are systematically low by as much as 3\\% relative to simulations. We argue that this offset could be interpreted as a measurement of the effective velocity bias that describes the ratio of our observed velocity dispersions and the intrinsic velocity dispersion of dark matter particles in a published simulation result. Here, by measuring velocity bias in this way suggests that large spectroscopic surveys can improve dispersion-based mass-observable scaling relations for cosmology even in the face of velocity biases, by quantifying and ultimately calibrating them out.« less

Velocity segregation and systematic biases in velocity dispersion estimates with the SPT-GMOS spectroscopic survey

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bayliss, Matthew. B.; Zengo, Kyle; Ruel, Jonathan

The velocity distribution of galaxies in clusters is not universal; rather, galaxies are segregated according to their spectral type and relative luminosity. We examine the velocity distributions of different populations of galaxies within 89 Sunyaev Zel'dovich (SZ) selected galaxy clusters spanningmore » $ 0.28 < z < 1.08$. Our sample is primarily draw from the SPT-GMOS spectroscopic survey, supplemented by additional published spectroscopy, resulting in a final spectroscopic sample of 4148 galaxy spectra---2868 cluster members. The velocity dispersion of star-forming cluster galaxies is $$17\\pm4$$% greater than that of passive cluster galaxies, and the velocity dispersion of bright ($$m < m^{*}-0.5$$) cluster galaxies is $$11\\pm4$$% lower than the velocity dispersion of our total member population. We find good agreement with simulations regarding the shape of the relationship between the measured velocity dispersion and the fraction of passive vs. star-forming galaxies used to measure it, but we find a small offset between this relationship as measured in data and simulations in which suggests that our dispersions are systematically low by as much as 3\\% relative to simulations. We argue that this offset could be interpreted as a measurement of the effective velocity bias that describes the ratio of our observed velocity dispersions and the intrinsic velocity dispersion of dark matter particles in a published simulation result. Here, by measuring velocity bias in this way suggests that large spectroscopic surveys can improve dispersion-based mass-observable scaling relations for cosmology even in the face of velocity biases, by quantifying and ultimately calibrating them out.« less

Velocity Segregation and Systematic Biases In Velocity Dispersion Estimates with the SPT-GMOS Spectroscopic Survey

NASA Astrophysics Data System (ADS)

Bayliss, Matthew. B.; Zengo, Kyle; Ruel, Jonathan; Benson, Bradford A.; Bleem, Lindsey E.; Bocquet, Sebastian; Bulbul, Esra; Brodwin, Mark; Capasso, Raffaella; Chiu, I.-non; McDonald, Michael; Rapetti, David; Saro, Alex; Stalder, Brian; Stark, Antony A.; Strazzullo, Veronica; Stubbs, Christopher W.; Zenteno, Alfredo

2017-03-01

The velocity distribution of galaxies in clusters is not universal; rather, galaxies are segregated according to their spectral type and relative luminosity. We examine the velocity distributions of different populations of galaxies within 89 Sunyaev Zel’dovich (SZ) selected galaxy clusters spanning 0.28< z< 1.08. Our sample is primarily draw from the SPT-GMOS spectroscopic survey, supplemented by additional published spectroscopy, resulting in a final spectroscopic sample of 4148 galaxy spectra—2868 cluster members. The velocity dispersion of star-forming cluster galaxies is 17 ± 4% greater than that of passive cluster galaxies, and the velocity dispersion of bright (m< {m}* -0.5) cluster galaxies is 11 ± 4% lower than the velocity dispersion of our total member population. We find good agreement with simulations regarding the shape of the relationship between the measured velocity dispersion and the fraction of passive versus star-forming galaxies used to measure it, but we find a small offset between this relationship as measured in data and simulations, which suggests that our dispersions are systematically low by as much as 3% relative to simulations. We argue that this offset could be interpreted as a measurement of the effective velocity bias that describes the ratio of our observed velocity dispersions and the intrinsic velocity dispersion of dark matter particles in a published simulation result. Measuring velocity bias in this way suggests that large spectroscopic surveys can improve dispersion-based mass-observable scaling relations for cosmology even in the face of velocity biases, by quantifying and ultimately calibrating them out.

