Does industrial waste taxation contribute to reduction of landfilled waste? Dynamic panel analysis considering industrial waste category in Japan.

PubMed

Sasao, Toshiaki

2014-11-01

Waste taxes, such as landfill and incineration taxes, have emerged as a popular option in developed countries to promote the 3Rs (reduce, reuse, and recycle). However, few studies have examined the effectiveness of waste taxes. In addition, quite a few studies have considered both dynamic relationships among dependent variables and unobserved individual heterogeneity among the jurisdictions. If dependent variables are persistent, omitted variables cause a bias, or common characteristics exist across the jurisdictions that have introduced waste taxes, the standard fixed effects model may lead to biased estimation results and misunderstood causal relationships. In addition, most existing studies have examined waste in terms of total amounts rather than by categories. Even if significant reductions in total waste amounts are not observed, some reduction within each category may, nevertheless, become evident. Therefore, this study analyzes the effects of industrial waste taxation on quantities of waste in landfill in Japan by applying the bias-corrected least-squares dummy variable (LSDVC) estimators; the general method of moments (difference GMM); and the system GMM. In addition, the study investigates effect differences attributable to industrial waste categories and taxation types. This paper shows that industrial waste taxes in Japan have minimal, significant effects on the reduction of final disposal amounts thus far, considering dynamic relationships and waste categories. Copyright © 2014 Elsevier Ltd. All rights reserved.

Is higher nursing home quality more costly?

PubMed

Giorgio, L Di; Filippini, M; Masiero, G

2016-11-01

Widespread issues regarding quality in nursing homes call for an improved understanding of the relationship with costs. This relationship may differ in European countries, where care is mainly delivered by nonprofit providers. In accordance with the economic theory of production, we estimate a total cost function for nursing home services using data from 45 nursing homes in Switzerland between 2006 and 2010. Quality is measured by means of clinical indicators regarding process and outcome derived from the minimum data set. We consider both composite and single quality indicators. Contrary to most previous studies, we use panel data and control for omitted variables bias. This allows us to capture features specific to nursing homes that may explain differences in structural quality or cost levels. Additional analysis is provided to address simultaneity bias using an instrumental variable approach. We find evidence that poor levels of quality regarding outcome, as measured by the prevalence of severe pain and weight loss, lead to higher costs. This may have important implications for the design of payment schemes for nursing homes.

Incomplete Reporting: Addressing the Prevalence of Outcome-Reporting Bias in Educational Research

ERIC Educational Resources Information Center

Trainor, Brian; Polanin, Joshua; Williams, Ryan; Pigott, Terri

2015-01-01

Outcome reporting bias refers to the practice of omitting from primary studies outcomes that were actually collected. When primary studies do not report on all the outcomes assessed, there is an incomplete understanding of a phenomenon that may be compounded when the study is included in a systematic review of research. Outcome reporting bias is…

Bridging Social Capital and Individual Earnings: Evidence for an Inverted U.

PubMed

Growiec, Katarzyna; Growiec, Jakub

Based on data on a cross section of individuals surveyed in the 1999-2002 wave of World and European Values Surveys, we investigate the multilateral associations between bridging social capital, individuals' earnings, as well as social trust and employment status. Our analysis provides robust evidence that the relationship between bridging social capital and earnings is inverted-U shaped. We carry out a range of tests in order to ascertain that this result is not driven by regressor endogeneity or omitted variables bias. We also identify significant interaction effects between bridging social capital, social trust, and employment status.

Testing the EKC hypothesis by considering trade openness, urbanization, and financial development: the case of Turkey.

PubMed

Ozatac, Nesrin; Gokmenoglu, Korhan K; Taspinar, Nigar

2017-07-01

This study investigates the environmental Kuznets curve (EKC) hypothesis for the case of Turkey from 1960 to 2013 by considering energy consumption, trade, urbanization, and financial development variables. Although previous literature examines various aspects of the EKC hypothesis for the case of Turkey, our model augments the basic model with several covariates to develop a better understanding of the relationship among the variables and to refrain from omitted variable bias. The results of the bounds test and the error correction model under autoregressive distributed lag mechanism suggest long-run relationships among the variables as well as proof of the EKC and the scale effect in Turkey. A conditional Granger causality test reveals that there are causal relationships among the variables. Our findings can have policy implications including the imposition of a "polluter pays" mechanism, such as the implementation of a carbon tax for pollution trading, to raise the urban population's awareness about the importance of adopting renewable energy and to support clean, environmentally friendly technology.

Forest cover, socioeconomics, and reported flood frequency in developing countries

NASA Astrophysics Data System (ADS)

Ferreira, Susana; Ghimire, Ramesh

2012-08-01

In this paper, we analyze the determinants of the number of large floods reported since 1990. Using the same sample of countries as Bradshaw et al. (2007), and, like them, omitting socioeconomic characteristics from the analysis, we found that a reduction in natural forest cover is associated with an increase in the reported count of large floods. This result does not hold in any of three new analyses we perform. First, we expand the sample to include all the developing countries and all countries for which data were available but were omitted in their study. Second, and more importantly, since forest management is just one possible channel through which humans can influence reported flood frequency, we account for other important human-flood interactions. People are typically responsible for deforestation, but they are also responsible for other land use changes (e.g., urbanization), for floodplain and flood emergency management, and for reporting the floods. Thus, in our analysis we account for population, urban population growth, income, and corruption. Third, we exploit the panel nature of the data to control for unobserved country and time heterogeneity. We conclude that not only is the link between forest cover and reported flood frequency at the country level not robust, it also seems to be driven by sample selection and omitted variable bias. The human impact on the reported frequency of large floods at the country level is not through deforestation.

Multiple Imputation For Combined-Survey Estimation With Incomplete Regressors In One But Not Both Surveys

PubMed Central

Rendall, Michael S.; Ghosh-Dastidar, Bonnie; Weden, Margaret M.; Baker, Elizabeth H.; Nazarov, Zafar

2013-01-01

Within-survey multiple imputation (MI) methods are adapted to pooled-survey regression estimation where one survey has more regressors, but typically fewer observations, than the other. This adaptation is achieved through: (1) larger numbers of imputations to compensate for the higher fraction of missing values; (2) model-fit statistics to check the assumption that the two surveys sample from a common universe; and (3) specificying the analysis model completely from variables present in the survey with the larger set of regressors, thereby excluding variables never jointly observed. In contrast to the typical within-survey MI context, cross-survey missingness is monotonic and easily satisfies the Missing At Random (MAR) assumption needed for unbiased MI. Large efficiency gains and substantial reduction in omitted variable bias are demonstrated in an application to sociodemographic differences in the risk of child obesity estimated from two nationally-representative cohort surveys. PMID:24223447

The Causal Effects of Father Absence

PubMed Central

McLanahan, Sara; Tach, Laura; Schneider, Daniel

2014-01-01

The literature on father absence is frequently criticized for its use of cross-sectional data and methods that fail to take account of possible omitted variable bias and reverse causality. We review studies that have responded to this critique by employing a variety of innovative research designs to identify the causal effect of father absence, including studies using lagged dependent variable models, growth curve models, individual fixed effects models, sibling fixed effects models, natural experiments, and propensity score matching models. Our assessment is that studies using more rigorous designs continue to find negative effects of father absence on offspring well-being, although the magnitude of these effects is smaller than what is found using traditional cross-sectional designs. The evidence is strongest and most consistent for outcomes such as high school graduation, children’s social-emotional adjustment, and adult mental health. PMID:24489431

Omitted Variable Sensitivity Analysis with the Annotated Love Plot

ERIC Educational Resources Information Center

Hansen, Ben B.; Fredrickson, Mark M.

2014-01-01

The goal of this research is to make sensitivity analysis accessible not only to empirical researchers but also to the various stakeholders for whom educational evaluations are conducted. To do this it derives anchors for the omitted variable (OV)-program participation association intrinsically, using the Love plot to present a wide range of…

Visualizing Statistical Mix Effects and Simpson's Paradox.

PubMed

Armstrong, Zan; Wattenberg, Martin

2014-12-01

We discuss how "mix effects" can surprise users of visualizations and potentially lead them to incorrect conclusions. This statistical issue (also known as "omitted variable bias" or, in extreme cases, as "Simpson's paradox") is widespread and can affect any visualization in which the quantity of interest is an aggregated value such as a weighted sum or average. Our first contribution is to document how mix effects can be a serious issue for visualizations, and we analyze how mix effects can cause problems in a variety of popular visualization techniques, from bar charts to treemaps. Our second contribution is a new technique, the "comet chart," that is meant to ameliorate some of these issues.

Testing competing forms of the Milankovitch hypothesis: A multivariate approach

NASA Astrophysics Data System (ADS)

Kaufmann, Robert K.; Juselius, Katarina

2016-02-01

We test competing forms of the Milankovitch hypothesis by estimating the coefficients and diagnostic statistics for a cointegrated vector autoregressive model that includes 10 climate variables and four exogenous variables for solar insolation. The estimates are consistent with the physical mechanisms postulated to drive glacial cycles. They show that the climate variables are driven partly by solar insolation, determining the timing and magnitude of glaciations and terminations, and partly by internal feedback dynamics, pushing the climate variables away from equilibrium. We argue that the latter is consistent with a weak form of the Milankovitch hypothesis and that it should be restated as follows: internal climate dynamics impose perturbations on glacial cycles that are driven by solar insolation. Our results show that these perturbations are likely caused by slow adjustment between land ice volume and solar insolation. The estimated adjustment dynamics show that solar insolation affects an array of climate variables other than ice volume, each at a unique rate. This implies that previous efforts to test the strong form of the Milankovitch hypothesis by examining the relationship between solar insolation and a single climate variable are likely to suffer from omitted variable bias.

Incorporating the sampling design in weighting adjustments for panel attrition

PubMed Central

Chen, Qixuan; Gelman, Andrew; Tracy, Melissa; Norris, Fran H.; Galea, Sandro

2015-01-01

We review weighting adjustment methods for panel attrition and suggest approaches for incorporating design variables, such as strata, clusters and baseline sample weights. Design information can typically be included in attrition analysis using multilevel models or decision tree methods such as the CHAID algorithm. We use simulation to show that these weighting approaches can effectively reduce bias in the survey estimates that would occur from omitting the effect of design factors on attrition while keeping the resulted weights stable. We provide a step-by-step illustration on creating weighting adjustments for panel attrition in the Galveston Bay Recovery Study, a survey of residents in a community following a disaster, and provide suggestions to analysts in decision making about weighting approaches. PMID:26239405

Polder maps: Improving OMIT maps by excluding bulk solvent

DOE PAGES

Liebschner, Dorothee; Afonine, Pavel V.; Moriarty, Nigel W.; ...

2017-02-01

The crystallographic maps that are routinely used during the structure-solution workflow are almost always model-biased because model information is used for their calculation. As these maps are also used to validate the atomic models that result from model building and refinement, this constitutes an immediate problem: anything added to the model will manifest itself in the map and thus hinder the validation. OMIT maps are a common tool to verify the presence of atoms in the model. The simplest way to compute an OMIT map is to exclude the atoms in question from the structure, update the corresponding structure factorsmore » and compute a residual map. It is then expected that if these atoms are present in the crystal structure, the electron density for the omitted atoms will be seen as positive features in this map. This, however, is complicated by the flat bulk-solvent model which is almost universally used in modern crystallographic refinement programs. This model postulates constant electron density at any voxel of the unit-cell volume that is not occupied by the atomic model. Consequently, if the density arising from the omitted atoms is weak then the bulk-solvent model may obscure it further. A possible solution to this problem is to prevent bulk solvent from entering the selected OMIT regions, which may improve the interpretative power of residual maps. This approach is called a polder (OMIT) map. Polder OMIT maps can be particularly useful for displaying weak densities of ligands, solvent molecules, side chains, alternative conformations and residues both in terminal regions and in loops. As a result, the tools described in this manuscript have been implemented and are available in PHENIX.« less

Variable methodological quality and use found in systematic reviews referenced in STEMI clinical practice guidelines.

PubMed

Scott, Jared; Howard, Benjamin; Sinnett, Philip; Schiesel, Michael; Baker, Jana; Henderson, Patrick; Vassar, Matt

2017-12-01

The objective of this study was to assess the methodological quality and clarity of reporting of the systematic reviews (SRs) supporting clinical practice guideline (CPG) recommendations in the management of ST-elevation myocardial infarction (STEMI) across international CPGs. We searched 13 guideline clearinghouses including the National Guideline Clearinghouse and Guidelines International Network (GIN). To meet inclusion criteria CPGs must be pertinent to the management of STEMI, endorsed by a governing body or national organization, and written in English. We retrieved SRs from the reference sections using a combination of keywords and hand searching. Two investigators scored eligible SRs using AMSTAR and PRISMA. We included four CPGs. We extracted 71 unique SRs. These SRs received AMSTAR scores ranging from 1 (low) to 9 (high) on an 11-point scale. All CPGs consistently underperformed in areas including disclosure of funding sources, risk of bias, and publication bias according to AMSTAR. PRISMA checklist completeness ranged from 44% to 96%. The PRISMA scores indicated that SRs did not provide a full search strategy, study protocol and registration, assessment of publication bias or report funding sources. Only one SR was referenced in all four CPGs. All CPGs omitted a large subset of available SRs cited by other guidelines. Our study demonstrates the variable quality of SRs used to establish recommendations within guidelines included in our sample. Although guideline developers have acknowledged this variability, it remains a significant finding that needs to be addressed further. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Copyright © 2017 Elsevier Inc. All rights reserved.

Associations of neighborhood disorganization and maternal spanking with children's aggression: A fixed-effects regression analysis.

PubMed

Ma, Julie; Grogan-Kaylor, Andrew; Lee, Shawna J

2018-02-01

This study employed fixed effects regression that controls for selection bias, omitted variables bias, and all time-invariant aspects of parent and child characteristics to examine the simultaneous associations between neighborhood disorganization, maternal spanking, and aggressive behavior in early childhood using data from the Fragile Families and Child Wellbeing Study (FFCWS). Analysis was based on 2,472 children and their mothers who participated in Wave 3 (2001-2003; child age 3) and Wave 4 (2003-2006; child age 5) of the FFCWS. Results indicated that higher rates of neighborhood crime and violence predicted higher levels of child aggression. Maternal spanking in the past year, whether frequent or infrequent, was also associated with increases in aggressive behavior. This study contributes statistically rigorous evidence that exposure to violence in the neighborhood as well as the family context are predictors of child aggression. We conclude with a discussion for the need for multilevel prevention and intervention approaches that target both community and parenting factors. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Making stronger causal inferences: Accounting for selection bias in associations between high performance work systems, leadership, and employee and customer satisfaction.

PubMed

Schmidt, Joseph A; Pohler, Dionne M

2018-05-17

We develop competing hypotheses about the relationship between high performance work systems (HPWS) with employee and customer satisfaction. Drawing on 8 years of employee and customer survey data from a financial services firm, we used a recently developed empirical technique-covariate balanced propensity score (CBPS) weighting-to examine if the proposed relationships between HPWS and satisfaction outcomes can be explained by reverse causality, selection effects, or commonly omitted variables such as leadership behavior. The results provide support for leader behaviors as a primary driver of customer satisfaction, rather than HPWS, and also suggest that the problem of reverse causality requires additional attention in future human resource (HR) systems research. Model comparisons suggest that the estimates and conclusions vary across CBPS, meta-analytic, cross-sectional, and time-lagged models (with and without a lagged dependent variable as a control). We highlight the theoretical and methodological implications of the findings for HR systems research. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Cost Growth in Weapons Systems: Re-examining Rubber Baselines and Economic Factors

DTIC Science & Technology

2007-03-01

committee members for their support in this endeavor. They allowed me to test my econometric limits without performing the analysis for me. I...Bruesch-Pagan Het Test Ramsey Omitted Variable Test 6 The adjusted r-squared value indicates that this model explains nearly 16% of the factors...Observations 1150 Chi2(1) 69.01 P(Chi) 0.0000 F(3, 1139) 7.22 P(F) 0.0001 Bruesch-Pagan Het Test Ramsey Omitted Variable Test The results of these two

The role of reporting standards in producing robust literature reviews

NASA Astrophysics Data System (ADS)

Haddaway, Neal Robert; Macura, Biljana

2018-06-01

Literature reviews can help to inform decision-making, yet they may be subject to fatal bias if not conducted rigorously as `systematic reviews'. Reporting standards help authors to provide sufficient methodological detail to allow verification and replication, clarifying when key steps, such as critical appraisal, have been omitted.

Federal Revenue Sharing and Nonmetropolitan Governments: "The Cumberland Gap".

ERIC Educational Resources Information Center

Hitzhusen, Fred J.

Exclusion of some forms of tax revenue and all forms of nontax revenue and support from measures of tax effort for allocating federal revenue sharing funds appears to introduce systematic bias against rural/nonmetropolitan local governments. Omitted tax revenues include those for schools and special districts (rural communitites raise…

Evidence of Cognitive Bias in Decision Making Around Implantable-Cardioverter Defibrillators: A Qualitative Framework Analysis.

PubMed

Matlock, Daniel D; Jones, Jacqueline; Nowels, Carolyn T; Jenkins, Amy; Allen, Larry A; Kutner, Jean S

2017-11-01

Studies have demonstrated that patients with primary prevention implantable cardioverter-defibrillators (ICDs) often misunderstand the ICD. Advances in behavioral economics demonstrate that some misunderstandings may be due to cognitive biases. We aimed to explore the influence of cognitive bias on ICD decision making. We used a qualitative framework analysis including 9 cognitive biases: affect heuristic, affective forecasting, anchoring, availability, default effects, halo effects, optimism bias, framing effects, and state dependence. We interviewed 48 patients from 4 settings in Denver. The majority were male (n = 32). Overall median age was 61 years. We found frequent evidence for framing, default, and halo effects; some evidence of optimism bias, affect heuristic, state dependence, anchoring and availability bias; and little or no evidence of affective forecasting. Framing effects were apparent in overestimation of benefits and downplaying or omitting potential harms. We found evidence of cognitive bias in decision making for ICD implantation. The majority of these biases appeared to encourage ICD treatment. Published by Elsevier Inc.

40 CFR 1039.505 - How do I test engines using steady-state duty cycles, including ramped-modal testing?

Code of Federal Regulations, 2013 CFR

2013-07-01

.... You may extend the sampling time to improve measurement accuracy of PM emissions, using good..., you may omit speed, torque, and power points from the duty-cycle regression statistics if the... mapped. (2) For variable-speed engines without low-speed governors, you may omit torque and power points...

40 CFR 1039.505 - How do I test engines using steady-state duty cycles, including ramped-modal testing?

Code of Federal Regulations, 2012 CFR

2012-07-01

.... You may extend the sampling time to improve measurement accuracy of PM emissions, using good..., you may omit speed, torque, and power points from the duty-cycle regression statistics if the... mapped. (2) For variable-speed engines without low-speed governors, you may omit torque and power points...

Nuclear Bashing in Chernobyl Coverage: Fact or Fiction?

ERIC Educational Resources Information Center

Friedman, Sharon M.; And Others

Critics of coverage of nuclear power have charged that the media overemphasize the importance of nuclear accidents, encourage public fear, and omit information vital to public understanding of nuclear power and risk. Some also feel there is an anti-nuclear bias among reporters and editors. A study was conducted to determine if such charges were…

Constructing diagnostic likelihood: clinical decisions using subjective versus statistical probability.

PubMed

Kinnear, John; Jackson, Ruth

2017-07-01

Although physicians are highly trained in the application of evidence-based medicine, and are assumed to make rational decisions, there is evidence that their decision making is prone to biases. One of the biases that has been shown to affect accuracy of judgements is that of representativeness and base-rate neglect, where the saliency of a person's features leads to overestimation of their likelihood of belonging to a group. This results in the substitution of 'subjective' probability for statistical probability. This study examines clinicians' propensity to make estimations of subjective probability when presented with clinical information that is considered typical of a medical condition. The strength of the representativeness bias is tested by presenting choices in textual and graphic form. Understanding of statistical probability is also tested by omitting all clinical information. For the questions that included clinical information, 46.7% and 45.5% of clinicians made judgements of statistical probability, respectively. Where the question omitted clinical information, 79.9% of clinicians made a judgement consistent with statistical probability. There was a statistically significant difference in responses to the questions with and without representativeness information (χ2 (1, n=254)=54.45, p<0.0001). Physicians are strongly influenced by a representativeness bias, leading to base-rate neglect, even though they understand the application of statistical probability. One of the causes for this representativeness bias may be the way clinical medicine is taught where stereotypic presentations are emphasised in diagnostic decision making. 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/.

Multiple Imputation for Incomplete Data in Epidemiologic Studies

PubMed Central

Harel, Ofer; Mitchell, Emily M; Perkins, Neil J; Cole, Stephen R; Tchetgen Tchetgen, Eric J; Sun, BaoLuo; Schisterman, Enrique F

2018-01-01

Abstract Epidemiologic studies are frequently susceptible to missing information. Omitting observations with missing variables remains a common strategy in epidemiologic studies, yet this simple approach can often severely bias parameter estimates of interest if the values are not missing completely at random. Even when missingness is completely random, complete-case analysis can reduce the efficiency of estimated parameters, because large amounts of available data are simply tossed out with the incomplete observations. Alternative methods for mitigating the influence of missing information, such as multiple imputation, are becoming an increasing popular strategy in order to retain all available information, reduce potential bias, and improve efficiency in parameter estimation. In this paper, we describe the theoretical underpinnings of multiple imputation, and we illustrate application of this method as part of a collaborative challenge to assess the performance of various techniques for dealing with missing data (Am J Epidemiol. 2018;187(3):568–575). We detail the steps necessary to perform multiple imputation on a subset of data from the Collaborative Perinatal Project (1959–1974), where the goal is to estimate the odds of spontaneous abortion associated with smoking during pregnancy. PMID:29165547

Environmental cost of using poor decision metrics to prioritize environmental projects.

PubMed

Pannell, David J; Gibson, Fiona L

2016-04-01

Conservation decision makers commonly use project-scoring metrics that are inconsistent with theory on optimal ranking of projects. As a result, there may often be a loss of environmental benefits. We estimated the magnitudes of these losses for various metrics that deviate from theory in ways that are common in practice. These metrics included cases where relevant variables were omitted from the benefits metric, project costs were omitted, and benefits were calculated using a faulty functional form. We estimated distributions of parameters from 129 environmental projects from Australia, New Zealand, and Italy for which detailed analyses had been completed previously. The cost of using poor prioritization metrics (in terms of lost environmental values) was often high--up to 80% in the scenarios we examined. The cost in percentage terms was greater when the budget was smaller. The most costly errors were omitting information about environmental values (up to 31% loss of environmental values), omitting project costs (up to 35% loss), omitting the effectiveness of management actions (up to 9% loss), and using a weighted-additive decision metric for variables that should be multiplied (up to 23% loss). The latter 3 are errors that occur commonly in real-world decision metrics, in combination often reducing potential benefits from conservation investments by 30-50%. Uncertainty about parameter values also reduced the benefits from investments in conservation projects but often not by as much as faulty prioritization metrics. © 2016 Society for Conservation Biology.

Assessing the Impact of Drug Use on Hospital Costs

PubMed Central

Stuart, Bruce C; Doshi, Jalpa A; Terza, Joseph V

2009-01-01

Objective To assess whether outpatient prescription drug utilization produces offsets in the cost of hospitalization for Medicare beneficiaries. Data Sources/Study Setting The study analyzed a sample (N=3,101) of community-dwelling fee-for-service U.S. Medicare beneficiaries drawn from the 1999 and 2000 Medicare Current Beneficiary Surveys. Study Design Using a two-part model specification, we regressed any hospital admission (part 1: probit) and hospital spending by those with one or more admissions (part 2: nonlinear least squares regression) on drug use in a standard model with strong covariate controls and a residual inclusion instrumental variable (IV) model using an exogenous measure of drug coverage as the instrument. Principal Findings The covariate control model predicted that each additional prescription drug used (mean=30) raised hospital spending by $16 (p<.001). The residual inclusion IV model prediction was that each additional prescription fill reduced hospital spending by $104 (p<.001). Conclusions The findings indicate that drug use is associated with cost offsets in hospitalization among Medicare beneficiaries, once omitted variable bias is corrected using an IV technique appropriate for nonlinear applications. PMID:18783453

Placing Families in Context: Challenges for Cross-National Family Research

PubMed Central

Yu, Wei-hsin

2015-01-01

Cross-national comparisons constitute a valuable strategy to assess how broader cultural, political, and institutional contexts shape family outcomes. One typical approach of cross-national family research is to use comparable data from a limited number of countries, fit similar regression models for each country, and compare results across country-specific models. Increasingly, researchers are adopting a second approach, which requires merging data from many more societies and testing multilevel models using the pooled sample. Although the second approach has the advantage of allowing direct estimates of the effects of nation-level characteristics, it is more likely to suffer from the problems of omitted-variable bias, influential cases, and measurement and construct nonequivalence. I discuss ways to improve the first approach's ability to infer macrolevel influences, as well as how to deal with challenges associated with the second one. I also suggest choosing analytical strategies according to whether the data meet multilevel models’ assumptions. PMID:25999603

Auditing Neonatal Intensive Care: Is PREM a Good Alternative to CRIB for Mortality Risk Adjustment in Premature Infants?

PubMed

Guenther, Kilian; Vach, Werner; Kachel, Walter; Bruder, Ingo; Hentschel, Roland

2015-01-01

Comparing outcomes at different neonatal intensive care units (NICUs) requires adjustment for intrinsic risk. The Clinical Risk Index for Babies (CRIB) is a widely used risk model, but it has been criticized for being affected by therapeutic decisions. The Prematurity Risk Evaluation Measure (PREM) is not supposed to be prone to treatment bias, but has not yet been validated. We aimed to validate the PREM, compare its accuracy to that of the original and modified versions of the CRIB and CRIB-II, and examine the congruence of risk categorization. Very-low-birth-weight (VLBW) infants with a gestational age (GA) <33 weeks, who were admitted to NICUs in Baden-Württemberg from 2003 to 2008, were identified from the German neonatal quality assurance program. CRIB, CRIB-II and PREM scores were calculated and modified. Omitting variables that directly reflected therapeutic decisions [the applied fraction of inspired oxygen (FiO2)] or that may have been prone to early-treatment bias (base excess and temperature), non-NICU-therapy-influenced scores were obtained. Score performance was assessed by the area under their ROC curve (AUC). The CRIB showed the largest AUC (0.89), which dropped significantly (to 0.85) after omitting the FiO2. The PREM birth condition model, PREM(bcm) (AUC 0.86), and the PREM birth model, PREM(bm) (AUC 0.82), also demonstrated good discrimination. PREM(bm) was superior to other non-therapy-affected scores and to GA, particularly in infants with <750 g birth weight. Congruence of risk categorization was low, especially among higher-risk cases. The CRIB score had the largest AUC, resulting from its inclusion of FiO2. PREM(bm), as the most accurate score among those unaffected by early treatment, seems to be a good alternative for strict risk adjustment in NICU auditing. It could be useful to combine scores. © 2015 S. Karger AG, Basel.

Reporting quality and risk of bias in randomised trials in health professions education.

PubMed

Horsley, Tanya; Galipeau, James; Petkovic, Jennifer; Zeiter, Jeanie; Hamstra, Stanley J; Cook, David A

2017-01-01

Complete reporting of research is essential to enable consumers to accurately appraise, interpret and apply findings. Quality appraisal checklists are giving way to tools that judge the risk for bias. We sought to determine the prevalence of these complementary aspects of research reports (completeness of reporting and perceived risk for bias) of randomised studies in health professions education. We searched bibliographic databases for randomised studies of health professions education. We appraised two cohorts representing different time periods (2008-2010 and 2014, respectively) and worked in duplicate to apply the CONSORT guidelines and Cochrane Risk of Bias tool. We explored differences between time periods using independent-samples t-tests or the chi-squared test, as appropriate. We systematically identified 180 randomised studies (2008-2010, n = 150; 2014, n = 30). Frequencies of reporting of CONSORT elements within full-text reports were highly variable and most elements were reported in fewer than 50% of studies. We found a statistically significant difference in the CONSORT reporting index (maximum score: 500) between the 2008-2010 (mean ± standard deviation [SD]: 242.7 ± 55.6) and 2014 (mean ± SD: 311.6 ± 53.2) cohorts (p < 0.001). High or unclear risk for bias was most common for allocation concealment (157, 87%) and blinding of participants (147, 82%), personnel (152, 84%) and outcome assessors (112, 62%). Most risk for bias elements were judged to be unclear (range: 51-84%). Risk for bias elements significantly improved over time for blinding of participants (p = 0.007), incomplete data (p < 0.001) and the presence of other sources of bias (p < 0.001). Reports of randomised studies in health professions education frequently omit elements recommended by the CONSORT statement. Most reports were assessed as having a high or unclear risk for bias. Greater attention to how studies are reported at study outset and in manuscript preparation could improve levels of complete transparent reporting. © 2016 John Wiley & Sons Ltd and The Association for the Study of Medical Education.

Fidelity of the representation of value in decision-making

PubMed Central

Dowding, Ben A.

2017-01-01

The ability to make optimal decisions depends on evaluating the expected rewards associated with different potential actions. This process is critically dependent on the fidelity with which reward value information can be maintained in the nervous system. Here we directly probe the fidelity of value representation following a standard reinforcement learning task. The results demonstrate a previously-unrecognized bias in the representation of value: extreme reward values, both low and high, are stored significantly more accurately and precisely than intermediate rewards. The symmetry between low and high rewards pertained despite substantially higher frequency of exposure to high rewards, resulting from preferential exploitation of more rewarding options. The observed variation in fidelity of value representation retrospectively predicted performance on the reinforcement learning task, demonstrating that the bias in representation has an impact on decision-making. A second experiment in which one or other extreme-valued option was omitted from the learning sequence showed that representational fidelity is primarily determined by the relative position of an encoded value on the scale of rewards experienced during learning. Both variability and guessing decreased with the reduction in the number of options, consistent with allocation of a limited representational resource. These findings have implications for existing models of reward-based learning, which typically assume defectless representation of reward value. PMID:28248958

Safety-in-numbers: Estimates based on a sample of pedestrian crossings in Norway.

PubMed

Elvik, Rune

2016-06-01

Safety-in-numbers denotes the tendency for the risk of accident for each road user to decline as the number of road users increases. Safety-in-numbers implies that a doubling of the number of road users will be associated with less than a doubling of the number of accidents. This paper investigates safety-in-numbers in 239 pedestrian crossings in Oslo and its suburbs. Accident prediction models were fitted by means of negative binomial regression. The models indicate a very strong safety-in-numbers effect. In the final model, the coefficients for traffic volume were 0.05 for motor vehicles, 0.07 for pedestrians and 0.12 for cyclists. The coefficient for motor vehicles implies that the number of accidents is almost independent of the number of motor vehicles. The safety-in-numbers effect found in this paper is stronger than reported in any other study dealing with safety-in-numbers. It should be noted that the model explained only 21% of the systematic variation in the number of accidents. It therefore cannot be ruled out that the results are influenced by omitted variable bias. Any such bias would, however, have to be very large to eliminate the safety-in-numbers effect. Copyright © 2016 Elsevier Ltd. All rights reserved.

Weekly Cycles in Daily Report Data: An Overlooked Issue.

PubMed

Liu, Yu; West, Stephen G

2016-10-01

Daily diaries and other everyday experience methods are increasingly used to study relationships between two time-varying variables X and Y. Although daily data potentially often have weekly cyclical patterns (e.g., stress may be higher on weekdays and lower on weekends), the majority of daily diary studies have ignored this possibility. In this study, we investigated the effect of ignoring existing weekly cycles. We reanalyzed an empirical dataset (stress and alcohol consumption) and performed Monte Carlo simulations to investigate the impact of omitting weekly cycles. In the empirical dataset, ignoring cycles led to the inference of a significant within-person X-Y relation whereas modeling cycles suggested that this relationship did not exist. Simulation results indicated that ignoring cycles that existed in both X and Y led to bias in the estimated within-person X-Y relationship. The amount and direction of bias depended on the magnitude of the cycles, magnitude of the true within-person X-Y relation, and synchronization of the cycles. We encourage researchers conducting daily diary studies to address potential weekly cycles in their data. We provide guidelines for detecting and modeling cycles to remove their influence and discuss challenges of causal inference in daily experience studies. © 2015 Wiley Periodicals, Inc.

Consequences of eliminating federal disability benefits for substance abusers.

PubMed

Chatterji, Pinka; Meara, Ellen

2010-03-01

Using annual, repeated cross-sections from national household surveys, we estimate how the January 1997 termination of federal disability insurance, Supplemental Security Income (SSI), for those with Drug Addiction and Alcoholism affected labor market outcomes among individuals targeted by the legislation. We also examine whether the policy change affected health insurance, health care utilization, and arrests. We employ propensity-score methods to address differences in observed characteristics between likely substance users and others, and we used a difference-in-difference-in-difference approach to mitigate potential omitted variables bias. In the short-run (1997-1998), declines in SSI receipt accompanied appreciable increases in labor force participation and current employment. There was little measurable effect of the policy change on insurance and utilization, but we have limited power to detect effects on these outcomes. In the later period after the policy change (1999-2002), the rate of SSI receipt rose, and short-run gains in labor market outcomes diminished. Copyright 2009 Elsevier B.V. All rights reserved.

The problem of natural funnel asymmetries: a simulation analysis of meta-analysis in macroeconomics.

PubMed

Callot, Laurent; Paldam, Martin

2011-06-01

Effect sizes in macroeconomic are estimated by regressions on data published by statistical agencies. Funnel plots are a representation of the distribution of the resulting regression coefficients. They are normally much wider than predicted by the t-ratio of the coefficients and often asymmetric. The standard method of meta-analysts in economics assumes that the asymmetries are because of publication bias causing censoring and adjusts the average accordingly. The paper shows that some funnel asymmetries may be 'natural' so that they occur without censoring. We investigate such asymmetries by simulating funnels by pairs of data generating processes (DGPs) and estimating models (EMs), in which the EM has the problem that it disregards a property of the DGP. The problems are data dependency, structural breaks, non-normal residuals, non-linearity, and omitted variables. We show that some of these problems generate funnel asymmetries. When they do, the standard method often fails. Copyright © 2011 John Wiley & Sons, Ltd. Copyright © 2011 John Wiley & Sons, Ltd.

A multiple indicator solution approach to endogeneity in discrete-choice models for environmental valuation.

PubMed

Mariel, Petr; Hoyos, David; Artabe, Alaitz; Guevara, C Angelo

2018-08-15

Endogeneity is an often neglected issue in empirical applications of discrete choice modelling despite its severe consequences in terms of inconsistent parameter estimation and biased welfare measures. This article analyses the performance of the multiple indicator solution method to deal with endogeneity arising from omitted explanatory variables in discrete choice models for environmental valuation. We also propose and illustrate a factor analysis procedure for the selection of the indicators in practice. Additionally, the performance of this method is compared with the recently proposed hybrid choice modelling framework. In an empirical application we find that the multiple indicator solution method and the hybrid model approach provide similar results in terms of welfare estimates, although the multiple indicator solution method is more parsimonious and notably easier to implement. The empirical results open a path to explore the performance of this method when endogeneity is thought to have a different cause or under a different set of indicators. Copyright © 2018 Elsevier B.V. All rights reserved.

Multiple regression and inverse moments improve the characterization of the spatial scaling behavior of daily streamflows in the Southeast United States

USGS Publications Warehouse

Farmer, William H.; Over, Thomas M.; Vogel, Richard M.

2015-01-01

Understanding the spatial structure of daily streamflow is essential for managing freshwater resources, especially in poorly-gaged regions. Spatial scaling assumptions are common in flood frequency prediction (e.g., index-flood method) and the prediction of continuous streamflow at ungaged sites (e.g. drainage-area ratio), with simple scaling by drainage area being the most common assumption. In this study, scaling analyses of daily streamflow from 173 streamgages in the southeastern US resulted in three important findings. First, the use of only positive integer moment orders, as has been done in most previous studies, captures only the probabilistic and spatial scaling behavior of flows above an exceedance probability near the median; negative moment orders (inverse moments) are needed for lower streamflows. Second, assessing scaling by using drainage area alone is shown to result in a high degree of omitted-variable bias, masking the true spatial scaling behavior. Multiple regression is shown to mitigate this bias, controlling for regional heterogeneity of basin attributes, especially those correlated with drainage area. Previous univariate scaling analyses have neglected the scaling of low-flow events and may have produced biased estimates of the spatial scaling exponent. Third, the multiple regression results show that mean flows scale with an exponent of one, low flows scale with spatial scaling exponents greater than one, and high flows scale with exponents less than one. The relationship between scaling exponents and exceedance probabilities may be a fundamental signature of regional streamflow. This signature may improve our understanding of the physical processes generating streamflow at different exceedance probabilities. 

Phonological Constraints on Children's Production of English Third Person Singular -S

ERIC Educational Resources Information Center

Song, Jae Yung; Sundara, Megha; Demuth, Katherine

2009-01-01

Purpose: Children variably produce grammatical morphemes at early stages of development, often omitting inflectional morphemes in obligatory contexts. This has typically been attributed to immature syntactic or semantic representations. In this study, the authors investigated the hypothesis that children's variable production of the 3rd person…

Mental health and risky sexual behaviors: evidence from DSM-IV Axis II disorders.

PubMed

Maclean, Johanna Catherine; Xu, Haiyong; French, Michael T; Ettner, Susan L

2013-12-01

Several economic studies link poor mental health and substance misuse with risky sexual behaviors. However, none have examined the relationships between DSM-IV Axis II mental health disorders (A2s) and risky sexual behaviors. A2 disorders are a poorly understood, yet prevalent and disabling class of mental health conditions. They develop early in life through an interaction of genetics and environment, and are persistent across the life course. Common features include poor impulse control, addiction, social isolation, and elevated sexual desires, although the defining features vary substantially across disorder. To investigate the association between A2 disorders and three measures of risky sexual behavior. We obtain data on adults age 20 to 50 years from Wave II of the National Epidemiological Survey of Alcohol and Related Conditions (NESARC). Our outcome measures include early initiation into sexual activity, and past year regular use of alcohol before sex and sexually transmitted disease diagnosis. NESARC administrators use the Alcohol Use Disorder and Associated Disabilities Interview Schedule to classify respondents as meeting criteria for the ten A2 disorders recognized by the American Psychiatric Association. We construct several measures of A2 disorders based on the NESARC administrators' classifications. Given their comorbidity with A2 disorders, we explore the importance of Axis I disorders in the estimated associations. We find that A2 disorders are generally associated with an increase in the probability of risky sexual behaviors among both men and women. In specifications that disaggregate disorders into clusters and specific conditions, the significant associations are not uniform, but are broadly consistent with the defining features of the cluster or disorder. Inclusion of A1 disorders attenuates estimated associations for some risky sexual behaviors among men, but not for women. We find positive associations between A2 disorders and our measures of risky sexual behaviors. Our findings are subject to several data limitations, however. The NESARC lacks information on more advanced risky sexual behaviors and our measure of early initiation into sexual activity is retrospective. Identifying the causal effects of mental health and risky sexual behaviors is complicated due to bias from reverse causality and omitted variables. We believe these sources of bias are less of a concern in our study, however. Specifically, A2 disorders develop early in life and pre-date the risky sexual behaviors, thus negating reverse causality. Because the NESARC contains a rich set of personal characteristics, we are also able to minimize potential omitted variable bias. A2 disorders are significantly associated with risky sexual behaviors, which could lead to greater utilization and cost of health care services. Health care providers should consider A2 disorders when developing health promotion recommendations as these disorders may place individuals at elevated risk for unsafe sexual behaviors. Future studies should examine the causal mechanisms between A2 disorders and risky sexual behaviors.

A model-based correction for outcome reporting bias in meta-analysis.

PubMed

Copas, John; Dwan, Kerry; Kirkham, Jamie; Williamson, Paula

2014-04-01

It is often suspected (or known) that outcomes published in medical trials are selectively reported. A systematic review for a particular outcome of interest can only include studies where that outcome was reported and so may omit, for example, a study that has considered several outcome measures but only reports those giving significant results. Using the methodology of the Outcome Reporting Bias (ORB) in Trials study of (Kirkham and others, 2010. The impact of outcome reporting bias in randomised controlled trials on a cohort of systematic reviews. British Medical Journal 340, c365), we suggest a likelihood-based model for estimating the effect of ORB on confidence intervals and p-values in meta-analysis. Correcting for bias has the effect of moving estimated treatment effects toward the null and hence more cautious assessments of significance. The bias can be very substantial, sometimes sufficient to completely overturn previous claims of significance. We re-analyze two contrasting examples, and derive a simple fixed effects approximation that can be used to give an initial estimate of the effect of ORB in practice.

Measurement error in performance studies of health information technology: lessons from the management literature.

PubMed

Litwin, A S; Avgar, A C; Pronovost, P J

2012-01-01

Just as researchers and clinicians struggle to pin down the benefits attendant to health information technology (IT), management scholars have long labored to identify the performance effects arising from new technologies and from other organizational innovations, namely the reorganization of work and the devolution of decision-making authority. This paper applies lessons from that literature to theorize the likely sources of measurement error that yield the weak statistical relationship between measures of health IT and various performance outcomes. In so doing, it complements the evaluation literature's more conceptual examination of health IT's limited performance impact. The paper focuses on seven issues, in particular, that likely bias downward the estimated performance effects of health IT. They are 1.) negative self-selection, 2.) omitted or unobserved variables, 3.) mis-measured contextual variables, 4.) mismeasured health IT variables, 5.) lack of attention to the specific stage of the adoption-to-use continuum being examined, 6.) too short of a time horizon, and 7.) inappropriate units-of-analysis. The authors offer ways to counter these challenges. Looking forward more broadly, they suggest that researchers take an organizationally-grounded approach that privileges internal validity over generalizability. This focus on statistical and empirical issues in health IT-performance studies should be complemented by a focus on theoretical issues, in particular, the ways that health IT creates value and apportions it to various stakeholders.

How Important is Parental Education for Child Nutrition?

PubMed

Alderman, Harold; Headey, Derek D

2017-06-01

Existing evidence on the impacts of parental education on child nutrition is plagued by both internal and external validity concerns. In this paper we try to address these concerns through a novel econometric analysis of 376,992 preschool children from 56 developing countries. We compare a naïve least square model to specifications that include cluster fixed effects and cohort-based educational rankings to reduce biases from omitted variables before gauging sensitivity to sub-samples and exploring potential explanations of education-nutrition linkages. We find that the estimated nutritional returns to parental education are: (a) substantially reduced in models that include fixed effects and cohort rankings; (b) larger for mothers than for fathers; (c) generally increasing, and minimal for primary education; (d) increasing with household wealth; (e) larger in countries/regions with higher burdens of undernutrition; (f) larger in countries/regions with higher schooling quality; and (g) highly variable across country sub-samples. These results imply substantial uncertainty and variability in the returns to education, but results from the more stringent models imply that even the achievement of very ambitious education targets would only lead to modest reductions in stunting rates in high-burden countries. We speculate that education might have more impact on the nutritional status of the next generation if school curricula focused on directly improving health and nutritional knowledge of future parents.

The effects of competition on premiums: using United Healthcare's 2015 entry into Affordable Care Act's marketplaces as an instrumental variable.

PubMed

Agirdas, Cagdas; Krebs, Robert J; Yano, Masato

2018-01-08

One goal of the Affordable Care Act is to increase insurance coverage by improving competition and lowering premiums. To facilitate this goal, the federal government enacted online marketplaces in the 395 rating areas spanning 34 states that chose not to establish their own state-run marketplaces. Few multivariate regression studies analyzing the effects of competition on premiums suffer from endogeneity, due to simultaneity and omitted variable biases. However, United Healthcare's decision to enter these marketplaces in 2015 provides the researcher with an opportunity to address this endogeneity problem. Exploiting the variation caused by United Healthcare's entry decision as an instrument for competition, we study the impact of competition on premiums during the first 2 years of these marketplaces. Combining panel data from five different sources and controlling for 12 variables, we find that one more insurer in a rating area leads to a 6.97% reduction in the second-lowest-priced silver plan premium, which is larger than the estimated effects in existing literature. Furthermore, we run a threshold analysis and find that competition's effects on premiums become statistically insignificant if there are four or more insurers in a rating area. These findings are robust to alternative measures of premiums, inclusion of a non-linear term in the regression models and a county-level analysis.