Effects of select and reject control on equivalence class formation and transfer of function.

PubMed

Perez, William F; Tomanari, Gerson Y; Vaidya, Manish

2015-09-01

The present study used a single-subject design to evaluate the effects of select or reject control on equivalence class formation and transfer of function. Adults were exposed to a matching-to-sample task with observing requirements (MTS-OR) in order to bias the establishment of sample/S+ (select) or sample/S- (reject) relations. In Experiment 1, four sets of baseline conditional relations were taught-two under reject control (A1B2C1, A2B1C2) and two under select control (D1E1F1, D2E2F2). Participants were tested for transitivity, symmetry, equivalence and reflexivity. They also learned a simple discrimination involving one of the stimuli from the equivalence classes and were tested for the transfer of the discriminative function. In general, participants performed with high accuracy on all equivalence-related probes as well as the transfer of function probes under select control. Under reject control, participants had high scores only on the symmetry test; transfer of function was attributed to stimuli programmed as S-. In Experiment 2, the equivalence class under reject control was expanded to four members (A1B2C1D2; A2B1C2D1). Participants had high scores only on symmetry and on transitivity and equivalence tests involving two nodes. Transfer of function was extended to the programmed S- added to each class. Results from both experiments suggest that select and reject controls might differently affect the formation of equivalence classes and the transfer of stimulus functions. © Society for the Experimental Analysis of Behavior.

Getting closer to the truth: overcoming research challenges when estimating the financial impact of worksite health promotion programs.

PubMed

Ozminkowski, R J; Goetzel, R Z

2001-01-01

The authors describe the most important methodological challenges often encountered in conducting research and evaluation on the financial impact of health promotion. These include selection bias, skewed data, small sample size, metrics. They discuss when these problems can and cannot be overcome and suggest how some of these problems can be overcome through a creating an appropriate framework for the study, and using state of the art statistical methods.

Improved Signal Chains for Readout of CMOS Imagers

NASA Technical Reports Server (NTRS)

Pain, Bedabrata; Hancock, Bruce; Cunningham, Thomas

2009-01-01

An improved generic design has been devised for implementing signal chains involved in readout from complementary metal oxide/semiconductor (CMOS) image sensors and for other readout integrated circuits (ICs) that perform equivalent functions. The design applies to any such IC in which output signal charges from the pixels in a given row are transferred simultaneously into sampling capacitors at the bottoms of the columns, then voltages representing individual pixel charges are read out in sequence by sequentially turning on column-selecting field-effect transistors (FETs) in synchronism with source-follower- or operational-amplifier-based amplifier circuits. The improved design affords the best features of prior source-follower-and operational- amplifier-based designs while overcoming the major limitations of those designs. The limitations can be summarized as follows: a) For a source-follower-based signal chain, the ohmic voltage drop associated with DC bias current flowing through the column-selection FET causes unacceptable voltage offset, nonlinearity, and reduced small-signal gain. b) For an operational-amplifier-based signal chain, the required bias current and the output noise increase superlinearly with size of the pixel array because of a corresponding increase in the effective capacitance of the row bus used to couple the sampled column charges to the operational amplifier. The effect of the bus capacitance is to simultaneously slow down the readout circuit and increase noise through the Miller effect.

Baseline participation in a health examination survey of the population 65 years and older: who is missed and why?

PubMed

Gaertner, Beate; Seitz, Ina; Fuchs, Judith; Busch, Markus A; Holzhausen, Martin; Martus, Peter; Scheidt-Nave, Christa

2016-01-19

Public health monitoring depends on valid health and disability estimates in the population 65+ years. This is hampered by high non-participation rates in this age group. There is limited insight into size and direction of potential baseline selection bias. We analyzed baseline non-participation in a register-based random sample of 1481 inner-city residents 65+ years, invited to a health examination survey according to demographics available for the entire sample, self-report information as available and reasons for non-participation. One year after recruitment, non-responders were revisited to assess their reasons. Five groups defined by participation status were differentiated: participants (N = 299), persons who had died or moved (N = 173), those who declined participation, but answered a short questionnaire (N = 384), those who declined participation and the short questionnaire (N = 324), and non-responders (N = 301). The results confirm substantial baseline selection bias with significant underrepresentation of persons 85+ years, persons in residential care or from disadvantaged neighborhoods, with lower education, foreign citizenship, or lower health-related quality of life. Finally, reasons for non-participation could be identified for 78% of all non-participants, including 183 non-responders. A diversity in health problems and barriers to participation exists among non-participants. Innovative study designs are needed for public health monitoring in aging populations.

Improved Estimates of the Benefits of Breastfeeding Using Sibling Comparisons to Reduce Selection Bias

PubMed Central

Evenhouse, Eirik; Reilly, Siobhan

2005-01-01

Objective Better measurement of the health and cognitive benefits of breastfeeding by using sibling comparisons to reduce sample selection bias. Data We use data on the breastfeeding history, physical and emotional health, academic performance, cognitive ability, and demographic characteristics of 16,903 adolescents from the first (1994) wave of the National Longitudinal Study of Adolescent Health. The sample includes 2,734 sibling pairs. Study Design We examine the relationship between breastfeeding history and 15 indicators of physical health, emotional health, and cognitive ability, using ordinary least squares and logit regression. For each indicator, we estimate, in addition to the usual between-family model, a within-family model to see whether differences in siblings' outcomes are associated with differences in the siblings' breastfeeding histories. Principal Findings Nearly all of the correlations found in the between-family model become statistically insignificant in the within-family model. The notable exception is a persistent positive correlation between breastfeeding and cognitive ability. These findings hold whether breastfeeding is measured in terms of duration or as a Yes/No variable. Conclusions This study provides persuasive evidence of a causal connection between breastfeeding and intelligence. However, it also suggests that nonexperimental studies of breastfeeding overstate some of its other long-term benefits, even if controls are included for race, ethnicity, income, and education. PMID:16336548

Eliminating Survivor Bias in Two-stage Instrumental Variable Estimators.

PubMed

Vansteelandt, Stijn; Walter, Stefan; Tchetgen Tchetgen, Eric

2018-07-01

Mendelian randomization studies commonly focus on elderly populations. This makes the instrumental variables analysis of such studies sensitive to survivor bias, a type of selection bias. A particular concern is that the instrumental variable conditions, even when valid for the source population, may be violated for the selective population of individuals who survive the onset of the study. This is potentially very damaging because Mendelian randomization studies are known to be sensitive to bias due to even minor violations of the instrumental variable conditions. Interestingly, the instrumental variable conditions continue to hold within certain risk sets of individuals who are still alive at a given age when the instrument and unmeasured confounders exert additive effects on the exposure, and moreover, the exposure and unmeasured confounders exert additive effects on the hazard of death. In this article, we will exploit this property to derive a two-stage instrumental variable estimator for the effect of exposure on mortality, which is insulated against the above described selection bias under these additivity assumptions.

Visual Selective Attention Biases Contribute to the Other-Race Effect Among 9-Month-Old Infants

PubMed Central

Oakes, Lisa M.; Amso, Dima

2016-01-01

During the first year of life, infants maintain their ability to discriminate faces from their own race but become less able to differentiate other-race faces. Though this is likely due to daily experience with own-race faces, the mechanisms linking repeated exposure to optimal face processing remain unclear. One possibility is that frequent experience with own-race faces generates a selective attention bias to these faces. Selective attention elicits enhancement of attended information and suppression of distraction to improve visual processing of attended objects. Thus attention biases to own-race faces may boost processing and discrimination of these faces relative to other-race faces. We used a spatial cueing task to bias attention to own- or other-race faces among Caucasian 9-month-old infants. Infants discriminated faces in the focus of the attention bias, regardless of race, indicating that infants remained sensitive to differences among other-race faces. Instead, efficacy of face discrimination reflected the extent of attention engagement. PMID:26486228

Visual selective attention biases contribute to the other-race effect among 9-month-old infants.