How easily can omission of patients, or selection amongst poorly-reproducible measurements, create artificial correlations? Methods for detection and implications for observational research design in cardiology.

PubMed

Francis, Darrel P

2013-07-15

When reported correlation coefficients seem too high to be true, does investigative verification of source data provide suitable reassurance? This study tests how easily omission of patients or selection amongst irreproducible measurements generate fictitious strong correlations, without data fabrication. Two forms of manipulation are applied to a pair of normally-distributed, uncorrelated variables: first, exclusion of patients least favourable to a hypothesised association and, second, making multiple poorly-reproducible measurements per patient and choosing the most supportive. Excluding patients raises correlations powerfully, from 0.0 ± 0.11 (no patients omitted) to 0.40 ± 0.11 (one-fifth omitted), 0.59 ± 0.08 (one-third omitted) and 0.78 ± 0.05 (half omitted). Study size offers no protection: omitting just one-fifth of 75 patients (i.e. publishing 60) makes 92% of correlations statistically significant. Worse, simply selecting the most favourable amongst several measurements raises correlations from 0.0 ± 0.12 (single measurement of each variable) to 0.73 ± 0.06 (best of 2), and 0.90 ± 0.03 (best of 4). 100% of correlation coefficients become statistically significant. Scatterplots may reveal a telltale "shave sign" or "bite sign". Simple statistical tests are presented for these suspicious signatures in single or multiple studies. Correlations are vulnerable to data manipulation. Cardiology is especially vulnerable to patient deletion (because cardiologists ourselves might completely control enrolment and measurement), and selection of "best" measurements (because alternative heartbeats are numerous, and some modalities poorly reproducible). Source data verification cannot detect these but tests might highlight suspicious data and--aggregating across studies--unreliable laboratories or research fields. Cardiological correlation research needs adequately-informed planning and guarantees of integrity, with teeth. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

New approaches to the analysis of population trends in land birds: Comment

USGS Publications Warehouse

Link, W.A.; Sauer, J.R.

1997-01-01

James et al. (1996, Ecology 77:13-27) used data from the North American Breeding Bird Survey (BBS) to examine geographic variability in patterns of population change for 26 species of wood warblers. They emphasized the importance of evaluating nonlinear patterns of change in bird populations, proposed LOESS-based non-parametric and semi-parametric analyses of BBS data, and contrasted their results with other analyses, including those of Robbins et al. (1989, Proceedings of the National Academy of Sciences 86: 7658-7662) and Peterjohn et al. (1995, Pages 3-39 in T. E. Martin and D. M. Finch, eds. Ecology and management of Neotropical migratory birds: a synthesis and review of critical issues. Oxford University Press, New York.). In this note, we briefly comment on some of the issues that arose from their analysis of BBS data, suggest a few aspects of the survey that should inspire caution in analysts, and review the differences between the LOESS-based procedures and other procedures (e.g., Link and Sauer 1994). We strongly discourage the use of James et al.'s completely non-parametric procedure, which fails to account for observer effects. Our comparisons of estimators adds to the evidence already present in the literature of the bias associated with omitting observer information in analyses of BBS data. Bias resulting from change in observer abilities should be a consideration in any analysis of BBS data.

Investigating the relationship between neighborhood poverty and mortality risk: A marginal structural modeling approach

PubMed Central

Do, D. Phuong; Wang, Lu; Elliott, Michael R.

2013-01-01

Extant observational studies generally support the existence of a link between neighborhood context and health. However, estimating the causal impact of neighborhood effects from observational data has proven to be a challenge. Omission of relevant factors may lead to overestimating the effects of neighborhoods on health while inclusion of time-varying confounders that may also be mediators (e.g., income, labor force status) may lead to underestimation. Using longitudinal data from the 1990 to 2007 years of the Panel Study of Income Dynamics, this study investigates the link between neighborhood poverty and overall mortality risk. A marginal structural modeling strategy is employed to appropriately adjust for simultaneous mediating and confounding factors. To address the issue of possible upward bias from the omission of key variables, sensitivity analysis to assess the robustness of results against unobserved confounding is conducted. We examine two continuous measures of neighborhood poverty – single-point and a running average. Both were specified as piece-wise linear splines with a knot at 20 percent. We found no evidence from the traditional naïve strategy that neighborhood context influences mortality risk. In contrast, for both the single-point and running average neighborhood poverty specifications, the marginal structural model estimates indicated a statistically significant increase in mortality risk with increasing neighborhood poverty above the 20 percent threshold. For example, below 20 percent neighborhood poverty, no association was found. However, after the 20 percent poverty threshold is reached, each 10 percentage point increase in running average neighborhood poverty was found to increase the odds for mortality by 89 percent [95% CI = 1.22, 2.91]. Sensitivity analysis indicated that estimates were moderately robust to omitted variable bias. PMID:23849239

Parental Psychological Control, Psychological Autonomy, and Acceptance as Predictors of Self-Esteem in Latino Adolescents

ERIC Educational Resources Information Center

Bean, Roy A.; Northrup, Jason C.

2009-01-01

This study examines several key parenting variables (psychological control, psychological autonomy, and acceptance) in predicting self-esteem among Latino adolescents using structural equation modeling analyses. Nested models are tested and parental acceptance variables are omitted from the model and group gender comparisons are examined. Two…

If looks could heal: Child health and paternal investment.

PubMed

Tracey, Marlon R; Polachek, Solomon W

2018-01-01

Data from the first two waves of the Fragile Family and Child Wellbeing study indicate that infants who look like their father at birth are healthier one year later. The reason is such father-child resemblance induces a father to spend more time engaged in positive parenting. An extra day (per month) of time-investment by a typical visiting father enhances child health by just over 10% of a standard deviation. This estimate is not biased by the effect of child health on father-involvement or omitted maternal ability, thereby eliminating endogeneity biases that plague existing studies. The result has implications regarding the role of a father's time in enhancing child health, especially in fragile families. Copyright © 2017 Elsevier B.V. All rights reserved.

It's about time: Cesarean sections and neonatal health.

PubMed

Costa-Ramón, Ana María; Rodríguez-González, Ana; Serra-Burriel, Miquel; Campillo-Artero, Carlos

2018-05-01

Cesarean sections have been associated in the literature with poorer newborn health, particularly with a higher incidence of respiratory morbidity. Most studies suffer, however, from potential omitted variable bias, as they are based on simple comparisons of mothers who give birth vaginally and those who give birth by cesarean section. We try to overcome this limitation and provide credible causal evidence by using variation in the probability of having a c-section that is arguably unrelated to maternal and fetal characteristics: variation by time of day. Previous literature documents that, while nature distributes births and associated problems uniformly, time-dependent variables related to physicians' demand for leisure are significant predictors of unplanned c-sections. Using a sample of public hospitals in Spain, we show that the rate of c-sections is higher during the early hours of the night compared to the rest of the day, while mothers giving birth at the different times are similar in observable characteristics. This exogenous variation provides us with a new instrument for type of birth: time of delivery. Our results suggest that non-medically indicated c-sections have a negative and significant impact on newborn health, as measured by Apgar scores, but that the effect is not severe enough to translate into more extreme outcomes. Copyright © 2018 Elsevier B.V. All rights reserved.

Variable Input and the Acquisition of Plural Morphology

ERIC Educational Resources Information Center

Miller, Karen L.; Schmitt, Cristina

2012-01-01

The present article examines the effect of variable input on the acquisition of plural morphology in two varieties of Spanish: Chilean Spanish, where the plural marker is sometimes omitted due to a phonological process of syllable final /s/ lenition, and Mexican Spanish (of Mexico City), with no such lenition process. The goal of the study is to…

The Younger Siblings of Childbearing Adolescents: Parenting Influences on Their Academic and Social-Emotional Adjustment

PubMed Central

East, Patricia L.

2013-01-01

The younger siblings of childbearing adolescents have poorer school outcomes and exhibit more internalizing and externalizing problems compared to their peers without a childbearing sister. We test a model where living with an adolescent childbearing sister constitutes a major family stressor that disrupts mothers’ parenting and well-being, and through which, adversely affect youths’ adjustment. Data came from 243 Latino younger siblings (62% female, M age 13.7 years) and their mothers, 121 of whom lived with a childbearing adolescent sister and 122 of whom did not. Individual fixed-effects models controlled for earlier measures of each respective model construct, thereby reducing omitted variable bias from pre-existing group differences. Results show that, for boys, the relationship between living with a childbearing adolescent sister and youth outcomes was sequentially mediated through mothers’ stress and parenting (i.e., monitoring and nurturance). For girls, however, the relationship was mediated through mothers’ monitoring only. Findings elucidate the within-family processes that contribute to the problematic outcomes of youth living with childbearing adolescent older sisters. PMID:21965104

Does a College Education Reduce Depressive Symptoms in American Young Adults?

PubMed Central

McFarland, Michael J.; Wagner, Brandon G.

2015-01-01

Higher levels of educational attainment are consistently associated with better mental health. Whether this association represents an effect of education on mental health, however, is less clear as omitted variable bias remains a pressing concern with education potentially serving as a proxy for unobserved factors including family background and genetics. To combat this threat and come closer to a causal estimate of the effect of education on depressive symptoms, this study uses data on 231 monozygotic twin pairs from The National Longitudinal Study of Adolescent to Adult Health and employs a twin-pair difference-in-difference design to account for both unobserved shared factors between twin pairs (e.g. home, school, and neighborhood environment throughout childhood) and a number of observed non-shared but theoretically relevant factors (e.g. cognitive ability, personality characteristics, adolescent health). We find an inverse association between possessing a college degree and depressive symptoms in both conventional and difference-in-difference models. Results of this study also highlight the potentially overlooked role of personality characteristics in the education and mental health literature. PMID:26513116

p-Curve and p-Hacking in Observational Research.

PubMed

Bruns, Stephan B; Ioannidis, John P A

2016-01-01

The p-curve, the distribution of statistically significant p-values of published studies, has been used to make inferences on the proportion of true effects and on the presence of p-hacking in the published literature. We analyze the p-curve for observational research in the presence of p-hacking. We show by means of simulations that even with minimal omitted-variable bias (e.g., unaccounted confounding) p-curves based on true effects and p-curves based on null-effects with p-hacking cannot be reliably distinguished. We also demonstrate this problem using as practical example the evaluation of the effect of malaria prevalence on economic growth between 1960 and 1996. These findings call recent studies into question that use the p-curve to infer that most published research findings are based on true effects in the medical literature and in a wide range of disciplines. p-values in observational research may need to be empirically calibrated to be interpretable with respect to the commonly used significance threshold of 0.05. Violations of randomization in experimental studies may also result in situations where the use of p-curves is similarly unreliable.

Does a college education reduce depressive symptoms in American young adults?

PubMed

McFarland, Michael J; Wagner, Brandon G

2015-12-01

Higher levels of educational attainment are consistently associated with better mental health. Whether this association represents an effect of education on mental health, however, is less clear as omitted variable bias remains a pressing concern with education potentially serving as a proxy for unobserved factors including family background and genetics. To combat this threat and come closer to a causal estimate of the effect of education on depressive symptoms, this study uses data on 231 monozygotic twin pairs from The National Longitudinal Study of Adolescent to Adult Health and employs a twin-pair difference-in-difference design to account for both unobserved shared factors between twin pairs (e.g. home, school, and neighborhood environment throughout childhood) and a number of observed non-shared but theoretically relevant factors (e.g. cognitive ability, personality characteristics, adolescent health). We find an inverse association between possessing a college degree and depressive symptoms in both conventional and difference-in-difference models. Results of this study also highlight the potentially overlooked role of personality characteristics in the education and mental health literature. Copyright © 2015 Elsevier Ltd. All rights reserved.

Economic cycles and heart disease in Mexico.

PubMed

Quast, Troy; Gonzalez, Fidel

2014-05-01

While a considerable literature has emerged regarding the relationship between the business cycles and mortality rates, relatively little is known regarding how economic fluctuations are related to morbidity. We investigate the relationship between business cycles and heart disease in Mexico using a unique state-level dataset of 512 observations consisting of real GDP and heart disease incidence rates (overall and by age group) from 1995 to 2010. Our study is one of the first to use a state-level panel approach to analyze the relationship between the business cycle and morbidity. Further, the state and year fixed effects employed in our econometric specification reduce possible omitted variable bias. We find a general procyclical, although largely statistically insignificant, contemporaneous relationship. However, an increase in GDP per capita sustained over five years is associated with considerable increases in the incidence rates of ischemic heart disease and hypertension. This procyclical relationship appears strongest in the states with the lowest levels of development and for the oldest age groups. Our results suggest that economic fluctuations may have important lagged effects on heart disease in developing countries. Copyright © 2014 Elsevier Ltd. All rights reserved.

Panel regressions to estimate low-flow response to rainfall variability in ungaged basins

USGS Publications Warehouse

Bassiouni, Maoya; Vogel, Richard M.; Archfield, Stacey A.

2016-01-01

Multicollinearity and omitted-variable bias are major limitations to developing multiple linear regression models to estimate streamflow characteristics in ungaged areas and varying rainfall conditions. Panel regression is used to overcome limitations of traditional regression methods, and obtain reliable model coefficients, in particular to understand the elasticity of streamflow to rainfall. Using annual rainfall and selected basin characteristics at 86 gaged streams in the Hawaiian Islands, regional regression models for three stream classes were developed to estimate the annual low-flow duration discharges. Three panel-regression structures (random effects, fixed effects, and pooled) were compared to traditional regression methods, in which space is substituted for time. Results indicated that panel regression generally was able to reproduce the temporal behavior of streamflow and reduce the standard errors of model coefficients compared to traditional regression, even for models in which the unobserved heterogeneity between streams is significant and the variance inflation factor for rainfall is much greater than 10. This is because both spatial and temporal variability were better characterized in panel regression. In a case study, regional rainfall elasticities estimated from panel regressions were applied to ungaged basins on Maui, using available rainfall projections to estimate plausible changes in surface-water availability and usable stream habitat for native species. The presented panel-regression framework is shown to offer benefits over existing traditional hydrologic regression methods for developing robust regional relations to investigate streamflow response in a changing climate.

Panel regressions to estimate low-flow response to rainfall variability in ungaged basins

NASA Astrophysics Data System (ADS)

Bassiouni, Maoya; Vogel, Richard M.; Archfield, Stacey A.

2016-12-01

Multicollinearity and omitted-variable bias are major limitations to developing multiple linear regression models to estimate streamflow characteristics in ungaged areas and varying rainfall conditions. Panel regression is used to overcome limitations of traditional regression methods, and obtain reliable model coefficients, in particular to understand the elasticity of streamflow to rainfall. Using annual rainfall and selected basin characteristics at 86 gaged streams in the Hawaiian Islands, regional regression models for three stream classes were developed to estimate the annual low-flow duration discharges. Three panel-regression structures (random effects, fixed effects, and pooled) were compared to traditional regression methods, in which space is substituted for time. Results indicated that panel regression generally was able to reproduce the temporal behavior of streamflow and reduce the standard errors of model coefficients compared to traditional regression, even for models in which the unobserved heterogeneity between streams is significant and the variance inflation factor for rainfall is much greater than 10. This is because both spatial and temporal variability were better characterized in panel regression. In a case study, regional rainfall elasticities estimated from panel regressions were applied to ungaged basins on Maui, using available rainfall projections to estimate plausible changes in surface-water availability and usable stream habitat for native species. The presented panel-regression framework is shown to offer benefits over existing traditional hydrologic regression methods for developing robust regional relations to investigate streamflow response in a changing climate.

The Impact of Childhood Obesity on Health and Health Service Use.

PubMed

Kinge, Jonas Minet; Morris, Stephen

2018-06-01

To test the impact of obesity on health and health care use in children, by the use of various methods to account for reverse causality and omitted variables. Fifteen rounds of the Health Survey for England (1998-2013), which is representative of children and adolescents in England. We use three methods to account for reverse causality and omitted variables in the relationship between BMI and health/health service use: regression with individual, parent, and household control variables; sibling fixed effects; and instrumental variables based on genetic variation in weight. We include all children and adolescents aged 4-18 years old. We find that obesity has a statistically significant and negative impact on self-rated health and a positive impact on health service use in girls, boys, younger children (aged 4-12), and adolescents (aged 13-18). The findings are comparable in each model in both boys and girls. Using econometric methods, we have mitigated several confounding factors affecting the impact of obesity in childhood on health and health service use. Our findings suggest that obesity has severe consequences for health and health service use even among children. © Health Research and Educational Trust.

Controlling for seasonal patterns and time varying confounders in time-series epidemiological models: a simulation study.

PubMed

Perrakis, Konstantinos; Gryparis, Alexandros; Schwartz, Joel; Le Tertre, Alain; Katsouyanni, Klea; Forastiere, Francesco; Stafoggia, Massimo; Samoli, Evangelia

2014-12-10

An important topic when estimating the effect of air pollutants on human health is choosing the best method to control for seasonal patterns and time varying confounders, such as temperature and humidity. Semi-parametric Poisson time-series models include smooth functions of calendar time and weather effects to control for potential confounders. Case-crossover (CC) approaches are considered efficient alternatives that control seasonal confounding by design and allow inclusion of smooth functions of weather confounders through their equivalent Poisson representations. We evaluate both methodological designs with respect to seasonal control and compare spline-based approaches, using natural splines and penalized splines, and two time-stratified CC approaches. For the spline-based methods, we consider fixed degrees of freedom, minimization of the partial autocorrelation function, and general cross-validation as smoothing criteria. Issues of model misspecification with respect to weather confounding are investigated under simulation scenarios, which allow quantifying omitted, misspecified, and irrelevant-variable bias. The simulations are based on fully parametric mechanisms designed to replicate two datasets with different mortality and atmospheric patterns. Overall, minimum partial autocorrelation function approaches provide more stable results for high mortality counts and strong seasonal trends, whereas natural splines with fixed degrees of freedom perform better for low mortality counts and weak seasonal trends followed by the time-season-stratified CC model, which performs equally well in terms of bias but yields higher standard errors. Copyright © 2014 John Wiley & Sons, Ltd.

Adjuvant radiotherapy after breast conserving surgery - a comparative effectiveness research study.

PubMed

Corradini, Stefanie; Niyazi, Maximilian; Niemoeller, Olivier M; Li, Minglun; Roeder, Falk; Eckel, Renate; Schubert-Fritschle, Gabriele; Scheithauer, Heike R; Harbeck, Nadia; Engel, Jutta; Belka, Claus

2015-01-01

The purpose of this retrospective outcome study was to validate the effectiveness of postoperative radiotherapy in breast conserving therapy (BCT) and to evaluate possible causes for omission of radiotherapy after breast conserving surgery (BCS) in a non-trial population. Data were provided by the population-based Munich Cancer Registry. The study included epidemiological data of 30.811 patients diagnosed with breast cancer from 1998 to 2012. The effect of omitting radiotherapy was analysed using Kaplan-Meier-estimates and Cox proportional hazard regression. Variables predicting omission of radiotherapy were analysed using multivariate logistic regression. Use of postoperative radiotherapy after BCS was associated with significant improvements in local control and survival. 10-year loco-regional recurrence-free-survival was 90.8% with postoperative radiotherapy vs. 77.6% with surgery alone (p<0.001). 10-year overall survival rates were 55.2% with surgery alone vs. 82.2% following postoperative radiotherapy (p<0.001). Variables predicting omission of postoperative radiotherapy included advanced age (women ⩾80 years; OR: 0.082; 95% CI: 0.071-0.094, p<0.001). This study shows a decrease in local control and a survival disadvantage if postoperative radiotherapy after breast conserving surgery is omitted in an unselected cohort of primary breast cancer patients. Due to its epidemiological nature, it cannot answer the question in whom postoperative radiotherapy can be safely omitted. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Workplace drug testing and worker drug use.

PubMed

Carpenter, Christopher S

2007-04-01

To examine the nature and extent of the association between workplace drug testing and worker drug use. Repeated cross-sections from the 2000 to 2001 National Household Surveys on Drug Abuse (NHSDA) and the 2002 National Survey on Drug Use and Health (NSDUH). Multivariate logistic regression models of the likelihood of marijuana use are estimated as a function of several different workplace drug policies, including drug testing. Specific questions about penalty severity and the likelihood of detection are used to further evaluate the nature of the association. Individuals whose employers perform drug tests are significantly less likely to report past month marijuana use, even after controlling for a wide array of worker and job characteristics. However, large negative associations are also found for variables indicating whether a firm has drug education, an employee assistance program, or a simple written policy about substance use. Accounting for these other workplace characteristics reduces-but does not eliminate-the testing differential. Frequent testing and severe penalties reduce the likelihood that workers use marijuana. Previous studies have interpreted the large negative correlation between workplace drug testing and employee substance use as representing a causal deterrent effect of drug testing. Our results using more comprehensive data suggest that these estimates have been slightly overstated due to omitted variables bias. The overall pattern of results remains largely consistent with the hypothesis that workplace drug testing deters worker drug use.

Multilevel Modeling with Correlated Effects

ERIC Educational Resources Information Center

Kim, Jee-Seon; Frees, Edward W.

2007-01-01

When there exist omitted effects, measurement error, and/or simultaneity in multilevel models, explanatory variables may be correlated with random components, and standard estimation methods do not provide consistent estimates of model parameters. This paper introduces estimators that are consistent under such conditions. By employing generalized…

Social Desirability Bias Against Admitting Anger: Bias in the Test-Taker or Bias in the Test?

PubMed

Fernandez, Ephrem; Woldgabreal, Yilma; Guharajan, Deepan; Day, Andrew; Kiageri, Vasiliki; Ramtahal, Nirvana

2018-05-09

The veracity of self-report is often questioned, especially in anger, which is particularly susceptible to social desirability bias (SDB). However, could tests of SDB be themselves susceptible to bias? This study aimed to replicate the inverse correlation between a common test of SDB and a test of anger, to deconstruct this relationship according to anger-related versus non-anger-related items, and to reevaluate factor structure and reliability of the SDB test. More than 200 students were administered the Marlowe-Crowne Social Desirability Scale Short Version [M-C1(10)] and the Anger Parameters Scale (APS). Results confirmed that anger and SDB scores were significantly and inversely correlated. This intercorrelation became nonsignificant when the 4 anger-related items were omitted from the M-C1(10). Confirmatory factor analyses showed excellent fit for a model comprising anger items of the M-C1(10) but not for models of the entire instrument or nonanger items. The first model also attained high internal consistency. Thus, the significant negative correlation between anger and SDB is attributable to 4 M-C1(10) anger items, for which low ratings are automatically scored as high SDB; this stems from a tenuous assumption that low anger reports are invariably biased. The SDB test risks false positives of faking good and should be used with caution.

What is the Uncertainty in MODIS Aerosol Optical Depth in the Vicinity of Clouds?

NASA Technical Reports Server (NTRS)

Patadia, Falguni; Levy, Rob; Mattoo, Shana

2017-01-01

MODIS dark-target (DT) algorithm retrieves aerosol optical depth (AOD) using a Look Up Table (LUT) approach. Global comparison of AOD (Collection 6 ) with ground-based sun photometer gives an Estimated Error (EE) of +/-(0.04 + 10%) over ocean. However, EE does not represent per-retrieval uncertainty. For retrievals that are biased high compared to AERONET, here we aim to closely examine the contribution of biases due to presence of clouds and per-pixel retrieval uncertainty. We have characterized AOD uncertainty at 550 nm, due to standard deviation of reflectance in 10 km retrieval region, uncertainty related to gas (H2O, O3) absorption, surface albedo, and aerosol models. The uncertainty in retrieved AOD seems to lie within the estimated over ocean error envelope of +/-(0.03+10%). Regions between broken clouds tend to have higher uncertainty. Compared to C6 AOD, a retrieval omitting observations in the vicinity of clouds (< or = 1 km) is biased by about +/- 0.05. For homogeneous aerosol distribution, clear sky retrievals show near zero bias. Close look at per-pixel reflectance histograms suggests retrieval possibility using median reflectance values.

Geometrical Meaning of Arithmetic Series [Image Omitted], [Image Omitted] and [Image Omitted] in Terms of the Elementary Combinatorics

ERIC Educational Resources Information Center

Kobayashi, Yukio

2011-01-01

The formula [image omitted] is closely related to combinatorics through an elementary geometric exercise. This approach can be expanded to the formulas [image omitted], [image omitted] and [image omitted]. These formulas are also nice examples of showing two approaches, one algebraic and one combinatoric, to a problem of counting. (Contains 6…

Empirical evidence on the demand for carve-outs in employment group mental health coverage.

PubMed

Salkever, David S.; Shinogle, Judith A.

2000-06-01

BACKGROUND AND AIMS OF THE STUDY: The use of specialized behavioral health companies to manage mental/health benefits has become widespread in recent years. Recent studies have reported on the cost and utilization impacts of behavioral health carve-outs. Yet little previous research has examined the factors which lead employer-based health plans to adopt a carve-out strategy for mental health benefits. The examination of these factors is the main focus of our study. Our empirical analysis is also intended to explore several hypotheses (moral hazard, adverse selection, economies of scale and alternate utilization management strategies) that have recently been advanced to explain the popularity of carve-outs. METHODS: The data for this study are from a survey of employers who have long-term disability contracts with one large insurer. The analysis uses data from 248 employers who offer mental health benefits combined with local market information (e.g. health care price proxies, state tax rates etc), state regulations (mental health and substance abuse mandate and parity laws) and employee characteristics. Two different measures of carve-out use were used as dependent variables in the analysis: (1) the fraction of health plans offered by the employer that contained carve-out provisions and (2) a dichotomous indicator for those employers who included a carve-out arrangement in all the health plans they offered. RESULTS: Our results tended to support the general cost-control hypothesis that factors associated with higher use and/or costs of mental health services increase the demand for carve-outs. Our results gave less consistent support to the argument that carve-outs are demanded to control adverse selection, though only a few variables provided a direct test of this hypothesis. The role of economies of scale (i.e., group size) and the effectiveness of alternative strategies for managing moral hazard costs (i.e., HMOs) were confirmed by our results. DISCUSSION: We considered a number of different hypotheses concerning employers' demands for mental health carve-outs and found varying degrees of support for these hypotheses in our data. Our results tended to support the general cost-control hypothesis that factors associated with higher use and/or costs of mental health services increase the demand for carve-outs. LIMITATIONS: Our database includes a small number of relatively large employers and is not representative of employers nationally. Our selection criteria, concerning size and the requirement that some employees are covered by LTD insurance, probably resulted in a study sample that offers richer benefits than do employers nationally. Our employers also report a higher percentage of salaried employees relative to the national data. Another deficiency in the current study is the lack of detailed information on the socio-demographic and behavioral characteristics of covered employees. Finally, the cross-sectional nature of our analysis raises concerns about susceptibility of our findings to omitted variables bias. IMPLICATIONS FOR FURTHER RESEARCH: Research with more information on covered employee characteristics will allow for a stronger test of the general hypothesis that factors associated with a higher demand for services are also associated with a higher demand for carve-outs. Also, future analyses that capture the experience of states that have recently passed mandate and parity laws, and that use pooled data to control for omitted variables bias, will provide more definitive evidence on the relationship between these laws and carve-out demand.

Is flood risk capitalized into real estate market values? : a Mahalanobis-metric matching approach to housing market in Busan, South Korea

NASA Astrophysics Data System (ADS)

Jung, E.; Yoon, H.

2016-12-01

Natural disasters are substantial source of social and economic damage around the globe. The amount of damage is larger when such catastrophe events happen in urbanized areas where the wealth is concentrated. Disasters cause losses in real estate assets, incurring additional cost of repair and maintenance of the properties. For this reason, natural hazard risk such as flooding and landslide is regarded as one of the important determinants of homebuyers' choice and preference. In this research, we aim to reveal whether the past records of flood affect real estate market values in Busan, Korea in 2014, under a hypothesis that homebuyers' perception of natural hazard is reflected on housing values, using the Mahalanobis-metric matching method. Unlike conventionally used hedonic pricing model to estimate capitalization of flood risk into the sales price of properties, the analytical method we adopt here enables inferring causal effects by efficiently controlling for observed/unobserved omitted variable bias. This matching approach pairs each inundated property (treatment variable) with a non-inundated property (control variable) with the closest Mahalanobis distance between them, and comparing their effects on residential property sales price (outcome variable). As a result, we expect price discounts for inundated properties larger than the one for comparable non-inundated properties. This research will be valuable in establishing the mitigation policies of future climate change to relieve the possible negative economic consequences from the disaster by estimating how people perceive and respond to natural hazard. This work was supported by the Korea Environmental Industry and Technology Institute (KEITI) under Grant (No. 2014-001-310007).

40 CFR 94.203 - Application for certification.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Section 94.203 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS... § 94.109(d) for Category 3 engines. Small-volume manufacturers may omit measurement and reporting of... application of the engine (e.g., used to propel planing vessels, use to propel vessels with variable-pitch...

Sensitivity of Coupled Tropical Pacific Model Biases to Convective Parameterization in CESM1

NASA Astrophysics Data System (ADS)

Woelfle, M. D.; Yu, S.; Bretherton, C. S.; Pritchard, M. S.

2018-01-01

Six month coupled hindcasts show the central equatorial Pacific cold tongue bias development in a GCM to be sensitive to the atmospheric convective parameterization employed. Simulations using the standard configuration of the Community Earth System Model version 1 (CESM1) develop a cold bias in equatorial Pacific sea surface temperatures (SSTs) within the first two months of integration due to anomalous ocean advection driven by overly strong easterly surface wind stress along the equator. Disabling the deep convection parameterization enhances the zonal pressure gradient leading to stronger zonal wind stress and a stronger equatorial SST bias, highlighting the role of pressure gradients in determining the strength of the cold bias. Superparameterized hindcasts show reduced SST bias in the cold tongue region due to a reduction in surface easterlies despite simulating an excessively strong low-level jet at 1-1.5 km elevation. This reflects inadequate vertical mixing of zonal momentum from the absence of convective momentum transport in the superparameterized model. Standard CESM1simulations modified to omit shallow convective momentum transport reproduce the superparameterized low-level wind bias and associated equatorial SST pattern. Further superparameterized simulations using a three-dimensional cloud resolving model capable of producing realistic momentum transport simulate a cold tongue similar to the default CESM1. These findings imply convective momentum fluxes may be an underappreciated mechanism for controlling the strength of the equatorial cold tongue. Despite the sensitivity of equatorial SST to these changes in convective parameterization, the east Pacific double-Intertropical Convergence Zone rainfall bias persists in all simulations presented in this study.

p-Curve and p-Hacking in Observational Research

PubMed Central

Bruns, Stephan B.; Ioannidis, John P. A.

2016-01-01

The p-curve, the distribution of statistically significant p-values of published studies, has been used to make inferences on the proportion of true effects and on the presence of p-hacking in the published literature. We analyze the p-curve for observational research in the presence of p-hacking. We show by means of simulations that even with minimal omitted-variable bias (e.g., unaccounted confounding) p-curves based on true effects and p-curves based on null-effects with p-hacking cannot be reliably distinguished. We also demonstrate this problem using as practical example the evaluation of the effect of malaria prevalence on economic growth between 1960 and 1996. These findings call recent studies into question that use the p-curve to infer that most published research findings are based on true effects in the medical literature and in a wide range of disciplines. p-values in observational research may need to be empirically calibrated to be interpretable with respect to the commonly used significance threshold of 0.05. Violations of randomization in experimental studies may also result in situations where the use of p-curves is similarly unreliable. PMID:26886098

Probabilities and statistics for backscatter estimates obtained by a scatterometer with applications to new scatterometer design data

NASA Technical Reports Server (NTRS)

Pierson, Willard J., Jr.

1989-01-01

The values of the Normalized Radar Backscattering Cross Section (NRCS), sigma (o), obtained by a scatterometer are random variables whose variance is a known function of the expected value. The probability density function can be obtained from the normal distribution. Models for the expected value obtain it as a function of the properties of the waves on the ocean and the winds that generated the waves. Point estimates of the expected value were found from various statistics given the parameters that define the probability density function for each value. Random intervals were derived with a preassigned probability of containing that value. A statistical test to determine whether or not successive values of sigma (o) are truly independent was derived. The maximum likelihood estimates for wind speed and direction were found, given a model for backscatter as a function of the properties of the waves on the ocean. These estimates are biased as a result of the terms in the equation that involve natural logarithms, and calculations of the point estimates of the maximum likelihood values are used to show that the contributions of the logarithmic terms are negligible and that the terms can be omitted.

Is video gaming, or video game addiction, associated with depression, academic achievement, heavy episodic drinking, or conduct problems?

PubMed

Brunborg, Geir Scott; Mentzoni, Rune Aune; Frøyland, Lars Roar

2014-03-01

While the relationships between video game use and negative consequences are debated, the relationships between video game addiction and negative consequences are fairly well established. However, previous studies suffer from methodological weaknesses that may have caused biased results. There is need for further investigation that benefits from the use of methods that avoid omitted variable bias. Two wave panel data was used from two surveys of 1,928 Norwegian adolescents aged 13 to 17 years. The surveys included measures of video game use, video game addiction, depression, heavy episodic drinking, academic achievement, and conduct problems. The data was analyzed using first-differencing, a regression method that is unbiased by time invariant individual factors. Video game addiction was related to depression, lower academic achievement, and conduct problems, but time spent on video games was not related to any of the studied negative outcomes. The findings were in line with a growing number of studies that have failed to find relationships between time spent on video games and negative outcomes. The current study is also consistent with previous studies in that video game addiction was related to other negative outcomes, but it made the added contribution that the relationships are unbiased by time invariant individual effects. However, future research should aim at establishing the temporal order of the supposed causal effects. Spending time playing video games does not involve negative consequences, but adolescents who experience problems related to video games are likely to also experience problems in other facets of life.

Is video gaming, or video game addiction, associated with depression, academic achievement, heavy episodic drinking, or conduct problems?

PubMed Central

Brunborg, Geir Scott; Mentzoni, Rune Aune; Frøyland, Lars Roar

2014-01-01

Background and aims: While the relationships between video game use and negative consequences are debated, the relationships between video game addiction and negative consequences are fairly well established. However, previous studies suffer from methodological weaknesses that may have caused biased results. There is need for further investigation that benefits from the use of methods that avoid omitted variable bias. Methods: Two wave panel data was used from two surveys of 1,928 Norwegian adolescents aged 13 to 17 years. The surveys included measures of video game use, video game addiction, depression, heavy episodic drinking, academic achievement, and conduct problems. The data was analyzed using first-differencing, a regression method that is unbiased by time invariant individual factors. Results: Video game addiction was related to depression, lower academic achievement, and conduct problems, but time spent on video games was not related to any of the studied negative outcomes. Discussion: The findings were in line with a growing number of studies that have failed to find relationships between time spent on video games and negative outcomes. The current study is also consistent with previous studies in that video game addiction was related to other negative outcomes, but it made the added contribution that the relationships are unbiased by time invariant individual effects. However, future research should aim at establishing the temporal order of the supposed causal effects. Conclusions: Spending time playing video games does not involve negative consequences, but adolescents who experience problems related to video games are likely to also experience problems in other facets of life. PMID:25215212

The effectiveness of a health promotion program after 3 years: evidence from the University of Minnesota.

PubMed

Nyman, John A; Abraham, Jean M; Jeffery, Molly Moore; Barleen, Nathan A

2012-09-01

Health promotion programs for the workplace are often sold to employers with the promise that they will pay for themselves with lowered health care expenditures and reduced absenteeism. In a recent review of the literature, it was noted that analysts often caution not to expect a positive return on investment until the third year of operation. This study investigates whether a positive return on investment was generated in the third year for the health promotion program used by the University of Minnesota. It further investigates what it is about the third year that would explain such a phenomenon. The study uses health care expenditure data and absenteeism data from 2004 to 2008 to investigate the effect of the University's lifestyle and disease management programs. It also investigates the effectiveness of participation in Minnesota's 10,000 Steps walking program and Miavita self-help programs. A differences-in-differences equations approach is used to address potential selection bias. Possible regression to the mean is dealt with by using only those who were eligible to participate as control observations. Propensity score weighting was used to balance the sample on observable characteristics and reduce bias due to omitted variables. The study finds that a 1.76 return on investment occurs in the third year of operation that is generated solely by the effect of disease management program participation in reducing health care expenditures. However, neither of the explanations for a third-year effect we tested seemed to be able to explain this phenomenon.

Sibling Position and Achievement. Reprint Series #239.

ERIC Educational Resources Information Center

Lindert, Peter H.

Past studies linking schooling and career attainment to sibling position (family size, birth order, and spacing) are vulnerable to suspicions about omitted variables. Since they were based on cross sections of individuals from different families, they may have attributed to sibling position an influence belonging to unobserved parental attributes.…

The Omitted Variable in Accounting Education Research: The Non-Traditional Student

ERIC Educational Resources Information Center

Mohrweis, Lawrence C.

2010-01-01

Few studies have examined the empirical question of whether nontraditional students are different from traditional students in learning performance. This study explores this issue. Specifically, is there a performance difference between traditional and nontraditional students in the first course in accounting? The model regressed students'…

Threat-Related Attention Bias Variability and Posttraumatic Stress.

PubMed

Naim, Reut; Abend, Rany; Wald, Ilan; Eldar, Sharon; Levi, Ofir; Fruchter, Eyal; Ginat, Karen; Halpern, Pinchas; Sipos, Maurice L; Adler, Amy B; Bliese, Paul D; Quartana, Phillip J; Pine, Daniel S; Bar-Haim, Yair

2015-12-01

Threat monitoring facilitates survival by allowing one to efficiently and accurately detect potential threats. Traumatic events can disrupt healthy threat monitoring, inducing biased and unstable threat-related attention deployment. Recent research suggests that greater attention bias variability, that is, attention fluctuations alternating toward and away from threat, occurs in participants with PTSD relative to healthy comparison subjects who were either exposed or not exposed to traumatic events. The current study extends findings on attention bias variability in PTSD. Previous measurement of attention bias variability was refined by employing a moving average technique. Analyses were conducted across seven independent data sets; in each, data on attention bias variability were collected by using variants of the dot-probe task. Trauma-related and anxiety symptoms were evaluated across samples by using structured psychiatric interviews and widely used self-report questionnaires, as specified for each sample. Analyses revealed consistent evidence of greater attention bias variability in patients with PTSD following various types of traumatic events than in healthy participants, participants with social anxiety disorder, and participants with acute stress disorder. Moreover, threat-related, and not positive, attention bias variability was correlated with PTSD severity. These findings carry possibilities for using attention bias variability as a specific cognitive marker of PTSD and for tailoring protocols for attention bias modification for this disorder.

Dependence of Halo Bias and Kinematics on Assembly Variables

NASA Astrophysics Data System (ADS)

Xu, Xiaoju; Zheng, Zheng

2018-06-01

Using dark matter haloes identified in a large N-body simulation, we study halo assembly bias, with halo formation time, peak maximum circular velocity, concentration, and spin as the assembly variables. Instead of grouping haloes at fixed mass into different percentiles of each assembly variable, we present the joint dependence of halo bias on the values of halo mass and each assembly variable. In the plane of halo mass and one assembly variable, the joint dependence can be largely described as halo bias increasing outward from a global minimum. We find it unlikely to have a combination of halo variables to absorb all assembly bias effects. We then present the joint dependence of halo bias on two assembly variables at fixed halo mass. The gradient of halo bias does not necessarily follow the correlation direction of the two assembly variables and it varies with halo mass. Therefore in general for two correlated assembly variables one cannot be used as a proxy for the other in predicting halo assembly bias trend. Finally, halo assembly is found to affect the kinematics of haloes. Low-mass haloes formed earlier can have much higher pairwise velocity dispersion than those of massive haloes. In general, halo assembly leads to a correlation between halo bias and halo pairwise velocity distribution, with more strongly clustered haloes having higher pairwise velocity and velocity dispersion. However, the correlation is not tight, and the kinematics of haloes at fixed halo bias still depends on halo mass and assembly variables.

Workplace Drug Testing and Worker Drug Use

PubMed Central

Carpenter, Christopher S

2007-01-01

Objective To examine the nature and extent of the association between workplace drug testing and worker drug use. Data Sources Repeated cross-sections from the 2000 to 2001 National Household Surveys on Drug Abuse (NHSDA) and the 2002 National Survey on Drug Use and Health (NSDUH). Study Design Multivariate logistic regression models of the likelihood of marijuana use are estimated as a function of several different workplace drug policies, including drug testing. Specific questions about penalty severity and the likelihood of detection are used to further evaluate the nature of the association. Principal Findings Individuals whose employers perform drug tests are significantly less likely to report past month marijuana use, even after controlling for a wide array of worker and job characteristics. However, large negative associations are also found for variables indicating whether a firm has drug education, an employee assistance program, or a simple written policy about substance use. Accounting for these other workplace characteristics reduces—but does not eliminate—the testing differential. Frequent testing and severe penalties reduce the likelihood that workers use marijuana. Conclusions Previous studies have interpreted the large negative correlation between workplace drug testing and employee substance use as representing a causal deterrent effect of drug testing. Our results using more comprehensive data suggest that these estimates have been slightly overstated due to omitted variables bias. The overall pattern of results remains largely consistent with the hypothesis that workplace drug testing deters worker drug use. PMID:17362218

Why Do Achievement Measures Underpredict Female Academic Performance?

ERIC Educational Resources Information Center

Mattern, Krista; Sanchez, Edgar; Ndum, Edwin

2017-01-01

In the context of college admissions, the current study examined whether differential prediction of first-year grade point average (FYGPA) by gender could be explained by an omitted variable problem--namely, academic discipline, or the amount of effort a student puts into schoolwork and the degree to which a student sees him/herself as hardworking…

Health and labour force participation of older people in Europe: what do objective health indicators add to the analysis?

PubMed

Kalwij, Adriaan; Vermeulen, Frederic

2008-05-01

This paper studies labour force participation of older individuals in 11 European countries. The data are drawn from the new Survey of Health, Ageing and Retirement in Europe (SHARE). We examine the value added of objective health indicators in relation to potentially endogenous self-reported health. We approach the endogeneity of self-reported health as an omitted variables problem. In line with the literature on the reliability of self-reported health ambiguous results are obtained. In some countries self-reported health does a fairly good job and controlling for objective health indicators does not add much to the analysis. In other countries, however, the results show that objective health indicators add significantly to the analysis and that self-reported health is endogenous due to omitted objective health indicators. These latter results illustrate the multi-dimensional nature of health and the need to control for objective health indicators when analysing the relation between health status and labour force participation. This makes an instrumental variables approach to deal with the endogeneity of self-reported health less appropriate.

Can statistical linkage of missing variables reduce bias in treatment effect estimates in comparative effectiveness research studies?

PubMed

Crown, William; Chang, Jessica; Olson, Melvin; Kahler, Kristijan; Swindle, Jason; Buzinec, Paul; Shah, Nilay; Borah, Bijan

2015-09-01

Missing data, particularly missing variables, can create serious analytic challenges in observational comparative effectiveness research studies. Statistical linkage of datasets is a potential method for incorporating missing variables. Prior studies have focused upon the bias introduced by imperfect linkage. This analysis uses a case study of hepatitis C patients to estimate the net effect of statistical linkage on bias, also accounting for the potential reduction in missing variable bias. The results show that statistical linkage can reduce bias while also enabling parameter estimates to be obtained for the formerly missing variables. The usefulness of statistical linkage will vary depending upon the strength of the correlations of the missing variables with the treatment variable, as well as the outcome variable of interest.

Application of pattern mixture models to address missing data in longitudinal data analysis using SPSS.