PubMed

Markant, Julie; Oakes, Lisa M; Amso, Dima

2016-04-01

During the first year of life, infants maintain their ability to discriminate faces from their own race but become less able to differentiate other-race faces. Though this is likely due to daily experience with own-race faces, the mechanisms linking repeated exposure to optimal face processing remain unclear. One possibility is that frequent experience with own-race faces generates a selective attention bias to these faces. Selective attention elicits enhancement of attended information and suppression of distraction to improve visual processing of attended objects. Thus attention biases to own-race faces may boost processing and discrimination of these faces relative to other-race faces. We used a spatial cueing task to bias attention to own- or other-race faces among Caucasian 9-month-old infants. Infants discriminated faces in the focus of the attention bias, regardless of race, indicating that infants remained sensitive to differences among other-race faces. Instead, efficacy of face discrimination reflected the extent of attention engagement. © 2015 Wiley Periodicals, Inc.

Validation sampling can reduce bias in health care database studies: an illustration using influenza vaccination effectiveness.

PubMed

Nelson, Jennifer Clark; Marsh, Tracey; Lumley, Thomas; Larson, Eric B; Jackson, Lisa A; Jackson, Michael L

2013-08-01

Estimates of treatment effectiveness in epidemiologic studies using large observational health care databases may be biased owing to inaccurate or incomplete information on important confounders. Study methods that collect and incorporate more comprehensive confounder data on a validation cohort may reduce confounding bias. We applied two such methods, namely imputation and reweighting, to Group Health administrative data (full sample) supplemented by more detailed confounder data from the Adult Changes in Thought study (validation sample). We used influenza vaccination effectiveness (with an unexposed comparator group) as an example and evaluated each method's ability to reduce bias using the control time period before influenza circulation. Both methods reduced, but did not completely eliminate, the bias compared with traditional effectiveness estimates that do not use the validation sample confounders. Although these results support the use of validation sampling methods to improve the accuracy of comparative effectiveness findings from health care database studies, they also illustrate that the success of such methods depends on many factors, including the ability to measure important confounders in a representative and large enough validation sample, the comparability of the full sample and validation sample, and the accuracy with which the data can be imputed or reweighted using the additional validation sample information. Copyright © 2013 Elsevier Inc. All rights reserved.

Validation sampling can reduce bias in healthcare database studies: an illustration using influenza vaccination effectiveness

PubMed Central

Nelson, Jennifer C.; Marsh, Tracey; Lumley, Thomas; Larson, Eric B.; Jackson, Lisa A.; Jackson, Michael

2014-01-01

Objective Estimates of treatment effectiveness in epidemiologic studies using large observational health care databases may be biased due to inaccurate or incomplete information on important confounders. Study methods that collect and incorporate more comprehensive confounder data on a validation cohort may reduce confounding bias. Study Design and Setting We applied two such methods, imputation and reweighting, to Group Health administrative data (full sample) supplemented by more detailed confounder data from the Adult Changes in Thought study (validation sample). We used influenza vaccination effectiveness (with an unexposed comparator group) as an example and evaluated each method’s ability to reduce bias using the control time period prior to influenza circulation. Results Both methods reduced, but did not completely eliminate, the bias compared with traditional effectiveness estimates that do not utilize the validation sample confounders. Conclusion Although these results support the use of validation sampling methods to improve the accuracy of comparative effectiveness findings from healthcare database studies, they also illustrate that the success of such methods depends on many factors, including the ability to measure important confounders in a representative and large enough validation sample, the comparability of the full sample and validation sample, and the accuracy with which data can be imputed or reweighted using the additional validation sample information. PMID:23849144

Selection Bias and Utilization of the Dual Eligibles in Medicare and Medicaid HMOs

PubMed Central

Zhang, Hui; Kane, Robert L; Dowd, Bryan; Feldman, Roger

2008-01-01

Objective To examine the existence of selection bias in the first 3 years of the Minnesota Senior Health Options (MSHO) demonstration and to estimate the MSHO effects on medical services utilization after adjusting for selection bias. Data Sources Monthly dual eligibility data and MSHO encounter data of March 1997–December 2000 and Medicaid encounter data of January 1995–December 2000 from the Minnesota Department of Human Services; Medicare fee-for-service claims data of January 1995–December 2000 from the Centers for Medicare and Medicaid Services. Study Design Quasi-experimental design comparing utilization between MSHO and control groups; multiple econometric and statistical models were estimated with time-invariant and time-varying covariates. Principal Findings Favorable MSHO selection was found in the nursing home (NH) and community populations, but selection bias did not substantially affect the findings. Enrollment in MSHO for more than 1 year reduced inpatient hospital admissions and days, emergency room and physician visits for NH residents, and lowered physician visits for community residents. Conclusions There was favorable selection in the first 3 years of the MSHO program. Enrollment in MSHO reduced several types of utilization for the NH group and physician visits for community enrollees. PMID:18479403

Measuring food intake in studies of obesity.

PubMed

Lissner, Lauren

2002-12-01

The problem of how to measure habitual food intake in studies of obesity remains an enigma in nutritional research. The existence of obesity-specific underreporting was rather controversial until the advent of the doubly labelled water technique gave credence to previously anecdotal evidence that such a bias does in fact exist. This paper reviews a number of issues relevant to interpreting dietary data in studies involving obesity. Topics covered include: participation biases, normative biases,importance of matching method to study, selective underreporting, and a brief discussion of the potential implications of generalised and selective underreporting in analytical epidemiology. It is concluded that selective underreporting of certain food types by obese individuals would produce consequences in analytical epidemiological studies that are both unpredictable and complex. Since it is becoming increasingly acknowledged that selective reporting error does occur, it is important to emphasise that correction for energy intake is not sufficient to eliminate the biases from this type of error. This is true both for obesity-related selective reporting errors and more universal types of selective underreporting, e.g. foods of low social desirability. Additional research is urgently required to examine the consequences of this type of error.

Dynamic nigrostriatal dopamine biases action selection

PubMed Central

Howard, Christopher D.; Li, Hao; Geddes, Claire E.; Jin, Xin

2017-01-01

Summary Dopamine is thought to play a critical role in reinforcement learning and goal-directed behavior, but its function in action selection remains largely unknown. Here, we demonstrate that nigrostriatal dopamine biases ongoing action selection. When mice were trained to dynamically switch the action selected at different time points, changes in firing rate of nigrostriatal dopamine neurons, as well as dopamine signaling in the dorsal striatum, were found to be associated with action selection. This dopamine profile is specific to behavioral choice, scalable with interval duration, and doesn’t reflect reward prediction error, timing, or value as single factors alone. Genetic deletion of NMDA receptors on dopamine or striatal neurons, or optogenetic manipulation of dopamine concentration, alters dopamine signaling and biases action selection. These results unveil a crucial role of nigrostriatal dopamine in integrating diverse information for regulating upcoming actions and have important implications for neurological disorders including Parkinson’s disease and substance dependence. PMID:28285820

Dynamic Nigrostriatal Dopamine Biases Action Selection.

PubMed

Howard, Christopher D; Li, Hao; Geddes, Claire E; Jin, Xin

2017-03-22

Dopamine is thought to play a critical role in reinforcement learning and goal-directed behavior, but its function in action selection remains largely unknown. Here we demonstrate that nigrostriatal dopamine biases ongoing action selection. When mice were trained to dynamically switch the action selected at different time points, changes in firing rate of nigrostriatal dopamine neurons, as well as dopamine signaling in the dorsal striatum, were found to be associated with action selection. This dopamine profile is specific to behavioral choice, scalable with interval duration, and doesn't reflect reward prediction error, timing, or value as single factors alone. Genetic deletion of NMDA receptors on dopamine or striatal neurons or optogenetic manipulation of dopamine concentration alters dopamine signaling and biases action selection. These results unveil a crucial role of nigrostriatal dopamine in integrating diverse information for regulating upcoming actions, and they have important implications for neurological disorders, including Parkinson's disease and substance dependence. Copyright © 2017 Elsevier Inc. All rights reserved.

Are adolescent treatment studies of eating disorders utilizing clinically relevant samples? A comparison of RCT and clinic treatment-seeking youth with eating disorders.