PubMed

Son, Heesook; Friedmann, Erika; Thomas, Sue A

2012-01-01

Longitudinal studies are used in nursing research to examine changes over time in health indicators. Traditional approaches to longitudinal analysis of means, such as analysis of variance with repeated measures, are limited to analyzing complete cases. This limitation can lead to biased results due to withdrawal or data omission bias or to imputation of missing data, which can lead to bias toward the null if data are not missing completely at random. Pattern mixture models are useful to evaluate the informativeness of missing data and to adjust linear mixed model (LMM) analyses if missing data are informative. The aim of this study was to provide an example of statistical procedures for applying a pattern mixture model to evaluate the informativeness of missing data and conduct analyses of data with informative missingness in longitudinal studies using SPSS. The data set from the Patients' and Families' Psychological Response to Home Automated External Defibrillator Trial was used as an example to examine informativeness of missing data with pattern mixture models and to use a missing data pattern in analysis of longitudinal data. Prevention of withdrawal bias, omitted data bias, and bias toward the null in longitudinal LMMs requires the assessment of the informativeness of the occurrence of missing data. Missing data patterns can be incorporated as fixed effects into LMMs to evaluate the contribution of the presence of informative missingness to and control for the effects of missingness on outcomes. Pattern mixture models are a useful method to address the presence and effect of informative missingness in longitudinal studies.

Including autapomorphies is important for paleontological tip-dating with clocklike data, but not with non-clock data.

PubMed

Matzke, Nicholas J; Irmis, Randall B

2018-01-01

Tip-dating, where fossils are included as dated terminal taxa in Bayesian dating inference, is an increasingly popular method. Data for these studies often come from morphological character matrices originally developed for non-dated, and usually parsimony, analyses. In parsimony, only shared derived characters (synapomorphies) provide grouping information, so many character matrices have an ascertainment bias: they omit autapomorphies (unique derived character states), which are considered uninformative. There has been no study of the effect of this ascertainment bias in tip-dating, but autapomorphies can be informative in model-based inference. We expected that excluding autapomorphies would shorten the morphological branchlengths of terminal branches, and thus bias downwards the time branchlengths inferred in tip-dating. We tested for this effect using a matrix for Carboniferous-Permian eureptiles where all autapomorphies had been deliberately coded. Surprisingly, date estimates are virtually unchanged when autapomorphies are excluded, although we find large changes in morphological rate estimates and small effects on topological and dating confidence. We hypothesized that the puzzling lack of effect on dating was caused by the non-clock nature of the eureptile data. We confirm this explanation by simulating strict clock and non-clock datasets, showing that autapomorphy exclusion biases dating only for the clocklike case. A theoretical solution to ascertainment bias is computing the ascertainment bias correction (M k parsinf ), but we explore this correction in detail, and show that it is computationally impractical for typical datasets with many character states and taxa. Therefore we recommend that palaeontologists collect autapomorphies whenever possible when assembling character matrices.

Morphological Errors in Spanish Second Language Learners and Heritage Speakers

ERIC Educational Resources Information Center

Montrul, Silvina

2011-01-01

Morphological variability and the source of these errors have been intensely debated in SLA. A recurrent finding is that postpuberty second language (L2) learners often omit or use the wrong affix for nominal and verbal inflections in oral production but less so in written tasks. According to the missing surface inflection hypothesis, L2 learners…

Use of Mobile Applications for Hospital Discharge Letters: Improving Handover at Point of Practice

ERIC Educational Resources Information Center

Maher, Bridget; Drachsler, Hendrik; Kalz, Marco; Hoare, Cathal; Sorensen, Humphrey; Lezcano, Leonardo; Henn, Pat; Specht, Marcus

2013-01-01

Handover of patient care is a time of particular risk and it is important that accurate and relevant information is clearly communicated. The hospital discharge letter is an important part of handover. However, the quality of hospital discharge letters is variable and letters frequently omit important information. The Cork Letter-Writing…

Visual Analysis among Novices: Training and Trend Lines as Graphic Aids

ERIC Educational Resources Information Center

Nelson, Peter M.; Van Norman, Ethan R.; Christ, Theodore J.

2017-01-01

The current study evaluated the degree to which novice visual analysts could discern trends in simulated time-series data across differing levels of variability and extreme values. Forty-five novice visual analysts were trained in general principles of visual analysis. One group received brief training on how to identify and omit extreme values.…

That Poor Laddie Cannae Tell His Thoughts Fae His Actions: A Reply to Sturmey

ERIC Educational Resources Information Center

Lindsay, William R.

2006-01-01

It is good that Peter Sturmey is scrutinizing the basis of cognitive therapy for people with intellectual disabilities. This response argues that behavioural therapies have always employed cognitive techniques and produced cognitive change but have omitted to measure them. It is further argued that unobservable variables are germane to scientific…

Using collective variables to drive molecular dynamics simulations

NASA Astrophysics Data System (ADS)

Fiorin, Giacomo; Klein, Michael L.; Hénin, Jérôme

2013-12-01

A software framework is introduced that facilitates the application of biasing algorithms to collective variables of the type commonly employed to drive massively parallel molecular dynamics (MD) simulations. The modular framework that is presented enables one to combine existing collective variables into new ones, and combine any chosen collective variable with available biasing methods. The latter include the classic time-dependent biases referred to as steered MD and targeted MD, the temperature-accelerated MD algorithm, as well as the adaptive free-energy biases called metadynamics and adaptive biasing force. The present modular software is extensible, and portable between commonly used MD simulation engines.

From Pubs to Scrubs: Alcohol Misuse and Health Care Use

PubMed Central

Balsa, Ana I; French, Michael T; Maclean, Johanna Catherine; Norton, Edward C

2009-01-01

Objective To analyze the relationships between alcohol misuse and two types of acute health care use—hospital admissions and emergency room (ER) episodes. Data Sources/Study Setting The first (2001/2002) and second (2004/2005) waves of the National Epidemiological Survey of Alcohol and Related Conditions (NESARC). Study Design Longitudinal study using a group of adults (18–60 years in Wave 1, N=23,079). Gender-stratified regression analysis adjusted for a range of covariates associated with health care use. First-difference methods corrected for potential omitted variable bias. Data Collection The target population of the NESARC was the civilian noninstitutionalized population aged 18 and older residing in the United States and the District of Columbia. The survey response rate was 81 percent in Wave 1 (N=43,093) and 65 percent in Wave 2 (N=34,653). Principal Findings Frequent drinking to intoxication was positively associated with hospital admissions for both men and women and increased the likelihood of using ER services for women. Alcohol dependence and/or abuse was related to higher use of ER services for both genders and increased hospitalizations for men. Conclusions These findings provide updated and nationally representative estimates of the relationships between alcohol misuse and health care use, and they underscore the potential implications of alcohol misuse on health care expenditures. PMID:19500163

Application of nonlinear least-squares regression to ground-water flow modeling, west-central Florida

USGS Publications Warehouse

Yobbi, D.K.

2000-01-01

A nonlinear least-squares regression technique for estimation of ground-water flow model parameters was applied to an existing model of the regional aquifer system underlying west-central Florida. The regression technique minimizes the differences between measured and simulated water levels. Regression statistics, including parameter sensitivities and correlations, were calculated for reported parameter values in the existing model. Optimal parameter values for selected hydrologic variables of interest are estimated by nonlinear regression. Optimal estimates of parameter values are about 140 times greater than and about 0.01 times less than reported values. Independently estimating all parameters by nonlinear regression was impossible, given the existing zonation structure and number of observations, because of parameter insensitivity and correlation. Although the model yields parameter values similar to those estimated by other methods and reproduces the measured water levels reasonably accurately, a simpler parameter structure should be considered. Some possible ways of improving model calibration are to: (1) modify the defined parameter-zonation structure by omitting and/or combining parameters to be estimated; (2) carefully eliminate observation data based on evidence that they are likely to be biased; (3) collect additional water-level data; (4) assign values to insensitive parameters, and (5) estimate the most sensitive parameters first, then, using the optimized values for these parameters, estimate the entire data set.

Buying a Better Air Force

DTIC Science & Technology

2006-03-01

identify if an explanatory variable may have been omitted due to model misspecification ( Ramsey , 1979). The RESET test resulted in failure to...Prob > F 0.0094 This model was also regressed using Huber-White estimators. Again, the Ramsey RESET test was done to ensure relevant...Aircraft. Annapolis, MD: Naval Institute Press, 2004. Ramsey , J. B. “ Tests for Specification Errors in Classical Least-Squares Regression Analysis

Stress in Latino families following an adolescent's childbearing: effects on family relationships and siblings.

PubMed

East, Patricia L; Chien, Nina C

2013-04-01

This study examined how increased stress in Latino families following an adolescent's childbearing impacts family relationships and the adolescent's siblings. Participants were 243 Mexican American youth (mean age: 13.7 years; 62% girls), or 121 youth who had a pregnant adolescent sister and 122 youth who had an adolescent sister who had never been pregnant. Youth and their mothers were studied at 4 time points across 15 months: The families of pregnant adolescents were studied when the adolescent sister was in her third trimester of pregnancy, and at 2 months, 6 months, and 12 months postpartum; the families of never-pregnant adolescents were studied at like intervals. Individual fixed-effects structural equation models were computed, which control for earlier measures of study constructs and thereby reduce omitted variable bias from preexisting family group differences. Results showed that an adolescent's childbearing was related to increases in family stress, which were related to increases in mothers' harsh parenting and mother-sibling conflict, which, in turn, were related to subsequent increases in siblings' problem behavior. Multiple group analyses revealed that the pathways through which a teenager's childbearing influences siblings operate similarly for girls and boys. Tests of an alternate ordering of model variables indicated a poor fit with the data. Findings provide evidence that the accumulation of stressful family changes following an adolescent's childbearing can negatively impact siblings. Findings also elucidate how family-level stress and disruption experienced across a family transition trickle down to affect family relationships and, in turn, child family members. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

Stress in Latino Families Following an Adolescent’s Childbearing: Effects on Family Relationships and Siblings

PubMed Central

East, Patricia L.; Chien, Nina C.

2013-01-01

This study examined how increased stress in Latino families following an adolescent’s childbearing impacts family relationships and the adolescent’s siblings. Participants were 243 Mexican American youth (mean age: 13.7 years; 62% girls), or 121 youth who had a pregnant adolescent sister and 122 youth who had an adolescent sister who had never been pregnant. Youth and their mothers were studied at 4 time points across 15 months: The families of pregnant adolescents were studied when the adolescent sister was in her third trimester of pregnancy, and at 2 months, 6 months, and 12 months postpartum; the families of never-pregnant adolescents were studied at like intervals. Individual fixed-effects structural equation models were computed, which control for earlier measures of study constructs and thereby reduce omitted variable bias from preexisting family group differences. Results showed that an adolescent’s childbearing was related to increases in family stress, which were related to increases in mothers’ harsh parenting and mother–sibling conflict, which, in turn, were related to subsequent increases in siblings’ problem behavior. Multiple group analyses revealed that the pathways through which a teenager’s childbearing influences siblings operate similarly for girls and boys. Tests of an alternate ordering of model variables indicated a poor fit with the data. Findings provide evidence that the accumulation of stressful family changes following an adolescent’s childbearing can negatively impact siblings. Findings also elucidate how family-level stress and disruption experienced across a family transition trickle down to affect family relationships and, in turn, child family members. PMID:23458699

Lost in translation: Review of identification bias, translation bias and research waste in dentistry.

PubMed

Layton, Danielle M; Clarke, Michael

2016-01-01

To review how articles are retrieved from bibliographic databases, what article identification and translation problems have affected research, and how these problems can contribute to research waste and affect clinical practice. This literature review sought and appraised articles regarding identification- and translation-bias in the medical and dental literature, which limit the ability of users to find research articles and to use these in practice. Articles can be retrieved from bibliographic databases by performing a word or index-term (for example, MeSH for MEDLINE) search. Identification of articles is challenging when it is not clear which words are most relevant, and which terms have been allocated to indexing fields. Poor reporting quality of abstracts and articles has been reported across the medical literature at large. Specifically in dentistry, research regarding time-to-event survival analyses found the allocation of MeSH terms to be inconsistent and inaccurate, important words were omitted from abstracts by authors, and the quality of reporting in the body of articles was generally poor. These shortcomings mean that articles will be difficult to identify, and difficult to understand if found. Use of specialized electronic search strategies can decrease identification bias, and use of tailored reporting guidelines can decrease translation bias. Research that cannot be found, or cannot be used results in research waste, and undermines clinical practice. Identification- and translation-bias have been shown to affect time-to-event dental articles, are likely affect other fields of research, and are largely unrecognized by authors and evidence seekers alike. By understanding that the problems exist, solutions can be sought to improve identification and translation of our research. Copyright © 2015 Academy of Dental Materials. Published by Elsevier Ltd. All rights reserved.

Reducing Cognitive Biases in Probabilistic Reasoning by the Use of Logarithm Formats

ERIC Educational Resources Information Center

Juslin, Peter; Nilsson, Hakan; Winman, Anders; Lindskog, Marcus

2011-01-01

Research on probability judgment has traditionally emphasized that people are susceptible to biases because they rely on "variable substitution": the assessment of normative variables is replaced by assessment of heuristic, subjective variables. A recent proposal is that many of these biases may rather derive from constraints on cognitive…

Deconstructing the smoking-preeclampsia paradox through a counterfactual framework.

PubMed

Luque-Fernandez, Miguel Angel; Zoega, Helga; Valdimarsdottir, Unnur; Williams, Michelle A

2016-06-01

Although smoking during pregnancy may lead to many adverse outcomes, numerous studies have reported a paradoxical inverse association between maternal cigarette smoking during pregnancy and preeclampsia. Using a counterfactual framework we aimed to explore the structure of this paradox as being a consequence of selection bias. Using a case-control study nested in the Icelandic Birth Registry (1309 women), we show how this selection bias can be explored and corrected for. Cases were defined as any case of pregnancy induced hypertension or preeclampsia occurring after 20 weeks' gestation and controls as normotensive mothers who gave birth in the same year. First, we used directed acyclic graphs to illustrate the common bias structure. Second, we used classical logistic regression and mediation analytic methods for dichotomous outcomes to explore the structure of the bias. Lastly, we performed both deterministic and probabilistic sensitivity analysis to estimate the amount of bias due to an uncontrolled confounder and corrected for it. The biased effect of smoking was estimated to reduce the odds of preeclampsia by 28 % (OR 0.72, 95 %CI 0.52, 0.99) and after stratification by gestational age at delivery (<37 vs. ≥37 gestation weeks) by 75 % (OR 0.25, 95 %CI 0.10, 0.68). In a mediation analysis, the natural indirect effect showed and OR > 1, revealing the structure of the paradox. The bias-adjusted estimation of the smoking effect on preeclampsia showed an OR of 1.22 (95 %CI 0.41, 6.53). The smoking-preeclampsia paradox appears to be an example of (1) selection bias most likely caused by studying cases prevalent at birth rather than all incident cases from conception in a pregnancy cohort, (2) omitting important confounders associated with both smoking and preeclampsia (preventing the outcome to develop) and (3) controlling for a collider (gestation weeks at delivery). Future studies need to consider these aspects when studying and interpreting the association between smoking and pregnancy outcomes.

Simultaneous Estimation of Model State Variables and Observation and Forecast Biases Using a Two-Stage Hybrid Kalman Filter

NASA Technical Reports Server (NTRS)

Pauwels, V. R. N.; DeLannoy, G. J. M.; Hendricks Franssen, H.-J.; Vereecken, H.

2013-01-01

In this paper, we present a two-stage hybrid Kalman filter to estimate both observation and forecast bias in hydrologic models, in addition to state variables. The biases are estimated using the discrete Kalman filter, and the state variables using the ensemble Kalman filter. A key issue in this multi-component assimilation scheme is the exact partitioning of the difference between observation and forecasts into state, forecast bias and observation bias updates. Here, the error covariances of the forecast bias and the unbiased states are calculated as constant fractions of the biased state error covariance, and the observation bias error covariance is a function of the observation prediction error covariance. In a series of synthetic experiments, focusing on the assimilation of discharge into a rainfall-runoff model, it is shown that both static and dynamic observation and forecast biases can be successfully estimated. The results indicate a strong improvement in the estimation of the state variables and resulting discharge as opposed to the use of a bias-unaware ensemble Kalman filter. Furthermore, minimal code modification in existing data assimilation software is needed to implement the method. The results suggest that a better performance of data assimilation methods should be possible if both forecast and observation biases are taken into account.

Using ERA-Interim reanalysis for creating datasets of energy-relevant climate variables

NASA Astrophysics Data System (ADS)

Jones, Philip D.; Harpham, Colin; Troccoli, Alberto; Gschwind, Benoit; Ranchin, Thierry; Wald, Lucien; Goodess, Clare M.; Dorling, Stephen

2017-07-01

The construction of a bias-adjusted dataset of climate variables at the near surface using ERA-Interim reanalysis is presented. A number of different, variable-dependent, bias-adjustment approaches have been proposed. Here we modify the parameters of different distributions (depending on the variable), adjusting ERA-Interim based on gridded station or direct station observations. The variables are air temperature, dewpoint temperature, precipitation (daily only), solar radiation, wind speed, and relative humidity. These are available on either 3 or 6 h timescales over the period 1979-2016. The resulting bias-adjusted dataset is available through the Climate Data Store (CDS) of the Copernicus Climate Change Data Store (C3S) and can be accessed at present from ftp://ecem.climate.copernicus.eu. The benefit of performing bias adjustment is demonstrated by comparing initial and bias-adjusted ERA-Interim data against gridded observational fields.

Accounting for control mislabeling in case-control biomarker studies.

PubMed

Rantalainen, Mattias; Holmes, Chris C

2011-12-02

In biomarker discovery studies, uncertainty associated with case and control labels is often overlooked. By omitting to take into account label uncertainty, model parameters and the predictive risk can become biased, sometimes severely. The most common situation is when the control set contains an unknown number of undiagnosed, or future, cases. This has a marked impact in situations where the model needs to be well-calibrated, e.g., when the prediction performance of a biomarker panel is evaluated. Failing to account for class label uncertainty may lead to underestimation of classification performance and bias in parameter estimates. This can further impact on meta-analysis for combining evidence from multiple studies. Using a simulation study, we outline how conventional statistical models can be modified to address class label uncertainty leading to well-calibrated prediction performance estimates and reduced bias in meta-analysis. We focus on the problem of mislabeled control subjects in case-control studies, i.e., when some of the control subjects are undiagnosed cases, although the procedures we report are generic. The uncertainty in control status is a particular situation common in biomarker discovery studies in the context of genomic and molecular epidemiology, where control subjects are commonly sampled from the general population with an established expected disease incidence rate.

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.

Decoupling global biases and local interactions between cell biological variables

PubMed Central

Zaritsky, Assaf; Obolski, Uri; Gan, Zhuo; Reis, Carlos R; Kadlecova, Zuzana; Du, Yi; Schmid, Sandra L; Danuser, Gaudenz

2017-01-01

Analysis of coupled variables is a core concept of cell biological inference, with co-localization of two molecules as a proxy for protein interaction being a ubiquitous example. However, external effectors may influence the observed co-localization independently from the local interaction of two proteins. Such global bias, although biologically meaningful, is often neglected when interpreting co-localization. Here, we describe DeBias, a computational method to quantify and decouple global bias from local interactions between variables by modeling the observed co-localization as the cumulative contribution of a global and a local component. We showcase four applications of DeBias in different areas of cell biology, and demonstrate that the global bias encapsulates fundamental mechanistic insight into cellular behavior. The DeBias software package is freely accessible online via a web-server at https://debias.biohpc.swmed.edu. DOI: http://dx.doi.org/10.7554/eLife.22323.001 PMID:28287393

Access to food outlets and children's nutritional intake in urban China: a difference-in-difference analysis.

PubMed

Wang, Rui; Shi, Lu

2012-06-30

In recent years supermarkets and fast food restaurants have been replacing those "wet markets" of independent vendors as the major food sources in urban China. Yet how these food outlets relate to children's nutritional intake remains largely unexplored. Using a longitudinal survey of households and communities in China, this study examines the effect of the urban built food environment (density of wet markets, density of supermarkets, and density of fast food restaurants) on children's nutritional intake (daily caloric intake, daily carbohydrate intake, daily protein intake, and daily fat intake). Children aged 6-18 (n = 185) living in cities were followed from 2004 to 2006, and difference-in-difference models are used to address the potential issue of omitted variable bias. Results suggest that the density of wet markets, rather than that of supermarkets, positively predicts children's four dimensions of nutritional intake. In the caloric intake model and the fat intake model, the positive effect of neighborhood wet market density on children's nutritional intake is stronger with children from households of lower income. With their cheaper prices and/or fresher food supply, wet markets are likely to contribute a substantial amount of nutritional intake for children living nearby, especially those in households with lower socioeconomic status. For health officials and urban planners, this study signals a sign of warning as wet markets are disappearing from urban China's food environment.

Full-time versus part-time employment: Does it influence frequency of grandparental childcare?

PubMed

Lakomý, Martin; Kreidl, Martin

2015-12-01

The impact of grandparents' employment on grandparental childcare has been examined repeatedly, but the findings have so far been inconsistent. We contend that these inconsistencies may have resulted from variations in model specification and crude measurement of employment status. Furthermore, we assert that earlier research overlooked gender differences in the ability to combine paid employment and caregiving as well as variations between maternal and paternal grandparents. We also question the causal interpretation of earlier findings that were based on cross-sectional data. We revisit the issue of the impact of the intensity of employment and analyze SHARE data from 19 countries. We find a significant positive association between part-time employment (as compared to full-time employment) and the frequency of grandparental childcare in a cross-sectional sample, but only among paternal grandmothers. Capitalizing on the panel component of SHARE, we use a within-person estimator to show that this association is unlikely to reflect a causal effect of the intensity of labor market attachment on the frequency of the care of grandchildren, but more probably results from omitted variable bias. We argue that grandparents most likely to provide (intensive) childcare are also most likely to adjust their employment in anticipation of caregiving. The paper documents the usefulness of role strain theory among grandparents and highlights that part-time jobs may reduce role conflict and may thus make grandparenting a more easily manageable experience.

A Cautious Note on Auxiliary Variables That Can Increase Bias in Missing Data Problems.

PubMed

Thoemmes, Felix; Rose, Norman

2014-01-01

The treatment of missing data in the social sciences has changed tremendously during the last decade. Modern missing data techniques such as multiple imputation and full-information maximum likelihood are used much more frequently. These methods assume that data are missing at random. One very common approach to increase the likelihood that missing at random is achieved consists of including many covariates as so-called auxiliary variables. These variables are either included based on data considerations or in an inclusive fashion; that is, taking all available auxiliary variables. In this article, we point out that there are some instances in which auxiliary variables exhibit the surprising property of increasing bias in missing data problems. In a series of focused simulation studies, we highlight some situations in which this type of biasing behavior can occur. We briefly discuss possible ways how one can avoid selecting bias-inducing covariates as auxiliary variables.

Neural mechanisms of motivated forgetting

PubMed Central

Anderson, Michael C.; Hanslmayr, Simon

2014-01-01

Not all memories are equally welcome in awareness. People limit the time they spend thinking about unpleasant experiences, a process that begins during encoding, but that continues when cues later remind someone of the memory. Here, we review the emerging behavioural and neuroimaging evidence that suppressing awareness of an unwelcome memory, at encoding or retrieval, is achieved by inhibitory control processes mediated by the lateral prefrontal cortex. These mechanisms interact with neural structures that represent experiences in memory, disrupting traces that support retention. Thus, mechanisms engaged to regulate momentary awareness introduce lasting biases in which experiences remain accessible. We argue that theories of forgetting that neglect the motivated control of awareness omit a powerful force shaping the retention of our past. PMID:24747000

Development of a core outcome set for clinical trials in squamous cell carcinoma: study protocol for a systematic review of the literature and identification of a core outcome set using a Delphi survey.

PubMed

Schlessinger, Daniel I; Iyengar, Sanjana; Yanes, Arianna F; Chiren, Sarah G; Godinez-Puig, Victoria; Chen, Brian R; Kurta, Anastasia O; Schmitt, Jochen; Deckert, Stefanie; Furlan, Karina C; Poon, Emily; Cartee, Todd V; Maher, Ian A; Alam, Murad; Sobanko, Joseph F

2017-07-12

Squamous cell carcinoma (SCC) is a common skin cancer that poses a risk of metastasis. Clinical investigations into SCC treatment are common, but the outcomes reported are highly variable, omitted, or clinically irrelevant. The outcome heterogeneity and reporting bias of these studies leave clinicians unable to accurately compare studies. Core outcome sets (COSs) are an agreed minimum set of outcomes recommended to be measured and reported in all clinical trials of a given condition or disease. Although COSs are under development for several dermatologic conditions, work has yet to be done to identify core outcomes specific for SCC. Outcome extraction for COS generation will occur via four methods: (1) systematic literature review; (2) patient interviews; (3) other published sources; and (4) input from stakeholders in medicine, pharmacy, and other relevant industries. The list of outcomes will be revaluated by the Measuring PRiority Outcome Variables via Excellence in Dermatologic surgery (IMPROVED) Steering Committee. Delphi processes will be performed separately by expert clinicians and patients to condense the list of outcomes generated. A consensus meeting with relevant stakeholders will be conducted after the Delphi exercise to further select outcomes, taking into account participant scores. At the end of the meeting, members will vote and decide on a final recommended set of core outcomes. The Core Outcome Measures in Effectiveness Trials (COMET) organization and the Cochrane Skin Group - Core Outcome Set Initiative (CSG-COUSIN) will serve as advisers throughout the COS generation process. Comparison of clinical trials via systematic reviews and meta-analyses is facilitated when investigators study outcomes that are relevant and similar. The aim of this project is to develop a COS to guide use for future clinical trials.

Estimating the effect of multiple environmental stressors on coral bleaching and mortality.

PubMed

Welle, Paul D; Small, Mitchell J; Doney, Scott C; Azevedo, Inês L

2017-01-01

Coral cover has been declining in recent decades due to increased temperatures and environmental stressors. However, the extent to which different stressors contribute both individually and in concert to bleaching and mortality is still very uncertain. We develop and use a novel regression approach, using non-linear parametric models that control for unobserved time invariant effects to estimate the effects on coral bleaching and mortality due to temperature, solar radiation, depth, hurricanes and anthropogenic stressors using historical data from a large bleaching event in 2005 across the Caribbean. Two separate models are created, one to predict coral bleaching, and the other to predict near-term mortality. A large ensemble of supporting data is assembled to control for omitted variable bias and improve fit, and a significant improvement in fit is observed from univariate linear regression based on temperature alone. The results suggest that climate stressors (temperature and radiation) far outweighed direct anthropogenic stressors (using distance from shore and nearby human population density as a proxy for such stressors) in driving coral health outcomes during the 2005 event. Indeed, temperature was found to play a role ~4 times greater in both the bleaching and mortality response than population density across their observed ranges. The empirical models tested in this study have large advantages over ordinary-least squares-they offer unbiased estimates for censored data, correct for spatial correlation, and are capable of handling more complex relationships between dependent and independent variables. The models offer a framework for preparing for future warming events and climate change; guiding monitoring and attribution of other bleaching and mortality events regionally and around the globe; and informing adaptive management and conservation efforts.

Estimating the effect of multiple environmental stressors on coral bleaching and mortality

PubMed Central

Welle, Paul D.; Small, Mitchell J.; Doney, Scott C.; Azevedo, Inês L.

2017-01-01

Coral cover has been declining in recent decades due to increased temperatures and environmental stressors. However, the extent to which different stressors contribute both individually and in concert to bleaching and mortality is still very uncertain. We develop and use a novel regression approach, using non-linear parametric models that control for unobserved time invariant effects to estimate the effects on coral bleaching and mortality due to temperature, solar radiation, depth, hurricanes and anthropogenic stressors using historical data from a large bleaching event in 2005 across the Caribbean. Two separate models are created, one to predict coral bleaching, and the other to predict near-term mortality. A large ensemble of supporting data is assembled to control for omitted variable bias and improve fit, and a significant improvement in fit is observed from univariate linear regression based on temperature alone. The results suggest that climate stressors (temperature and radiation) far outweighed direct anthropogenic stressors (using distance from shore and nearby human population density as a proxy for such stressors) in driving coral health outcomes during the 2005 event. Indeed, temperature was found to play a role ~4 times greater in both the bleaching and mortality response than population density across their observed ranges. The empirical models tested in this study have large advantages over ordinary-least squares–they offer unbiased estimates for censored data, correct for spatial correlation, and are capable of handling more complex relationships between dependent and independent variables. The models offer a framework for preparing for future warming events and climate change; guiding monitoring and attribution of other bleaching and mortality events regionally and around the globe; and informing adaptive management and conservation efforts. PMID:28472031

Mismeasurement and the resonance of strong confounders: uncorrelated errors.

PubMed

Marshall, J R; Hastrup, J L

1996-05-15

Greenland first documented (Am J Epidemiol 1980; 112:564-9) that error in the measurement of a confounder could resonate--that it could bias estimates of other study variables, and that the bias could persist even with statistical adjustment for the confounder as measured. An important question is raised by this finding: can such bias be more than trivial within the bounds of realistic data configurations? The authors examine several situations involving dichotomous and continuous data in which a confounder and a null variable are measured with error, and they assess the extent of resultant bias in estimates of the effect of the null variable. They show that, with continuous variables, measurement error amounting to 40% of observed variance in the confounder could cause the observed impact of the null study variable to appear to alter risk by as much as 30%. Similarly, they show, with dichotomous independent variables, that 15% measurement error in the form of misclassification could lead the null study variable to appear to alter risk by as much as 50%. Such bias would result only from strong confounding. Measurement error would obscure the evidence that strong confounding is a likely problem. These results support the need for every epidemiologic inquiry to include evaluations of measurement error in each variable considered.

Incorporating Anthropogenic Influences into Fire Probability Models: Effects of Human Activity and Climate Change on Fire Activity in California.

PubMed

Mann, Michael L; Batllori, Enric; Moritz, Max A; Waller, Eric K; Berck, Peter; Flint, Alan L; Flint, Lorraine E; Dolfi, Emmalee

2016-01-01

The costly interactions between humans and wildfires throughout California demonstrate the need to understand the relationships between them, especially in the face of a changing climate and expanding human communities. Although a number of statistical and process-based wildfire models exist for California, there is enormous uncertainty about the location and number of future fires, with previously published estimates of increases ranging from nine to fifty-three percent by the end of the century. Our goal is to assess the role of climate and anthropogenic influences on the state's fire regimes from 1975 to 2050. We develop an empirical model that integrates estimates of biophysical indicators relevant to plant communities and anthropogenic influences at each forecast time step. Historically, we find that anthropogenic influences account for up to fifty percent of explanatory power in the model. We also find that the total area burned is likely to increase, with burned area expected to increase by 2.2 and 5.0 percent by 2050 under climatic bookends (PCM and GFDL climate models, respectively). Our two climate models show considerable agreement, but due to potential shifts in rainfall patterns, substantial uncertainty remains for the semiarid inland deserts and coastal areas of the south. Given the strength of human-related variables in some regions, however, it is clear that comprehensive projections of future fire activity should include both anthropogenic and biophysical influences. Previous findings of substantially increased numbers of fires and burned area for California may be tied to omitted variable bias from the exclusion of human influences. The omission of anthropogenic variables in our model would overstate the importance of climatic ones by at least 24%. As such, the failure to include anthropogenic effects in many models likely overstates the response of wildfire to climatic change.

Incorporating Anthropogenic Influences into Fire Probability Models: Effects of Human Activity and Climate Change on Fire Activity in California

PubMed Central

Batllori, Enric; Moritz, Max A.; Waller, Eric K.; Berck, Peter; Flint, Alan L.; Flint, Lorraine E.; Dolfi, Emmalee

2016-01-01

The costly interactions between humans and wildfires throughout California demonstrate the need to understand the relationships between them, especially in the face of a changing climate and expanding human communities. Although a number of statistical and process-based wildfire models exist for California, there is enormous uncertainty about the location and number of future fires, with previously published estimates of increases ranging from nine to fifty-three percent by the end of the century. Our goal is to assess the role of climate and anthropogenic influences on the state’s fire regimes from 1975 to 2050. We develop an empirical model that integrates estimates of biophysical indicators relevant to plant communities and anthropogenic influences at each forecast time step. Historically, we find that anthropogenic influences account for up to fifty percent of explanatory power in the model. We also find that the total area burned is likely to increase, with burned area expected to increase by 2.2 and 5.0 percent by 2050 under climatic bookends (PCM and GFDL climate models, respectively). Our two climate models show considerable agreement, but due to potential shifts in rainfall patterns, substantial uncertainty remains for the semiarid inland deserts and coastal areas of the south. Given the strength of human-related variables in some regions, however, it is clear that comprehensive projections of future fire activity should include both anthropogenic and biophysical influences. Previous findings of substantially increased numbers of fires and burned area for California may be tied to omitted variable bias from the exclusion of human influences. The omission of anthropogenic variables in our model would overstate the importance of climatic ones by at least 24%. As such, the failure to include anthropogenic effects in many models likely overstates the response of wildfire to climatic change. PMID:27124597

An Elementary Proof of the Identity cot [theta] = [image omitted

ERIC Educational Resources Information Center

Ho, Weng Kin; Ho, Foo Him; Lee, Tuo Yeong

2012-01-01

This article gives an elementary proof of the famous identity [image omitted]. Using nothing more than freshman calculus, the present proof is far simpler than many existing ones. This result also leads directly to Euler's and Neville's identities, as well as the identity [image omitted].

Towards an Ancient Chinese-Inspired Theory of Music Education

ERIC Educational Resources Information Center

Tan, Leonard

2016-01-01

In this philosophical paper, I propose a theory of music education inspired by ancient Chinese philosophy. In particular, I draw on five classical Chinese philosophical texts: the Analects (lunyu [Chinese characters omitted]), the Mencius (Mengzi [Chinese characters omitted]), the Zhuangzi ([Chinese characters omitted]), the Xunzi ([Chinese…

VizieR Online Data Catalog: The distance modulus of the LMC (Kovacs, 2000)

NASA Astrophysics Data System (ADS)

Kovacs, G.

2000-11-01

This table provides periods, intensity averaged V magnitudes and magnitude averaged V-Rc (Johnson V & Kron-Cousins R) colors of the MACHO LMC double-mode RR Lyrae variables employed in the above paper. For the calculation of the averages, an iterative 3-sigma condition was used to omit outliers. Further references (coordinates, amplitude ratios, etc.) to these variables can be found in Alcock et al. (1997ApJ...482...89A) and in Alcock et al. (2000, Cat. ). Trasformation to the standard system has been performed in accordance with Alcock et al. (1999PASP..111.1539A). (1 data file).

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.

Is Filial Piety a Virtue? a Reading of the "Xiao Jing" ("Classic of Filial Piety") from the Perspective of Ideology Critique

ERIC Educational Resources Information Center

Yan, Hektor K. T.

2017-01-01

The recent revival of Confucianism in the PRC raises questions regarding the legitimacy of cultivating Confucian virtues such as "ren" ([Chinese characters omitted] benevolence), "li" ([Chinese characters omitted] propriety) and "xiao" ([Chinese characters omitted] "filial piety" or "family…

LES study of microphysical variability bias in shallow cumulus

NASA Astrophysics Data System (ADS)

Kogan, Yefim

2017-05-01

Subgrid-scale (SGS) variability of cloud microphysical variables over the mesoscale numerical weather prediction (NWP) model has been evaluated by means of joint probability distribution functions (JPDFs). The latter were obtained using dynamically balanced Large Eddy Simulation (LES) model dataset from a case of marine trade cumulus initialized with soundings from Rain in Cumulus Over the Ocean (RICO) field project. Bias in autoconversion and accretion rates from different formulations of the JPDFs was analyzed. Approximating the 2-D PDF using a generic (fixed-in-time), but variable-in-height JPDFs give an acceptable level of accuracy, whereas neglecting the SGS variability altogether results in a substantial underestimate of the grid-mean total conversion rate and producing negative bias in rain water. Nevertheless the total effect on rain formation may be uncertain in the long run due to the fact that the negative bias in rain water may be counterbalanced by the positive bias in cloud water. Consequently, the overall effect of SGS neglect needs to be investigated in direct simulations with a NWP model.

Dark Energy Survey Year 1 results: the impact of galaxy neighbours on weak lensing cosmology with IM3SHAPE

NASA Astrophysics Data System (ADS)

Samuroff, S.; Bridle, S. L.; Zuntz, J.; Troxel, M. A.; Gruen, D.; Rollins, R. P.; Bernstein, G. M.; Eifler, T. F.; Huff, E. M.; Kacprzak, T.; Krause, E.; MacCrann, N.; Abdalla, F. B.; Allam, S.; Annis, J.; Bechtol, K.; Benoit-Lévy, A.; Bertin, E.; Brooks, D.; Buckley-Geer, E.; Carnero Rosell, A.; Carrasco Kind, M.; Carretero, J.; Crocce, M.; D'Andrea, C. B.; da Costa, L. N.; Davis, C.; Desai, S.; Doel, P.; Fausti Neto, A.; Flaugher, B.; Fosalba, P.; Frieman, J.; García-Bellido, J.; Gerdes, D. W.; Gruendl, R. A.; Gschwend, J.; Gutierrez, G.; Honscheid, K.; James, D. J.; Jarvis, M.; Jeltema, T.; Kirk, D.; Kuehn, K.; Kuhlmann, S.; Li, T. S.; Lima, M.; Maia, M. A. G.; March, M.; Marshall, J. L.; Martini, P.; Melchior, P.; Menanteau, F.; Miquel, R.; Nord, B.; Ogando, R. L. C.; Plazas, A. A.; Roodman, A.; Sanchez, E.; Scarpine, V.; Schindler, R.; Schubnell, M.; Sevilla-Noarbe, I.; Sheldon, E.; Smith, M.; Soares-Santos, M.; Sobreira, F.; Suchyta, E.; Tarle, G.; Thomas, D.; Tucker, D. L.; DES Collaboration

2018-04-01

We use a suite of simulated images based on Year 1 of the Dark Energy Survey to explore the impact of galaxy neighbours on shape measurement and shear cosmology. The HOOPOE image simulations include realistic blending, galaxy positions, and spatial variations in depth and point spread function properties. Using the IM3SHAPE maximum-likelihood shape measurement code, we identify four mechanisms by which neighbours can have a non-negligible influence on shear estimation. These effects, if ignored, would contribute a net multiplicative bias of m ˜ 0.03-0.09 in the Year One of the Dark Energy Survey (DES Y1) IM3SHAPE catalogue, though the precise impact will be dependent on both the measurement code and the selection cuts applied. This can be reduced to percentage level or less by removing objects with close neighbours, at a cost to the effective number density of galaxies neff of 30 per cent. We use the cosmological inference pipeline of DES Y1 to explore the cosmological implications of neighbour bias and show that omitting blending from the calibration simulation for DES Y1 would bias the inferred clustering amplitude S8 ≡ σ8(Ωm/0.3)0.5 by 2σ towards low values. Finally, we use the HOOPOE simulations to test the effect of neighbour-induced spatial correlations in the multiplicative bias. We find the impact on the recovered S8 of ignoring such correlations to be subdominant to statistical error at the current level of precision.

Dark Energy Survey Year 1 results: the impact of galaxy neighbours on weak lensing cosmology with IM3SHAPE

DOE PAGES

Samuroff, S.

2017-12-26

We use a suite of simulated images based on Year 1 of the Dark Energy Survey to explore the impact of galaxy neighbours on shape measurement and shear cosmology. The hoopoe image simulations include realistic blending, galaxy positions, and spatial variations in depth and PSF properties. Using the im3shape maximum-likelihood shape measurement code, we identify four mechanisms by which neighbours can have a non-negligible influence on shear estimation. These effects, if ignored, would contribute a net multiplicative bias ofmore » $$m \\sim 0.03 - 0.09$$ in the DES Y1 im3shape catalogue, though the precise impact will be dependent on both the measurement code and the selection cuts applied. This can be reduced to percentage level or less by removing objects with close neighbours, at a cost to the effective number density of galaxies $$n_\\mathrm{eff}$$ of 30%. We use the cosmological inference pipeline of DES Y1 to explore the cosmological implications of neighbour bias and show that omitting blending from the calibration simulation for DES Y1 would bias the inferred clustering amplitude $$S_8\\equiv \\sigma_8 (\\Omega _\\mathrm{m} /0.3)^{0.5}$$ by $$2 \\sigma$$ towards low values. Lastly, we use the hoopoe simulations to test the effect of neighbour-induced spatial correlations in the multiplicative bias. We find the impact on the recovered $$S_8$$ of ignoring such correlations to be subdominant to statistical error at the current level of precision.« less

Dark Energy Survey Year 1 results: the impact of galaxy neighbours on weak lensing cosmology with IM3SHAPE

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

Samuroff, S.

We use a suite of simulated images based on Year 1 of the Dark Energy Survey to explore the impact of galaxy neighbours on shape measurement and shear cosmology. The hoopoe image simulations include realistic blending, galaxy positions, and spatial variations in depth and PSF properties. Using the im3shape maximum-likelihood shape measurement code, we identify four mechanisms by which neighbours can have a non-negligible influence on shear estimation. These effects, if ignored, would contribute a net multiplicative bias ofmore » $$m \\sim 0.03 - 0.09$$ in the DES Y1 im3shape catalogue, though the precise impact will be dependent on both the measurement code and the selection cuts applied. This can be reduced to percentage level or less by removing objects with close neighbours, at a cost to the effective number density of galaxies $$n_\\mathrm{eff}$$ of 30%. We use the cosmological inference pipeline of DES Y1 to explore the cosmological implications of neighbour bias and show that omitting blending from the calibration simulation for DES Y1 would bias the inferred clustering amplitude $$S_8\\equiv \\sigma_8 (\\Omega _\\mathrm{m} /0.3)^{0.5}$$ by $$2 \\sigma$$ towards low values. Lastly, we use the hoopoe simulations to test the effect of neighbour-induced spatial correlations in the multiplicative bias. We find the impact on the recovered $$S_8$$ of ignoring such correlations to be subdominant to statistical error at the current level of precision.« less

Measuring Variability in the Presence of Noise

NASA Astrophysics Data System (ADS)

Welsh, W. F.

Quantitative measurements of a variable signal in the presence of noise requires very careful attention to subtle affects which can easily bias the measurements. This is not limited to the low-count rate regime, nor is the bias error necessarily small. In this talk I will mention some of the dangers in applying standard techniques which are appropriate for high signal to noise data but fail in the cases where the S/N is low. I will discuss methods for correcting the bias in the these cases, both for periodic and non-periodic variability, and will introduce the concept of the ``filtered de-biased RMS''. I will also illustrate some common abuses of power spectrum interpretation. All of these points will be illustrated with examples from recent work on CV and AGN variability.

Judgments of Omitted BE and DO in Questions as Extended Finiteness Clinical Markers of SLI to Fifteen Years: A Study of Growth and Asymptote

PubMed Central

Rice, Mabel L; Hoffman, Lesa; Wexler, Ken

2009-01-01

Purpose Clinical grammar markers are needed for children with SLI older than 8 years. This study followed children studied earlier on sentences with omitted finiteness to determine if affected children continue to perform at low levels and to examine possible predictors of low performance. This is the first longitudinal report of grammaticality judgments of questions. Method Three groups of children participated: 20 SLI, 20 age controls and 18 language-matched controls, followed from ages 6–15 years. An experimental grammaticality judgment task was administered with BE copula/auxiliary and DO auxiliary in Wh- and Yes/No questions for 9 times of measurement. Predictors were indices of vocabulary, nonverbal intelligence, and maternal education. Results Growth curve analyses show that the affected group performed below the younger controls at each time of measurement, for each variable. Growth analyses show linear and quadratic effects for both groups across variables, with the exception of BE acquisition which was flat for both groups. The control children reached ceiling levels; the affected children reached a lower asymptote. Conclusions The results suggest an on-going maturational lag in finiteness marking for affected children with promise as a clinical marker for language impairment in school-aged and adolescent children and probably adults as well. PMID:19786705

Parity-time-symmetry enhanced optomechanically-induced-transparency

PubMed Central

Li, Wenlin; Jiang, Yunfeng; Li, Chong; Song, Heshan

2016-01-01

We propose and analyze a scheme to enhance optomechanically-induced-transparency (OMIT) based on parity-time-symmetric optomechanical system. Our results predict that an OMIT window which does not exist originally can appear in weak optomechanical coupling and driving system via coupling an auxiliary active cavity with optical gain. This phenomenon is quite different from these reported in previous works in which the gain is considered just to damage OMIT phenomenon even leads to electromagnetically induced absorption or inverted-OMIT. Such enhanced OMIT effects are ascribed to the additional gain which can increase photon number in cavity without reducing effective decay. We also discuss the scheme feasibility by analyzing recent experiment parameters. Our work provide a promising platform for the coherent manipulation and slow light operation, which has potential applications for quantum information processing and quantum optical device. PMID:27489193

Model-Based Control of Observer Bias for the Analysis of Presence-Only Data in Ecology

PubMed Central

Warton, David I.; Renner, Ian W.; Ramp, Daniel

2013-01-01

Presence-only data, where information is available concerning species presence but not species absence, are subject to bias due to observers being more likely to visit and record sightings at some locations than others (hereafter “observer bias”). In this paper, we describe and evaluate a model-based approach to accounting for observer bias directly – by modelling presence locations as a function of known observer bias variables (such as accessibility variables) in addition to environmental variables, then conditioning on a common level of bias to make predictions of species occurrence free of such observer bias. We implement this idea using point process models with a LASSO penalty, a new presence-only method related to maximum entropy modelling, that implicitly addresses the “pseudo-absence problem” of where to locate pseudo-absences (and how many). The proposed method of bias-correction is evaluated using systematically collected presence/absence data for 62 plant species endemic to the Blue Mountains near Sydney, Australia. It is shown that modelling and controlling for observer bias significantly improves the accuracy of predictions made using presence-only data, and usually improves predictions as compared to pseudo-absence or “inventory” methods of bias correction based on absences from non-target species. Future research will consider the potential for improving the proposed bias-correction approach by estimating the observer bias simultaneously across multiple species. PMID:24260167

Bias and Bias Correction in Multi-Site Instrumental Variables Analysis of Heterogeneous Mediator Effects

ERIC Educational Resources Information Center

Reardon, Sean F.; Unlu, Faith; Zhu, Pei; Bloom, Howard

2013-01-01

We explore the use of instrumental variables (IV) analysis with a multi-site randomized trial to estimate the effect of a mediating variable on an outcome in cases where it can be assumed that the observed mediator is the only mechanism linking treatment assignment to outcomes, as assumption known in the instrumental variables literature as the…

The role of cognitive biases and personality variables in subclinical delusional ideation.

PubMed

Menon, Mahesh; Quilty, Lena Catherine; Zawadzki, John Anthony; Woodward, Todd Stephen; Sokolowski, Helen Moriah; Boon, Heather Shirley; Wong, Albert Hung Choy

2013-05-01

A number of cognitive biases, most notably a data gathering bias characterised by "jumping to conclusions" (JTC), and the "bias against disconfirmatory evidence" (BADE), have been shown to be associated with delusions and subclinical delusional ideation. Certain personality variables, particularly "openness to experience", are thought to be associated with schizotypy. Using structural equation modelling, we examined the association between two higher order subfactors ("aspects") of "openness to experience" (labelled "openness" and "intellect"), these cognitive biases, and their relationship to subclinical delusional ideation in 121 healthy, nonpsychiatric controls. Our results suggest that cognitive biases (specifically the data gathering bias and BADE) and the "openness" aspect are independently associated with subclinical delusional ideation, and the data gathering bias is weakly associated with "positive schizotypy". "Intellect" is negatively associated with delusional ideation and might play a potential protective role. Cognitive biases and personality are likely to be independent risk factors for the development of delusions.

Educational Philosophy--East, West, and Beyond: A Reading and Discussion of Xueji (Chinese characters omitted)

ERIC Educational Resources Information Center

Di, Xu

2017-01-01

This article analyzes Xueji (Chinese characters omitted) and discusses some of the myths and facts in Western perceptions of Chinese educational practice. It also looks at the similarities and contrasts between Eastern and Western conceptions of teaching and learning. A careful study of Xueji (Chinese characters omitted) will help in understanding…

Empirical spatial econometric modelling of small scale neighbourhood

NASA Astrophysics Data System (ADS)

Gerkman, Linda

2012-07-01

The aim of the paper is to model small scale neighbourhood in a house price model by implementing the newest methodology in spatial econometrics. A common problem when modelling house prices is that in practice it is seldom possible to obtain all the desired variables. Especially variables capturing the small scale neighbourhood conditions are hard to find. If there are important explanatory variables missing from the model, the omitted variables are spatially autocorrelated and they are correlated with the explanatory variables included in the model, it can be shown that a spatial Durbin model is motivated. In the empirical application on new house price data from Helsinki in Finland, we find the motivation for a spatial Durbin model, we estimate the model and interpret the estimates for the summary measures of impacts. By the analysis we show that the model structure makes it possible to model and find small scale neighbourhood effects, when we know that they exist, but we are lacking proper variables to measure them.

Simple and rapid quantification of serotonin transporter binding using [11C]DASB bolus plus constant infusion.

PubMed

Gryglewski, G; Rischka, L; Philippe, C; Hahn, A; James, G M; Klebermass, E; Hienert, M; Silberbauer, L; Vanicek, T; Kautzky, A; Berroterán-Infante, N; Nics, L; Traub-Weidinger, T; Mitterhauser, M; Wadsak, W; Hacker, M; Kasper, S; Lanzenberger, R

2017-04-01

In-vivo quantification of serotonin transporters (SERT) in human brain has been a mainstay of molecular imaging in the field of neuropsychiatric disorders and helped to explore the underpinnings of several medical conditions, therapeutic and environmental influences. The emergence of PET/MR hybrid systems and the heterogeneity of SERT binding call for the development of efficient methods making the investigation of larger or vulnerable populations with limited scanner time and simultaneous changes in molecular and functional measures possible. We propose [ 11 C]DASB bolus plus constant infusion for these applications and validate it against standard analyses of dynamic PET data. [ 11 C]DASB bolus/infusion optimization was performed on data acquired after [ 11 C]DASB bolus in 8 healthy subjects. Subsequently, 16 subjects underwent one scan using [ 11 C]DASB bolus plus constant infusion with K bol 160-179min and one scan after [ 11 C]DASB bolus for inter-method reliability analysis. Arterial blood sampling and metabolite analysis were performed for all scans. Distribution volumes (V T ) were obtained using Logan plots for bolus scans and ratios between tissue and plasma parent activity for bolus plus infusion scans for different time spans of the scan (V T-70 for 60-70min after start of tracer infusion, V T-90 for 75-90min, V T-120 for 100-120min) in 9 subjects. Omitting blood data, binding potentials (BP ND ) obtained using multilinear reference tissue modeling (MRTM2) and cerebellar gray matter as reference region were compared in 11 subjects. A K bol of 160min was observed to be optimal for rapid equilibration in thalamus and striatum. V T-70 showed good intraclass correlation coefficients (ICCs) of 0.61-0.70 for thalamus, striatal regions and olfactory cortex with bias ≤5.1% compared to bolus scans. ICCs increased to 0.72-0.78 for V T-90 and 0.77-0.93 for V T-120 in these regions. BP ND-90 had negligible bias ≤2.5%, low variability ≤7.9% and ICCs of 0.74-0.87; BP ND-120 had ICCs of 0.73-0.90. Low-binding cortical regions and cerebellar gray matter showed a positive bias of ~8% and ICCs 0.57-0.68 at V T-90 . Cortical BP ND suffered from high variability and bias, best results were obtained for olfactory cortex and anterior cingulate cortex with ICC=0.74-0.75 for BP ND-90 . High-density regions amygdala and midbrain had a negative bias of -5.5% and -22.5% at V T-90 with ICC 0.70 and 0.63, respectively. We have optimized the equilibrium method with [ 11 C]DASB bolus plus constant infusion and demonstrated good inter-method reliability with accepted standard methods and for SERT quantification using both V T and BP ND in a range of different brain regions. With as little as 10-15min of scanning valid estimates of SERT V T and BP ND in thalamus, amygdala, striatal and high-binding cortical regions could be obtained. Blood sampling seems vital for valid quantification of SERT in low-binding cortical regions. These methods allow the investigation of up to three subjects with a single radiosynthesis. Copyright © 2017 Elsevier Inc. All rights reserved.

Comparisons of estimates of annual exceedance-probability discharges for small drainage basins in Iowa, based on data through water year 2013

USGS Publications Warehouse

Eash, David A.

2015-01-01

An examination was conducted to understand why the 1987 single-variable RREs seem to provide better accuracy and less bias than either of the 2013 multi- or single-variable RREs. A comparison of 1-percent annual exceedance-probability regression lines for hydrologic regions 1-4 from the 1987 single-variable RREs and for flood regions 1-3 from the 2013 single-variable RREs indicates that the 1987 single-variable regional-regression lines generally have steeper slopes and lower discharges when compared to 2013 single-variable regional-regression lines for corresponding areas of Iowa. The combination of the definition of hydrologic regions, the lower discharges, and the steeper slopes of regression lines associated with the 1987 single-variable RREs seem to provide better accuracy and less bias when compared to the 2013 multi- or single-variable RREs; better accuracy and less bias was determined particularly for drainage areas less than 2 mi2, and also for some drainage areas between 2 and 20 mi2. The 2013 multi- and single-variable RREs are considered to provide better accuracy and less bias for larger drainage areas. Results of this study indicate that additional research is needed to address the curvilinear relation between drainage area and AEPDs for areas of Iowa.

A method for estimation of bias and variability of continuous gas monitor data: application to carbon monoxide monitor accuracy.

PubMed

Shulman, Stanley A; Smith, Jerome P

2002-01-01

A method is presented for the evaluation of the bias, variability, and accuracy of gas monitors. This method is based on using the parameters for the fitted response curves of the monitors. Thereby, variability between calibrations, between dates within each calibration period, and between different units can be evaluated at several different standard concentrations. By combining variability information with bias information, accuracy can be assessed. An example using carbon monoxide monitor data is provided. Although the most general statistical software required for these tasks is not available on a spreadsheet, when the same number of dates in a calibration period are evaluated for each monitor unit, the calculations can be done on a spreadsheet. An example of such calculations, together with the formulas needed for their implementation, is provided. In addition, the methods can be extended by use of appropriate statistical models and software to evaluate monitor trends within calibration periods, as well as consider the effects of other variables, such as humidity and temperature, on monitor variability and bias.

Constructing a multidimensional free energy surface like a spider weaving a web.

PubMed

Chen, Changjun

2017-10-15

Complete free energy surface in the collective variable space provides important information of the reaction mechanisms of the molecules. But, sufficient sampling in the collective variable space is not easy. The space expands quickly with the number of the collective variables. To solve the problem, many methods utilize artificial biasing potentials to flatten out the original free energy surface of the molecule in the simulation. Their performances are sensitive to the definitions of the biasing potentials. Fast-growing biasing potential accelerates the sampling speed but decreases the accuracy of the free energy result. Slow-growing biasing potential gives an optimized result but needs more simulation time. In this article, we propose an alternative method. It adds the biasing potential to a representative point of the molecule in the collective variable space to improve the conformational sampling. And the free energy surface is calculated from the free energy gradient in the constrained simulation, not given by the negative of the biasing potential as previous methods. So the presented method does not require the biasing potential to remove all the barriers and basins on the free energy surface exactly. Practical applications show that the method in this work is able to produce the accurate free energy surfaces for different molecules in a short time period. The free energy errors are small in the cases of various biasing potentials. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Stability and Composition of Functional Synergies for Speech Movements in Children with Developmental Speech Disorders

ERIC Educational Resources Information Center

Terband, H.; Maassen, B.; van Lieshout, P.; Nijland, L.

2011-01-01

The aim of this study was to investigate the consistency and composition of functional synergies for speech movements in children with developmental speech disorders. Kinematic data were collected on the reiterated productions of syllables spa(/spa[image omitted]/) and paas(/pa[image omitted]s/) by 10 6- to 9-year-olds with developmental speech…

A Comparison of Hispanic and White Non-Hispanic Students' Omit Patterns on the Scholastic Aptitude Test.

ERIC Educational Resources Information Center

Rivera, Charlene; Schmitt, Alicia P.

Standardization methodology was used to analyze omitted responses of Hispanic examinees on the Scholastic Aptitude Test. Study or focal groups were 2,956 Mexican-Americans, 3,230 Puerto Ricans, and 278,009 White test-takers. Results indicate that both Mexican-Americans and Puerto Rican students omitted fewer items than White students of comparable…

Liquid-Vapor Phase Transition: Thermomechanical Theory, Entropy Stable Numerical Formulation, and Boiling Simulations

DTIC Science & Technology

2015-05-01

will be omitted for notational simplicity. 32 3.3 Functional entropy variables The mathematical entropy function H is defined to be H := −ρs = 8 27 ρ log...ρ 1− ρ ) − 8 27(γ − 1) ρ log (θ) . (104) With this definition and the relation (72), the second law of thermodynamics can be written in terms of H ...as ∂ H ∂t +∇ · (Hu)−∇ · (q θ ) + ρr θ = −1 θ τ : ∇u− 1 θ2 κ|∇θ|2 ≤ 0. In three dimensions, the conservation variables can be written as UT = [U1, U2

On the wavelength dependence of the effects of turbulence on average refraction angles in occultations by planetary atmospheres

NASA Technical Reports Server (NTRS)

Haugstad, B. S.; Eshleman, V. R.

1979-01-01

The dependence of the effects of planetary atmospheric turbulence on radio or optical wavelength in occultation experiments is discussed, and the analysis of Hubbard and Jokipii (1977) is criticized. It is argued that in deriving a necessary condition for the applicability of their method, Hubbard and Jokipii neglect a factor proportional to the square of the ratio of atmospheric or local Fresnel zone radius and the inner scale of turbulence, and fail to establish sufficient conditions, thereby omitting the square of the ratio of atmospheric scale height and the local Fresnel zone radius. The total discrepancy is said to mean that the results correspond to geometrical optics instead of wave optics, as claimed, thus being inapplicable in a dicussion of wavelength dependence. Calculations based on geometrical optics show that the bias in the average bending angle depends on the wavelength in the same way as does the bias in phase path caused by turbulence in a homogeneous atmosphere. Hubbard and Jokipii comment that the criterion of Haugstad and Eshleman is incorrect and show that there is a large wave optical domain where the results are independent of wavelength.

Comparison of two trajectory based models for locating particle sources for two rural New York sites

NASA Astrophysics Data System (ADS)

Zhou, Liming; Hopke, Philip K.; Liu, Wei

Two back trajectory-based statistical models, simplified quantitative transport bias analysis and residence-time weighted concentrations (RTWC) have been compared for their capabilities of identifying likely locations of source emissions contributing to observed particle concentrations at Potsdam and Stockton, New York. Quantitative transport bias analysis (QTBA) attempts to take into account the distribution of concentrations around the directions of the back trajectories. In full QTBA approach, deposition processes (wet and dry) are also considered. Simplified QTBA omits the consideration of deposition. It is best used with multiple site data. Similarly the RTWC approach uses concentrations measured at different sites along with the back trajectories to distribute the concentration contributions across the spatial domain of the trajectories. In this study, these models are used in combination with the source contribution values obtained by the previous positive matrix factorization analysis of particle composition data from Potsdam and Stockton. The six common sources for the two sites, sulfate, soil, zinc smelter, nitrate, wood smoke and copper smelter were analyzed. The results of the two methods are consistent and locate large and clearly defined sources well. RTWC approach can find more minor sources but may also give unrealistic estimations of the source locations.

Formal Compiler Implementation in a Logical Framework

DTIC Science & Technology

2003-04-29

variable set [], we omit the brackets and use the simpler notation v. MetaPRL is a tactic-based prover that uses OCaml [20] as its meta-language. When a...rewrite is defined in MetaPRL, the framework creates an OCaml expression that can be used to apply the rewrite. Code to guide the application of...rewrites is written in OCaml , using a rich set of primitives provided by MetaPRL. MetaPRL automates the construction of most guidance code; we describe

Testing the Normality of Residuals.

DTIC Science & Technology

1982-09-01

work, Ramsey (1969) and Ransey and Gilbert (1972) investigate tests for detection of regression specification errors such as omitted variables, incorrect...estimator of scale. Biometrika, 68, 331-333. Nelson, L.S. (1981). A simple test for normality. J. of Quality Technology, 13 , 76-77. Ramsey , J.B. (1969...AD-A120 997 TESTING THE NORMALT O F RESIDU ALS(U WISCONSIN UNV MAO ISON MATRENAT C S RESEARCH CENT ER NRDAPER FT AL SEP 92 NRC- SR- 2 42 6 DAAG29-90

Further Validation of Stahl and Harrell’s Job Choice Exercise in Measuring McClelland’s Trichotomy of Needs.

DTIC Science & Technology

1982-09-01

factor which could influence one’s needs levels is that of birth order . A review of the literature indicated a strong correlation between birth order and...questionnaire that accompanied the JCE is located at Appendix A. Missing Values Within the sample, one SOS subject omitted birth order information. Information...compare the regression methodology that con- trolled for other variables such as school, years service, supervisory status, and birth order , we performed

An analysis of input errors in precipitation-runoff models using regression with errors in the independent variables

USGS Publications Warehouse

Troutman, Brent M.

1982-01-01

Errors in runoff prediction caused by input data errors are analyzed by treating precipitation-runoff models as regression (conditional expectation) models. Independent variables of the regression consist of precipitation and other input measurements; the dependent variable is runoff. In models using erroneous input data, prediction errors are inflated and estimates of expected storm runoff for given observed input variables are biased. This bias in expected runoff estimation results in biased parameter estimates if these parameter estimates are obtained by a least squares fit of predicted to observed runoff values. The problems of error inflation and bias are examined in detail for a simple linear regression of runoff on rainfall and for a nonlinear U.S. Geological Survey precipitation-runoff model. Some implications for flood frequency analysis are considered. A case study using a set of data from Turtle Creek near Dallas, Texas illustrates the problems of model input errors.

Patient safety ward round checklist via an electronic app: implications for harm prevention.

PubMed

Keller, C; Arsenault, S; Lamothe, M; Bostan, S R; O'Donnell, R; Harbison, J; Doherty, C P

2017-11-06

Patient safety is a value at the core of modern healthcare. Though awareness in the medical community is growing, implementing systematic approaches similar to those used in other high reliability industries is proving difficult. The aim of this research was twofold, to establish a baseline for patient safety practices on routine ward rounds and to test the feasibility of implementing an electronic patient safety checklist application. Two research teams were formed; one auditing a medical team to establish a procedural baseline of "usual care" practice and an intervention team concurrently was enforcing the implementation of the checklist. The checklist was comprised of eight standard clinical practice items. The program was conducted over a 2-week period and 1 month later, a retrospective analysis of patient charts was conducted using a global trigger tool to determine variance between the experimental groups. Finally, feedback from the physician participants was considered. The results demonstrated a statistically significant difference on five variables of a total of 16. The auditing team observed low adherence to patient identification (0.0%), hand decontamination (5.5%), and presence of nurse on ward rounds (6.8%). Physician feedback was generally positive. The baseline audit demonstrated significant practice bias on daily ward rounds which tended to omit several key-proven patient safety practices such as prompting hand decontamination and obtaining up to date reports from nursing staff. Results of the intervention arm demonstrate the feasibility of using the Checklist App on daily ward rounds.

Negative Effects of Paternal Age on Children's Neurocognitive Outcomes Can Be Explained by Maternal Education and Number of Siblings

PubMed Central

Edwards, Ryan D.; Roff, Jennifer

2010-01-01

Background Recent findings suggest advanced paternal age may be associated with impaired child outcomes, in particular, neurocognitive skills. Such patterns are worrisome given relatively universal trends in advanced countries toward delayed nuptiality and fertility. But nature and nurture are both important for child outcomes, and it is important to control for both when drawing inferences about either pathway. Methods and Findings We examined cross-sectional patterns in six developmental outcome measures among children in the U.S. Collaborative Perinatal Project (n = 31,346). Many of these outcomes at 8 mo, 4 y, and 7 y of age (Bayley scales, Stanford Binet Intelligence Scale, Graham-Ernhart Block Sort Test, Wechsler Intelligence Scale for Children, Wide Range Achievement Test) are negatively correlated with paternal age when important family characteristics such as maternal education and number of siblings are not included as covariates. But controlling for family characteristics in general and mother's education in particular renders the effect of paternal age statistically insignificant for most developmental measures. Conclusions Assortative mating produces interesting relationships between maternal and paternal characteristics that can inject spurious correlation into observational studies via omitted variable bias. Controlling for both nature and nurture reveals little residual evidence of a link between child neurocognitive outcomes and paternal age in these data. Results suggest that benefits associated with the upward trend in maternal education may offset any negative effects of advancing paternal age. PMID:20856853

The Role of Feature Selection and Statistical Weighting in ...

EPA Pesticide Factsheets

Our study assesses the value of both in vitro assay and quantitative structure activity relationship (QSAR) data in predicting in vivo toxicity using numerous statistical models and approaches to process the data. Our models are built on datasets of (i) 586 chemicals for which both in vitro and in vivo data are currently available in EPA’s Toxcast and ToxRefDB databases, respectively, and (ii) 769 chemicals for which both QSAR data and in vivo data exist. Similar to a previous study (based on just 309 chemicals, Thomas et al. 2012), after converting the continuous values from each dataset to binary values, the majority of more than 1,000 in vivo endpoints are poorly predicted. Even for the endpoints that are well predicted (about 40 with an F1 score of >0.75), imbalances in in vivo endpoint data or cytotoxicity across in vitro assays may be skewing results. In order to better account for these types of considerations, we examine best practices in data preprocessing and model fitting in real-world contexts where data are rife with imperfections. We discuss options for dealing with missing data, including omitting observations, aggregating variables, and imputing values. We also examine the impacts of feature selection (from both a statistical and biological perspective) on performance and efficiency, and we weight outcome data to reduce endpoint imbalances to account for potential chemical selection bias and assess revised performance. For example, initial weig

Adaptive Variable Bias Magnetic Bearing Control

NASA Technical Reports Server (NTRS)

Johnson, Dexter; Brown, Gerald V.; Inman, Daniel J.

1998-01-01

Most magnetic bearing control schemes use a bias current with a superimposed control current to linearize the relationship between the control current and the force it delivers. With the existence of the bias current, even in no load conditions, there is always some power consumption. In aerospace applications, power consumption becomes an important concern. In response to this concern, an alternative magnetic bearing control method, called Adaptive Variable Bias Control (AVBC), has been developed and its performance examined. The AVBC operates primarily as a proportional-derivative controller with a relatively slow, bias current dependent, time-varying gain. The AVBC is shown to reduce electrical power loss, be nominally stable, and provide control performance similar to conventional bias control. Analytical, computer simulation, and experimental results are presented in this paper.

Patterns of Local-Regional Management Following Neoadjuvant Chemotherapy in Breast Cancer: Results From ACOSOG Z1071 (Alliance)

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

Haffty, Bruce G., E-mail: hafftybg@cinj.rutgers.edu; McCall, Linda M.; Ballman, Karla V.

2016-03-01

Purpose: American College of Surgeons Oncology Group Z1071 was a prospective trial evaluating the false negative rate of sentinel lymph node (SLN) surgery after neoadjuvant chemotherapy (NAC) in breast cancer patients with initial node-positive disease. Radiation therapy (RT) decisions were made at the discretion of treating physicians, providing an opportunity to evaluate variability in practice patterns following NAC. Methods and Materials: Of 756 patients enrolled from July 2009 to June 2011, 685 met all eligibility requirements. Surgical approach, RT, and radiation field design were analyzed based on presenting clinical and pathologic factors. Results: Of 401 node-positive patients, mastectomy was performed inmore » 148 (36.9%), mastectomy with immediate reconstruction in 107 (26.7%), and breast-conserving surgery (BCS) in 146 patients (36.4%). Of the 284 pathologically node-negative patients, mastectomy was performed in 84 (29.6%), mastectomy with immediate reconstruction in 69 (24.3%), and BCS in 131 patients (46.1%). Bilateral mastectomy rates were higher in women undergoing reconstruction than in those without (66.5% vs 32.2%, respectively, P<.0001). Use of internal mammary RT was low (7.8%-11.2%) and did not differ between surgical approaches. Supraclavicular RT rate ranged from 46.6% to 52.2% and did not differ between surgical approaches but was omitted in 193 or 408 node-positive patients (47.3%). Rate of axillary RT was more frequent in patients who remained node-positive (P=.002). However, 22% of patients who converted to node-negative still received axillary RT. Post-mastectomy RT was more frequently omitted after reconstruction than mastectomy (23.9% vs 12.1%, respectively, P=.002) and was omitted in 19 of 107 patients (17.8%) with residual node-positive disease in the reconstruction group. Conclusions: Most clinically node-positive patients treated with NAC undergoing mastectomy receive RT. RT is less common in patients undergoing reconstruction. There is wide variability in RT fields. These practice patterns conflict with expert recommendations and ongoing trial guidelines. There is a significant need for greater uniformity and guidelines regarding RT following NAC.« less

Optimizing Illumina next-generation sequencing library preparation for extremely AT-biased genomes.

PubMed

Oyola, Samuel O; Otto, Thomas D; Gu, Yong; Maslen, Gareth; Manske, Magnus; Campino, Susana; Turner, Daniel J; Macinnis, Bronwyn; Kwiatkowski, Dominic P; Swerdlow, Harold P; Quail, Michael A

2012-01-03

Massively parallel sequencing technology is revolutionizing approaches to genomic and genetic research. Since its advent, the scale and efficiency of Next-Generation Sequencing (NGS) has rapidly improved. In spite of this success, sequencing genomes or genomic regions with extremely biased base composition is still a great challenge to the currently available NGS platforms. The genomes of some important pathogenic organisms like Plasmodium falciparum (high AT content) and Mycobacterium tuberculosis (high GC content) display extremes of base composition. The standard library preparation procedures that employ PCR amplification have been shown to cause uneven read coverage particularly across AT and GC rich regions, leading to problems in genome assembly and variation analyses. Alternative library-preparation approaches that omit PCR amplification require large quantities of starting material and hence are not suitable for small amounts of DNA/RNA such as those from clinical isolates. We have developed and optimized library-preparation procedures suitable for low quantity starting material and tolerant to extremely high AT content sequences. We have used our optimized conditions in parallel with standard methods to prepare Illumina sequencing libraries from a non-clinical and a clinical isolate (containing ~53% host contamination). By analyzing and comparing the quality of sequence data generated, we show that our optimized conditions that involve a PCR additive (TMAC), produces amplified libraries with improved coverage of extremely AT-rich regions and reduced bias toward GC neutral templates. We have developed a robust and optimized Next-Generation Sequencing library amplification method suitable for extremely AT-rich genomes. The new amplification conditions significantly reduce bias and retain the complexity of either extremes of base composition. This development will greatly benefit sequencing clinical samples that often require amplification due to low mass of DNA starting material.

Reducing bias and analyzing variability in the time-left procedure.

PubMed

Trujano, R Emmanuel; Orduña, Vladimir

2015-04-01

The time-left procedure was designed to evaluate the psychophysical function for time. Although previous results indicated a linear relationship, it is not clear what role the observed bias toward the time-left option plays in this procedure and there are no reports of how variability changes with predicted indifference. The purposes of this experiment were to reduce bias experimentally, and to contrast the difference limen (a measure of variability around indifference) with predictions from scalar expectancy theory (linear timing) and behavioral economic model (logarithmic timing). A control group of 6 rats performed the original time-left procedure with C=60 s and S=5, 10,…, 50, 55 s, whereas a no-bias group of 6 rats performed the same conditions in a modified time-left procedure in which only a single response per choice trial was allowed. Results showed that bias was reduced for the no-bias group, observed indifference grew linearly with predicted indifference for both groups, and difference limen and Weber ratios decreased as expected indifference increased for the control group, which is consistent with linear timing, whereas for the no-bias group they remained constant, consistent with logarithmic timing. Therefore, the time-left procedure generates results consistent with logarithmic perceived time once bias is experimentally reduced. Copyright © 2015 Elsevier B.V. All rights reserved.

Study Protocol, Sample Characteristics, and Loss to Follow-Up: The OPPERA Prospective Cohort Study

PubMed Central

Bair, Eric; Brownstein, Naomi C.; Ohrbach, Richard; Greenspan, Joel D.; Dubner, Ron; Fillingim, Roger B.; Maixner, William; Smith, Shad; Diatchenko, Luda; Gonzalez, Yoly; Gordon, Sharon; Lim, Pei-Feng; Ribeiro-Dasilva, Margarete; Dampier, Dawn; Knott, Charles; Slade, Gary D.

2013-01-01

When studying incidence of pain conditions such as temporomandibular disorders (TMDs), repeated monitoring is needed in prospective cohort studies. However, monitoring methods usually have limitations and, over a period of years, some loss to follow-up is inevitable. The OPPERA prospective cohort study of first-onset TMD screened for symptoms using quarterly questionnaires and examined symptomatic participants to definitively ascertain TMD incidence. During the median 2.8-year observation period, 16% of the 3,263 enrollees completed no follow-up questionnaires, others provided incomplete follow-up, and examinations were not conducted for one third of symptomatic episodes. Although screening methods and examinations were found to have excellent reliability and validity, they were not perfect. Loss to follow-up varied according to some putative TMD risk factors, although multiple imputation to correct the problem suggested that bias was minimal. A second method of multiple imputation that evaluated bias associated with omitted and dubious examinations revealed a slight underestimate of incidence and some small biases in hazard ratios used to quantify effects of risk factors. Although “bottom line” statistical conclusions were not affected, multiply-imputed estimates should be considered when evaluating the large number of risk factors under investigation in the OPPERA study. Perspective These findings support the validity of the OPPERA prospective cohort study for the purpose of investigating the etiology of first-onset TMD, providing the foundation for other papers investigating risk factors hypothesized in the OPPERA project. PMID:24275220

75 FR 33801 - Sunshine Act Meeting Notice

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-15

..., Inc. E-3 ER09-1050-000, ER09-1192-000.... Southwest Power Pool, Inc. E-4 OMITTED. E-5 ER09-1063-000..., Inc. E-7 ER10-1069-000 Southwest Power Pool, Inc. E-8 OMITTED. E-9 RM10-23-000 Transmission Planning... Owners. E-16 OMITTED. E-17 ER09-1051-000 ISO New England Inc. and New England Power Pool. E-18 EL10-52...

Access Control to Information in Pervasive Computing Environments

DTIC Science & Technology

2005-08-01

for foo’s public key. (Digital signatures are omitted.) indicate a set of location and time intervals. A service will return location information only...stands for foo’s public key. (Digital signatures are omitted.) it describes the resource to which access is granted. Currently, we allow only resources...information relationship. Alice’s location information is bun- dled in her personal information. (The digital signature is omitted.) We use extended

Bias and Bias Correction in Multisite Instrumental Variables Analysis of Heterogeneous Mediator Effects

ERIC Educational Resources Information Center

Reardon, Sean F.; Unlu, Fatih; Zhu, Pei; Bloom, Howard S.

2014-01-01

We explore the use of instrumental variables (IV) analysis with a multisite randomized trial to estimate the effect of a mediating variable on an outcome in cases where it can be assumed that the observed mediator is the only mechanism linking treatment assignment to outcomes, an assumption known in the IV literature as the exclusion restriction.…

Design and Testing of an Automated System using Thermochromatic Liquid Crystals to Determine Local Heat Transfer Coefficients for an Impinging Jet

NASA Technical Reports Server (NTRS)

Tan, Benjamin

1995-01-01

Using thermochromatic liquid crystal to measure surface temperature, an automated transient method with time-varying free-stream temperature is developed to determine local heat transfer coefficients. By allowing the free-stream temperature to vary with time, the need for complicated mechanical components to achieve a step temperature change is eliminated, and by using the thermochromatic liquid crystals as temperature indicators, the labor intensive task of installing many thermocouples is omitted. Bias associated with human perception of the transition of the thermochromatic liquid crystal is eliminated by using a high speed digital camera and a computer. The method is validated by comparisons with results obtained by the steady-state method for a circular Jet impinging on a flat plate. Several factors affecting the accuracy of the method are evaluated.

Do Wealth Shocks Affect Health? New Evidence from the Housing Boom

PubMed Central

Fichera, Eleonora

2016-01-01

Abstract We exploit large exogenous changes in housing wealth to examine the impact of wealth gains and losses on individual health. In UK household, panel data house price increases, which endow owners with greater wealth, lower the likelihood of home owners exhibiting a range of non‐chronic health conditions and improve their self‐assessed health with no effect on their psychological health. These effects are not transitory and persist over a 10‐year period. Using a range of fixed effects models, we provide robust evidence that these results are not biased by reverse causality or omitted factors. For owners' wealth gains affect labour supply and leisure choices indicating that house price increases allow individuals to reduce intensity of work with commensurate health benefits. © 2016 The Authors. Health Economics Published by John Wiley & Sons, Ltd. PMID:27870303

A method to perform spinal motion analysis from functional X-ray images.

PubMed

Schulze, Martin; Trautwein, Frank; Vordemvenne, Thomas; Raschke, Michael; Heuer, Frank

2011-06-03

Identifying spinal instability is an important aim for proper surgical treatment. Analysis of functional X-ray images delivers measurements of the range of motion (RoM) and the center of rotation (CoR). In today's practice, CoR determination is often omitted, due to the lack of accurate methods. The aim of this work was to investigate the accuracy of a new analysis software (FXA™) based on an in vitro experiment. Six bovine spinal specimens (L3-4) were mounted in a robot (KR125, Kuka). CoRs were predefined by locking the robot actuator tool center point to the estimated position of the physiologic CoR and taking a baseline X-ray. Specimens were deflected to various RoM(preset) flexion/extension angles about the CoR(preset). Lateral functional radiographs were acquired and specimen movements were recorded using an optical motion tracking system (Optotrak Certus). RoM and CoR errors were calculated from presets for both methods. Prior to the experiment, the FXA™ software was verified with artificially generated images. For the artificial images, FXA™ yielded a mean RoM-error of 0.01 ± 0.03° (bias ± standard deviation). In the experiment, RoM-error of the FXA™-software (deviation from presets) was 0.04 ± 0.13°, and 0.10 ± 0.16° for the Optotrak, respectively. Both correlated with 0.998 (p < 0.001). For RoM < 1.0°, FXA™ determined CoR positions with a bias>20mm. This bias progressively decreased from RoM = 1° (bias = 6.0mm) to RoM = 9° (bias<1.5mm). Under the assumption that CoR location variances <5mm are clinically irrelevant on the lumbar spine, the FXA™ method can accurately determine CoRs for RoMs > 1°. Utilizing FXA™, polysegmental RoMs, CoRs and implant migration measurements could be performed in daily practice. Copyright © 2011 Elsevier Ltd. All rights reserved.

Evidence of selective reporting bias in hematology journals: A systematic review

PubMed Central

Scheckel, Caleb; Hicks, Chandler; Nissen, Timothy; Leduc, Linda; Som, Mousumi; Vassar, Matt

2017-01-01

Introduction Selective reporting bias occurs when chance or selective outcome reporting rather than the intervention contributes to group differences. The prevailing concern about selective reporting bias is the possibility of results being modified towards specific conclusions. In this study, we evaluate randomized controlled trials (RCTs) published in hematology journals, a group in which selective outcome reporting has not yet been explored. Methods Our primary goal was to examine discrepancies between the reported primary and secondary outcomes in registered and published RCTs concerning hematological malignancies reported in hematology journals with a high impact factor. The secondary goals were to address whether outcome reporting discrepancies favored statistically significant outcomes, whether a pattern existed between the funding source and likelihood of outcome reporting bias, and whether temporal trends were present in outcome reporting bias. For trials with major outcome discrepancies, we contacted trialists to determine reasons for these discrepancies. Trials published between January 1, 2010 and December 31, 2015 in Blood; British Journal of Haematology; American Journal of Hematology; Leukemia; and Haematologica were included. Results Of 499 RCTs screened, 109 RCTs were included. Our analysis revealed 118 major discrepancies and 629 total discrepancies. Among the 118 discrepancies, 30 (25.4%) primary outcomes were demoted, 47 (39.8%) primary outcomes were omitted, and 30 (25.4%) primary outcomes were added. Three (2.5%) secondary outcomes were upgraded to a primary outcome. The timing of assessment for a primary outcome changed eight (6.8%) times. Thirty-one major discrepancies were published with a P-value and twenty-five (80.6%) favored statistical significance. A majority of authors whom we contacted cited a pre-planned subgroup analysis as a reason for outcome changes. Conclusion Our results suggest that outcome changes occur frequently in hematology trials. Because RCTs ultimately underpin clinical judgment and guide policy implementation, selective reporting could pose a threat to medical decision making. PMID:28570573

Study of Bias in 2012-Placement Test through Rasch Model in Terms of Gender Variable

ERIC Educational Resources Information Center

Turkan, Azmi; Cetin, Bayram

2017-01-01

Validity and reliability are among the most crucial characteristics of a test. One of the steps to make sure that a test is valid and reliable is to examine the bias in test items. The purpose of this study was to examine the bias in 2012 Placement Test items in terms of gender variable using Rasch Model in Turkey. The sample of this study was…

Lower Risk of Death With SGLT2 Inhibitors in Observational Studies: Real or Bias?

PubMed

Suissa, Samy

2018-01-01

Two recent observational studies reported a remarkably lower rate of all-cause death associated with sodium-glucose cotransporter 2 inhibitor (-SGLT2i) use in all patients with type 2 diabetes and not only those at increased cardiovascular risk. The >50% lower mortality rates reported in these studies are much greater than those found in the BI 10773 (Empagliflozin) Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients (EMPA-REG OUTCOME) and CANagliflozin cardioVascular Assessment Study (CANVAS) randomized trials. We show that these observational studies are affected by time-related biases, including immortal time bias and time-lag bias, which tend to exaggerate the benefits observed with a drug. The Comparative Effectiveness of Cardiovascular Outcomes in New Users of SGLT-2 Inhibitors (CVD-REAL) study, based on 166,033 users of SGLT2i and 1,226,221 users of other glucose-lowering drugs (oGLD) identified from health care databases of six countries, was affected by immortal time bias. Indeed, the immortal time between the first oGLD prescription and the first SGLT2i prescription was omitted from the analysis, which resulted in increasing the rate of death in the oGLD group and thus producing the appearance of a lower risk of death with SGLT2i use. The Swedish study compared 10,879 SGLT2i/dipeptidyl peptidase 4 inhibitor (DPP-4i) users with 10,879 matched insulin users. Such comparisons involving second-line therapies with a third-line therapy can introduce time-lag bias, as the patients may not be at the same stage of diabetes. This bias is compounded by the fact that the users of insulin had already started their insulin before cohort entry, unlike the new users of SGLT2i. Finally, the study also introduces immortal time bias with respect to the effects of SGLT2i relative to DPP-4i. In conclusion, the >50% lower rate of death with SGLT2i in type 2 diabetes reported by two recent observational studies is likely exaggerated by immortal time and time-lag biases. It thus remains uncertain whether the benefit seen with empagliflozin in the EMPA-REG OUTCOME trial applies to all SGLT2i and to all patients with type 2 diabetes, not only those at increased cardiovascular risk. While observational studies can provide crucial real-world evidence for the effects of medications, they need to be carefully conducted to avoid such major time-related biases. © 2017 by the American Diabetes Association.

Nurse staffing and patient outcomes: Strengths and limitations of the evidence to inform policy and practice. A review and discussion paper based on evidence reviewed for the National Institute for Health and Care Excellence Safe Staffing guideline development.

PubMed

Griffiths, Peter; Ball, Jane; Drennan, Jonathan; Dall'Ora, Chiara; Jones, Jeremy; Maruotti, Antonello; Pope, Catherine; Recio Saucedo, Alejandra; Simon, Michael

2016-11-01

A large and increasing number of studies have reported a relationship between low nurse staffing levels and adverse outcomes, including higher mortality rates. Despite the evidence being extensive in size, and having been sometimes described as "compelling" and "overwhelming", there are limitations that existing studies have not yet been able to address. One result of these weaknesses can be observed in the guidelines on safe staffing in acute hospital wards issued by the influential body that sets standards for the National Health Service in England, the National Institute for Health and Care Excellence, which concluded there is insufficient good quality evidence available to fully inform practice. In this paper we explore this apparent contradiction. After summarising the evidence review that informed the National Institute for Health and Care Excellence guideline on safe staffing and related evidence, we move on to discussing the complex challenges that arise when attempting to apply this evidence to practice. Among these, we introduce the concept of endogeneity, a form of bias in the estimation of causal effects. Although current evidence is broadly consistent with a cause and effect relationship, endogeneity means that estimates of the size of effect, essential for building an economic case, may be biased and in some cases qualitatively wrong. We expand on three limitations that are likely to lead to endogeneity in many previous studies: omitted variables, which refers to the absence of control for variables such as medical staffing and patient case mix; simultaneity, which occurs when the outcome can influence the level of staffing just as staffing influences outcome; and common-method variance, which may be present when both outcomes and staffing levels variables are derived from the same survey. Thus while current evidence is important and has influenced policy because it illustrates the potential risks and benefits associated with changes in nurse staffing, it may not provide operational solutions. We conclude by posing a series of questions about design and methods for future researchers who intend to further explore this complex relationship between nurse staffing levels and outcomes. These questions are intended to reflect on the potential added value of new research given what is already known, and to encourage those conducting research to take opportunities to produce research that fills gaps in the existing knowledge for practice. By doing this we hope that future studies can better quantify both the benefits and costs of changes in nurse staffing levels and, therefore, serve as a more useful tool for those delivering services. Copyright © 2016 Elsevier Ltd. All rights reserved.

An improved standardization procedure to remove systematic low frequency variability biases in GCM simulations

NASA Astrophysics Data System (ADS)

Mehrotra, Rajeshwar; Sharma, Ashish

2012-12-01

The quality of the absolute estimates of general circulation models (GCMs) calls into question the direct use of GCM outputs for climate change impact assessment studies, particularly at regional scales. Statistical correction of GCM output is often necessary when significant systematic biasesoccur between the modeled output and observations. A common procedure is to correct the GCM output by removing the systematic biases in low-order moments relative to observations or to reanalysis data at daily, monthly, or seasonal timescales. In this paper, we present an extension of a recently published nested bias correction (NBC) technique to correct for the low- as well as higher-order moments biases in the GCM-derived variables across selected multiple time-scales. The proposed recursive nested bias correction (RNBC) approach offers an improved basis for applying bias correction at multiple timescales over the original NBC procedure. The method ensures that the bias-corrected series exhibits improvements that are consistently spread over all of the timescales considered. Different variations of the approach starting from the standard NBC to the more complex recursive alternatives are tested to assess their impacts on a range of GCM-simulated atmospheric variables of interest in downscaling applications related to hydrology and water resources. Results of the study suggest that three to five iteration RNBCs are the most effective in removing distributional and persistence related biases across the timescales considered.

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.

Detection and Attribution of Simulated Climatic Extreme Events and Impacts: High Sensitivity to Bias Correction

NASA Astrophysics Data System (ADS)

Sippel, S.; Otto, F. E. L.; Forkel, M.; Allen, M. R.; Guillod, B. P.; Heimann, M.; Reichstein, M.; Seneviratne, S. I.; Kirsten, T.; Mahecha, M. D.