PubMed

Stiles-Shields, Colleen; Goldschmidt, Andrea B; Lock, James; Le Grange, Daniel

2013-09-01

To assess potential selection bias in participant recruitment for randomized controlled trials (RCTs) of adolescent eating disorders (EDs), we compared participants recruited for RCTs evaluating psychosocial treatments with individuals seeking fee-for-service outpatient ED treatment [clinic treatment-seeking (CTS)]. Participants were 214 adolescents presenting to an outpatient ED research-clinical program (92.1% female; M age = 15.4 ± 1.8 years). ANOVA and chi-square tests assessed differences between CTS participants and those presenting for no-cost treatment through RCTs. A secondary analysis compared RCT participants to participants eligible for the RCTs that opted for fee-for-service treatment. RCT participants had greater baseline ED and general psychopathology (p < .001); however, CTS participants were more likely to present with a comorbid psychiatric disorder (p < .05) and higher family income (p < .05). Results suggest that RCT participants did not have less pathology than CTS participants. While preliminary, results do not indicate a systematic population bias in selecting healthier patients for RCTs involving adolescent ED. Copyright © 2013 John Wiley & Sons, Ltd and Eating Disorders Association.

[Potential selection bias in telephone surveys: landline and mobile phones].

PubMed

Garcia-Continente, Xavier; Pérez-Giménez, Anna; López, María José; Nebot, Manel

2014-01-01

The increasing use of mobile phones in the last decade has decreased landline telephone coverage in Spanish households. This study aimed to analyze sociodemographic characteristics and health indicators by type of telephone service (mobile phone vs. landline or landline and mobile phone). Two telephone surveys were conducted in Spanish samples (February 2010 and February 2011). Multivariate logistic regression analyses were performed to analyze differences in the main sociodemographic characteristics and health indicators according to the type of telephone service available in Spanish households. We obtained 2027 valid responses (1627 landline telephones and 400 mobile phones). Persons contacted through a mobile phone were more likely to be a foreigner, to belong to the manual social class, to have a lower educational level, and to be a smoker than those contacted through a landline telephone. The profile of the population that has only a mobile phone differs from that with a landline telephone. Therefore, telephone surveys that exclude mobile phones could show a selection bias. Copyright © 2013 SESPAS. Published by Elsevier Espana. All rights reserved.

Guidelines for reporting methodological challenges and evaluating potential bias in dementia research.

PubMed

Weuve, Jennifer; Proust-Lima, Cécile; Power, Melinda C; Gross, Alden L; Hofer, Scott M; Thiébaut, Rodolphe; Chêne, Geneviève; Glymour, M Maria; Dufouil, Carole

2015-09-01

Clinical and population research on dementia and related neurologic conditions, including Alzheimer's disease, faces several unique methodological challenges. Progress to identify preventive and therapeutic strategies rests on valid and rigorous analytic approaches, but the research literature reflects little consensus on "best practices." We present findings from a large scientific working group on research methods for clinical and population studies of dementia, which identified five categories of methodological challenges as follows: (1) attrition/sample selection, including selective survival; (2) measurement, including uncertainty in diagnostic criteria, measurement error in neuropsychological assessments, and practice or retest effects; (3) specification of longitudinal models when participants are followed for months, years, or even decades; (4) time-varying measurements; and (5) high-dimensional data. We explain why each challenge is important in dementia research and how it could compromise the translation of research findings into effective prevention or care strategies. We advance a checklist of potential sources of bias that should be routinely addressed when reporting dementia research. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Estimating Dungeness crab (Cancer magister) abundance: Crab pots and dive transects compared

USGS Publications Warehouse

Taggart, S. James; O'Clair, Charles E.; Shirley, Thomas C.; Mondragon, Jennifer

2004-01-01

Dungeness crabs (Cancer magister) were sampled with commercial pots and counted by scuba divers on benthic transects at eight sites near Glacier Bay, Alaska. Catch per unit of effort (CPUE) from pots was compared to the density estimates from dives to evaluate the bias and power of the two techniques. Yearly sampling was conducted in two seasons: April and September, from 1992 to 2000. Male CPUE estimates from pots were significantly lower in April than in the following September; a step-wise regression demonstrated that season accounted for more of the variation in male CPUE than did temperature. In both April and September, pot sampling was significantly biased against females. When females were categorized as ovigerous and nonovigerous, it was clear that ovigerous females accounted for the majority of the bias because pots were not biased against nonovigerous females. We compared the power of pots and dive transects in detecting trends in populations and found that pots had much higher power than dive transects. Despite their low power, the dive transects were very useful for detecting bias in our pot sampling and in identifying the optimal times of year to sample so that pot bias could be avoided.