2015-12-01

Understanding, quantifying and attributing the impacts of climatic extreme events and variability is crucial for societal adaptation in a changing climate. However, climate model simulations generated for this purpose typically exhibit pronounced biases in their output that hinders any straightforward assessment of impacts. To overcome this issue, various bias correction strategies are routinely used to alleviate climate model deficiencies most of which have been criticized for physical inconsistency and the non-preservation of the multivariate correlation structure. We assess how biases and their correction affect the quantification and attribution of simulated extremes and variability in i) climatological variables and ii) impacts on ecosystem functioning as simulated by a terrestrial biosphere model. Our study demonstrates that assessments of simulated climatic extreme events and impacts in the terrestrial biosphere are highly sensitive to bias correction schemes with major implications for the detection and attribution of these events. We introduce a novel ensemble-based resampling scheme based on a large regional climate model ensemble generated by the distributed weather@home setup[1], which fully preserves the physical consistency and multivariate correlation structure of the model output. We use extreme value statistics to show that this procedure considerably improves the representation of climatic extremes and variability. Subsequently, biosphere-atmosphere carbon fluxes are simulated using a terrestrial ecosystem model (LPJ-GSI) to further demonstrate the sensitivity of ecosystem impacts to the methodology of bias correcting climate model output. We find that uncertainties arising from bias correction schemes are comparable in magnitude to model structural and parameter uncertainties. The present study consists of a first attempt to alleviate climate model biases in a physically consistent way and demonstrates that this yields improved simulations of climate extremes and associated impacts. [1] http://www.climateprediction.net/weatherathome/

Bias estimation for the Landsat 8 operational land imager

USGS Publications Warehouse

Morfitt, Ron; Vanderwerff, Kelly

2011-01-01

The Operational Land Imager (OLI) is a pushbroom sensor that will be a part of the Landsat Data Continuity Mission (LDCM). This instrument is the latest in the line of Landsat imagers, and will continue to expand the archive of calibrated earth imagery. An important step in producing a calibrated image from instrument data is accurately accounting for the bias of the imaging detectors. Bias variability is one factor that contributes to error in bias estimation for OLI. Typically, the bias is simply estimated by averaging dark data on a per-detector basis. However, data acquired during OLI pre-launch testing exhibited bias variation that correlated well with the variation in concurrently collected data from a special set of detectors on the focal plane. These detectors are sensitive to certain electronic effects but not directly to incoming electromagnetic radiation. A method of using data from these special detectors to estimate the bias of the imaging detectors was developed, but found not to be beneficial at typical radiance levels as the detectors respond slightly when the focal plane is illuminated. In addition to bias variability, a systematic bias error is introduced by the truncation performed by the spacecraft of the 14-bit instrument data to 12-bit integers. This systematic error can be estimated and removed on average, but the per pixel quantization error remains. This paper describes the variability of the bias, the effectiveness of a new approach to estimate and compensate for it, as well as the errors due to truncation and how they are reduced.

Impacts of correcting the inter-variable correlation of climate model outputs on hydrological modeling

NASA Astrophysics Data System (ADS)

Chen, Jie; Li, Chao; Brissette, François P.; Chen, Hua; Wang, Mingna; Essou, Gilles R. C.

2018-05-01

Bias correction is usually implemented prior to using climate model outputs for impact studies. However, bias correction methods that are commonly used treat climate variables independently and often ignore inter-variable dependencies. The effects of ignoring such dependencies on impact studies need to be investigated. This study aims to assess the impacts of correcting the inter-variable correlation of climate model outputs on hydrological modeling. To this end, a joint bias correction (JBC) method which corrects the joint distribution of two variables as a whole is compared with an independent bias correction (IBC) method; this is considered in terms of correcting simulations of precipitation and temperature from 26 climate models for hydrological modeling over 12 watersheds located in various climate regimes. The results show that the simulated precipitation and temperature are considerably biased not only in the individual distributions, but also in their correlations, which in turn result in biased hydrological simulations. In addition to reducing the biases of the individual characteristics of precipitation and temperature, the JBC method can also reduce the bias in precipitation-temperature (P-T) correlations. In terms of hydrological modeling, the JBC method performs significantly better than the IBC method for 11 out of the 12 watersheds over the calibration period. For the validation period, the advantages of the JBC method are greatly reduced as the performance becomes dependent on the watershed, GCM and hydrological metric considered. For arid/tropical and snowfall-rainfall-mixed watersheds, JBC performs better than IBC. For snowfall- or rainfall-dominated watersheds, however, the two methods behave similarly, with IBC performing somewhat better than JBC. Overall, the results emphasize the advantages of correcting the P-T correlation when using climate model-simulated precipitation and temperature to assess the impact of climate change on watershed hydrology. However, a thorough validation and a comparison with other methods are recommended before using the JBC method, since it may perform worse than the IBC method for some cases due to bias nonstationarity of climate model outputs.

Non-equilibrium Green's functions study of discrete dopants variability on an ultra-scaled FinFET

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

Valin, R., E-mail: r.valinferreiro@swansea.ac.uk; Martinez, A., E-mail: a.e.Martinez@swansea.ac.uk; Barker, J. R., E-mail: john.barker@glasgow.ac.uk

In this paper, we study the effect of random discrete dopants on the performance of a 6.6 nm channel length silicon FinFET. The discrete dopants have been distributed randomly in the source/drain region of the device. Due to the small dimensions of the FinFET, a quantum transport formalism based on the non-equilibrium Green's functions has been deployed. The transfer characteristics for several devices that differ in location and number of dopants have been calculated. Our results demonstrate that discrete dopants modify the effective channel length and the height of the source/drain barrier, consequently changing the channel control of the charge. Thismore » effect becomes more significant at high drain bias. As a consequence, there is a strong effect on the variability of the on-current, off-current, sub-threshold slope, and threshold voltage. Finally, we have also calculated the mean and standard deviation of these parameters to quantify their variability. The obtained results show that the variability at high drain bias is 1.75 larger than at low drain bias. However, the variability of the on-current, off-current, and sub-threshold slope remains independent of the drain bias. In addition, we have found that a large source to drain current by tunnelling current occurs at low gate bias.« less

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.

Role Appropriateness of Educational Fields: Bias in Selection.

ERIC Educational Resources Information Center

Smith, Elizabeth P.; And Others

Bias towards women exists in the selection of applicants to professional and other positions. This research investigated the effects of two rater variables--sex and attitude toward women--and three applicant variables--sex, field (engineering-dietetics), and attributes--(feminine-masculine) upon ratings of competency and personal charm. Analyses…

Evaluating the Bias of Alternative Cost Progress Models: Tests Using Aerospace Industry Acquisition Programs

DTIC Science & Technology

1992-12-01

suspect :mat, -n2 extent predict:.on cas jas ccsiziveiv crrei:=e amonc e v:arious models, :he fandom *.;aik, learn ha r ur e, i;<ea- variable and Bemis...Functions, Production Rate Adjustment Model, Learning Curve Model. Random Walk Model. Bemis Model. Evaluating Model Bias, Cost Prediction Bias. Cost...of four cost progress models--a random walk model, the tradiuonai learning curve model, a production rate model Ifixed-variable model). and a model

Time Preferences, Mental Health and Treatment Utilization.

PubMed

Eisenberg, Daniel; Druss, Benjamin G

2015-09-01

In all countries of the world, fewer than half of people with mental disorders receive treatment. This treatment gap is commonly attributed to factors such as consumers' limited knowledge, negative attitudes, and financial constraints. In the context of other health behaviors, such as diet and exercise, behavioral economists have emphasized time preferences and procrastination as additional barriers. These factors might also be relevant to mental health. We examine conceptually and empirically how lack of help-seeking for mental health conditions might be related to time preferences and procrastination. Our conceptual discussion explores how the interrelationships between time preferences and mental health treatment utilization could fit into basic microeconomic theory. The empirical analysis uses survey data of student populations from 12 colleges and universities in 2011 (the Healthy Minds Study, N=8,806). Using standard brief measures of discounting, procrastination, and mental health (depression and anxiety symptoms), we examine the conditional correlations between indicators of present-orientation (discount rate and procrastination) and mental health symptoms. The conceptual discussion reveals a number of potential relationships that would be useful to examine empirically. In the empirical analysis depression is significantly associated with procrastination and discounting. Treatment utilization is significantly associated with procrastination but not discounting. The empirical results are generally consistent with the idea that depression increases present orientation (reduces future orientation), as measured by discounting and procrastination. These analyses have notable limitations that will require further examination in future research: the measures are simple and brief, and the estimates may be biased from true causal effects because of omitted variables and reverse causality. There are several possibilities for future research, including: (i) observational, longitudinal studies with detailed data on mental health, time preferences, and help-seeking; (ii) experimental studies that examine immediate or short-term responses and connections between these variables; (iii) randomized trials of mental health therapies that include outcome measures of time preferences and procrastination; and, (iv) intervention studies that test strategies to influence help-seeking by addressing time preferences and present orientation.

Contrasting patterns of fine-scale herb layer species composition in temperate forests

NASA Astrophysics Data System (ADS)

Chudomelová, Markéta; Zelený, David; Li, Ching-Feng

2017-04-01

Although being well described at the landscape level, patterns in species composition of forest herb layer are rarely studied at smaller scales. Here, we examined fine-scale environmental determinants and spatial structures of herb layer communities in thermophilous oak- and hornbeam dominated forests of the south-eastern part of the Czech Republic. Species composition of herb layer vegetation and environmental variables were recorded within a fixed grid of 2 × 2 m subplots regularly distributed within 1-ha quadrate plots in three forest stands. For each site, environmental models best explaining species composition were constructed using constrained ordination analysis. Spatial eigenvector mapping was used to model and account for spatial structures in community variation. Mean Ellenberg indicator values calculated for each subplot were used for ecological interpretation of spatially structured residual variation. The amount of variation explained by environmental and spatial models as well as the selection of variables with the best explanatory power differed among sites. As an important environmental factor, relative elevation was common to all three sites, while pH and canopy openness were shared by two sites. Both environmental and community variation was mostly coarse-scaled, as was the spatially structured portion of residual variation. When corrected for bias due to spatial autocorrelation, those environmental factors with already weak explanatory power lost their significance. Only a weak evidence of possibly omitted environmental predictor was found for autocorrelated residuals of site models using mean Ellenberg indicator values. Community structure was determined by different factors at different sites. The relative importance of environmental filtering vs. spatial processes was also site specific, implying that results of fine-scale studies tend to be shaped by local conditions. Contrary to expectations based on other studies, overall dominance of spatial processes at fine scale has not been detected. Ecologists should keep this in mind when making generalizations about community dynamics.

Patient-specific lean body mass can be estimated from limited-coverage computed tomography images.

PubMed

Devriese, Joke; Beels, Laurence; Maes, Alex; van de Wiele, Christophe; Pottel, Hans

2018-06-01

In PET/CT, quantitative evaluation of tumour metabolic activity is possible through standardized uptake values, usually normalized for body weight (BW) or lean body mass (LBM). Patient-specific LBM can be estimated from whole-body (WB) CT images. As most clinical indications only warrant PET/CT examinations covering head to midthigh, the aim of this study was to develop a simple and reliable method to estimate LBM from limited-coverage (LC) CT images and test its validity. Head-to-toe PET/CT examinations were retrospectively retrieved and semiautomatically segmented into tissue types based on thresholding of CT Hounsfield units. LC was obtained by omitting image slices. Image segmentation was validated on the WB CT examinations by comparing CT-estimated BW with actual BW, and LBM estimated from LC images were compared with LBM estimated from WB images. A direct method and an indirect method were developed and validated on an independent data set. Comparing LBM estimated from LC examinations with estimates from WB examinations (LBMWB) showed a significant but limited bias of 1.2 kg (direct method) and nonsignificant bias of 0.05 kg (indirect method). This study demonstrates that LBM can be estimated from LC CT images with no significant difference from LBMWB.

Use of a genetic algorithm for the analysis of eye movements from the linear vestibulo-ocular reflex

NASA Technical Reports Server (NTRS)

Shelhamer, M.

2001-01-01

It is common in vestibular and oculomotor testing to use a single-frequency (sine) or combination of frequencies [sum-of-sines (SOS)] stimulus for head or target motion. The resulting eye movements typically contain a smooth tracking component, which follows the stimulus, in which are interspersed rapid eye movements (saccades or fast phases). The parameters of the smooth tracking--the amplitude and phase of each component frequency--are of interest; many methods have been devised that attempt to identify and remove the fast eye movements from the smooth. We describe a new approach to this problem, tailored to both single-frequency and sum-of-sines stimulation of the human linear vestibulo-ocular reflex. An approximate derivative is used to identify fast movements, which are then omitted from further analysis. The remaining points form a series of smooth tracking segments. A genetic algorithm is used to fit these segments together to form a smooth (but disconnected) wave form, by iteratively removing biases due to the missing fast phases. A genetic algorithm is an iterative optimization procedure; it provides a basis for extending this approach to more complex stimulus-response situations. In the SOS case, the genetic algorithm estimates the amplitude and phase values of the component frequencies as well as removing biases.

What is the contribution of different cognitive biases and stressful childhood events to the presence and number of previous depressive episodes?

PubMed

Vrijsen, Janna N; Becker, Eni S; Arias-Vásquez, Alejandro; van Dijk, Maarten K; Speckens, Anne; Oostrom, Iris van

2014-07-30

Negative cognitive biases as well as stressful childhood events are well-known risk factors for depression. Few studies have compared the association of different types of biases and events with depression. The current study examined whether different cognitive biases and stressful childhood events variables were associated with depression and recurrence. Three types of childhood events were assessed in 83 never-depressed and 337 formerly depressed individuals: trauma within the family, trauma outside the family, and adverse events. Furthermore, after a sad mood induction procedure, participants executed a Dot Probe task (selective attentional bias), an Emotional Stroop task (attentional interference bias) and an incidental learning task (memory bias). The association of these measures with case status and recurrence status (one or multiple past episodes) was examined. Negative memory bias and traumatic childhood events within the family were associated with case status, whereas none of the bias measures or childhood events variables were associated with recurrence status. The results indicate that memory bias as well as the experience of aggression and/or abuse within the family during childhood are independently associated with depression. Biases and stressful childhood events did not offer differentiation between individuals with one or multiple past episodes. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Biasogram: Visualization of Confounding Technical Bias in Gene Expression Data

PubMed Central

Krzystanek, Marcin; Szallasi, Zoltan; Eklund, Aron C.

2013-01-01

Gene expression profiles of clinical cohorts can be used to identify genes that are correlated with a clinical variable of interest such as patient outcome or response to a particular drug. However, expression measurements are susceptible to technical bias caused by variation in extraneous factors such as RNA quality and array hybridization conditions. If such technical bias is correlated with the clinical variable of interest, the likelihood of identifying false positive genes is increased. Here we describe a method to visualize an expression matrix as a projection of all genes onto a plane defined by a clinical variable and a technical nuisance variable. The resulting plot indicates the extent to which each gene is correlated with the clinical variable or the technical variable. We demonstrate this method by applying it to three clinical trial microarray data sets, one of which identified genes that may have been driven by a confounding technical variable. This approach can be used as a quality control step to identify data sets that are likely to yield false positive results. PMID:23613961

The contribution of natural variability to GCM bias: Can we effectively bias-correct climate projections?

NASA Astrophysics Data System (ADS)

McAfee, S. A.; DeLaFrance, A.

2017-12-01

Investigating the impacts of climate change often entails using projections from inherently imperfect general circulation models (GCMs) to drive models that simulate biophysical or societal systems in great detail. Error or bias in the GCM output is often assessed in relation to observations, and the projections are adjusted so that the output from impacts models can be compared to historical or observed conditions. Uncertainty in the projections is typically accommodated by running more than one future climate trajectory to account for differing emissions scenarios, model simulations, and natural variability. The current methods for dealing with error and uncertainty treat them as separate problems. In places where observed and/or simulated natural variability is large, however, it may not be possible to identify a consistent degree of bias in mean climate, blurring the lines between model error and projection uncertainty. Here we demonstrate substantial instability in mean monthly temperature bias across a suite of GCMs used in CMIP5. This instability is greatest in the highest latitudes during the cool season, where shifts from average temperatures below to above freezing could have profound impacts. In models with the greatest degree of bias instability, the timing of regional shifts from below to above average normal temperatures in a single climate projection can vary by about three decades, depending solely on the degree of bias assessed. This suggests that current bias correction methods based on comparison to 20- or 30-year normals may be inappropriate, particularly in the polar regions.

Do Wealth Shocks Affect Health? New Evidence from the Housing Boom.

PubMed

Fichera, Eleonora; Gathergood, John

2016-11-01

We exploit large exogenous changes in housing wealth to examine the impact of wealth gains and losses on individual health. In UK household, panel data house price increases, which endow owners with greater wealth, lower the likelihood of home owners exhibiting a range of non-chronic health conditions and improve their self-assessed health with no effect on their psychological health. These effects are not transitory and persist over a 10-year period. Using a range of fixed effects models, we provide robust evidence that these results are not biased by reverse causality or omitted factors. For owners' wealth gains affect labour supply and leisure choices indicating that house price increases allow individuals to reduce intensity of work with commensurate health benefits. © 2016 The Authors. Health Economics Published by John Wiley & Sons, Ltd. © 2016 The Authors. Health Economics Published by John Wiley & Sons, Ltd.

OmniSearch: a semantic search system based on the Ontology for MIcroRNA Target (OMIT) for microRNA-target gene interaction data.

PubMed

Huang, Jingshan; Gutierrez, Fernando; Strachan, Harrison J; Dou, Dejing; Huang, Weili; Smith, Barry; Blake, Judith A; Eilbeck, Karen; Natale, Darren A; Lin, Yu; Wu, Bin; Silva, Nisansa de; Wang, Xiaowei; Liu, Zixing; Borchert, Glen M; Tan, Ming; Ruttenberg, Alan

2016-01-01

As a special class of non-coding RNAs (ncRNAs), microRNAs (miRNAs) perform important roles in numerous biological and pathological processes. The realization of miRNA functions depends largely on how miRNAs regulate specific target genes. It is therefore critical to identify, analyze, and cross-reference miRNA-target interactions to better explore and delineate miRNA functions. Semantic technologies can help in this regard. We previously developed a miRNA domain-specific application ontology, Ontology for MIcroRNA Target (OMIT), whose goal was to serve as a foundation for semantic annotation, data integration, and semantic search in the miRNA field. In this paper we describe our continuing effort to develop the OMIT, and demonstrate its use within a semantic search system, OmniSearch, designed to facilitate knowledge capture of miRNA-target interaction data. Important changes in the current version OMIT are summarized as: (1) following a modularized ontology design (with 2559 terms imported from the NCRO ontology); (2) encoding all 1884 human miRNAs (vs. 300 in previous versions); and (3) setting up a GitHub project site along with an issue tracker for more effective community collaboration on the ontology development. The OMIT ontology is free and open to all users, accessible at: http://purl.obolibrary.org/obo/omit.owl. The OmniSearch system is also free and open to all users, accessible at: http://omnisearch.soc.southalabama.edu/index.php/Software.

Estimating the price elasticity of beer: meta-analysis of data with heterogeneity, dependence, and publication bias.

PubMed

Nelson, Jon P

2014-01-01

Precise estimates of price elasticities are important for alcohol tax policy. Using meta-analysis, this paper corrects average beer elasticities for heterogeneity, dependence, and publication selection bias. A sample of 191 estimates is obtained from 114 primary studies. Simple and weighted means are reported. Dependence is addressed by restricting number of estimates per study, author-restricted samples, and author-specific variables. Publication bias is addressed using funnel graph, trim-and-fill, and Egger's intercept model. Heterogeneity and selection bias are examined jointly in meta-regressions containing moderator variables for econometric methodology, primary data, and precision of estimates. Results for fixed- and random-effects regressions are reported. Country-specific effects and sample time periods are unimportant, but several methodology variables help explain the dispersion of estimates. In models that correct for selection bias and heterogeneity, the average beer price elasticity is about -0.20, which is less elastic by 50% compared to values commonly used in alcohol tax policy simulations. Copyright © 2013 Elsevier B.V. All rights reserved.

Microarray image analysis: background estimation using quantile and morphological filters.

PubMed

Bengtsson, Anders; Bengtsson, Henrik

2006-02-28

In a microarray experiment the difference in expression between genes on the same slide is up to 103 fold or more. At low expression, even a small error in the estimate will have great influence on the final test and reference ratios. In addition to the true spot intensity the scanned signal consists of different kinds of noise referred to as background. In order to assess the true spot intensity background must be subtracted. The standard approach to estimate background intensities is to assume they are equal to the intensity levels between spots. In the literature, morphological opening is suggested to be one of the best methods for estimating background this way. This paper examines fundamental properties of rank and quantile filters, which include morphological filters at the extremes, with focus on their ability to estimate between-spot intensity levels. The bias and variance of these filter estimates are driven by the number of background pixels used and their distributions. A new rank-filter algorithm is implemented and compared to methods available in Spot by CSIRO and GenePix Pro by Axon Instruments. Spot's morphological opening has a mean bias between -47 and -248 compared to a bias between 2 and -2 for the rank filter and the variability of the morphological opening estimate is 3 times higher than for the rank filter. The mean bias of Spot's second method, morph.close.open, is between -5 and -16 and the variability is approximately the same as for morphological opening. The variability of GenePix Pro's region-based estimate is more than ten times higher than the variability of the rank-filter estimate and with slightly more bias. The large variability is because the size of the background window changes with spot size. To overcome this, a non-adaptive region-based method is implemented. Its bias and variability are comparable to that of the rank filter. The performance of more advanced rank filters is equal to the best region-based methods. However, in order to get unbiased estimates these filters have to be implemented with great care. The performance of morphological opening is in general poor with a substantial spatial-dependent bias.

Direct estimation and correction of bias from temporally variable non-stationary noise in a channelized Hotelling model observer.

PubMed

Fetterly, Kenneth A; Favazza, Christopher P

2016-08-07

Channelized Hotelling model observer (CHO) methods were developed to assess performance of an x-ray angiography system. The analytical methods included correction for known bias error due to finite sampling. Detectability indices ([Formula: see text]) corresponding to disk-shaped objects with diameters in the range 0.5-4 mm were calculated. Application of the CHO for variable detector target dose (DTD) in the range 6-240 nGy frame(-1) resulted in [Formula: see text] estimates which were as much as 2.9×  greater than expected of a quantum limited system. Over-estimation of [Formula: see text] was presumed to be a result of bias error due to temporally variable non-stationary noise. Statistical theory which allows for independent contributions of 'signal' from a test object (o) and temporally variable non-stationary noise (ns) was developed. The theory demonstrates that the biased [Formula: see text] is the sum of the detectability indices associated with the test object [Formula: see text] and non-stationary noise ([Formula: see text]). Given the nature of the imaging system and the experimental methods, [Formula: see text] cannot be directly determined independent of [Formula: see text]. However, methods to estimate [Formula: see text] independent of [Formula: see text] were developed. In accordance with the theory, [Formula: see text] was subtracted from experimental estimates of [Formula: see text], providing an unbiased estimate of [Formula: see text]. Estimates of [Formula: see text] exhibited trends consistent with expectations of an angiography system that is quantum limited for high DTD and compromised by detector electronic readout noise for low DTD conditions. Results suggest that these methods provide [Formula: see text] estimates which are accurate and precise for [Formula: see text]. Further, results demonstrated that the source of bias was detector electronic readout noise. In summary, this work presents theory and methods to test for the presence of bias in Hotelling model observers due to temporally variable non-stationary noise and correct this bias when the temporally variable non-stationary noise is independent and additive with respect to the test object signal.

Use of paired simple and complex models to reduce predictive bias and quantify uncertainty

NASA Astrophysics Data System (ADS)

Doherty, John; Christensen, Steen

2011-12-01

Modern environmental management and decision-making is based on the use of increasingly complex numerical models. Such models have the advantage of allowing representation of complex processes and heterogeneous system property distributions inasmuch as these are understood at any particular study site. The latter are often represented stochastically, this reflecting knowledge of the character of system heterogeneity at the same time as it reflects a lack of knowledge of its spatial details. Unfortunately, however, complex models are often difficult to calibrate because of their long run times and sometimes questionable numerical stability. Analysis of predictive uncertainty is also a difficult undertaking when using models such as these. Such analysis must reflect a lack of knowledge of spatial hydraulic property details. At the same time, it must be subject to constraints on the spatial variability of these details born of the necessity for model outputs to replicate observations of historical system behavior. In contrast, the rapid run times and general numerical reliability of simple models often promulgates good calibration and ready implementation of sophisticated methods of calibration-constrained uncertainty analysis. Unfortunately, however, many system and process details on which uncertainty may depend are, by design, omitted from simple models. This can lead to underestimation of the uncertainty associated with many predictions of management interest. The present paper proposes a methodology that attempts to overcome the problems associated with complex models on the one hand and simple models on the other hand, while allowing access to the benefits each of them offers. It provides a theoretical analysis of the simplification process from a subspace point of view, this yielding insights into the costs of model simplification, and into how some of these costs may be reduced. It then describes a methodology for paired model usage through which predictive bias of a simplified model can be detected and corrected, and postcalibration predictive uncertainty can be quantified. The methodology is demonstrated using a synthetic example based on groundwater modeling environments commonly encountered in northern Europe and North America.

Biases associated with genotype and sex in prediction of fat-free lean mass and carcass value in hogs.

PubMed

Hicks, C; Schinckel, A P; Forrest, J C; Akridge, J T; Wagner, J R; Chen, W

1998-09-01

Carcass and live measurements of 165 market hogs that represented seven genotypes were used to investigate genotype and sex biases associated with the prediction of fat-free lean mass (FFLM) and carcass value. Carcass value was determined as the sum of the product of weight of individual cuts and their average unit prices adjusted for slaughter and processing costs. Independent variables used in the prediction equations included carcass measurements, such as optical probe, midline ruler, ribbed carcass measurements, and electromagnetic scanning (EMSCAN), and live animal ultrasonic scanning. The effect of including subpopulation mean values of independent variables in the prediction equations for FFLM and carcass value was also investigated. Genotype and sex biases were found in equations in which midline backfat, ribbed carcass, EMSCAN, and live ultrasonic scanning were used as single technology sets of measurements. The prediction equations generally undervalued genotypes with above-average carcass value. Biases were reduced when measurements of combined technologies and mean adjusted variables were used. The FFLM and carcass value of gilts were underestimated, and they were overestimated of barrows. Equations that combined OP and EMSCAN technologies were the most accurate and least biased for both FFLM and carcass value. Equations that included carcass weight and midline last-rib backfat thickness measurements were the least accurate and most biased. Genotype and sex biases must be considered when predicting FFLM and carcass value.

Rater variables associated with ITER ratings.

PubMed

Paget, Michael; Wu, Caren; McIlwrick, Joann; Woloschuk, Wayne; Wright, Bruce; McLaughlin, Kevin

2013-10-01

Advocates of holistic assessment consider the ITER a more authentic way to assess performance. But this assessment format is subjective and, therefore, susceptible to rater bias. Here our objective was to study the association between rater variables and ITER ratings. In this observational study our participants were clerks at the University of Calgary and preceptors who completed online ITERs between February 2008 and July 2009. Our outcome variable was global rating on the ITER (rated 1-5), and we used a generalized estimating equation model to identify variables associated with this rating. Students were rated "above expected level" or "outstanding" on 66.4 % of 1050 online ITERs completed during the study period. Two rater variables attenuated ITER ratings: the log transformed time taken to complete the ITER [β = -0.06, 95 % confidence interval (-0.10, -0.02), p = 0.002], and the number of ITERs that a preceptor completed over the time period of the study [β = -0.008 (-0.02, -0.001), p = 0.02]. In this study we found evidence of leniency bias that resulted in two thirds of students being rated above expected level of performance. This leniency bias appeared to be attenuated by delay in ITER completion, and was also blunted in preceptors who rated more students. As all biases threaten the internal validity of the assessment process, further research is needed to confirm these and other sources of rater bias in ITER ratings, and to explore ways of limiting their impact.

Bias correction by use of errors-in-variables regression models in studies with K-X-ray fluorescence bone lead measurements.

PubMed

Lamadrid-Figueroa, Héctor; Téllez-Rojo, Martha M; Angeles, Gustavo; Hernández-Ávila, Mauricio; Hu, Howard

2011-01-01

In-vivo measurement of bone lead by means of K-X-ray fluorescence (KXRF) is the preferred biological marker of chronic exposure to lead. Unfortunately, considerable measurement error associated with KXRF estimations can introduce bias in estimates of the effect of bone lead when this variable is included as the exposure in a regression model. Estimates of uncertainty reported by the KXRF instrument reflect the variance of the measurement error and, although they can be used to correct the measurement error bias, they are seldom used in epidemiological statistical analyzes. Errors-in-variables regression (EIV) allows for correction of bias caused by measurement error in predictor variables, based on the knowledge of the reliability of such variables. The authors propose a way to obtain reliability coefficients for bone lead measurements from uncertainty data reported by the KXRF instrument and compare, by the use of Monte Carlo simulations, results obtained using EIV regression models vs. those obtained by the standard procedures. Results of the simulations show that Ordinary Least Square (OLS) regression models provide severely biased estimates of effect, and that EIV provides nearly unbiased estimates. Although EIV effect estimates are more imprecise, their mean squared error is much smaller than that of OLS estimates. In conclusion, EIV is a better alternative than OLS to estimate the effect of bone lead when measured by KXRF. Copyright © 2010 Elsevier Inc. All rights reserved.

Adjustment of QT dispersion assessed from 12 lead electrocardiograms for different numbers of analysed electrocardiographic leads: comparison of stability of different methods.

PubMed Central

Hnatkova, K; Malik, M; Kautzner, J; Gang, Y; Camm, A J

1994-01-01

OBJECTIVE--Normal electrocardiographic recordings were analysed to establish the influence of measurement of different numbers of electrocardiographic leads on the results of different formulas expressing QT dispersion and the effects of adjustment of QT dispersion obtained from a subset of an electrocardiogram to approximate to the true QT dispersion obtained from a complete electrocardiogram. SUBJECTS AND METHODS--Resting 12 lead electrocardiograms of 27 healthy people were investigated. In each lead, the QT interval was measured with a digitising board and QT dispersion was evaluated by three formulas: (A) the difference between the longest and the shortest QT interval among all leads; (B) the difference between the second longest and the second shortest QT interval; (C) SD of QT intervals in different leads. For each formula, the "true" dispersion was assessed from all measurable leads and then different combinations of leads were omitted. The mean relative differences between the QT dispersion with a given number of omitted leads and the "true" QT dispersion (mean relative errors) and the coefficients of variance of the results of QT dispersion obtained when omitting combinations of leads were compared for the different formulas. The procedure was repeated with an adjustment of each formula dividing its results by the square root of the number of measured leads. The same approach was used for the measurement of QT dispersion from the chest leads including a fourth formula (D) the SD of interlead differences weighted according to the distances between leads. For different formulas, the mean relative errors caused by omitting individual electrocardiographic leads were also assessed and the importance of individual leads for correct measurement of QT dispersion was investigated. RESULTS--The study found important differences between different formulas for assessment of QT dispersion with respect to compensation for missing measurements of QT interval. The standard max-min formula (A) performed poorly (mean relative errors of 6.1% to 18.5% for missing one to four leads) but was appropriately adjusted with the factor of 1/square root of n (n = number of measured leads). In a population of healthy people such an adjustment removed the systematic bias introduced by missing leads of the 12 lead electrocardiogram and significantly reduced the mean relative errors caused by the omission of several leads. The unadjusted SD was the optimum formula (C) for the analysis of 12 lead electrocardiograms, and the weighted standard deviation (D) was the optimum for the analysis of six lead chest electrocardiograms. The coefficients of variance of measurements of QT dispersion with different missing leads were very large (about 3 to 7 for one to four missing leads). Independently of the formula for measurement of QT dispersion, omission of different leads produced substantially different relative errors. In 12 lead electrocardiograms the largest relative errors (> 10%) were caused by omitting lead aVL or lead V1. CONCLUSIONS--Because of the large coefficients of variance, the concept of adjusting the QT dispersion for different numbers of electrocardiographic leads used in its assessment is difficult if not impossible to fulfil. Thus it is likely to be more appropriate to assess QT dispersion from standardised constant sets of electrocardiographic leads. PMID:7833200

Photometry Using Kepler "Superstamps" of Open Clusters NGC 6791 & NGC 6819

NASA Astrophysics Data System (ADS)

Kuehn, Charles A.; Drury, Jason A.; Bellamy, Beau R.; Stello, Dennis; Bedding, Timothy R.; Reed, Mike; Quick, Breanna

2015-09-01

The Kepler space telescope has proven to be a gold mine for the study of variable stars. Usually, Kepler only reads out a handful of pixels around each pre-selected target star, omitting a large number of stars in the Kepler field. Fortunately, for the open clusters NGC 6791 and NGC 6819, Kepler also read out larger "superstamps" which contained complete images of the central region of each cluster. These cluster images can be used to study additional stars in the open clusters that were not originally on Kepler's target list. We discuss our work on using two photometric techniques to analyze these superstamps and present sample results from this project to demonstrate the value of this technique for a wide variety of variable stars.

Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables

NASA Astrophysics Data System (ADS)

Cannon, Alex J.

2018-01-01

Most bias correction algorithms used in climatology, for example quantile mapping, are applied to univariate time series. They neglect the dependence between different variables. Those that are multivariate often correct only limited measures of joint dependence, such as Pearson or Spearman rank correlation. Here, an image processing technique designed to transfer colour information from one image to another—the N-dimensional probability density function transform—is adapted for use as a multivariate bias correction algorithm (MBCn) for climate model projections/predictions of multiple climate variables. MBCn is a multivariate generalization of quantile mapping that transfers all aspects of an observed continuous multivariate distribution to the corresponding multivariate distribution of variables from a climate model. When applied to climate model projections, changes in quantiles of each variable between the historical and projection period are also preserved. The MBCn algorithm is demonstrated on three case studies. First, the method is applied to an image processing example with characteristics that mimic a climate projection problem. Second, MBCn is used to correct a suite of 3-hourly surface meteorological variables from the Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) across a North American domain. Components of the Canadian Forest Fire Weather Index (FWI) System, a complicated set of multivariate indices that characterizes the risk of wildfire, are then calculated and verified against observed values. Third, MBCn is used to correct biases in the spatial dependence structure of CanRCM4 precipitation fields. Results are compared against a univariate quantile mapping algorithm, which neglects the dependence between variables, and two multivariate bias correction algorithms, each of which corrects a different form of inter-variable correlation structure. MBCn outperforms these alternatives, often by a large margin, particularly for annual maxima of the FWI distribution and spatiotemporal autocorrelation of precipitation fields.

Gender Bias in the College Classroom: A Critical Review of the Literature and Implications for Future Research.

ERIC Educational Resources Information Center

Brady, Kristine L.; Eisler, Richard M.

1995-01-01

Summarizes eight studies on gender bias in college classrooms, examining the range of variables assessed and adequacy of evidence supporting the existence of bias. Inconsistent findings and significant methodological flaws in existing literature suggest that more empirical research is needed to investigate the existence of gender bias in college…

Constraints on early-type galaxy structure from spectroscopically selected gravitational lenses

NASA Astrophysics Data System (ADS)

Bolton, Adam Stallard

2005-11-01

This thesis describes all aspects of a unique spectroscopic survey for strong galaxy-galaxy gravitational lenses: motivation, candidate selection, ground- based spectroscopic follow-up, Hubble Space Telescope imaging, data analysis, and results on the radial density profile of the lens galaxies. The lens candidates are selected from within the spectroscopic database of the Sloan Digital Sky Survey (SDSS) based on the appearance of two significantly different redshifts along the same line of sight, and lenses are confirmed within the candidate sample by follow-up imaging and spectroscopy. The sample of [approximate]20 early-type lenses presented in this thesis represents the largest single strong-lens galaxy sample discovered and published to date. These lenses probe the mass of the lens galaxies on scales roughly equal to one-half effective radius. We find a dynamical normalization between isothermal lens-model velocity dispersions and aperture-corrected SDSS stellar velocity dispersions of f = s lens /s stars = 0.95 +/- 0.03. By combining lens-model Einstein radii and de Vaucouleurs effective radii with stellar velocity dispersions through the Jeans equation, we find that the logarithmic slope [Special characters omitted.] of the density profile in our lens galaxies (r 0 ( [Special characters omitted.] ) is on average slightly steeper than isothermal ([Special characters omitted.] = 2) with a modest intrinsic scatter. Parameterizing the intrinsic distribution in [Special characters omitted.] as Gaussian, we find a maximum-likelihood mean of [Special characters omitted. ] and standard deviation of s[Special characters omitted.] = [Special characters omitted.] (68% confidence, for isotropic velocity-dispersion models). Our results rule out a single universal logarithmic density slope at >99.995% confidence. The success of this spectroscopic lens survey suggests that similar projects should be considered as an explicit science goal of future redshift surveys. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)

Social network based dynamic transit service through the OMITS system.

DOT National Transportation Integrated Search

2014-02-01

The Open Mode Integrated Transportation System (OMITS) forms a sustainable information infrastructure for communication within and between the mobile/Internet network, the roadway : network, and the users social network. It manipulates the speed g...

Decadal evaluation of regional climate, air quality, and their interactions over the continental US and their interactions using WRF/Chem version 3.6.1

NASA Astrophysics Data System (ADS)

Yahya, Khairunnisa; Wang, Kai; Campbell, Patrick; Glotfelty, Timothy; He, Jian; Zhang, Yang

2016-02-01

The Weather Research and Forecasting model with Chemistry (WRF/Chem) v3.6.1 with the Carbon Bond 2005 (CB05) gas-phase mechanism is evaluated for its first decadal application during 2001-2010 using the Representative Concentration Pathway 8.5 (RCP 8.5) emissions to assess its capability and appropriateness for long-term climatological simulations. The initial and boundary conditions are downscaled from the modified Community Earth System Model/Community Atmosphere Model (CESM/CAM5) v1.2.2. The meteorological initial and boundary conditions are bias-corrected using the National Center for Environmental Protection's Final (FNL) Operational Global Analysis data. Climatological evaluations are carried out for meteorological, chemical, and aerosol-cloud-radiation variables against data from surface networks and satellite retrievals. The model performs very well for the 2 m temperature (T2) for the 10-year period, with only a small cold bias of -0.3 °C. Biases in other meteorological variables including relative humidity at 2 m, wind speed at 10 m, and precipitation tend to be site- and season-specific; however, with the exception of T2, consistent annual biases exist for most of the years from 2001 to 2010. Ozone mixing ratios are slightly overpredicted at both urban and rural locations with a normalized mean bias (NMB) of 9.7 % but underpredicted at rural locations with an NMB of -8.8 %. PM2.5 concentrations are moderately overpredicted with an NMB of 23.3 % at rural sites but slightly underpredicted with an NMB of -10.8 % at urban/suburban sites. In general, the model performs relatively well for chemical and meteorological variables, and not as well for aerosol-cloud-radiation variables. Cloud-aerosol variables including aerosol optical depth, cloud water path, cloud optical thickness, and cloud droplet number concentration are generally underpredicted on average across the continental US. Overpredictions of several cloud variables over the eastern US result in underpredictions of radiation variables (such as net shortwave radiation - GSW - with a mean bias - MB - of -5.7 W m-2) and overpredictions of shortwave and longwave cloud forcing (MBs of ˜ 7 to 8 W m-2), which are important climate variables. While the current performance is deemed to be acceptable, improvements to the bias-correction method for CESM downscaling and the model parameterizations of cloud dynamics and thermodynamics, as well as aerosol-cloud interactions, can potentially improve model performance for long-term climate simulations.

Design, analysis, and interpretation of field quality-control data for water-sampling projects

USGS Publications Warehouse

Mueller, David K.; Schertz, Terry L.; Martin, Jeffrey D.; Sandstrom, Mark W.

2015-01-01

The report provides extensive information about statistical methods used to analyze quality-control data in order to estimate potential bias and variability in environmental data. These methods include construction of confidence intervals on various statistical measures, such as the mean, percentiles and percentages, and standard deviation. The methods are used to compare quality-control results with the larger set of environmental data in order to determine whether the effects of bias and variability might interfere with interpretation of these data. Examples from published reports are presented to illustrate how the methods are applied, how bias and variability are reported, and how the interpretation of environmental data can be qualified based on the quality-control analysis.

Compensatory lengthening in the Spanish of Havana, Cuba: Acoustic analyses of word-internal, post-nuclear /l/ and /r/

NASA Astrophysics Data System (ADS)

Carlson, Kristin M.

Given the geographic, demographic, and historical importance of Cuba vis-a-vis the dissemination of language and culture throughout the Hispanic Caribbean, one would naturally anticipate a larger corpus of scientifically-noteworthy linguistic publications on Cuban Spanish, which is far from the actual case. Moreover, the gemination of an onset positionally subsequent to the deletion of a syllable-final liquid (generally termed liquid gemination in the literature) has been repeatedly claimed yet remarkably unsubstantiated as a pervasive characteristic of Cuban Spanish, particularly of the western dialect region (cf. Alfaraz (2000, 2007, 2008), Casanellas and Alamo (1985), Choy Lopez (1985, 1988, 1989), Costa Sanchez (1987), Darias Concepcion (2001, 2005), Dohotaru (2002, 2007), Figueroa Esteva and Dohotaru (1994), Garcia Perez (2006), Garcia Riveron (1991), Haden and Matluck (1973, 1974, 1977), Isbǎsescu (1965, 1968), Lamb (1968), Levina (1970), Montero Bernal (1990, 2002, 2007a, b), Ringer Uber (1986), Ruiz Hernandez (1978), Sosa (1974), Terrell (1976), Trista and Valdes (1978), Valdes Acosta (1980), and Vera Riveron (2000)). As a result, in the interest of supplementing all antecedent work concerning the allophony of final liquids as well as affording a more descriptively-precise account of the allophony of word-internal, post-nuclear /l/ and /[Special character omitted]/ in Cuban Spanish in addition to expressly addressing the need for empirical data-collection and analysis processes, the present investigation was specifically designed and implemented to acoustically investigate the phenomenon of gemination as it is purported to occur in the Spanish of the region of Havana, Cuba: more specifically, (1) to acoustically examine the qualitative and quantitative patternings of post-nuclear /l/ and /[Special character omitted]/ within the word; and (2) to statistically evaluate the relationship between gemination and eight independent variables: gender, age group, educational level, morphological significance of the syllable closed by the liquid phoneme, position of the syllable closed by the liquid phoneme relative to stress placement, voicing specification, manner of articulation, and place of articulation of the surface realization of the immediately-following onset. Qualitative acoustic analyses of the 1,895 tokens of word-internal, post-nuclear /l/ (n=469) and /[Special character omitted]/ (n=1,426) extracted from the corpus of informal data demonstrated seven allophones for the lateral liquid phoneme (O, [l], [d[Special character omitted

Omitting chest tube drainage after thoracoscopic major lung resection.

PubMed

Ueda, Kazuhiro; Hayashi, Masataro; Tanaka, Toshiki; Hamano, Kimikazu

2013-08-01

Absorbable mesh and fibrin glue applied to prevent alveolar air leakage contribute to reducing the length of chest tube drainage, length of hospitalization and the rate of pulmonary complications. This study investigated the feasibility of omitting chest tube drainage in selected patients undergoing thoracoscopic major lung resection. Intraoperative air leakages were sealed with fibrin glue and absorbable mesh in patients undergoing thoracoscopic major lung resection. The chest tube was removed just after tracheal extubation if no air leakages were detected in a suction-induced air leakage test, which is an original technique to confirm pneumostasis. Patients with bleeding tendency or extensive thoracic adhesions were excluded. Chest tube drainage was omitted in 29 (58%) of 50 eligible patients and was used in 21 (42%) on the basis of suction-induced air leakage test results. Male gender and compromised pulmonary function were significantly associated with the failure to omit chest tube drainage (both, P < 0.05). Regardless of omitting the chest tube drainage, there were no adverse events during hospitalization, such as subcutaneous emphysema, pneumothorax, pleural effusion or haemothorax, requiring subsequent drainage. Furthermore, there was no prolonged air leakage in any patients: The mean length of chest tube drainage was only 0.9 days. Omitting the chest tube drainage was associated with reduced pain on the day of the operation (P = 0.046). The refined strategy for pneumostasis allowed the omission of chest tube drainage in the majority of patients undergoing thoracoscopic major lung resection without increasing the risk of adverse events, which may contribute to a fast-track surgery.

Measuring temporal stability of positron emission tomography standardized uptake value bias using long-lived sources in a multicenter network.

PubMed

Byrd, Darrin; Christopfel, Rebecca; Arabasz, Grae; Catana, Ciprian; Karp, Joel; Lodge, Martin A; Laymon, Charles; Moros, Eduardo G; Budzevich, Mikalai; Nehmeh, Sadek; Scheuermann, Joshua; Sunderland, John; Zhang, Jun; Kinahan, Paul

2018-01-01

Positron emission tomography (PET) is a quantitative imaging modality, but the computation of standardized uptake values (SUVs) requires several instruments to be correctly calibrated. Variability in the calibration process may lead to unreliable quantitation. Sealed source kits containing traceable amounts of [Formula: see text] were used to measure signal stability for 19 PET scanners at nine hospitals in the National Cancer Institute's Quantitative Imaging Network. Repeated measurements of the sources were performed on PET scanners and in dose calibrators. The measured scanner and dose calibrator signal biases were used to compute the bias in SUVs at multiple time points for each site over a 14-month period. Estimation of absolute SUV accuracy was confounded by bias from the solid phantoms' physical properties. On average, the intrascanner coefficient of variation for SUV measurements was 3.5%. Over the entire length of the study, single-scanner SUV values varied over a range of 11%. Dose calibrator bias was not correlated with scanner bias. Calibration factors from the image metadata were nearly as variable as scanner signal, and were correlated with signal for many scanners. SUVs often showed low intrascanner variability between successive measurements but were also prone to shifts in apparent bias, possibly in part due to scanner recalibrations that are part of regular scanner quality control. Biases of key factors in the computation of SUVs were not correlated and their temporal variations did not cancel out of the computation. Long-lived sources and image metadata may provide a check on the recalibration process.

Intrapersonal Variability in Negative Affect as a Moderator of Accuracy and Bias in Interpersonal Perception.

PubMed

Sadikaj, Gentiana; Moskowitz, D S; Zuroff, David C

2015-08-01

High intrapersonal variability has frequently been found to be related to poor personal and interpersonal outcomes. Little research has examined processes by which intrapersonal variability influences outcomes. This study explored the relation of intrapersonal variability in negative affect (negative affect flux) to accuracy and bias in the perception of a romantic partner's quarrelsome behavior. A sample of 93 cohabiting couples participated in a study using an event-contingent recording (ECR) methodology in which they reported their negative affect, quarrelsome behavior, and perception of their partner's quarrelsome behavior in interactions with each other during a 20-day period. Negative affect flux was operationalized as the within-person standard deviation of negative affect scores across couple interactions. Findings suggested that participants were both accurate in tracking changes in their partner's quarrelsome behavior and biased in assuming their partner's quarrelsome behavior mirrored their own quarrelsome behavior. Negative affect flux moderated both accuracy and bias of assumed similarity such that participants with higher flux manifested both greater tracking accuracy and larger bias of assumed similarity. Negative affect flux may be related to enhanced vigilance to close others' negative behavior, which may explain higher tracking accuracy and propensity to rely on a person's own negative behavior as a means of judging others' negative behavior. These processes may augment these individuals' negative interpersonal behavior, enhance cycles of negative social interactions, and lead to poor intrapersonal and interpersonal outcomes.

Dealing with missing standard deviation and mean values in meta-analysis of continuous outcomes: a systematic review.

PubMed

Weir, Christopher J; Butcher, Isabella; Assi, Valentina; Lewis, Stephanie C; Murray, Gordon D; Langhorne, Peter; Brady, Marian C

2018-03-07

Rigorous, informative meta-analyses rely on availability of appropriate summary statistics or individual participant data. For continuous outcomes, especially those with naturally skewed distributions, summary information on the mean or variability often goes unreported. While full reporting of original trial data is the ideal, we sought to identify methods for handling unreported mean or variability summary statistics in meta-analysis. We undertook two systematic literature reviews to identify methodological approaches used to deal with missing mean or variability summary statistics. Five electronic databases were searched, in addition to the Cochrane Colloquium abstract books and the Cochrane Statistics Methods Group mailing list archive. We also conducted cited reference searching and emailed topic experts to identify recent methodological developments. Details recorded included the description of the method, the information required to implement the method, any underlying assumptions and whether the method could be readily applied in standard statistical software. We provided a summary description of the methods identified, illustrating selected methods in example meta-analysis scenarios. For missing standard deviations (SDs), following screening of 503 articles, fifteen methods were identified in addition to those reported in a previous review. These included Bayesian hierarchical modelling at the meta-analysis level; summary statistic level imputation based on observed SD values from other trials in the meta-analysis; a practical approximation based on the range; and algebraic estimation of the SD based on other summary statistics. Following screening of 1124 articles for methods estimating the mean, one approximate Bayesian computation approach and three papers based on alternative summary statistics were identified. Illustrative meta-analyses showed that when replacing a missing SD the approximation using the range minimised loss of precision and generally performed better than omitting trials. When estimating missing means, a formula using the median, lower quartile and upper quartile performed best in preserving the precision of the meta-analysis findings, although in some scenarios, omitting trials gave superior results. Methods based on summary statistics (minimum, maximum, lower quartile, upper quartile, median) reported in the literature facilitate more comprehensive inclusion of randomised controlled trials with missing mean or variability summary statistics within meta-analyses.

Dynamic modulation of decision biases by brainstem arousal systems.

PubMed

de Gee, Jan Willem; Colizoli, Olympia; Kloosterman, Niels A; Knapen, Tomas; Nieuwenhuis, Sander; Donner, Tobias H

2017-04-11

Decision-makers often arrive at different choices when faced with repeated presentations of the same evidence. Variability of behavior is commonly attributed to noise in the brain's decision-making machinery. We hypothesized that phasic responses of brainstem arousal systems are a significant source of this variability. We tracked pupil responses (a proxy of phasic arousal) during sensory-motor decisions in humans, across different sensory modalities and task protocols. Large pupil responses generally predicted a reduction in decision bias. Using fMRI, we showed that the pupil-linked bias reduction was (i) accompanied by a modulation of choice-encoding pattern signals in parietal and prefrontal cortex and (ii) predicted by phasic, pupil-linked responses of a number of neuromodulatory brainstem centers involved in the control of cortical arousal state, including the noradrenergic locus coeruleus. We conclude that phasic arousal suppresses decision bias on a trial-by-trial basis, thus accounting for a significant component of the variability of choice behavior.

Dynamic modulation of decision biases by brainstem arousal systems

PubMed Central

de Gee, Jan Willem; Colizoli, Olympia; Kloosterman, Niels A; Knapen, Tomas; Nieuwenhuis, Sander; Donner, Tobias H

2017-01-01

Decision-makers often arrive at different choices when faced with repeated presentations of the same evidence. Variability of behavior is commonly attributed to noise in the brain’s decision-making machinery. We hypothesized that phasic responses of brainstem arousal systems are a significant source of this variability. We tracked pupil responses (a proxy of phasic arousal) during sensory-motor decisions in humans, across different sensory modalities and task protocols. Large pupil responses generally predicted a reduction in decision bias. Using fMRI, we showed that the pupil-linked bias reduction was (i) accompanied by a modulation of choice-encoding pattern signals in parietal and prefrontal cortex and (ii) predicted by phasic, pupil-linked responses of a number of neuromodulatory brainstem centers involved in the control of cortical arousal state, including the noradrenergic locus coeruleus. We conclude that phasic arousal suppresses decision bias on a trial-by-trial basis, thus accounting for a significant component of the variability of choice behavior. DOI: http://dx.doi.org/10.7554/eLife.23232.001 PMID:28383284

Predictive Inference Using Latent Variables with Covariates*

PubMed Central

Schofield, Lynne Steuerle; Junker, Brian; Taylor, Lowell J.; Black, Dan A.

2014-01-01

Plausible Values (PVs) are a standard multiple imputation tool for analysis of large education survey data that measures latent proficiency variables. When latent proficiency is the dependent variable, we reconsider the standard institutionally-generated PV methodology and find it applies with greater generality than shown previously. When latent proficiency is an independent variable, we show that the standard institutional PV methodology produces biased inference because the institutional conditioning model places restrictions on the form of the secondary analysts’ model. We offer an alternative approach that avoids these biases based on the mixed effects structural equations (MESE) model of Schofield (2008). PMID:25231627

Performance of nonlinear mixed effects models in the presence of informative dropout.

PubMed

Björnsson, Marcus A; Friberg, Lena E; Simonsson, Ulrika S H

2015-01-01

Informative dropout can lead to bias in statistical analyses if not handled appropriately. The objective of this simulation study was to investigate the performance of nonlinear mixed effects models with regard to bias and precision, with and without handling informative dropout. An efficacy variable and dropout depending on that efficacy variable were simulated and model parameters were reestimated, with or without including a dropout model. The Laplace and FOCE-I estimation methods in NONMEM 7, and the stochastic simulations and estimations (SSE) functionality in PsN, were used in the analysis. For the base scenario, bias was low, less than 5% for all fixed effects parameters, when a dropout model was used in the estimations. When a dropout model was not included, bias increased up to 8% for the Laplace method and up to 21% if the FOCE-I estimation method was applied. The bias increased with decreasing number of observations per subject, increasing placebo effect and increasing dropout rate, but was relatively unaffected by the number of subjects in the study. This study illustrates that ignoring informative dropout can lead to biased parameters in nonlinear mixed effects modeling, but even in cases with few observations or high dropout rate, the bias is relatively low and only translates into small effects on predictions of the underlying effect variable. A dropout model is, however, crucial in the presence of informative dropout in order to make realistic simulations of trial outcomes.

Comparative Evaluation of Accuracy of Pulse Oximeters and Factors Affecting Their Performance in a Tertiary Intensive Care Unit

PubMed Central

Sahi, Malvinder Singh; Mahawar, Bablesh; Rajpurohit, Sajjan

2017-01-01

Introduction Pulse oximetry is a widely used tool, unfortunately there is a paucity of data investigating its accuracy in Intensive Care Units (ICU) and if they are able to meet mandated FDA criteria as claimed by them in critically ill patients. Aim To assess bias, precision and accuracy of pulse oximeters used in ICU and factors affecting them. Materials and Methods A prospective cohort study, including 129 patients admitted to the ICU of a tertiary referral centre. Pulse oximetry and blood gas were done simultaneously. Pulse oximetry was done using two pulse oximetres: Nonin and Philips. All physiological variables like haemoglobin, lactate, use of vasopressors and blood pressure were recorded. Bland Altman curves were constructed to determine bias and limits of agreement. Effect of physiological variables on bias and difference between performance characteristics of bias was determined using SPSS. Results Pulse oximetry overestimated arterial oxygen saturation (SaO2) by 1.44%. There was negative correlation between bias and SaO2 (r=-0.32) and positive correlation with lactate (r=0.16). The Philips pulse oximeter had significant higher bias and variability than Nonin pulse oximeter. (2.49±2.99 versus 0.46±1.68, mean difference = 1.98, 95% C.I. = 1.53 – 2.43, p-value <0.001). Conclusion Pulse oximetry overestimates SaO2. Bias tends to increase with rising lactate and hypoxia. There is heterogeneity in performance of various pulse oximetry devices in ICU. PMID:28764215

Comparative Evaluation of Accuracy of Pulse Oximeters and Factors Affecting Their Performance in a Tertiary Intensive Care Unit.

PubMed

Singh, Anupam Kumar; Sahi, Malvinder Singh; Mahawar, Bablesh; Rajpurohit, Sajjan

2017-06-01

Pulse oximetry is a widely used tool, unfortunately there is a paucity of data investigating its accuracy in Intensive Care Units (ICU) and if they are able to meet mandated FDA criteria as claimed by them in critically ill patients. To assess bias, precision and accuracy of pulse oximeters used in ICU and factors affecting them. A prospective cohort study, including 129 patients admitted to the ICU of a tertiary referral centre. Pulse oximetry and blood gas were done simultaneously. Pulse oximetry was done using two pulse oximetres: Nonin and Philips. All physiological variables like haemoglobin, lactate, use of vasopressors and blood pressure were recorded. Bland Altman curves were constructed to determine bias and limits of agreement. Effect of physiological variables on bias and difference between performance characteristics of bias was determined using SPSS. Pulse oximetry overestimated arterial oxygen saturation (SaO 2 ) by 1.44%. There was negative correlation between bias and SaO 2 (r=-0.32) and positive correlation with lactate (r=0.16). The Philips pulse oximeter had significant higher bias and variability than Nonin pulse oximeter. (2.49±2.99 versus 0.46±1.68, mean difference = 1.98, 95% C.I. = 1.53 - 2.43, p-value <0.001). Pulse oximetry overestimates SaO 2 . Bias tends to increase with rising lactate and hypoxia. There is heterogeneity in performance of various pulse oximetry devices in ICU.

Single- and Dual-Process Models of Biased Contingency Detection.

PubMed

Vadillo, Miguel A; Blanco, Fernando; Yarritu, Ion; Matute, Helena

2016-01-01

Decades of research in causal and contingency learning show that people's estimations of the degree of contingency between two events are easily biased by the relative probabilities of those two events. If two events co-occur frequently, then people tend to overestimate the strength of the contingency between them. Traditionally, these biases have been explained in terms of relatively simple single-process models of learning and reasoning. However, more recently some authors have found that these biases do not appear in all dependent variables and have proposed dual-process models to explain these dissociations between variables. In the present paper we review the evidence for dissociations supporting dual-process models and we point out important shortcomings of this literature. Some dissociations seem to be difficult to replicate or poorly generalizable and others can be attributed to methodological artifacts. Overall, we conclude that support for dual-process models of biased contingency detection is scarce and inconclusive.

Omitting details from post-event information: are true and false memory affected in the same way?

PubMed

Loehr, Janeen D; Marche, Tammy A

2006-01-01

Participants who witness an event and later receive post-event information that omits a critical scene are less likely to recall and to recognise that scene than are participants who receive no post-event information (Wright, Loftus, & Hall, 2001). The present study used the Deese-Roediger-McDermott (DRM) paradigm, in which participants study lists of semantic associates (e.g., hot, snow, warm, winter) that commonly elicit false memories of critical non-presented words (e.g., cold), to determine whether omitting information from a second presentation decreases memory for both presented and non-presented information. Participants were presented with a list of the semantic associates of six non-presented words. For half the participants, this list was presented a second time with the semantic associates of one of the non-presented words omitted. As expected, participants were less likely to recall and to recognise the presented words when they had been omitted from the second presentation. Omission also decreased the rate at which non-presented words were recalled, although false recognition of these words was not reduced. These results suggest that false recognition may be particularly difficult to attenuate and that post-event omission may be more detrimental to memory accuracy than previously thought.

A non-parametric postprocessor for bias-correcting multi-model ensemble forecasts of hydrometeorological and hydrologic variables

NASA Astrophysics Data System (ADS)

Brown, James; Seo, Dong-Jun

2010-05-01

Operational forecasts of hydrometeorological and hydrologic variables often contain large uncertainties, for which ensemble techniques are increasingly used. However, the utility of ensemble forecasts depends on the unbiasedness of the forecast probabilities. We describe a technique for quantifying and removing biases from ensemble forecasts of hydrometeorological and hydrologic variables, intended for use in operational forecasting. The technique makes no a priori assumptions about the distributional form of the variables, which is often unknown or difficult to model parametrically. The aim is to estimate the conditional cumulative distribution function (ccdf) of the observed variable given a (possibly biased) real-time ensemble forecast from one or several forecasting systems (multi-model ensembles). The technique is based on Bayesian optimal linear estimation of indicator variables, and is analogous to indicator cokriging (ICK) in geostatistics. By developing linear estimators for the conditional expectation of the observed variable at many thresholds, ICK provides a discrete approximation of the full ccdf. Since ICK minimizes the conditional error variance of the indicator expectation at each threshold, it effectively minimizes the Continuous Ranked Probability Score (CRPS) when infinitely many thresholds are employed. However, the ensemble members used as predictors in ICK, and other bias-correction techniques, are often highly cross-correlated, both within and between models. Thus, we propose an orthogonal transform of the predictors used in ICK, which is analogous to using their principal components in the linear system of equations. This leads to a well-posed problem in which a minimum number of predictors are used to provide maximum information content in terms of the total variance explained. The technique is used to bias-correct precipitation ensemble forecasts from the NCEP Global Ensemble Forecast System (GEFS), for which independent validation results are presented. Extension to multimodel ensembles from the NCEP GFS and Short Range Ensemble Forecast (SREF) systems is also proposed.

Variability and bias between magnetoencephalography systems in non-invasive localization of the primary somatosensory cortex.

PubMed

Bardouille, Timothy; Power, Lindsey; Lalancette, Marc; Bishop, Ronald; Beyea, Steven; Taylor, Margot J; Dunkley, Benjamin T

2018-05-26

Magnetoencephalography (MEG) provides functional neuroimaging data for pre-surgical planning in patients with epilepsy or brain tumour. For mapping the primary somatosensory cortex (S1), MEG data are acquired while a patient undergoes median nerve stimulation (MNS) to localize components of the somatosensory evoked field (SEF). In clinical settings, only one MEG imaging session is usually possible due to limited resources. As such, it is important to have an a priori estimate of the expected variability in localization. Variability in S1 localization between mapping sessions using the same MEG system has been previously measured as 8 mm. There are different types of MEG systems available with varied hardware and software, and it is not known how using a different MEG system will impact on S1 localization. In our study, healthy participants underwent the MNS procedure with two different MEG systems (Vector View and CTF). We compared the location, amplitude and latency of SEF components between data from each system to quantify variability and bias between MEG systems. We found 8-11 mm variability in S1 localization between the two MEG systems, and no evidence for a systematic bias in location, amplitude or latency between the two systems. These findings suggest that S1 localization is not biased by the type of MEG system used, and that differences between the two systems are not a major contributor to variability in localization. Copyright © 2018. Published by Elsevier B.V.

43 CFR 30.126 - What happens if property was omitted from the inventory of the estate?

Code of Federal Regulations, 2010 CFR

2010-10-01

... property was omitted from the inventory of the estate? This section applies when, after issuance of a... of any modification order to the agency and to all interested parties who share in the estate. (b...

Personal Variables and Bias in Educational Decision-Making.

ERIC Educational Resources Information Center

Huebner, E. Scott; And Others

1984-01-01

Findings regarding the influence of four potential sources of bias (sex, socioeconimic status, race, physical attractiveness) upon decision-making stages of the assessment process are selectively reviewed. It is concluded that, though further research is needed, convincing evidence of bias in later stages of decision making has yet to be…

Estimation and correction of different flavors of surface observation biases in ensemble Kalman filter

NASA Astrophysics Data System (ADS)

Lorente-Plazas, Raquel; Hacker, Josua P.; Collins, Nancy; Lee, Jared A.

2017-04-01

The impact of assimilating surface observations has been shown in several publications, for improving weather prediction inside of the boundary layer as well as the flow aloft. However, the assimilation of surface observations is often far from optimal due to the presence of both model and observation biases. The sources of these biases can be diverse: an instrumental offset, errors associated to the comparison of point-based observations and grid-cell average, etc. To overcome this challenge, a method was developed using the ensemble Kalman filter. The approach consists on representing each observation bias as a parameter. These bias parameters are added to the forward operator and they extend the state vector. As opposed to the observation bias estimation approaches most common in operational systems (e.g. for satellite radiances), the state vector and parameters are simultaneously updated by applying the Kalman filter equations to the augmented state. The method to estimate and correct the observation bias is evaluated using observing system simulation experiments (OSSEs) with the Weather Research and Forecasting (WRF) model. OSSEs are constructed for the conventional observation network including radiosondes, aircraft observations, atmospheric motion vectors, and surface observations. Three different kinds of biases are added to 2-meter temperature for synthetic METARs. From the simplest to more sophisticated, imposed biases are: (1) a spatially invariant bias, (2) a spatially varying bias proportional to topographic height differences between the model and the observations, and (3) bias that is proportional to the temperature. The target region characterized by complex terrain is the western U.S. on a domain with 30-km grid spacing. Observations are assimilated every 3 hours using an 80-member ensemble during September 2012. Results demonstrate that the approach is able to estimate and correct the bias when it is spatially invariant (experiment 1). More complex bias structure in experiments (2) and (3) are more difficult to estimate, but still possible. Estimated the parameter in experiments with unbiased observations results in spatial and temporal parameter variability about zero, and establishes a threshold on the accuracy of the parameter in further experiments. When the observations are biased, the mean parameter value is close to the true bias, but temporal and spatial variability in the parameter estimates is similar to the parameters used when estimating a zero bias in the observations. The distributions are related to other errors in the forecasts, indicating that the parameters are absorbing some of the forecast error from other sources. In this presentation we elucidate the reasons for the resulting parameter estimates, and their variability.

HIV Salvage Therapy Does Not Require Nucleoside Reverse Transcriptase Inhibitors: A Randomized, Controlled Trial.

PubMed

Tashima, Karen T; Smeaton, Laura M; Fichtenbaum, Carl J; Andrade, Adriana; Eron, Joseph J; Gandhi, Rajesh T; Johnson, Victoria A; Klingman, Karin L; Ritz, Justin; Hodder, Sally; Santana, Jorge L; Wilkin, Timothy; Haubrich, Richard H

2015-12-15

Nucleoside reverse transcriptase inhibitors (NRTIs) are often included in antiretroviral regimens in treatment-experienced patients in the absence of data from randomized trials. To compare treatment success between participants who omit versus those who add NRTIs to an optimized antiretroviral regimen of 3 or more agents. Multicenter, randomized, controlled trial. (ClinicalTrials.gov: NCT00537394). Outpatient HIV clinics. Treatment-experienced patients with HIV infection and viral resistance. Open-label optimized regimens (not including NRTIs) were selected on the basis of treatment history and susceptibility testing. Participants were randomly assigned to omit or add NRTIs. The primary efficacy outcome was regimen failure through 48 weeks using a noninferiority margin of 15%. The primary safety outcome was time to initial episode of a severe sign, symptom, or laboratory abnormality before discontinuation of NRTI assignment. 360 participants were randomly assigned, and 93% completed a 48-week visit. The cumulative probability of regimen failure was 29.8% in the omit-NRTIs group versus 25.9% in the add-NRTIs group (difference, 3.2 percentage points [95% CI, -6.1 to 12.5 percentage points]). No significant between-group differences were found in the primary safety end points or the proportion of participants with HIV RNA level less than 50 copies/mL. No deaths occurred in the omit-NRTIs group compared with 7 deaths in the add-NRTIs group. Unblinded study design, and the study may not be applicable to resource-poor settings. Treatment-experienced patients with HIV infection starting a new optimized regimen can safely omit NRTIs without compromising virologic efficacy. Omitting NRTIs will reduce pill burden, cost, and toxicity in this patient population. National Institute of Allergy and Infectious Diseases, Boehringer Ingelheim, Janssen, Merck, ViiV Healthcare, Roche, and Monogram Biosciences (LabCorp).

HIV salvage therapy does not require nucleoside reverse transcriptase inhibitors: a randomized trial

PubMed Central

Tashima, Karen T; Smeaton, Laura M; Fichtenbaum, Carl J; Andrade, Adriana; Eron, Joseph J; Gandhi, Rajesh T; Johnson, Victoria A; Klingman, Karin L; Ritz, Justin; Hodder, Sally; Santana, Jorge L; Wilkin, Timothy; Haubrich, Richard H

2015-01-01

Background Nucleoside reverse transcriptase inhibitors (NRTIs) are often included in antiretroviral (ARV) regimens in treatment-experienced patients in the absence of data from randomized trials. Objective To compare treatment success between participants who omit versus Add NRTIs to an optimized ARV regimen of three or more agents. Design Multisite, randomized, controlled trial. Setting Outpatient HIV clinics. Participants HIV-infected patients with three-class ARV experience and/or viral resistance. Intervention Open-label optimized regimens (not including NRTIs) were selected based upon treatment history and susceptibility testing. Participants were randomized to Omit or Add NRTIs. Measurements The primary efficacy outcome was regimen failure through week 48, using a non-inferiority margin of 15%. The primary safety outcome was time to initial episode of severe sign/symptom or laboratory abnormality prior to discontinuation of NRTI assignment. Results 360 participants were randomized and 93% completed a week 48 visit. The cumulative probability of regimen failure was 29.8% in the Omit NRTI arm versus 25.9% in the Add NRTI arm (difference= 3.2%: 95% CI, −6.1 to 12.5). There were no significant differences in the primary safety endpoints or the proportion of participants with HIV RNA <50 copies/mL between arms. No deaths occurred in the Omit NRTIs arm, compared with 7 deaths in the Add NRTIs arm. Limitations Non-blinded study design and may not be applicable to resource poor settings. Conclusion HIV-infected treatment-experienced patients starting a new optimized regimen can safely omit NRTIs without compromising virologic efficacy. Omitting NRTIs will reduce pill burden, cost, and toxicity in this patient population. PMID:26595748

Proximate factors associated with speech intelligibility in children with cochlear implants: A preliminary study.

PubMed

Chin, Steven B; Kuhns, Matthew J

2014-01-01

The purpose of this descriptive pilot study was to examine possible relationships among speech intelligibility and structural characteristics of speech in children who use cochlear implants. The Beginners Intelligibility Test (BIT) was administered to 10 children with cochlear implants, and the intelligibility of the words in the sentences was judged by panels of naïve adult listeners. Additionally, several qualitative and quantitative measures of word omission, segment correctness, duration, and intonation variability were applied to the sentences used to assess intelligibility. Correlational analyses were conducted to determine if BIT scores and the other speech parameters were related. There was a significant correlation between BIT score and percent words omitted, but no other variables correlated significantly with BIT score. The correlation between intelligibility and word omission may be task-specific as well as reflective of memory limitations.

Diagnosing GCM errors over West Africa using relaxation experiments. Part I: summer monsoon climatology and interannual variability

NASA Astrophysics Data System (ADS)

Pohl, Benjamin; Douville, Hervé

2011-10-01

The CNRM atmospheric general circulation model Arpege-Climat is relaxed towards atmospheric reanalyses outside the 10°S-32°N 30°W-50°E domain in order to disentangle the regional versus large-scale sources of climatological biases and interannual variability of the West African monsoon (WAM). On the one hand, the main climatological features of the monsoon, including the spatial distribution of summer precipitation, are only weakly improved by the nudging, thereby suggesting the regional origin of the Arpege-Climat biases. On the other hand, the nudging technique is relatively efficient to control the interannual variability of the WAM dynamics, though the impact on rainfall variability is less clear. Additional sensitivity experiments focusing on the strong 1994 summer monsoon suggest that the weak sensitivity of the model biases is not an artifact of the nudging design, but the evidence that regional physical processes are the main limiting factors for a realistic simulation of monsoon circulation and precipitation in the Arpege-Climat model. Sensitivity experiments to soil moisture boundary conditions are also conducted and highlight the relevance of land-atmosphere coupling for the amplification of precipitation biases. Nevertheless, the land surface hydrology is not the main explanation for the model errors that are rather due to deficiencies in the atmospheric physics. The intraseasonal timescale and the model internal variability are discussed in a companion paper.

Confounding, causality, and confusion: the role of intermediate variables in interpreting observational studies in obstetrics.

PubMed

Ananth, Cande V; Schisterman, Enrique F

2017-08-01

Prospective and retrospective cohorts and case-control studies are some of the most important study designs in epidemiology because, under certain assumptions, they can mimic a randomized trial when done well. These assumptions include, but are not limited to, properly accounting for 2 important sources of bias: confounding and selection bias. While not adjusting the causal association for an intermediate variable will yield an unbiased estimate of the exposure-outcome's total causal effect, it is often that obstetricians will want to adjust for an intermediate variable to assess if the intermediate is the underlying driver of the association. Such a practice must be weighed in light of the underlying research question and whether such an adjustment is necessary should be carefully considered. Gestational age is, by far, the most commonly encountered variable in obstetrics that is often mislabeled as a confounder when, in fact, it may be an intermediate. If, indeed, gestational age is an intermediate but if mistakenly labeled as a confounding variable and consequently adjusted in an analysis, the conclusions can be unexpected. The implications of this overadjustment of an intermediate as though it were a confounder can render an otherwise persuasive study downright meaningless. This commentary provides an exposition of confounding bias, collider stratification, and selection biases, with applications in obstetrics and perinatal epidemiology. Copyright © 2017 Elsevier Inc. All rights reserved.

Equal Potential: A Collective Fraud.

ERIC Educational Resources Information Center

Gottfredson, Linda S.

2000-01-01

Critiques the College Board's report, "Reaching the Top," asserting that it illustrates collective fraud in the social sciences, which sustains an egalitarian fiction that intelligence is clustered equally across all human populations. Suggests that while the report omits certain popular falsehoods, it also omits crucial truths about…

Midwest guardrail system (MGS) with an omitted post.

DOT National Transportation Integrated Search

2016-02-22

The objective of this research study was to evaluate the MGS (31 tall W-beam guardrail) with an omitted post according to the safety performance criteria provided in MASH. A single full-scale crash test was conducted with the 2270P pickup truck in...

77 FR 63309 - Sunshine Act Meetings

Federal Register 2010, 2011, 2012, 2013, 2014

2012-10-16

... Southwest Power Pool, Inc. ER12-1179-001. E-5 ER12-550-000 Southwest Power Pool, Inc. E-6 OMITTED. E-7 ER12.... GAS G-1 OMITTED. G-2 RM12-14-000 Annual Charge Filing Procedures for Natural Gas Pipelines. G-3 RM12...

40 CFR 350.1 - Definitions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... confidential has been omitted or withheld. Senior management official means an official with management... confidential has not been withheld or omitted. Working day is any day on which Federal government offices are open for normal business. Saturdays, Sundays, and official Federal holidays are not working days; all...

78 FR 32574 - Azoxystrobin; Pesticide Tolerance; Technical Correction

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-31

... ENVIRONMENTAL PROTECTION AGENCY 40 CFR Part 180 [EPA-HQ-OPP-2012-0283; FRL-9387-4] Azoxystrobin..., establishing new and modifying existing tolerances for residues of azoxystrobin. EPA inadvertently omitted the... for residues of the fungicide azoxystrobin in or on various commodities. EPA inadvertently omitted the...

Decadal evaluation of regional climate, air quality, and their interactions using WRF/Chem Version 3.6.1

NASA Astrophysics Data System (ADS)

Yahya, K.; Wang, K.; Campbell, P.; Glotfelty, T.; He, J.; Zhang, Y.

2015-08-01

The Weather Research and Forecasting model with Chemistry (WRF/Chem) v3.6.1 with the Carbon Bond 2005 (CB05) gas-phase mechanism is evaluated for its first decadal application during 2001-2010 using the Representative Concentration Pathway (RCP 8.5) emissions to assess its capability and appropriateness for long-term climatological simulations. The initial and boundary conditions are downscaled from the modified Community Earth System Model/Community Atmosphere Model (CESM/CAM5) v1.2.2. The meteorological initial and boundary conditions are bias-corrected using the National Center for Environmental Protection's Final (FNL) Operational Global Analysis data. Climatological evaluations are carried out for meteorological, chemical, and aerosol-cloud-radiation variables against data from surface networks and satellite retrievals. The model performs very well for the 2 m temperature (T2) for the 10 year period with only a small cold bias of -0.3 °C. Biases in other meteorological variables including relative humidity at 2 m, wind speed at 10 m, and precipitation tend to be site- and season-specific; however, with the exception of T2, consistent annual biases exist for most of the years from 2001 to 2010. Ozone mixing ratios are slightly overpredicted at both urban and rural locations but underpredicted at rural locations. PM2.5 concentrations are slightly overpredicted at rural sites, but slightly underpredicted at urban/suburban sites. In general, the model performs relatively well for chemical and meteorological variables, and not as well for aerosol-cloud-radiation variables. Cloud-aerosol variables including aerosol optical depth, cloud water path, cloud optical thickness, and cloud droplet number concentration are generally underpredicted on average across the continental US. Overpredictions of several cloud variables over eastern US result in underpredictions of radiation variables and overpredictions of shortwave and longwave cloud forcing which are important climate variables. While the current performance is deemed to be acceptable, improvements to the bias-correction method for CESM downscaling and the model parameterizations of cloud dynamics and thermodynamics, as well as aerosol-cloud interactions can potentially improve model performance for long-term climate simulations.

Reconstructing the equilibrium Boltzmann distribution from well-tempered metadynamics.

PubMed

Bonomi, M; Barducci, A; Parrinello, M

2009-08-01

Metadynamics is a widely used and successful method for reconstructing the free-energy surface of complex systems as a function of a small number of suitably chosen collective variables. This is achieved by biasing the dynamics of the system. The bias acting on the collective variables distorts the probability distribution of the other variables. Here we present a simple reweighting algorithm for recovering the unbiased probability distribution of any variable from a well-tempered metadynamics simulation. We show the efficiency of the reweighting procedure by reconstructing the distribution of the four backbone dihedral angles of alanine dipeptide from two and even one dimensional metadynamics simulation. 2009 Wiley Periodicals, Inc.

The quality of registration of clinical trials.

PubMed

Viergever, Roderik F; Ghersi, Davina

2011-02-24

Lack of transparency in clinical trial conduct, publication bias and selective reporting bias are still important problems in medical research. Through clinical trials registration, it should be possible to take steps towards resolving some of these problems. However, previous evaluations of registered records of clinical trials have shown that registered information is often incomplete and non-meaningful. If these studies are accurate, this negates the possible benefits of registration of clinical trials. A 5% sample of records of clinical trials that were registered between 17 June 2008 and 17 June 2009 was taken from the International Clinical Trials Registry Platform (ICTRP) database and assessed for the presence of contact information, the presence of intervention specifics in drug trials and the quality of primary and secondary outcome reporting. 731 records were included. More than half of the records were registered after recruitment of the first participant. The name of a contact person was available in 94.4% of records from non-industry funded trials and 53.7% of records from industry funded trials. Either an email address or a phone number was present in 76.5% of non-industry funded trial records and in 56.5% of industry funded trial records. Although a drug name or company serial number was almost always provided, other drug intervention specifics were often omitted from registration. Of 3643 reported outcomes, 34.9% were specific measures with a meaningful time frame. Clinical trials registration has the potential to contribute substantially to improving clinical trial transparency and reducing publication bias and selective reporting. These potential benefits are currently undermined by deficiencies in the provision of information in key areas of registered records.

The Quality of Registration of Clinical Trials

PubMed Central

Viergever, Roderik F.; Ghersi, Davina

2011-01-01

Background Lack of transparency in clinical trial conduct, publication bias and selective reporting bias are still important problems in medical research. Through clinical trials registration, it should be possible to take steps towards resolving some of these problems. However, previous evaluations of registered records of clinical trials have shown that registered information is often incomplete and non-meaningful. If these studies are accurate, this negates the possible benefits of registration of clinical trials. Methods and Findings A 5% sample of records of clinical trials that were registered between 17 June 2008 and 17 June 2009 was taken from the International Clinical Trials Registry Platform (ICTRP) database and assessed for the presence of contact information, the presence of intervention specifics in drug trials and the quality of primary and secondary outcome reporting. 731 records were included. More than half of the records were registered after recruitment of the first participant. The name of a contact person was available in 94.4% of records from non-industry funded trials and 53.7% of records from industry funded trials. Either an email address or a phone number was present in 76.5% of non-industry funded trial records and in 56.5% of industry funded trial records. Although a drug name or company serial number was almost always provided, other drug intervention specifics were often omitted from registration. Of 3643 reported outcomes, 34.9% were specific measures with a meaningful time frame. Conclusions Clinical trials registration has the potential to contribute substantially to improving clinical trial transparency and reducing publication bias and selective reporting. These potential benefits are currently undermined by deficiencies in the provision of information in key areas of registered records. PMID:21383991

The Box-Cox power transformation on nursing sensitive indicators: Does it matter if structural effects are omitted during the estimation of the transformation parameter?

PubMed Central

2011-01-01

Background Many nursing and health related research studies have continuous outcome measures that are inherently non-normal in distribution. The Box-Cox transformation provides a powerful tool for developing a parsimonious model for data representation and interpretation when the distribution of the dependent variable, or outcome measure, of interest deviates from the normal distribution. The objectives of this study was to contrast the effect of obtaining the Box-Cox power transformation parameter and subsequent analysis of variance with or without a priori knowledge of predictor variables under the classic linear or linear mixed model settings. Methods Simulation data from a 3 × 4 factorial treatments design, along with the Patient Falls and Patient Injury Falls from the National Database of Nursing Quality Indicators (NDNQI®) for the 3rd quarter of 2007 from a convenience sample of over one thousand US hospitals were analyzed. The effect of the nonlinear monotonic transformation was contrasted in two ways: a) estimating the transformation parameter along with factors with potential structural effects, and b) estimating the transformation parameter first and then conducting analysis of variance for the structural effect. Results Linear model ANOVA with Monte Carlo simulation and mixed models with correlated error terms with NDNQI examples showed no substantial differences on statistical tests for structural effects if the factors with structural effects were omitted during the estimation of the transformation parameter. Conclusions The Box-Cox power transformation can still be an effective tool for validating statistical inferences with large observational, cross-sectional, and hierarchical or repeated measure studies under the linear or the mixed model settings without prior knowledge of all the factors with potential structural effects. PMID:21854614

The Box-Cox power transformation on nursing sensitive indicators: does it matter if structural effects are omitted during the estimation of the transformation parameter?

PubMed

Hou, Qingjiang; Mahnken, Jonathan D; Gajewski, Byron J; Dunton, Nancy

2011-08-19

Many nursing and health related research studies have continuous outcome measures that are inherently non-normal in distribution. The Box-Cox transformation provides a powerful tool for developing a parsimonious model for data representation and interpretation when the distribution of the dependent variable, or outcome measure, of interest deviates from the normal distribution. The objectives of this study was to contrast the effect of obtaining the Box-Cox power transformation parameter and subsequent analysis of variance with or without a priori knowledge of predictor variables under the classic linear or linear mixed model settings. Simulation data from a 3 × 4 factorial treatments design, along with the Patient Falls and Patient Injury Falls from the National Database of Nursing Quality Indicators (NDNQI® for the 3rd quarter of 2007 from a convenience sample of over one thousand US hospitals were analyzed. The effect of the nonlinear monotonic transformation was contrasted in two ways: a) estimating the transformation parameter along with factors with potential structural effects, and b) estimating the transformation parameter first and then conducting analysis of variance for the structural effect. Linear model ANOVA with Monte Carlo simulation and mixed models with correlated error terms with NDNQI examples showed no substantial differences on statistical tests for structural effects if the factors with structural effects were omitted during the estimation of the transformation parameter. The Box-Cox power transformation can still be an effective tool for validating statistical inferences with large observational, cross-sectional, and hierarchical or repeated measure studies under the linear or the mixed model settings without prior knowledge of all the factors with potential structural effects.

Alleviating tropical Atlantic sector biases in the Kiel climate model by enhancing horizontal and vertical atmosphere model resolution: climatology and interannual variability

NASA Astrophysics Data System (ADS)

Harlaß, Jan; Latif, Mojib; Park, Wonsun

2018-04-01

We investigate the quality of simulating tropical Atlantic (TA) sector climatology and interannual variability in integrations of the Kiel climate model (KCM) with varying atmosphere model resolution. The ocean model resolution is kept fixed. A reasonable simulation of TA sector annual-mean climate, seasonal cycle and interannual variability can only be achieved at sufficiently high horizontal and vertical atmospheric resolution. Two major reasons for the improvements are identified. First, the western equatorial Atlantic westerly surface wind bias in spring can be largely eliminated, which is explained by a better representation of meridional and especially vertical zonal momentum transport. The enhanced atmospheric circulation along the equator in turn greatly improves the thermal structure of the upper equatorial Atlantic with much reduced warm sea surface temperature (SST) biases. Second, the coastline in the southeastern TA and steep orography are better resolved at high resolution, which improves wind structure and in turn reduces warm SST biases in the Benguela upwelling region. The strongly diminished wind and SST biases at high atmosphere model resolution allow for a more realistic latitudinal position of the intertropical convergence zone. Resulting stronger cross-equatorial winds, in conjunction with a shallower thermocline, enable a rapid cold tongue development in the eastern TA in boreal spring. This enables simulation of realistic interannual SST variability and its seasonal phase locking in the KCM, which primarily is the result of a stronger thermocline feedback. Our findings suggest that enhanced atmospheric resolution, both vertical and horizontal, could be a key to achieving more realistic simulation of TA climatology and interannual variability in climate models.

The second Southern African Bird Atlas Project: Causes and consequences of geographical sampling bias.

PubMed

Hugo, Sanet; Altwegg, Res

2017-09-01

Using the Southern African Bird Atlas Project (SABAP2) as a case study, we examine the possible determinants of spatial bias in volunteer sampling effort and how well such biased data represent environmental gradients across the area covered by the atlas. For each province in South Africa, we used generalized linear mixed models to determine the combination of variables that explain spatial variation in sampling effort (number of visits per 5' × 5' grid cell, or "pentad"). The explanatory variables were distance to major road and exceptional birding locations or "sampling hubs," percentage cover of protected, urban, and cultivated area, and the climate variables mean annual precipitation, winter temperatures, and summer temperatures. Further, we used the climate variables and plant biomes to define subsets of pentads representing environmental zones across South Africa, Lesotho, and Swaziland. For each environmental zone, we quantified sampling intensity, and we assessed sampling completeness with species accumulation curves fitted to the asymptotic Lomolino model. Sampling effort was highest close to sampling hubs, major roads, urban areas, and protected areas. Cultivated area and the climate variables were less important. Further, environmental zones were not evenly represented by current data and the zones varied in the amount of sampling required representing the species that are present. SABAP2 volunteers' preferences in birding locations cause spatial bias in the dataset that should be taken into account when analyzing these data. Large parts of South Africa remain underrepresented, which may restrict the kind of ecological questions that may be addressed. However, sampling bias may be improved by directing volunteers toward undersampled regions while taking into account volunteer preferences.

Problems and Progress regarding Sex Bias and Omission in Neuroscience Research

PubMed Central

Will, Tyler R.; Proaño, Stephanie B.; Thomas, Anly M.; Kunz, Lindsey M.; Thompson, Kelly C.; Ginnari, Laura A.; Jones, Clay H.; Lucas, Sarah-Catherine; Reavis, Elizabeth M.

2017-01-01

Neuroscience research has historically ignored female animals. This neglect comes in two general forms. The first is sex bias, defined as favoring one sex over another; in this case, male over female. The second is sex omission, which is the lack of reporting sex. The recognition of this phenomenon has generated fierce debate across the sciences. Here we test whether sex bias and omission are still present in the neuroscience literature, whether studies employing both males and females neglect sex as an experimental variable, and whether sex bias and omission differs between animal models and journals. To accomplish this, we analyzed the largest-ever number of neuroscience articles for sex bias and omission: 6636 articles using mice or rats in 6 journals published from 2010 to 2014. Sex omission is declining, as increasing numbers of articles report sex. Sex bias remains present, as increasing numbers of articles report the sole use of males. Articles using both males and females are also increasing, but few report assessing sex as an experimental variable. Sex bias and omission varies substantially by animal model and journal. These findings are essential for understanding the complex status of sex bias and omission in neuroscience research and may inform effective decisions regarding policy action. PMID:29134192

Errors in causal inference: an organizational schema for systematic error and random error.

PubMed

Suzuki, Etsuji; Tsuda, Toshihide; Mitsuhashi, Toshiharu; Mansournia, Mohammad Ali; Yamamoto, Eiji

2016-11-01

To provide an organizational schema for systematic error and random error in estimating causal measures, aimed at clarifying the concept of errors from the perspective of causal inference. We propose to divide systematic error into structural error and analytic error. With regard to random error, our schema shows its four major sources: nondeterministic counterfactuals, sampling variability, a mechanism that generates exposure events and measurement variability. Structural error is defined from the perspective of counterfactual reasoning and divided into nonexchangeability bias (which comprises confounding bias and selection bias) and measurement bias. Directed acyclic graphs are useful to illustrate this kind of error. Nonexchangeability bias implies a lack of "exchangeability" between the selected exposed and unexposed groups. A lack of exchangeability is not a primary concern of measurement bias, justifying its separation from confounding bias and selection bias. Many forms of analytic errors result from the small-sample properties of the estimator used and vanish asymptotically. Analytic error also results from wrong (misspecified) statistical models and inappropriate statistical methods. Our organizational schema is helpful for understanding the relationship between systematic error and random error from a previously less investigated aspect, enabling us to better understand the relationship between accuracy, validity, and precision. Copyright © 2016 Elsevier Inc. All rights reserved.