Growth differences and competition between Listeria monocytogenes strains determine their predominance on ham slices and lead to bias during selective enrichment with the ISO protocol.

PubMed

Zilelidou, Evangelia; Manthou, Evanthia; Skandamis, Panagiotis

2016-10-17

Listeria monocytogenes strains are widespread in the environment where they live well mixed, often resulting in multiple strains contaminating a single food sample. The occurrence of different strains in the same food might trigger strain competition, contributing to uneven growth of strains in food and to bias during selective procedures. We tested the growth of seven L. monocytogenes strains (C5, 6179, ScottA, PL24, PL25, PL26, PL27) on ham slices and on nutrient-rich agar at 10°C, singly and in combinations. Strains were made resistant to different antibiotics for their selective enumeration. In addition, growth of single strains (axenic culture) and competition between strains in xenic cultures of two strains was evaluated in enrichment broth and on selective agar. According to ISO 11290-1:1996/Amd 1:2004 standard protocol for detection of L. monocytogenes, two enrichment steps both followed by streaking on ALOA were performed. Strain cultures were directly added in the enrichment broth or used to inoculate minced beef and sliced hams which were then mixed with enrichment broth. 180-360 colonies were used to determine the relative percentage of each strain recovered on plates per enrichment step. The data showed a significant impact of co-cultivation on the growth of six out of seven strains on ham and a bias towards certain strains during selective enrichment. Competition was manifested by: (i) cessation of growth for the outcompeted strain when the dominant strain reached stationary phase, (ii) reduction of growth rates or (iii) total suppression of growth (both on ham and in enrichment broth or ALOA). Outgrowth of strains by their competitors on ALOA resulted in limited to no recovery, with the outcompeting strain accounting for up to 100% of the total recovered colonies. The observed bias was associated with the enrichment conditions (i.e. food type added to the enrichment broth) and the strain-combination. The outcome of growth competition on food or nonselective agar surface did not necessarily coincide with the results of competition during enrichment. The results show that certain strains present in foods may be missed during classical detection due to strain competition and such likelihood should be taken into consideration when resolving a listeriosis outbreak. Copyright © 2016 Elsevier B.V. All rights reserved.

Born at the Wrong Time: Selection Bias in the NHL Draft

PubMed Central

Deaner, Robert O.; Lowen, Aaron; Cobley, Stephen

2013-01-01

Relative age effects (RAEs) occur when those who are relatively older for their age group are more likely to succeed. RAEs occur reliably in some educational and athletic contexts, yet the causal mechanisms remain unclear. Here we provide the first direct test of one mechanism, selection bias, which can be defined as evaluators granting fewer opportunities to relatively younger individuals than is warranted by their latent ability. Because RAEs are well-established in hockey, we analyzed National Hockey League (NHL) drafts from 1980 to 2006. Compared to those born in the first quarter (i.e., January–March), those born in the third and fourth quarters were drafted more than 40 slots later than their productivity warranted, and they were roughly twice as likely to reach career benchmarks, such as 400 games played or 200 points scored. This selection bias in drafting did not decrease over time, apparently continues to occur, and reduces the playing opportunities of relatively younger players. This bias is remarkable because it is exhibited by professional decision makers evaluating adults in a context where RAEs have been widely publicized. Thus, selection bias based on relative age may be pervasive. PMID:23460902

Nonneutral GC3 and retroelement codon mimicry in Phytophthora.

PubMed

Jiang, Rays H Y; Govers, Francine

2006-10-01

Phytophthora is a genus entirely comprised of destructive plant pathogens. It belongs to the Stramenopila, a unique branch of eukaryotes, phylogenetically distinct from plants, animals, or fungi. Phytophthora genes show a strong preference for usage of codons ending with G or C (high GC3). The presence of high GC3 in genes can be utilized to differentiate coding regions from noncoding regions in the genome. We found that both selective pressure and mutation bias drive codon bias in Phytophthora. Indicative for selection pressure is the higher GC3 value of highly expressed genes in different Phytophthora species. Lineage specific GC increase of noncoding regions is reminiscent of whole-genome mutation bias, whereas the elevated Phytophthora GC3 is primarily a result of translation efficiency-driven selection. Heterogeneous retrotransposons exist in Phytophthora genomes and many of them vary in their GC content. Interestingly, the most widespread groups of retroelements in Phytophthora show high GC3 and a codon bias that is similar to host genes. Apparently, selection pressure has been exerted on the retroelement's codon usage, and such mimicry of host codon bias might be beneficial for the propagation of retrotransposons.

Efficient global biopolymer sampling with end-transfer configurational bias Monte Carlo

NASA Astrophysics Data System (ADS)

Arya, Gaurav; Schlick, Tamar

2007-01-01

We develop an "end-transfer configurational bias Monte Carlo" method for efficient thermodynamic sampling of complex biopolymers and assess its performance on a mesoscale model of chromatin (oligonucleosome) at different salt conditions compared to other Monte Carlo moves. Our method extends traditional configurational bias by deleting a repeating motif (monomer) from one end of the biopolymer and regrowing it at the opposite end using the standard Rosenbluth scheme. The method's sampling efficiency compared to local moves, pivot rotations, and standard configurational bias is assessed by parameters relating to translational, rotational, and internal degrees of freedom of the oligonucleosome. Our results show that the end-transfer method is superior in sampling every degree of freedom of the oligonucleosomes over other methods at high salt concentrations (weak electrostatics) but worse than the pivot rotations in terms of sampling internal and rotational sampling at low-to-moderate salt concentrations (strong electrostatics). Under all conditions investigated, however, the end-transfer method is several orders of magnitude more efficient than the standard configurational bias approach. This is because the characteristic sampling time of the innermost oligonucleosome motif scales quadratically with the length of the oligonucleosomes for the end-transfer method while it scales exponentially for the traditional configurational-bias method. Thus, the method we propose can significantly improve performance for global biomolecular applications, especially in condensed systems with weak nonbonded interactions and may be combined with local enhancements to improve local sampling.

Transient stimulation of distinct subpopulations of striatal neurons mimics changes in action value

PubMed Central

Tai, Lung-Hao; Lee, A. Moses; Benavidez, Nora; Bonci, Antonello; Wilbrecht, Linda

2012-01-01

In changing environments animals must adaptively select actions to achieve their goals. In tasks involving goal-directed action selection, striatal neural activity has been shown to represent the value of competing actions. Striatal representations of action value could potentially bias responses toward actions of higher value. However, no study to date has demonstrated the direct impact of distinct striatal pathways in goal-directed action selection. Here we show in mice that transient optogenetic stimulation of dorsal striatal dopamine D1 and D2 receptor-expressing neurons during decision-making introduces opposing biases in the distribution of choices. The effect of stimulation on choice is dependent on recent reward history and mimics an additive change in the action value. While stimulation prior to and during movement initiation produces a robust bias in choice behavior, this bias is significantly diminished when stimulation is delayed after response initiation. Together, our data demonstrate the role of striatal activity in goal-directed action selection. PMID:22902719

Unconscious biases in task choices depend on conscious expectations.

PubMed

González-García, Carlos; Tudela, Pío; Ruz, María

2015-12-01

Recent studies highlight the influence of non-conscious information on task-set selection. However, it has not yet been tested whether this influence depends on conscious settings, as some theoretical models propose. In a series of three experiments, we explored whether non-conscious abstract cues could bias choices between a semantic and a perceptual task. In Experiment 1, we observed a non-conscious influence on task-set selection even when perceptual priming and cue-target compound confounds did not apply. Experiments 2 and 3 showed that, under restrictive conditions of visibility, cues only biased task selection when the conscious task-setting mindset led participants to search for information during the time period of the cue. However, this conscious strategy did not modulate the effect found when a subjective measure of consciousness was used. Altogether, our results show that the configuration of the conscious mindset determines the potential bias of non-conscious information on task-set selection. Copyright © 2015 Elsevier Inc. All rights reserved.