43 CFR 4.355 - Omitted compensation.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 43 Public Lands: Interior 1 2010-10-01 2010-10-01 false Omitted compensation. 4.355 Section 4.355 Public Lands: Interior Office of the Secretary of the Interior DEPARTMENT HEARINGS AND APPEALS PROCEDURES Rules Applicable in Indian Affairs Hearings and Appeals White Earth Reservation Land Settlement Act of...

43 CFR 4.355 - Omitted compensation.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 43 Public Lands: Interior 1 2011-10-01 2011-10-01 false Omitted compensation. 4.355 Section 4.355 Public Lands: Interior Office of the Secretary of the Interior DEPARTMENT HEARINGS AND APPEALS PROCEDURES Rules Applicable in Indian Affairs Hearings and Appeals White Earth Reservation Land Settlement Act of...

43 CFR 4.355 - Omitted compensation.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 43 Public Lands: Interior 1 2012-10-01 2011-10-01 true Omitted compensation. 4.355 Section 4.355 Public Lands: Interior Office of the Secretary of the Interior DEPARTMENT HEARINGS AND APPEALS PROCEDURES Rules Applicable in Indian Affairs Hearings and Appeals White Earth Reservation Land Settlement Act of...

43 CFR 4.355 - Omitted compensation.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 43 Public Lands: Interior 1 2013-10-01 2013-10-01 false Omitted compensation. 4.355 Section 4.355 Public Lands: Interior Office of the Secretary of the Interior DEPARTMENT HEARINGS AND APPEALS PROCEDURES Rules Applicable in Indian Affairs Hearings and Appeals White Earth Reservation Land Settlement Act of...

43 CFR 4.355 - Omitted compensation.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 43 Public Lands: Interior 1 2014-10-01 2014-10-01 false Omitted compensation. 4.355 Section 4.355 Public Lands: Interior Office of the Secretary of the Interior DEPARTMENT HEARINGS AND APPEALS PROCEDURES Rules Applicable in Indian Affairs Hearings and Appeals White Earth Reservation Land Settlement Act of...

Harmony as Language Policy in China: An Internet Perspective

ERIC Educational Resources Information Center

Wang, Xuan; Juffermans, Kasper; Du, Caixia

2016-01-01

This paper provides an ethnographic understanding of harmony as language policy in China, grounded in a historical analysis of "harmony" ([character omitted] "he") as a distinct traditional Chinese (Confucian) ideal that gradually finds its new expressions through the policy of Harmonious Society ([characters omitted]…

Towards process-informed bias correction of climate change simulations

NASA Astrophysics Data System (ADS)

Maraun, Douglas; Shepherd, Theodore G.; Widmann, Martin; Zappa, Giuseppe; Walton, Daniel; Gutiérrez, José M.; Hagemann, Stefan; Richter, Ingo; Soares, Pedro M. M.; Hall, Alex; Mearns, Linda O.

2017-11-01

Biases in climate model simulations introduce biases in subsequent impact simulations. Therefore, bias correction methods are operationally used to post-process regional climate projections. However, many problems have been identified, and some researchers question the very basis of the approach. Here we demonstrate that a typical cross-validation is unable to identify improper use of bias correction. Several examples show the limited ability of bias correction to correct and to downscale variability, and demonstrate that bias correction can cause implausible climate change signals. Bias correction cannot overcome major model errors, and naive application might result in ill-informed adaptation decisions. We conclude with a list of recommendations and suggestions for future research to reduce, post-process, and cope with climate model biases.

Food-related attentional bias. Word versus pictorial stimuli and the importance of stimuli calorific value in the dot probe task.

PubMed

Freijy, Tanya; Mullan, Barbara; Sharpe, Louise

2014-12-01

The primary aim of this study was to extend previous research on food-related attentional biases by examining biases towards pictorial versus word stimuli, and foods of high versus low calorific value. It was expected that participants would demonstrate greater biases to pictures over words, and to high-calorie over low-calorie foods. A secondary aim was to examine associations between BMI, dietary restraint, external eating and attentional biases. It was expected that high scores on these individual difference variables would be associated with a bias towards high-calorie stimuli. Undergraduates (N = 99) completed a dot probe task including matched word and pictorial food stimuli in a controlled setting. Questionnaires assessing eating behaviour were administered, and height and weight were measured. Contrary to predictions, there were no main effects for stimuli type (pictures vs words) or calorific value (high vs low). There was, however, a significant interaction effect suggesting a bias towards high-calorie pictures, but away from high-calorie words; and a bias towards low-calorie words, but away from low-calorie pictures. No associations between attentional bias and any of the individual difference variables were found. The presence of a stimulus type by calorific value interaction demonstrates the importance of stimuli type in the dot probe task, and may help to explain inconsistencies in prior research. Further research is needed to clarify associations between attentional bias and BMI, restraint, and external eating. Copyright © 2014 Elsevier Ltd. All rights reserved.

On-chip optical phase locking of single growth monolithically integrated Slotted Fabry Perot lasers.

PubMed

Morrissey, P E; Cotter, W; Goulding, D; Kelleher, B; Osborne, S; Yang, H; O'Callaghan, J; Roycroft, B; Corbett, B; Peters, F H

2013-07-15

This work investigates the optical phase locking performance of Slotted Fabry Perot (SFP) lasers and develops an integrated variable phase locked system on chip for the first time to our knowledge using these lasers. Stable phase locking is demonstrated between two SFP lasers coupled on chip via a variable gain waveguide section. The two lasers are biased differently, one just above the threshold current of the device with the other at three times this value. The coupling between the lasers can be controlled using the variable gain section which can act as a variable optical attenuator or amplifier depending on bias. Using this, the width of the stable phase locking region on chip is shown to be variable.

Collinear Latent Variables in Multilevel Confirmatory Factor Analysis

PubMed Central

van de Schoot, Rens; Hox, Joop

2014-01-01

Because variables may be correlated in the social and behavioral sciences, multicollinearity might be problematic. This study investigates the effect of collinearity manipulated in within and between levels of a two-level confirmatory factor analysis by Monte Carlo simulation. Furthermore, the influence of the size of the intraclass correlation coefficient (ICC) and estimation method; maximum likelihood estimation with robust chi-squares and standard errors and Bayesian estimation, on the convergence rate are investigated. The other variables of interest were rate of inadmissible solutions and the relative parameter and standard error bias on the between level. The results showed that inadmissible solutions were obtained when there was between level collinearity and the estimation method was maximum likelihood. In the within level multicollinearity condition, all of the solutions were admissible but the bias values were higher compared with the between level collinearity condition. Bayesian estimation appeared to be robust in obtaining admissible parameters but the relative bias was higher than for maximum likelihood estimation. Finally, as expected, high ICC produced less biased results compared to medium ICC conditions. PMID:29795827

Collinear Latent Variables in Multilevel Confirmatory Factor Analysis: A Comparison of Maximum Likelihood and Bayesian Estimations.

PubMed

Can, Seda; van de Schoot, Rens; Hox, Joop

2015-06-01

Because variables may be correlated in the social and behavioral sciences, multicollinearity might be problematic. This study investigates the effect of collinearity manipulated in within and between levels of a two-level confirmatory factor analysis by Monte Carlo simulation. Furthermore, the influence of the size of the intraclass correlation coefficient (ICC) and estimation method; maximum likelihood estimation with robust chi-squares and standard errors and Bayesian estimation, on the convergence rate are investigated. The other variables of interest were rate of inadmissible solutions and the relative parameter and standard error bias on the between level. The results showed that inadmissible solutions were obtained when there was between level collinearity and the estimation method was maximum likelihood. In the within level multicollinearity condition, all of the solutions were admissible but the bias values were higher compared with the between level collinearity condition. Bayesian estimation appeared to be robust in obtaining admissible parameters but the relative bias was higher than for maximum likelihood estimation. Finally, as expected, high ICC produced less biased results compared to medium ICC conditions.

Inferring Master Painters' Esthetic Biases from the Statistics of Portraits

PubMed Central

Aleem, Hassan; Correa-Herran, Ivan; Grzywacz, Norberto M.

2017-01-01

The Processing Fluency Theory posits that the ease of sensory information processing in the brain facilitates esthetic pleasure. Accordingly, the theory would predict that master painters should display biases toward visual properties such as symmetry, balance, and moderate complexity. Have these biases been occurring and if so, have painters been optimizing these properties (fluency variables)? Here, we address these questions with statistics of portrait paintings from the Early Renaissance period. To do this, we first developed different computational measures for each of the aforementioned fluency variables. Then, we measured their statistics in 153 portraits from 26 master painters, in 27 photographs of people in three controlled poses, and in 38 quickly snapped photographs of individual persons. A statistical comparison between Early Renaissance portraits and quickly snapped photographs revealed that painters showed a bias toward balance, symmetry, and moderate complexity. However, a comparison between portraits and controlled-pose photographs showed that painters did not optimize each of these properties. Instead, different painters presented biases toward different, narrow ranges of fluency variables. Further analysis suggested that the painters' individuality stemmed in part from having to resolve the tension between complexity vs. symmetry and balance. We additionally found that constraints on the use of different painting materials by distinct painters modulated these fluency variables systematically. In conclusion, the Processing Fluency Theory of Esthetic Pleasure would need expansion if we were to apply it to the history of visual art since it cannot explain the lack of optimization of each fluency variables. To expand the theory, we propose the existence of a Neuroesthetic Space, which encompasses the possible values that each of the fluency variables can reach in any given art period. We discuss the neural mechanisms of this Space and propose that it has a distributed representation in the human brain. We further propose that different artists reside in different, small sub-regions of the Space. This Neuroesthetic-Space hypothesis raises the question of how painters and their paintings evolve across art periods. PMID:28337133

Catalan's Intriguing Factorial Problem

ERIC Educational Resources Information Center

Koshy, Thomas

2012-01-01

This article investigates the numbers [image omitted], originally studied by Catalan. We re-confirm that they are indeed integers. Using the close relationship between them and the Catalan numbers C[subscript n], we develop some divisibility properties for C[subscript n]. In particular, we establish that [image omitted], where f[subscript k]…

An Inventory on Rotational Kinematics of a Particle: Unravelling Misconceptions and Pitfalls in Reasoning

ERIC Educational Resources Information Center

Mashood, K. K.; Singh, Vijay A.

2012-01-01

Student difficulties regarding the angular velocity ([image omitted]) and angular acceleration ([image omitted]) of a particle have remained relatively unexplored in contrast to their linear counterparts. We present an inventory comprising multiple choice questions aimed at probing misconceptions and eliciting ill-suited reasoning patterns. The…

Examining the influence of link function misspecification in conventional regression models for developing crash modification factors.

PubMed

Wu, Lingtao; Lord, Dominique

2017-05-01

This study further examined the use of regression models for developing crash modification factors (CMFs), specifically focusing on the misspecification in the link function. The primary objectives were to validate the accuracy of CMFs derived from the commonly used regression models (i.e., generalized linear models or GLMs with additive linear link functions) when some of the variables have nonlinear relationships and quantify the amount of bias as a function of the nonlinearity. Using the concept of artificial realistic data, various linear and nonlinear crash modification functions (CM-Functions) were assumed for three variables. Crash counts were randomly generated based on these CM-Functions. CMFs were then derived from regression models for three different scenarios. The results were compared with the assumed true values. The main findings are summarized as follows: (1) when some variables have nonlinear relationships with crash risk, the CMFs for these variables derived from the commonly used GLMs are all biased, especially around areas away from the baseline conditions (e.g., boundary areas); (2) with the increase in nonlinearity (i.e., nonlinear relationship becomes stronger), the bias becomes more significant; (3) the quality of CMFs for other variables having linear relationships can be influenced when mixed with those having nonlinear relationships, but the accuracy may still be acceptable; and (4) the misuse of the link function for one or more variables can also lead to biased estimates for other parameters. This study raised the importance of the link function when using regression models for developing CMFs. Copyright © 2017 Elsevier Ltd. All rights reserved.

The dynamics of motivated perception: Effects of control and status on the perception of ambivalent stimuli.

PubMed

Voss, Andreas; Schwieren, Christiane

2015-01-01

Previous studies on attentional biases often show contradictory results. This suggests that important moderating variables have been neglected so far. We suggest that (1) control over potential consequences and (2) satisfaction with the current status are important factors that need to be considered. We explored the influence of these variables using a colour classification task, where colours are associated with financial gains and losses. Data were analysed with hierarchical logistic regression models and with stochastic diffusion models. The latter approach has the special advantage that it allows separating perceptual and judgemental biases. Results show an overall positive judgemental bias. In the absence of control, this positivity bias increases with the amount of money that has been gained, whereas the opposite pattern is present when dangers can be controlled. In the second experiment, no general feedback was given, which led to an increasing negativity bias. Results are discussed within an action theoretic framework.

Single- and Dual-Process Models of Biased Contingency Detection

PubMed Central

2016-01-01

Abstract. Decades of research in causal and contingency learning show that people’s estimations of the degree of contingency between two events are easily biased by the relative probabilities of those two events. If two events co-occur frequently, then people tend to overestimate the strength of the contingency between them. Traditionally, these biases have been explained in terms of relatively simple single-process models of learning and reasoning. However, more recently some authors have found that these biases do not appear in all dependent variables and have proposed dual-process models to explain these dissociations between variables. In the present paper we review the evidence for dissociations supporting dual-process models and we point out important shortcomings of this literature. Some dissociations seem to be difficult to replicate or poorly generalizable and others can be attributed to methodological artifacts. Overall, we conclude that support for dual-process models of biased contingency detection is scarce and inconclusive. PMID:27025532

Burden of blood transfusion in knee and hip surgery in the US and Belgium.

PubMed

Blanchette, Christopher M; Joshi, Ashish V; Szpalski, Marek; Gunzburg, Robert; Du Bois, Mark; Donceel, Peter; Saunders, William B

2009-09-01

Transfusion services in orthopaedic surgery can lead to unnecessary complications and increased healthcare costs. The objective of this study was to assess treatments and costs associated with blood and blood product transfusions in a historical cohort of 189,457 inpatients in the US and 34,987 inpatients in Belgium undergoing knee or hip surgery. Descriptive analysis, logistic regression and ordinary least squares regression were used to describe the factors associated with the use and cost of allogeneic blood transfusion. Hospitalisation costs for joint replacement surgery totalled $12,718 (SD=6,356) and averaged 4.33 days in the US, while costs in Belgium were $6,526 (SD=3,192) and averaged 17.1 days. The use of low molecular weight heparin and tranexamic acid was much higher in Belgium than the US (36% and 99% compared to 0% and 40%, respectively). Patients in the US spent 12.7 (p<0.0001) fewer days in the hospital, 0.3 (p<0.0001) fewer days in the intensive care unit and were 88% less likely to have allogeneic blood transfusions (OR=0.22, 95% CI 0.22-0.23), but incurred $6,483 (p<0.0001) more costs per hospitalisation than patients in Belgium. While hospital costs for patients were greater in the US, length of stay was shorter and patients were less likely to have transfusion services than those patients in Belgium. While this study is limited by factors inherent to observational studies, such as omitted variable bias, misclassification, and disease comorbidity, there are substantial differences in the use of blood products between Belgium and the US.

Using incentives to attract nurses to remote areas of Tanzania: a contingent valuation study.

PubMed

Munga, Michael A; Torsvik, Gaute; Mæstad, Ottar

2014-03-01

This article analyses (1) how financial incentives (salary top-ups) and non-financial incentives (housing and education) affect nurses' willingness to work in remote areas of Tanzania and (2) how the magnitude of the incentives needed to attract health workers varies with the nurses' geographic origin and their intrinsic motivation. A contingent valuation method was used to elicit the location preferences of 362 nursing students. Without any interventions, 19% of the nurses were willing to work in remote places. With the provision of free housing, this share increased by 15 percentage points. Better education opportunities increased the share by 28 percentage points from the baseline. For a salary top-up to have the same effect as provision of free housing, the top-up needs to be between 80 and 100% of the base salary. Similarly, for salary top-ups to have the same effect as provision of better education opportunities, the top-up should be between 120 and 140%. Our study confirms results from previous research, that those with a strong intrinsic motivation to provide health care are more motivated to work in a remote location. A more surprising finding is that students of older age are more prepared to take a job in remote areas. Several studies have found that individuals who grew up in a remote area are more willing to work in such locations. A novel finding of our analysis is that only nursing students with a 'very' remote origin (i.e. those who grew up farther from a district centre than the suggested remote working place) express a higher willingness to take the remote job. Although we do control for nursing school effects, our results could be biased due to omitted variables capturing individual characteristics.

What types of social interactions reduce the risk of psychological distress? Fixed effects longitudinal analysis of a cohort of 30,271 middle-to-older aged Australians.

PubMed

Feng, Xiaoqi; Astell-Burt, Thomas

2016-11-01

Research on the impact of social interactions on psychological distress tends to be limited to particular forms of support, cross-sectional designs and by the spectre of omitted variables bias. A baseline sample with 3.4±0.95 years follow-up time was extracted from the 45 and Up Study. Change in the risk of psychological distress (Kessler Psychological Distress Scale) was assessed using fixed effects logistic regressions in relation to the number of times in the past week a participant: i) spent time with friends or family they did not live with; ii) talked to friends, relatives or others on the telephone; iii) attended meetings at social clubs or religious groups; and the count of people outside their home, but within one hour travel-time, participants felt close to. Separate models were fitted for men and women, adjusting for age, income, economic and couple status. An increase in the number of social interactions was associated with a reduction in the risk of psychological distress, with some gender differences. Interactions with friends or family were important for women (adjusted OR 0.85, 95%CI 0.74, 0.98, p=0.024), whereas telephone calls were effective among men (adjusted OR 0.83, 95%CI 0.72, 0.96, p=0.011). Strong effects for the number of people that can be relied on were observed for men and women, but attendance at clubs and groups was not. No age-specific effects were observed. No indicator of positive mental health. Policies targeting greater social interactions in middle-to-older age may help protect mental health. Copyright © 2016 Elsevier B.V. All rights reserved.

Risk aversion and religious behaviour: Analysis using a sample of Danish twins.

PubMed

Nielsen, Jytte Seested; Bech, Mickael; Christensen, Kaare; Kiil, Astrid; Hvidt, Niels Christian

2017-08-01

Economics offers an analytical framework to consider human behaviour including religious behaviour. Within the realm of Expected Utility Theory, religious belief and activity could be interpreted as an insurance both for current life events and for afterlife rewards. Based on that framework, we would expect that risk averse individuals would demand a more generous protection plan which they may do by devoting more effort and resources into religious activities such as church attendance and prayer, which seems to be in accordance with previous empirical results. However, a general concern regards the problems of spurious correlations due to underlying omitted or unobservable characteristics shaping both religious activities and risk attitudes. This paper examines empirically the demand for religion by analysing the association between risk attitudes on the one hand, and church attandance and prayer frequency on the other controlling for unobservable variables using survey data of Danish same-sex twin pairs. We verify the correlation between risk preferences and religion found previously by carrying out cross-sectional analyses. We also show that the association between risk attitudes and religious behaviour is driven by the subgroup of individuals who believe in an afterlife. In addition, when re-analysing our results using panel data analyses which cancel out shared factors among twin pairs, we find that the correlation found between risk aversion and religious behaviour is no longer significant indicating that other factors might explain differences in religious behaviour. Caution is needed in the interpretation of our results as the insignificant association between risk aversion and religious behaviour in the panel data analyses potentially might be due to measurement error causing attenuation bias or lack of variation within twin pairs rather than the actual absence of an association. Copyright © 2017 Elsevier B.V. All rights reserved.

Gender Bias in the Diagnosis of a Geriatric Standardized Patient: A Potential Confounding Variable

ERIC Educational Resources Information Center

Lewis, Roya; Lamdan, Ruth M.; Wald, David; Curtis, Michael

2006-01-01

Background: Gender bias has been reported in the diagnosis and treatment of patients with a variety of illnesses. In the context of our 10-station fourth year Objective Structured Clinical Evaluation, we queried whether this could influence diagnosis in a geriatric case. Case writers hypothesized that, due to this bias, the female standardized…

Unfolding the neutron spectrum of a NE213 scintillator using artificial neural networks.

PubMed

Sharghi Ido, A; Bonyadi, M R; Etaati, G R; Shahriari, M

2009-10-01

Artificial neural networks technology has been applied to unfold the neutron spectra from the pulse height distribution measured with NE213 liquid scintillator. Here, both the single and multi-layer perceptron neural network models have been implemented to unfold the neutron spectrum from an Am-Be neutron source. The activation function and the connectivity of the neurons have been investigated and the results have been analyzed in terms of the network's performance. The simulation results show that the neural network that utilizes the Satlins transfer function has the best performance. In addition, omitting the bias connection of the neurons improve the performance of the network. Also, the SCINFUL code is used for generating the response functions in the training phase of the process. Finally, the results of the neural network simulation have been compared with those of the FORIST unfolding code for both (241)Am-Be and (252)Cf neutron sources. The results of neural network are in good agreement with FORIST code.

Potential fitting biases resulting from grouping data into variable width bins

NASA Astrophysics Data System (ADS)

Towers, S.

2014-07-01

When reading peer-reviewed scientific literature describing any analysis of empirical data, it is natural and correct to proceed with the underlying assumption that experiments have made good faith efforts to ensure that their analyses yield unbiased results. However, particle physics experiments are expensive and time consuming to carry out, thus if an analysis has inherent bias (even if unintentional), much money and effort can be wasted trying to replicate or understand the results, particularly if the analysis is fundamental to our understanding of the universe. In this note we discuss the significant biases that can result from data binning schemes. As we will show, if data are binned such that they provide the best comparison to a particular (but incorrect) model, the resulting model parameter estimates when fitting to the binned data can be significantly biased, leading us to too often accept the model hypothesis when it is not in fact true. When using binned likelihood or least squares methods there is of course no a priori requirement that data bin sizes need to be constant, but we show that fitting to data grouped into variable width bins is particularly prone to produce biased results if the bin boundaries are chosen to optimize the comparison of the binned data to a wrong model. The degree of bias that can be achieved simply with variable binning can be surprisingly large. Fitting the data with an unbinned likelihood method, when possible to do so, is the best way for researchers to show that their analyses are not biased by binning effects. Failing that, equal bin widths should be employed as a cross-check of the fitting analysis whenever possible.

Dual Systems Competence [Image Omitted] Procedural Processing: A Relational Developmental Systems Approach to Reasoning

ERIC Educational Resources Information Center

Ricco, Robert B.; Overton, Willis F.

2011-01-01

Many current psychological models of reasoning minimize the role of deductive processes in human thought. In the present paper, we argue that deduction is an important part of ordinary cognition and we propose that a dual systems Competence [image omitted] Procedural processing model conceptualized within relational developmental systems theory…

E-Halagat: An E-Learning System for Teaching the Holy Quran

ERIC Educational Resources Information Center

Elhadj, Yahya O. Mohamed

2010-01-01

Recently, there has been a great interest in Islamic software that try to harness computer to serve the religion. This brought about some applications and programs for the Holy Quran and its sciences, Hadith "[image omitted]" (Prophet's Tradition) and its methodology, Fiqh "[image omitted]" (Islamic jurisdiction), and Islamic law in general.…

Variation in Angular Velocity and Angular Acceleration of a Particle in Rectilinear Motion

ERIC Educational Resources Information Center

Mashood, K. K.; Singh, V. A.

2012-01-01

We discuss the angular velocity ([image omitted]) and angular acceleration ([image omitted]) associated with a particle in rectilinear motion with constant acceleration. The discussion was motivated by an observation that students and even teachers have difficulty in ascribing rotational motion concepts to a particle when the trajectory is a…

Improved omit set displacement recoveries in dynamic analysis

NASA Technical Reports Server (NTRS)

Allen, Tom; Cook, Greg; Walls, Bill

1993-01-01

Two related methods for improving the dependent (OMIT set) displacements after performing a Guyan reduction are presented. The theoretical bases for the methods are derived. The NASTRAN DMAP ALTERs used to implement the methods in a NASTRAN execution are described. Data are presented that verify the methods and the NASTRAN DMAP ALTERs.

Specific Language Impairment as a Period of Extended Optional Infinitive.

ERIC Educational Resources Information Center

Rice, Mabel L.; And Others

1995-01-01

This study evaluated an Extended Optional Infinitive theory of specific language impairment (SLI) in children, which suggests that SLI children omit finiteness markers longer than do normally developing children. Comparison of 18 SLI 5-year olds with 2 normally developing groups (ages 5 and 3) found that SLI subjects omitted finiteness markers…

A Functional Study of "zhi [Chinese character omitted]" in the Chinese Nominal Group

ERIC Educational Resources Information Center

Zhang, Weiwei; Li, Manliang

2017-01-01

Over the past decades, subjects concerned with the Chinese character "zhi [Chinese character omitted]", i.e. grammatical structure, in ancient Chinese language, have been widely explored. This paper conducts a research from a new dimension: the Cardiff Grammar, an integral part of Systemic Functional Linguistics (SFL) which is famous for…

Management of Adolescent Low-Risk Classical Hodgkin Lymphoma: Which Chemotherapy Backbone Gives the Best Chance of Omitting Radiotherapy Safely.

PubMed

Algiraigri, Ali H; Essa, Mohammed F

2016-03-01

Even though more than 90% of adolescents with low-risk classical Hodgkin lymphoma (LRcHL) will be cured with first-line therapy, many will suffer serious late toxic effects from radiotherapy (RT). The goals for care have shifted toward minimizing late toxic effects without compromising the outstanding cure rates by adapting a risk and response-based therapy. Recent published and ongoing randomized clinical trials, using functional imaging, may allow for better identification of those patients for whom RT may be safely omitted while maintaining excellent cure rates. To evaluate the best chemotherapy regimens with a reasonable toxicity profile and that are expected to have a high chance of omitting RT based on a response-directed therapy while maintaining high cure rates, a mini review was conducted of the recent clinical trials in pediatric and adult LRcHL. The UK RAPID trial chemotherapy backbone (3 × ABVD) followed by a response-based positron emission tomography scan offers up to a 75% chance of safely omitting RT without compromising the cure rate, which remained well above 90%.

Mortality and Morbidity Risks and Economic Behavior

PubMed Central

Stoler, Avraham; Meltzer, David

2012-01-01

There are theoretical reasons to expect that high risk of mortality or morbidity during young adulthood decreases investment in human capital. However, investigation of this hypothesis is complicated by a variety of empirical challenges, including difficulties in inferring causation due to omitted variables and reverse causation. For example, to compare two groups with substantially different mortality rates, one typically has to use samples from different countries or time periods, making it difficult to control for other relevant variables. Reverse causation is important because human capital investment can affect mortality and morbidity. To counter these problems, we collected data on human capital investments, fertility decisions, and other economic choices of people at risk for Huntington’s disease. Huntington’s disease is a fatal genetic disorder that introduces a large and exogenous risk of early mortality and morbidity. We find a strong negative relation between mortality and morbidity risks and human capital investment. PMID:22308067

Intercomparison of Downscaling Methods on Hydrological Impact for Earth System Model of NE United States

NASA Astrophysics Data System (ADS)

Yang, P.; Fekete, B. M.; Rosenzweig, B.; Lengyel, F.; Vorosmarty, C. J.

2012-12-01

Atmospheric dynamics are essential inputs to Regional-scale Earth System Models (RESMs). Variables including surface air temperature, total precipitation, solar radiation, wind speed and humidity must be downscaled from coarse-resolution, global General Circulation Models (GCMs) to the high temporal and spatial resolution required for regional modeling. However, this downscaling procedure can be challenging due to the need to correct for bias from the GCM and to capture the spatiotemporal heterogeneity of the regional dynamics. In this study, the results obtained using several downscaling techniques and observational datasets were compared for a RESM of the Northeast Corridor of the United States. Previous efforts have enhanced GCM model outputs through bias correction using novel techniques. For example, the Climate Impact Research at Potsdam Institute developed a series of bias-corrected GCMs towards the next generation climate change scenarios (Schiermeier, 2012; Moss et al., 2010). Techniques to better represent the heterogeneity of climate variables have also been improved using statistical approaches (Maurer, 2008; Abatzoglou, 2011). For this study, four downscaling approaches to transform bias-corrected HADGEM2-ES Model output (daily at .5 x .5 degree) to the 3'*3'(longitude*latitude) daily and monthly resolution required for the Northeast RESM were compared: 1) Bilinear Interpolation, 2) Daily bias-corrected spatial downscaling (D-BCSD) with Gridded Meteorological Datasets (developed by Abazoglou 2011), 3) Monthly bias-corrected spatial disaggregation (M-BCSD) with CRU(Climate Research Unit) and 4) Dynamic Downscaling based on Weather Research and Forecast (WRF) model. Spatio-temporal analysis of the variability in precipitation was conducted over the study domain. Validation of the variables of different downscaling methods against observational datasets was carried out for assessment of the downscaled climate model outputs. The effects of using the different approaches to downscale atmospheric variables (specifically air temperature and precipitation) for use as inputs to the Water Balance Model (WBMPlus, Vorosmarty et al., 1998;Wisser et al., 2008) for simulation of daily discharge and monthly stream flow in the Northeast US for a 100-year period in the 21st century were also assessed. Statistical techniques especially monthly bias-corrected spatial disaggregation (M-BCSD) showed potential advantage among other methods for the daily discharge and monthly stream flow simulation. However, Dynamic Downscaling will provide important complements to the statistical approaches tested.

Persistent states in vision break universality and time invariance

PubMed Central

Wexler, Mark; Duyck, Marianne; Mamassian, Pascal

2015-01-01

Studies of perception usually emphasize processes that are largely universal across observers and—except for short-term fluctuations—stationary over time. Here we test the universality and stationarity assumptions with two families of ambiguous visual stimuli. Each stimulus can be perceived in two different ways, parameterized by two opposite directions from a continuous circular variable. A large-sample study showed that almost all observers have preferred directions or biases, with directions lying within 90 degrees of the bias direction nearly always perceived and opposite directions almost never perceived. The biases differ dramatically from one observer to the next, and although nearly every bias direction occurs in the population, the population distributions of the biases are nonuniform, featuring asymmetric peaks in the cardinal directions. The biases for the two families of stimuli are independent and have distinct population distributions. Following external perturbations and spontaneous fluctuations, the biases decay over tens of seconds toward their initial values. Persistent changes in the biases are found on time scales of several minutes to 1 hour. On scales of days to months, the biases undergo a variety of dynamical processes such as drifts, jumps, and oscillations. The global statistics of a majority of these long-term time series are well modeled as random walk processes. The measurable fluctuations of these hitherto unknown degrees of freedom show that the assumptions of universality and stationarity in perception may be unwarranted and that models of perception must include both directly observable variables as well as covert, persistent states. PMID:26627250

Omitting late-night eating may cause hypoglycemia in "well controlled" basal insulin-treated type 2 diabetes.

PubMed

King, Allen B; Clark, Dawn

2015-03-01

To assess hypoglycemia caused by eating the last meal of the day earlier or its omission in "well controlled" type 2 diabetes mellitus patients treated with once-nightly basal insulin. Previously basal insulin-titrated subjects (n = 20) (fasting plasma glucose, FPG, <110 mg/dL and no self-reported hypoglycemia) underwent continuous glucose monitoring (CGM) during 3 consecutive eating conditions of 3 days each; (1) usual eating, (2) the last meal restricted to 18:00, and (3) 1 sequential meal omitted/day thereby creating a fasting day after transposing the 4-hour period after a meal with that when the meal was omitted. One 24-hour (00:00 to 00:00) period within each eating condition was selected for comparison. The mean duration in all hypoglycemic ranges doubled (P = .0584 or greater) when the last meal was omitted or eaten at 18:09 ± 0:39 instead of 19:43 ± 1:01, the usual time for the subjects' undisturbed eating. The mean duration of hypoglycemia was greatest between 00:00 to 06:00 compared to the 3 other 6-hour periods of the day. Increased hypoglycemia occurs when the subject's last meal is eaten earlier or omitted and may not be recognized because it occurs predominately during sleep. When titrating basal insulin from the morning FPG, considerations should be given to the effect of the last meal of the day and possible hypoglycemia between 00:00 and 06:00 to avoid excessive basal insulin treatment.

An assessment of change in risk perception and optimistic bias for hurricanes among Gulf Coast residents.

PubMed

Trumbo, Craig; Meyer, Michelle A; Marlatt, Holly; Peek, Lori; Morrissey, Bridget

2014-06-01

This study focuses on levels of concern for hurricanes among individuals living along the Gulf Coast during the quiescent two-year period following the exceptionally destructive 2005 hurricane season. A small study of risk perception and optimistic bias was conducted immediately following Hurricanes Katrina and Rita. Two years later, a follow-up was done in which respondents were recontacted. This provided an opportunity to examine changes, and potential causal ordering, in risk perception and optimistic bias. The analysis uses 201 panel respondents who were matched across the two mail surveys. Measures included hurricane risk perception, optimistic bias for hurricane evacuation, past hurricane experience, and a small set of demographic variables (age, sex, income, and education). Paired t-tests were used to compare scores across time. Hurricane risk perception declined and optimistic bias increased. Cross-lagged correlations were used to test the potential causal ordering between risk perception and optimistic bias, with a weak effect suggesting the former affects the latter. Additional cross-lagged analysis using structural equation modeling was used to look more closely at the components of optimistic bias (risk to self vs. risk to others). A significant and stronger potentially causal effect from risk perception to optimistic bias was found. Analysis of the experience and demographic variables' effects on risk perception and optimistic bias, and their change, provided mixed results. The lessening of risk perception and increase in optimistic bias over the period of quiescence suggest that risk communicators and emergency managers should direct attention toward reversing these trends to increase disaster preparedness. © 2013 Society for Risk Analysis.

Associations between lifestyle and air pollution exposure: Potential for confounding in large administrative data cohorts.

PubMed

Strak, Maciej; Janssen, Nicole; Beelen, Rob; Schmitz, Oliver; Karssenberg, Derek; Houthuijs, Danny; van den Brink, Carolien; Dijst, Martin; Brunekreef, Bert; Hoek, Gerard

2017-07-01

Cohorts based on administrative data have size advantages over individual cohorts in investigating air pollution risks, but often lack in-depth information on individual risk factors related to lifestyle. If there is a correlation between lifestyle and air pollution, omitted lifestyle variables may result in biased air pollution risk estimates. Correlations between lifestyle and air pollution can be induced by socio-economic status affecting both lifestyle and air pollution exposure. Our overall aim was to assess potential confounding by missing lifestyle factors on air pollution mortality risk estimates. The first aim was to assess associations between long-term exposure to several air pollutants and lifestyle factors. The second aim was to assess whether these associations were sensitive to adjustment for individual and area-level socioeconomic status (SES), and whether they differed between subgroups of the population. Using the obtained air pollution-lifestyle associations and indirect adjustment methods, our third aim was to investigate the potential bias due to missing lifestyle information on air pollution mortality risk estimates in administrative cohorts. We used a recent Dutch national health survey of 387,195 adults to investigate the associations of PM 10 , PM 2.5 , PM 2.5-10 , PM 2.5 absorbance, OP DTT, OP ESR and NO 2 annual average concentrations at the residential address from land use regression models with individual smoking habits, alcohol consumption, physical activity and body mass index. We assessed the associations with and without adjustment for neighborhood and individual SES characteristics typically available in administrative data cohorts. We illustrated the effect of including lifestyle information on the air pollution mortality risk estimates in administrative cohort studies using a published indirect adjustment method. Current smoking and alcohol consumption were generally positively associated with air pollution. Physical activity and overweight were negatively associated with air pollution. The effect estimates were small (mostly <5% of the air pollutant standard deviations). Direction and magnitude of the associations depended on the pollutant, use of continuous vs. categorical scale of the lifestyle variable, and level of adjustment for individual and area-level SES. Associations further differed between subgroups (age, sex) in the population. Despite the small associations between air pollution and smoking intensity, indirect adjustment resulted in considerable changes of air pollution risk estimates for cardiovascular and especially lung cancer mortality. Individual lifestyle-related risk factors were weakly associated with long-term exposure to air pollution in the Netherlands. Indirect adjustment for missing lifestyle factors in administrative data cohort studies may substantially affect air pollution mortality risk estimates. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Development of Spatiotemporal Bias-Correction Techniques for Downscaling GCM Predictions

NASA Astrophysics Data System (ADS)

Hwang, S.; Graham, W. D.; Geurink, J.; Adams, A.; Martinez, C. J.

2010-12-01

Accurately representing the spatial variability of precipitation is an important factor for predicting watershed response to climatic forcing, particularly in small, low-relief watersheds affected by convective storm systems. Although Global Circulation Models (GCMs) generally preserve spatial relationships between large-scale and local-scale mean precipitation trends, most GCM downscaling techniques focus on preserving only observed temporal variability on point by point basis, not spatial patterns of events. Downscaled GCM results (e.g., CMIP3 ensembles) have been widely used to predict hydrologic implications of climate variability and climate change in large snow-dominated river basins in the western United States (Diffenbaugh et al., 2008; Adam et al., 2009). However fewer applications to smaller rain-driven river basins in the southeastern US (where preserving spatial variability of rainfall patterns may be more important) have been reported. In this study a new method was developed to bias-correct GCMs to preserve both the long term temporal mean and variance of the precipitation data, and the spatial structure of daily precipitation fields. Forty-year retrospective simulations (1960-1999) from 16 GCMs were collected (IPCC, 2007; WCRP CMIP3 multi-model database: https://esg.llnl.gov:8443/), and the daily precipitation data at coarse resolution (i.e., 280km) were interpolated to 12km spatial resolution and bias corrected using gridded observations over the state of Florida (Maurer et al., 2002; Wood et al, 2002; Wood et al, 2004). In this method spatial random fields which preserved the observed spatial correlation structure of the historic gridded observations and the spatial mean corresponding to the coarse scale GCM daily rainfall were generated. The spatiotemporal variability of the spatio-temporally bias-corrected GCMs were evaluated against gridded observations, and compared to the original temporally bias-corrected and downscaled CMIP3 data for the central Florida. The hydrologic response of two southwest Florida watersheds to the gridded observation data, the original bias corrected CMIP3 data, and the new spatiotemporally corrected CMIP3 predictions was compared using an integrated surface-subsurface hydrologic model developed by Tampa Bay Water.

North Atlantic climate model bias influence on multiyear predictability

NASA Astrophysics Data System (ADS)

Wu, Y.; Park, T.; Park, W.; Latif, M.

2018-01-01

The influences of North Atlantic biases on multiyear predictability of unforced surface air temperature (SAT) variability are examined in the Kiel Climate Model (KCM). By employing a freshwater flux correction over the North Atlantic to the model, which strongly alleviates both North Atlantic sea surface salinity (SSS) and sea surface temperature (SST) biases, the freshwater flux-corrected integration depicts significantly enhanced multiyear SAT predictability in the North Atlantic sector in comparison to the uncorrected one. The enhanced SAT predictability in the corrected integration is due to a stronger and more variable Atlantic Meridional Overturning Circulation (AMOC) and its enhanced influence on North Atlantic SST. Results obtained from preindustrial control integrations of models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) support the findings obtained from the KCM: models with large North Atlantic biases tend to have a weak AMOC influence on SAT and exhibit a smaller SAT predictability over the North Atlantic sector.

Steady-State Density Functional Theory for Finite Bias Conductances.

PubMed

Stefanucci, G; Kurth, S

2015-12-09

In the framework of density functional theory, a formalism to describe electronic transport in the steady state is proposed which uses the density on the junction and the steady current as basic variables. We prove that, in a finite window around zero bias, there is a one-to-one map between the basic variables and both local potential on as well as bias across the junction. The resulting Kohn-Sham system features two exchange-correlation (xc) potentials, a local xc potential, and an xc contribution to the bias. For weakly coupled junctions the xc potentials exhibit steps in the density-current plane which are shown to be crucial to describe the Coulomb blockade diamonds. At small currents these steps emerge as the equilibrium xc discontinuity bifurcates. The formalism is applied to a model benzene junction, finding perfect agreement with the orthodox theory of Coulomb blockade.

Inference for binomial probability based on dependent Bernoulli random variables with applications to meta-analysis and group level studies.

PubMed

Bakbergenuly, Ilyas; Kulinskaya, Elena; Morgenthaler, Stephan

2016-07-01

We study bias arising as a result of nonlinear transformations of random variables in random or mixed effects models and its effect on inference in group-level studies or in meta-analysis. The findings are illustrated on the example of overdispersed binomial distributions, where we demonstrate considerable biases arising from standard log-odds and arcsine transformations of the estimated probability p̂, both for single-group studies and in combining results from several groups or studies in meta-analysis. Our simulations confirm that these biases are linear in ρ, for small values of ρ, the intracluster correlation coefficient. These biases do not depend on the sample sizes or the number of studies K in a meta-analysis and result in abysmal coverage of the combined effect for large K. We also propose bias-correction for the arcsine transformation. Our simulations demonstrate that this bias-correction works well for small values of the intraclass correlation. The methods are applied to two examples of meta-analyses of prevalence. © 2016 The Authors. Biometrical Journal Published by Wiley-VCH Verlag GmbH & Co. KGaA.

Validation of satellite-based rainfall in Kalahari

NASA Astrophysics Data System (ADS)

Lekula, Moiteela; Lubczynski, Maciek W.; Shemang, Elisha M.; Verhoef, Wouter

2018-06-01

Water resources management in arid and semi-arid areas is hampered by insufficient rainfall data, typically obtained from sparsely distributed rain gauges. Satellite-based rainfall estimates (SREs) are alternative sources of such data in these areas. In this study, daily rainfall estimates from FEWS-RFE∼11 km, TRMM-3B42∼27 km, CMOPRH∼27 km and CMORPH∼8 km were evaluated against nine, daily rain gauge records in Central Kalahari Basin (CKB), over a five-year period, 01/01/2001-31/12/2005. The aims were to evaluate the daily rainfall detection capabilities of the four SRE algorithms, analyze the spatio-temporal variability of rainfall in the CKB and perform bias-correction of the four SREs. Evaluation methods included scatter plot analysis, descriptive statistics, categorical statistics and bias decomposition. The spatio-temporal variability of rainfall, was assessed using the SREs' mean annual rainfall, standard deviation, coefficient of variation and spatial correlation functions. Bias correction of the four SREs was conducted using a Time-Varying Space-Fixed bias-correction scheme. The results underlined the importance of validating daily SREs, as they had different rainfall detection capabilities in the CKB. The FEWS-RFE∼11 km performed best, providing better results of descriptive and categorical statistics than the other three SREs, although bias decomposition showed that all SREs underestimated rainfall. The analysis showed that the most reliable SREs performance analysis indicator were the frequency of "miss" rainfall events and the "miss-bias", as they directly indicated SREs' sensitivity and bias of rainfall detection, respectively. The Time Varying and Space Fixed (TVSF) bias-correction scheme, improved some error measures but resulted in the reduction of the spatial correlation distance, thus increased, already high, spatial rainfall variability of all the four SREs. This study highlighted SREs as valuable source of daily rainfall data providing good spatio-temporal data coverage especially suitable for areas with limited rain gauges, such as the CKB, but also emphasized SREs' drawbacks, creating avenue for follow up research.

Subseasonal forecast skills and biases of global summer monsoons in the NCEP Climate Forecast System version 2

NASA Astrophysics Data System (ADS)

Liu, Xiangwen; Yang, Song; Li, Qiaoping; Kumar, Arun; Weaver, Scott; Liu, Shi

2014-03-01

Subseasonal forecast skills and biases of global summer monsoons are diagnosed using daily data from the hindcasts of 45-day integrations by the NCEP Climate Forecast System version 2. Predictions for subseasonal variability of zonal wind and precipitation are generally more skillful over the Asian and Australian monsoon regions than other monsoon regions. Climatologically, forecasts for the variations of dynamical monsoon indices have high skills at leads of about 2 weeks. However, apparent interannual differences exist, with high skills up to 5 weeks in exceptional cases. Comparisons for the relationships of monsoon indices with atmospheric circulation and precipitation patterns between skillful and unskillful forecasts indicate that skills for subseasonal variability of a monsoon index depend partially on the degree to which the observed variability of the index attributes to the variation of large-scale circulation. Thus, predictions are often more skillful when the index is closely linked to atmospheric circulation over a broad region than over a regional and narrow range. It is also revealed that, the subseasonal variations of biases of winds, precipitation, and surface temperature over various monsoon regions are captured by a first mode with seasonally independent biases and a second mode with apparent phase transition of biases during summer. The first mode indicates the dominance of overall weaker-than-observed summer monsoons over major monsoon regions. However, at certain stages of monsoon evolution, these underestimations are regionally offset or intensified by the time evolving biases portrayed by the second mode. This feature may be partially related to factors such as the shifts of subtropical highs and intertropical convergence zones, the reversal of biases of surface temperature over some monsoon regions, and the transition of regional circulation system. The significant geographical differences in bias growth with increasing lead time reflect the distinctions of initial memory capability of the climate system over different monsoon regions.

Impacts of Model Bias on the Climate Change Signal and Effects of Weighted Ensembles of Regional Climate Model Simulations: A Case Study over Southern Québec, Canada

DOE PAGES

Eum, Hyung-Il; Gachon, Philippe; Laprise, René

2016-01-01

This study examined the impact of model biases on climate change signals for daily precipitation and for minimum and maximum temperatures. Through the use of multiple climate scenarios from 12 regional climate model simulations, the ensemble mean, and three synthetic simulations generated by a weighting procedure, we investigated intermodel seasonal climate change signals between current and future periods, for both median and extreme precipitation/temperature values. A significant dependence of seasonal climate change signals on the model biases over southern Québec in Canada was detected for temperatures, but not for precipitation. This suggests that the regional temperature change signal is affectedmore » by local processes. Seasonally, model bias affects future mean and extreme values in winter and summer. In addition, potentially large increases in future extremes of temperature and precipitation values were projected. For three synthetic scenarios, systematically less bias and a narrow range of mean change for all variables were projected compared to those of climate model simulations. In addition, synthetic scenarios were found to better capture the spatial variability of extreme cold temperatures than the ensemble mean scenario. Finally, these results indicate that the synthetic scenarios have greater potential to reduce the uncertainty of future climate projections and capture the spatial variability of extreme climate events.« less

Impacts of Model Bias on the Climate Change Signal and Effects of Weighted Ensembles of Regional Climate Model Simulations: A Case Study over Southern Québec, Canada

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

Eum, Hyung-Il; Gachon, Philippe; Laprise, René

This study examined the impact of model biases on climate change signals for daily precipitation and for minimum and maximum temperatures. Through the use of multiple climate scenarios from 12 regional climate model simulations, the ensemble mean, and three synthetic simulations generated by a weighting procedure, we investigated intermodel seasonal climate change signals between current and future periods, for both median and extreme precipitation/temperature values. A significant dependence of seasonal climate change signals on the model biases over southern Québec in Canada was detected for temperatures, but not for precipitation. This suggests that the regional temperature change signal is affectedmore » by local processes. Seasonally, model bias affects future mean and extreme values in winter and summer. In addition, potentially large increases in future extremes of temperature and precipitation values were projected. For three synthetic scenarios, systematically less bias and a narrow range of mean change for all variables were projected compared to those of climate model simulations. In addition, synthetic scenarios were found to better capture the spatial variability of extreme cold temperatures than the ensemble mean scenario. Finally, these results indicate that the synthetic scenarios have greater potential to reduce the uncertainty of future climate projections and capture the spatial variability of extreme climate events.« less

Spatial scaling of net primary productivity using subpixel landcover information

NASA Astrophysics Data System (ADS)

Chen, X. F.; Chen, Jing M.; Ju, Wei M.; Ren, L. L.

2008-10-01

Gridding the land surface into coarse homogeneous pixels may cause important biases on ecosystem model estimations of carbon budget components at local, regional and global scales. These biases result from overlooking subpixel variability of land surface characteristics. Vegetation heterogeneity is an important factor introducing biases in regional ecological modeling, especially when the modeling is made on large grids. This study suggests a simple algorithm that uses subpixel information on the spatial variability of land cover type to correct net primary productivity (NPP) estimates, made at coarse spatial resolutions where the land surface is considered as homogeneous within each pixel. The algorithm operates in such a way that NPP obtained from calculations made at coarse spatial resolutions are multiplied by simple functions that attempt to reproduce the effects of subpixel variability of land cover type on NPP. Its application to a carbon-hydrology coupled model(BEPS-TerrainLab model) estimates made at a 1-km resolution over a watershed (named Baohe River Basin) located in the southwestern part of Qinling Mountains, Shaanxi Province, China, improved estimates of average NPP as well as its spatial variability.

Sex Differences in the Tendency to Omit Items on Multiple-Choice Tests: 1980-2000

ERIC Educational Resources Information Center

von Schrader, Sarah; Ansley, Timothy

2006-01-01

Much has been written concerning the potential group differences in responding to multiple-choice achievement test items. This discussion has included references to possible disparities in tendency to omit such test items. When test scores are used for high-stakes decision making, even small differences in scores and rankings that arise from male…

78 FR 40382 - Modification of Class D and E Airspace; Twin Falls, ID

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-05

...-0258; Airspace Docket No. 13-ANM-12] Modification of Class D and E Airspace; Twin Falls, ID AGENCY... Class D airspace, omitted from the Title in the notice of proposed rulemaking is included in this rule... were received. Subsequent to publication, the FAA found that the Class D airspace reference was omitted...

Quality of care and the demand for health services in Bamako, Mali: the specific roles of structural, process, and outcome components.

PubMed

Mariko, Mamadou

2003-03-01

The public finance and foreign exchange crisis of the 1980s aggravated the unfavourable economic trends in many developing countries and resulted in budget cuts in the health sector. Policymakers, following the suggestions of World Bank experts, introduced user fees. Economic analysis of the demand for health care in these countries focused on the impact of price and income on health service utilisation. But the lesson to date from experiences in cost recovery is that without visible and fairly immediate improvements in the quality of care, the implementation of user fees will cause service utilisation to drop. For this reason, the role of quality of health care has been recently a subject of investigation in a number of health care demand studies. In spite of using the data from both households and facilities, recent studies are quite limited because they measure quality only by structural attributes (availability of drugs, equipment, number and qualifications of staff, and so on). Structural attributes of quality are necessary but not sufficient conditions for demand. A unique feature of this study is that it also considers the processes followed by practitioners and the outcome of care, to determine simultaneously the respective influence of price and quality on decision making. A nested multinomial logit was used to examine the choice between six alternatives (self-treatment, modern treatment at home, public hospital, public dispensary, for-profit facility and non-profit facility). The estimations are based on data from a statistically representative sample of 1104 patients from 1191 households and the data from a stratified random sample of 42 out of 84 facilities identified. The results indicate that omitting the process quality variables from the demand model produces a bias not only in the estimated coefficient of the "price" variable but also in coefficients of some structural attributes of the quality. The simulations suggest that price has a minor effect on utilisation of health services, and that health authorities can simultaneously double user fees and increase utilisation by emphasising improvement of both the structural and process quality of care in public health facilities.

A critical issue in model-based inference for studying trait-based community assembly and a solution.

PubMed

Ter Braak, Cajo J F; Peres-Neto, Pedro; Dray, Stéphane

2017-01-01

Statistical testing of trait-environment association from data is a challenge as there is no common unit of observation: the trait is observed on species, the environment on sites and the mediating abundance on species-site combinations. A number of correlation-based methods, such as the community weighted trait means method (CWM), the fourth-corner correlation method and the multivariate method RLQ, have been proposed to estimate such trait-environment associations. In these methods, valid statistical testing proceeds by performing two separate resampling tests, one site-based and the other species-based and by assessing significance by the largest of the two p -values (the p max test). Recently, regression-based methods using generalized linear models (GLM) have been proposed as a promising alternative with statistical inference via site-based resampling. We investigated the performance of this new approach along with approaches that mimicked the p max test using GLM instead of fourth-corner. By simulation using models with additional random variation in the species response to the environment, the site-based resampling tests using GLM are shown to have severely inflated type I error, of up to 90%, when the nominal level is set as 5%. In addition, predictive modelling of such data using site-based cross-validation very often identified trait-environment interactions that had no predictive value. The problem that we identify is not an "omitted variable bias" problem as it occurs even when the additional random variation is independent of the observed trait and environment data. Instead, it is a problem of ignoring a random effect. In the same simulations, the GLM-based p max test controlled the type I error in all models proposed so far in this context, but still gave slightly inflated error in more complex models that included both missing (but important) traits and missing (but important) environmental variables. For screening the importance of single trait-environment combinations, the fourth-corner test is shown to give almost the same results as the GLM-based tests in far less computing time.

Treatment Effect Estimation Using Nonlinear Two-Stage Instrumental Variable Estimators: Another Cautionary Note.

PubMed

Chapman, Cole G; Brooks, John M

2016-12-01

To examine the settings of simulation evidence supporting use of nonlinear two-stage residual inclusion (2SRI) instrumental variable (IV) methods for estimating average treatment effects (ATE) using observational data and investigate potential bias of 2SRI across alternative scenarios of essential heterogeneity and uniqueness of marginal patients. Potential bias of linear and nonlinear IV methods for ATE and local average treatment effects (LATE) is assessed using simulation models with a binary outcome and binary endogenous treatment across settings varying by the relationship between treatment effectiveness and treatment choice. Results show that nonlinear 2SRI models produce estimates of ATE and LATE that are substantially biased when the relationships between treatment and outcome for marginal patients are unique from relationships for the full population. Bias of linear IV estimates for LATE was low across all scenarios. Researchers are increasingly opting for nonlinear 2SRI to estimate treatment effects in models with binary and otherwise inherently nonlinear dependent variables, believing that it produces generally unbiased and consistent estimates. This research shows that positive properties of nonlinear 2SRI rely on assumptions about the relationships between treatment effect heterogeneity and choice. © Health Research and Educational Trust.

Towards identification of relevant variables in the observed aerosol optical depth bias between MODIS and AERONET observations

NASA Astrophysics Data System (ADS)

Malakar, N. K.; Lary, D. J.; Gencaga, D.; Albayrak, A.; Wei, J.

2013-08-01

Measurements made by satellite remote sensing, Moderate Resolution Imaging Spectroradiometer (MODIS), and globally distributed Aerosol Robotic Network (AERONET) are compared. Comparison of the two datasets measurements for aerosol optical depth values show that there are biases between the two data products. In this paper, we present a general framework towards identifying relevant set of variables responsible for the observed bias. We present a general framework to identify the possible factors influencing the bias, which might be associated with the measurement conditions such as the solar and sensor zenith angles, the solar and sensor azimuth, scattering angles, and surface reflectivity at the various measured wavelengths, etc. Specifically, we performed analysis for remote sensing Aqua-Land data set, and used machine learning technique, neural network in this case, to perform multivariate regression between the ground-truth and the training data sets. Finally, we used mutual information between the observed and the predicted values as the measure of similarity to identify the most relevant set of variables. The search is brute force method as we have to consider all possible combinations. The computations involves a huge number crunching exercise, and we implemented it by writing a job-parallel program.

Measurement error in epidemiologic studies of air pollution based on land-use regression models.

PubMed

Basagaña, Xavier; Aguilera, Inmaculada; Rivera, Marcela; Agis, David; Foraster, Maria; Marrugat, Jaume; Elosua, Roberto; Künzli, Nino

2013-10-15

Land-use regression (LUR) models are increasingly used to estimate air pollution exposure in epidemiologic studies. These models use air pollution measurements taken at a small set of locations and modeling based on geographical covariates for which data are available at all study participant locations. The process of LUR model development commonly includes a variable selection procedure. When LUR model predictions are used as explanatory variables in a model for a health outcome, measurement error can lead to bias of the regression coefficients and to inflation of their variance. In previous studies dealing with spatial predictions of air pollution, bias was shown to be small while most of the effect of measurement error was on the variance. In this study, we show that in realistic cases where LUR models are applied to health data, bias in health-effect estimates can be substantial. This bias depends on the number of air pollution measurement sites, the number of available predictors for model selection, and the amount of explainable variability in the true exposure. These results should be taken into account when interpreting health effects from studies that used LUR models.

Bias in Prediction: A Test of Three Models with Elementary School Children

ERIC Educational Resources Information Center

Frazer, William G.; And Others

1975-01-01

Explores the differences among the traditional single-equation prediction model of test bias, the Cleary and the Thorndike model in a situation involving typical educational variables with young female and male children. (Author/DEP)

Genetic instrumental variable regression: Explaining socioeconomic and health outcomes in nonexperimental data

PubMed Central

DiPrete, Thomas A.; Burik, Casper A. P.; Koellinger, Philipp D.

2018-01-01

Identifying causal effects in nonexperimental data is an enduring challenge. One proposed solution that recently gained popularity is the idea to use genes as instrumental variables [i.e., Mendelian randomization (MR)]. However, this approach is problematic because many variables of interest are genetically correlated, which implies the possibility that many genes could affect both the exposure and the outcome directly or via unobserved confounding factors. Thus, pleiotropic effects of genes are themselves a source of bias in nonexperimental data that would also undermine the ability of MR to correct for endogeneity bias from nongenetic sources. Here, we propose an alternative approach, genetic instrumental variable (GIV) regression, that provides estimates for the effect of an exposure on an outcome in the presence of pleiotropy. As a valuable byproduct, GIV regression also provides accurate estimates of the chip heritability of the outcome variable. GIV regression uses polygenic scores (PGSs) for the outcome of interest which can be constructed from genome-wide association study (GWAS) results. By splitting the GWAS sample for the outcome into nonoverlapping subsamples, we obtain multiple indicators of the outcome PGSs that can be used as instruments for each other and, in combination with other methods such as sibling fixed effects, can address endogeneity bias from both pleiotropy and the environment. In two empirical applications, we demonstrate that our approach produces reasonable estimates of the chip heritability of educational attainment (EA) and show that standard regression and MR provide upwardly biased estimates of the effect of body height on EA. PMID:29686100

Genetic instrumental variable regression: Explaining socioeconomic and health outcomes in nonexperimental data.

PubMed

DiPrete, Thomas A; Burik, Casper A P; Koellinger, Philipp D

2018-05-29

Identifying causal effects in nonexperimental data is an enduring challenge. One proposed solution that recently gained popularity is the idea to use genes as instrumental variables [i.e., Mendelian randomization (MR)]. However, this approach is problematic because many variables of interest are genetically correlated, which implies the possibility that many genes could affect both the exposure and the outcome directly or via unobserved confounding factors. Thus, pleiotropic effects of genes are themselves a source of bias in nonexperimental data that would also undermine the ability of MR to correct for endogeneity bias from nongenetic sources. Here, we propose an alternative approach, genetic instrumental variable (GIV) regression, that provides estimates for the effect of an exposure on an outcome in the presence of pleiotropy. As a valuable byproduct, GIV regression also provides accurate estimates of the chip heritability of the outcome variable. GIV regression uses polygenic scores (PGSs) for the outcome of interest which can be constructed from genome-wide association study (GWAS) results. By splitting the GWAS sample for the outcome into nonoverlapping subsamples, we obtain multiple indicators of the outcome PGSs that can be used as instruments for each other and, in combination with other methods such as sibling fixed effects, can address endogeneity bias from both pleiotropy and the environment. In two empirical applications, we demonstrate that our approach produces reasonable estimates of the chip heritability of educational attainment (EA) and show that standard regression and MR provide upwardly biased estimates of the effect of body height on EA. Copyright © 2018 the Author(s). Published by PNAS.

Detecting and correcting the bias of unmeasured factors using perturbation analysis: a data-mining approach.

PubMed

Lee, Wen-Chung

2014-02-05

The randomized controlled study is the gold-standard research method in biomedicine. In contrast, the validity of a (nonrandomized) observational study is often questioned because of unknown/unmeasured factors, which may have confounding and/or effect-modifying potential. In this paper, the author proposes a perturbation test to detect the bias of unmeasured factors and a perturbation adjustment to correct for such bias. The proposed method circumvents the problem of measuring unknowns by collecting the perturbations of unmeasured factors instead. Specifically, a perturbation is a variable that is readily available (or can be measured easily) and is potentially associated, though perhaps only very weakly, with unmeasured factors. The author conducted extensive computer simulations to provide a proof of concept. Computer simulations show that, as the number of perturbation variables increases from data mining, the power of the perturbation test increased progressively, up to nearly 100%. In addition, after the perturbation adjustment, the bias decreased progressively, down to nearly 0%. The data-mining perturbation analysis described here is recommended for use in detecting and correcting the bias of unmeasured factors in observational studies.

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.

Estimating Causal Effects of Local Air Pollution on Daily Deaths: Effect of Low Levels.

PubMed

Schwartz, Joel; Bind, Marie-Abele; Koutrakis, Petros

2017-01-01

Although many time-series studies have established associations of daily pollution variations with daily deaths, there are fewer at low concentrations, or focused on locally generated pollution, which is becoming more important as regulations reduce regional transport. Causal modeling approaches are also lacking. We used causal modeling to estimate the impact of local air pollution on mortality at low concentrations. Using an instrumental variable approach, we developed an instrument for variations in local pollution concentrations that is unlikely to be correlated with other causes of death, and examined its association with daily deaths in the Boston, Massachusetts, area. We combined height of the planetary boundary layer and wind speed, which affect concentrations of local emissions, to develop the instrument for particulate matter ≤ 2.5 μm (PM2.5), black carbon (BC), or nitrogen dioxide (NO2) variations that were independent of year, month, and temperature. We also used Granger causality to assess whether omitted variable confounding existed. We estimated that an interquartile range increase in the instrument for local PM2.5 was associated with a 0.90% increase in daily deaths (95% CI: 0.25, 1.56). A similar result was found for BC, and a weaker association with NO2. The Granger test found no evidence of omitted variable confounding for the instrument. A separate test confirmed the instrument was not associated with mortality independent of pollution. Furthermore, the association remained when all days with PM2.5 concentrations > 30 μg/m3 were excluded from the analysis (0.84% increase in daily deaths; 95% CI: 0.19, 1.50). We conclude that there is a causal association of local air pollution with daily deaths at concentrations below U.S. EPA standards. The estimated attributable risk in Boston exceeded 1,800 deaths during the study period, indicating that important public health benefits can follow from further control efforts. Citation: Schwartz J, Bind MA, Koutrakis P. 2017. Estimating causal effects of local air pollution on daily deaths: effect of low levels. Environ Health Perspect 125:23-29; http://dx.doi.org/10.1289/EHP232.

Pluck or Luck: Does Trait Variation or Chance Drive Variation in Lifetime Reproductive Success?

PubMed

Snyder, Robin E; Ellner, Stephen P

2018-04-01

While there has been extensive interest in how intraspecific trait variation affects ecological processes, outcomes are highly variable even when individuals are identical: some are lucky, while others are not. Trait variation is therefore important only if it adds substantially to the variability produced by luck. We ask when trait variation has a substantial effect on variability in lifetime reproductive success (LRS), using two approaches: (1) we partition the variation in LRS into contributions from luck and trait variation and (2) we ask what can be inferred about an individual's traits and with what certainty, given their observed LRS. In theoretical stage- and size-structured models and two empirical case studies, we find that luck usually dominates the variance of LRS. Even when individuals differ substantially in ways that affect expected LRS, unless the effects of luck are substantially reduced (e.g., low variability in reproductive life span or annual fecundity), most variance in lifetime outcomes is due to luck, implying that departures from "null" models omitting trait variation will be hard to detect. Luck also obscures the relationship between realized LRS and individual traits. While trait variation may influence the fate of populations, luck often governs the lives of individuals.

Quality of narrative operative reports in pancreatic surgery

PubMed Central

Wiebe, Meagan E.; Sandhu, Lakhbir; Takata, Julie L.; Kennedy, Erin D.; Baxter, Nancy N.; Gagliardi, Anna R.; Urbach, David R.; Wei, Alice C.

2013-01-01

Background Quality in health care can be evaluated using quality indicators (QIs). Elements contained in the surgical operative report are potential sources for QI data, but little is known about the completeness of the narrative operative report (NR). We evaluated the completeness of the NR for patients undergoing a pancreaticoduodenectomy. Methods We reviewed NRs for patients undergoing a pancreaticoduodenectomy over a 1-year period. We extracted 79 variables related to patient and narrator characteristics, process of care measures, surgical technique and oncology-related outcomes by document analysis. Data were coded and evaluated for completeness. Results We analyzed 74 NRs. The median number of variables reported was 43.5 (range 13–54). Variables related to surgical technique were most complete. Process of care and oncology-related variables were often omitted. Completeness of the NR was associated with longer operative duration. Conclusion The NRs were often incomplete and of poor quality. Important elements, including process of care and oncology-related data, were frequently missing. Thus, the NR is an inadequate data source for QI. Development and use of alternative reporting methods, including standardized synoptic operative reports, should be encouraged to improve documentation of care and serve as a measure of quality of surgical care. PMID:24067527

Quality of narrative operative reports in pancreatic surgery.

PubMed

Wiebe, Meagan E; Sandhu, Lakhbir; Takata, Julie L; Kennedy, Erin D; Baxter, Nancy N; Gagliardi, Anna R; Urbach, David R; Wei, Alice C

2013-10-01

Quality in health care can be evaluated using quality indicators (QIs). Elements contained in the surgical operative report are potential sources for QI data, but little is known about the completeness of the narrative operative report (NR). We evaluated the completeness of the NR for patients undergoing a pancreaticoduodenectomy. We reviewed NRs for patients undergoing a pancreaticoduodenectomy over a 1-year period. We extracted 79 variables related to patient and narrator characteristics, process of care measures, surgical technique and oncology-related outcomes by document analysis. Data were coded and evaluated for completeness. We analyzed 74 NRs. The median number of variables reported was 43.5 (range 13-54). Variables related to surgical technique were most complete. Process of care and oncology-related variables were often omitted. Completeness of the NR was associated with longer operative duration. The NRs were often incomplete and of poor quality. Important elements, including process of care and oncology-related data, were frequently missing. Thus, the NR is an inadequate data source for QI. Development and use of alternative reporting methods, including standardized synoptic operative reports, should be encouraged to improve documentation of care and serve as a measure of quality of surgical care.

An Investigation of Gender, Income, and Special Education Status Bias on Curriculum-Based Measurement Slope in Reading

ERIC Educational Resources Information Center

Yeo, Seungsoo; Fearrington, Jamie; Christ, Theodore J.

2011-01-01

This study investigated slope bias on student background variables for both Curriculum Based Measurement of Oral Reading (CBM-R) and Curriculum Based Measurement Maze Reading (Maze). Benchmark scores from 1,738 students in Grades 3 through 8 were used to examine potential slope bias in CBM-R and Maze. Latent growth modeling was used to both…

Body Mass Index, Sex, Interview Protocol, and Children’s Accuracy for Reporting Kilocalories Observed Eaten at School Meals

PubMed Central

Baxter, Suzanne Domel; Smith, Albert F.; Litaker, Mark S.; Guinn, Caroline H.; Nichols, Michele D.; Miller, Patricia H.; Kipp, Katherine

2008-01-01

This pilot study investigated body mass index (BMI), sex, interview protocol, and children’s accuracy for reporting kilocalories. Forty fourth-grade children (20 low BMI [LBMI; ≥5th and <50th percentiles; 10 boys; 15 black], 20 high BMI [HBMI;≥ 85th percentile; 10 boys; 15 black]) were observed eating school meals (breakfast, lunch) and interviewed either that evening about the prior 24 hours (24E) or the next morning about the previous day (PDM), with 10 LBMI (5 boys) and 10 HBMI (5 boys) per interview protocol. Five kilocalorie variables were analyzed using separate 4-factor (BMI group, sex, race, interview protocol) analyses of variance. No effects were found for reported or matched kilocalories. More kilocalories were observed (p<0.02) and omitted (p<0.05) by HBMI than LBMI children. For intruded kilocalories, means were smaller (better) for HBMI girls than HBMI boys, but larger for LBMI girls than LBMI boys (interaction p<0.04); LBMI girls intruded the most while HBMI girls intruded the least. For interview protocol, omitted and intruded kilocalories were higher (worse), although not significantly so (ps<0.11), for PDM than 24E. These results illuminate relations of BMI, sex, interview protocol, and children’s reporting accuracy, and are consistent with results concerning BMI and sex from studies with adults. PMID:17000199

Nonconscious attention bias to threat is correlated with anterior cingulate cortex gray matter volume: a voxel-based morphometry result and replication.

PubMed

Carlson, Joshua M; Beacher, Felix; Reinke, Karen S; Habib, Reza; Harmon-Jones, Eddie; Mujica-Parodi, Lilianne R; Hajcak, Greg

2012-01-16

An important aspect of the fear response is the allocation of spatial attention toward threatening stimuli. This response is so powerful that modulations in spatial attention can occur automatically without conscious awareness. Functional neuroimaging research suggests that the amygdala and anterior cingulate cortex (ACC) form a network involved in the rapid orienting of attention to threat. A hyper-responsive attention bias to threat is a common component of anxiety disorders. Yet, little is known of how individual differences in underlying brain morphometry relate to variability in attention bias to threat. Here, we performed two experiments using dot-probe tasks that measured individuals' attention bias to backward masked fearful faces. We collected whole-brain structural magnetic resonance images and used voxel-based morphometry to measure brain morphometry. We tested the hypothesis that reduced gray matter within the amygdala and ACC would be associated with reduced attention bias to threat. In Experiment 1, we found that backward masked fearful faces captured spatial attention and that elevated attention bias to masked threat was associated with greater ACC gray matter volumes. In Experiment 2, this association was replicated in a separate sample. Thus, we provide initial and replicating evidence that ACC gray matter volume is correlated with biased attention to threat. Importantly, we demonstrate that variability in affective attention bias within the healthy population is associated with ACC morphometry. This result opens the door for future research into the underlying brain morphometry associated with attention bias in clinically anxious populations. Copyright © 2011 Elsevier Inc. All rights reserved.

The Observed Behavior of the Bias in MODIS-retrieved Cloud Droplet Effective Radius through MISR-MODIS Data Fusion

NASA Astrophysics Data System (ADS)

Fu, D.; Di Girolamo, L.; Liang, L.; Zhao, G.

2017-12-01

Listed as one of the Essential Climate Variables by the Global Climate Observing System, the effective radius (Re) of the cloud drop size distribution plays an important role in the energy and water cycles of the Earth system. Re is retrieved from several passive sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), based on a visible and near-infrared bi-spectral technique that had its foundation more than a quarter century ago. This technique makes a wide range of assumptions, including 1-D radiative transfer, assumed single-mode drop size distribution, and cloud horizontal and vertical homogeneity. It is well known that deviations from these assumptions lead to bias in the retrieved Re. Recently, an effort to characterize the bias in MODIS-retrieved Re through MISR-MODIS data fusion revealed biases in the zonal-mean values of MODIS-retrieved Re that varied from 2 to 11 µm, depending on latitude (Liang et al., 2015). Here, in a push towards bias-correction of MODIS-retrieved Re, we further examine the bias with MISR-MODIS data fusion as it relates to other observed cloud properties, such as cloud-top height and the spatial variability of the radiance field, sun-view geometry, and the driving meteorology had from reanalysis data. Our results show interesting relationships in Re bias behavior with these observed properties, revealing that while Re bias do show a certain degree of dependence on some properties, no single property dominates the behavior in MODIS-retrieved Re bias.

A new dynamical downscaling approach with GCM bias corrections and spectral nudging

NASA Astrophysics Data System (ADS)

Xu, Zhongfeng; Yang, Zong-Liang

2015-04-01

To improve confidence in regional projections of future climate, a new dynamical downscaling (NDD) approach with both general circulation model (GCM) bias corrections and spectral nudging is developed and assessed over North America. GCM biases are corrected by adjusting GCM climatological means and variances based on reanalysis data before the GCM output is used to drive a regional climate model (RCM). Spectral nudging is also applied to constrain RCM-based biases. Three sets of RCM experiments are integrated over a 31 year period. In the first set of experiments, the model configurations are identical except that the initial and lateral boundary conditions are derived from either the original GCM output, the bias-corrected GCM output, or the reanalysis data. The second set of experiments is the same as the first set except spectral nudging is applied. The third set of experiments includes two sensitivity runs with both GCM bias corrections and nudging where the nudging strength is progressively reduced. All RCM simulations are assessed against North American Regional Reanalysis. The results show that NDD significantly improves the downscaled mean climate and climate variability relative to other GCM-driven RCM downscaling approach in terms of climatological mean air temperature, geopotential height, wind vectors, and surface air temperature variability. In the NDD approach, spectral nudging introduces the effects of GCM bias corrections throughout the RCM domain rather than just limiting them to the initial and lateral boundary conditions, thereby minimizing climate drifts resulting from both the GCM and RCM biases.

The use of cognitive task analysis to reveal the instructional limitations of experts in the teaching of procedural skills.

PubMed

Sullivan, Maura E; Yates, Kenneth A; Inaba, Kenji; Lam, Lydia; Clark, Richard E

2014-05-01

Because of the automated nature of knowledge, experts tend to omit information when describing a task. A potential solution is cognitive task analysis (CTA). The authors investigated the percentage of knowledge experts omitted when teaching a cricothyrotomy to determine the percentage of additional knowledge gained during a CTA interview. Three experts were videotaped teaching a cricothyrotomy in 2010 at the University of Southern California. After transcription, they participated in CTA interviews for the same procedure. Three additional surgeons were recruited to perform a CTA for the procedure, and a "gold standard" task list was created. Transcriptions from the teaching sessions were compared with the task list to identify omitted steps (both "what" and "how" to do). Transcripts from the CTA interviews were compared against the task list to determine the percentage of knowledge articulated by each expert during the initial "free recall" (unprompted) phase of the CTA interview versus the amount of knowledge gained by using CTA elicitation techniques (prompted). Experts omitted an average of 71% (10/14) of clinical knowledge steps, 51% (14/27) of action steps, and 73% (3.6/5) of decision steps. For action steps, experts described "how to do it" only 13% (3.6/27) of the time. The average number of steps that were described increased from 44% (20/46) when unprompted to 66% (31/46) when prompted. This study supports previous research that experts unintentionally omit knowledge when describing a procedure. CTA is a useful method to extract automated knowledge and augment expert knowledge recall during teaching.

Sensitivity analysis for linear structural equation models, longitudinal mediation with latent growth models and blended learning in biostatistics education

NASA Astrophysics Data System (ADS)

Sullivan, Adam John

In chapter 1, we consider the biases that may arise when an unmeasured confounder is omitted from a structural equation model (SEM) and sensitivity analysis techniques to correct for such biases. We give an analysis of which effects in an SEM are and are not biased by an unmeasured confounder. It is shown that a single unmeasured confounder will bias not just one but numerous effects in an SEM. We present sensitivity analysis techniques to correct for biases in total, direct, and indirect effects when using SEM analyses, and illustrate these techniques with a study of aging and cognitive function. In chapter 2, we consider longitudinal mediation with latent growth curves. We define the direct and indirect effects using counterfactuals and consider the assumptions needed for identifiability of those effects. We develop models with a binary treatment/exposure followed by a model where treatment/exposure changes with time allowing for treatment/exposure-mediator interaction. We thus formalize mediation analysis with latent growth curve models using counterfactuals, makes clear the assumptions and extends these methods to allow for exposure mediator interactions. We present and illustrate the techniques with a study on Multiple Sclerosis(MS) and depression. In chapter 3, we report on a pilot study in blended learning that took place during the Fall 2013 and Summer 2014 semesters here at Harvard. We blended the traditional BIO 200: Principles of Biostatistics and created ID 200: Principles of Biostatistics and epidemiology. We used materials from the edX course PH207x: Health in Numbers: Quantitative Methods in Clinical & Public Health Research and used. These materials were used as a video textbook in which students would watch a given number of these videos prior to class. Using surveys as well as exam data we informally assess these blended classes from the student's perspective as well as a comparison of these students with students in another course, BIO 201: Introduction to Statistical Methods in Fall 2013 as well as students from BIO 200 in Fall semesters of 1992 and 1993. We then suggest improvements upon our original course designs and follow up with an informal look at how these implemented changes affected the second offering of the newly blended ID 200 in Summer 2014.

Effect of study design on the reported effect of cardiac resynchronization therapy (CRT) on quantitative physiological measures: stratified meta-analysis in narrow-QRS heart failure and implications for planning future studies.

PubMed

Jabbour, Richard J; Shun-Shin, Matthew J; Finegold, Judith A; Afzal Sohaib, S M; Cook, Christopher; Nijjer, Sukhjinder S; Whinnett, Zachary I; Manisty, Charlotte H; Brugada, Josep; Francis, Darrel P

2015-01-06

Biventricular pacing (CRT) shows clear benefits in heart failure with wide QRS, but results in narrow QRS have appeared conflicting. We tested the hypothesis that study design might have influenced findings. We identified all reports of CRT-P/D therapy in subjects with narrow QRS reporting effects on continuous physiological variables. Twelve studies (2074 patients) met these criteria. Studies were stratified by presence of bias-resistance steps: the presence of a randomized control arm over a single arm, and blinded outcome measurement. Change in each endpoint was quantified using a standardized effect size (Cohen's d). We conducted separate meta-analyses for each variable in turn, stratified by trial quality. In non-randomized, non-blinded studies, the majority of variables (10 of 12, 83%) showed significant improvement, ranging from a standardized mean effect size of +1.57 (95%CI +0.43 to +2.7) for ejection fraction to +2.87 (+1.78 to +3.95) for NYHA class. In the randomized, non-blinded study, only 3 out of 6 variables (50%) showed improvement. For the randomized blinded studies, 0 out of 9 variables (0%) showed benefit, ranging from -0.04 (-0.31 to +0.22) for ejection fraction to -0.1 (-0.73 to +0.53) for 6-minute walk test. Differences in degrees of resistance to bias, rather than choice of endpoint, explain the variation between studies of CRT in narrow-QRS heart failure addressing physiological variables. When bias-resistance features are implemented, it becomes clear that these patients do not improve in any tested physiological variable. Guidance from studies without careful planning to resist bias may be far less useful than commonly perceived. © 2015 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

Student Interpretations of Equations Related to the First Law of Thermodynamics

ERIC Educational Resources Information Center

Hadfield, Linda C.; Wieman, Carl E.

2010-01-01

Student interpretations of the equation for the first law of thermodynamics, [delta]U = q + w, an expression defining work done on or by a gas, w = -[image omitted]PdV, and an expression defining heat, q = [image omitted]C[subscript v]dT were investigated through a multiple-choice survey, a free-response written survey, and interviews. The…

13 CFR 126.308 - What happens if SBA inadvertently omits a qualified HUBZone SBC from the List?

Code of Federal Regulations, 2010 CFR

2010-01-01

... writing at U.S. Small Business Administration, 409 Third Street, SW., Washington, DC 20416 or via e-mail... 13 Business Credit and Assistance 1 2010-01-01 2010-01-01 false What happens if SBA inadvertently omits a qualified HUBZone SBC from the List? 126.308 Section 126.308 Business Credit and Assistance...

Racial/Ethnic Differences in the Criterion-Related Validity of Cognitive Ability Tests: A Qualitative and Quantitative Review

ERIC Educational Resources Information Center

Berry, Christopher M.; Clark, Malissa A.; McClure, Tara K.

2011-01-01

The correlation between cognitive ability test scores and performance was separately meta-analyzed for Asian, Black, Hispanic, and White racial/ethnic subgroups. Compared to the average White observed correlation ([image omitted] = 0.33, N = 903,779), average correlations were lower for Black samples ([image omitted] = 0.24, N = 112,194) and…

Individual-scale inference to anticipate climate-change vulnerability of biodiversity.

PubMed

Clark, James S; Bell, David M; Kwit, Matthew; Stine, Anne; Vierra, Ben; Zhu, Kai

2012-01-19

Anticipating how biodiversity will respond to climate change is challenged by the fact that climate variables affect individuals in competition with others, but interest lies at the scale of species and landscapes. By omitting the individual scale, models cannot accommodate the processes that determine future biodiversity. We demonstrate how individual-scale inference can be applied to the problem of anticipating vulnerability of species to climate. The approach places climate vulnerability in the context of competition for light and soil moisture. Sensitivities to climate and competition interactions aggregated from the individual tree scale provide estimates of which species are vulnerable to which variables in different habitats. Vulnerability is explored in terms of specific demographic responses (growth, fecundity and survival) and in terms of the synthetic response (the combination of demographic rates), termed climate tracking. These indices quantify risks for individuals in the context of their competitive environments. However, by aggregating in specific ways (over individuals, years, and other input variables), we provide ways to summarize and rank species in terms of their risks from climate change.

Is my study system good enough? A case study for identifying maternal effects.

PubMed

Holand, Anna Marie; Steinsland, Ingelin

2016-06-01

In this paper, we demonstrate how simulation studies can be used to answer questions about identifiability and consequences of omitting effects from a model. The methodology is presented through a case study where identifiability of genetic and/or individual (environmental) maternal effects is explored. Our study system is a wild house sparrow ( Passer domesticus ) population with known pedigree. We fit pedigree-based (generalized) linear mixed models (animal models), with and without additive genetic and individual maternal effects, and use deviance information criterion (DIC) for choosing between these models. Pedigree and R-code for simulations are available. For this study system, the simulation studies show that only large maternal effects can be identified. The genetic maternal effect (and similar for individual maternal effect) has to be at least half of the total genetic variance to be identified. The consequences of omitting a maternal effect when it is present are explored. Our results indicate that the total (genetic and individual) variance are accounted for. When an individual (environmental) maternal effect is omitted from the model, this only influences the estimated (direct) individual (environmental) variance. When a genetic maternal effect is omitted from the model, both (direct) genetic and (direct) individual variance estimates are overestimated.

Meta-analysis of associations between human brain volume and intelligence differences: How strong are they and what do they mean?

PubMed

Pietschnig, Jakob; Penke, Lars; Wicherts, Jelte M; Zeiler, Michael; Voracek, Martin

2015-10-01

Positive associations between human intelligence and brain size have been suspected for more than 150 years. Nowadays, modern non-invasive measures of in vivo brain volume (Magnetic Resonance Imaging) make it possible to reliably assess associations with IQ. By means of a systematic review of published studies and unpublished results obtained by personal communications with researchers, we identified 88 studies examining effect sizes of 148 healthy and clinical mixed-sex samples (>8000 individuals). Our results showed significant positive associations of brain volume and IQ (r=.24, R(2)=.06) that generalize over age (children vs. adults), IQ domain (full-scale, performance, and verbal IQ), and sex. Application of a number of methods for detection of publication bias indicates that strong and positive correlation coefficients have been reported frequently in the literature whilst small and non-significant associations appear to have been often omitted from reports. We show that the strength of the positive association of brain volume and IQ has been overestimated in the literature, but remains robust even when accounting for different types of dissemination bias, although reported effects have been declining over time. While it is tempting to interpret this association in the context of human cognitive evolution and species differences in brain size and cognitive ability, we show that it is not warranted to interpret brain size as an isomorphic proxy of human intelligence differences. Copyright © 2015 Elsevier Ltd. All rights reserved.

Systematic review: Outcome reporting bias is a problem in high impact factor neurology journals

PubMed Central

Scott, Jared T.; Blubaugh, Mark; Roepke, Brie; Scheckel, Caleb; Vassar, Matt

2017-01-01

Background Selective outcome reporting is a significant methodological concern. Comparisons between the outcomes reported in clinical trial registrations and those later published allow investigators to understand the extent of selection bias among trialists. We examined the possibility of selective outcome reporting in randomized controlled trials (RCTs) published in neurology journals. Methods We searched PubMed for randomized controlled trials from Jan 1, 2010 –Dec 31, 2015 published in the top 3 impact factor neurology journals. These articles were screened according to specific inclusion criteria. Each author individually extracted data from trials following a standardized protocol. A second author verified each extracted element and discrepancies were resolved. Consistency between registered and published outcomes was evaluated and correlations between discrepancies and funding, journal, and temporal trends were examined. Results 180 trials were included for analysis. 10 (6%) primary outcomes were demoted, 38 (21%) primary outcomes were omitted from the publication, and 61 (34%) unregistered primary outcomes were added to the published report. There were 18 (10%) cases of secondary outcomes being upgraded to primary outcomes in the publication, and there were 53 (29%) changes in timing of assessment. Of 82 (46%) major discrepancies with reported p-values, 54 (66.0%) favored publication of statistically significant results. Conclusion Across trials, we found 180 major discrepancies. 66% of major discrepancies with a reported p-value (n = 82) favored statistically significant results. These results suggest a need within neurology to provide more consistent and timely registration of outcomes. PMID:28727834

Systematic review: Outcome reporting bias is a problem in high impact factor neurology journals.

PubMed

Howard, Benjamin; Scott, Jared T; Blubaugh, Mark; Roepke, Brie; Scheckel, Caleb; Vassar, Matt

2017-01-01

Selective outcome reporting is a significant methodological concern. Comparisons between the outcomes reported in clinical trial registrations and those later published allow investigators to understand the extent of selection bias among trialists. We examined the possibility of selective outcome reporting in randomized controlled trials (RCTs) published in neurology journals. We searched PubMed for randomized controlled trials from Jan 1, 2010 -Dec 31, 2015 published in the top 3 impact factor neurology journals. These articles were screened according to specific inclusion criteria. Each author individually extracted data from trials following a standardized protocol. A second author verified each extracted element and discrepancies were resolved. Consistency between registered and published outcomes was evaluated and correlations between discrepancies and funding, journal, and temporal trends were examined. 180 trials were included for analysis. 10 (6%) primary outcomes were demoted, 38 (21%) primary outcomes were omitted from the publication, and 61 (34%) unregistered primary outcomes were added to the published report. There were 18 (10%) cases of secondary outcomes being upgraded to primary outcomes in the publication, and there were 53 (29%) changes in timing of assessment. Of 82 (46%) major discrepancies with reported p-values, 54 (66.0%) favored publication of statistically significant results. Across trials, we found 180 major discrepancies. 66% of major discrepancies with a reported p-value (n = 82) favored statistically significant results. These results suggest a need within neurology to provide more consistent and timely registration of outcomes.

Social desirability bias in dietary self-report may compromise the validity of dietary intake measures.

PubMed

Hebert, J R; Clemow, L; Pbert, L; Ockene, I S; Ockene, J K

1995-04-01

Self-report of dietary intake could be biased by social desirability or social approval thus affecting risk estimates in epidemiological studies. These constructs produce response set biases, which are evident when testing in domains characterized by easily recognizable correct or desirable responses. Given the social and psychological value ascribed to diet, assessment methodologies used most commonly in epidemiological studies are particularly vulnerable to these biases. Social desirability and social approval biases were tested by comparing nutrient scores derived from multiple 24-hour diet recalls (24HR) on seven randomly assigned days with those from two 7-day diet recalls (7DDR) (similar in some respects to commonly used food frequency questionnaires), one administered at the beginning of the test period (pre) and one at the end (post). Statistical analysis included correlation and multiple linear regression. Cross-sectionally, no relationships between social approval score and the nutritional variables existed. Social desirability score was negatively correlated with most nutritional variables. In linear regression analysis, social desirability score produced a large downward bias in nutrient estimation in the 7DDR relative to the 24HR. For total energy, this bias equalled about 50 kcal/point on the social desirability scale or about 450 kcal over its interquartile range. The bias was approximately twice as large for women as for men and only about half as large in the post measures. Individuals having the highest 24HR-derived fat and total energy intake scores had the largest downward bias due to social desirability. We observed a large downward bias in reporting food intake related to social desirability score. These results are consistent with the theoretical constructs on which the hypothesis is based. The effect of social desirability bias is discussed in terms of its influence on epidemiological estimates of effect. Suggestions are made for future work aimed at improving dietary assessment methodologies and adjusting risk estimates for this bias.

Lindley frailty model for a class of compound Poisson processes

NASA Astrophysics Data System (ADS)

Kadilar, Gamze Özel; Ata, Nihal

2013-10-01

The Lindley distribution gain importance in survival analysis for the similarity of exponential distribution and allowance for the different shapes of hazard function. Frailty models provide an alternative to proportional hazards model where misspecified or omitted covariates are described by an unobservable random variable. Despite of the distribution of the frailty is generally assumed to be continuous, it is appropriate to consider discrete frailty distributions In some circumstances. In this paper, frailty models with discrete compound Poisson process for the Lindley distributed failure time are introduced. Survival functions are derived and maximum likelihood estimation procedures for the parameters are studied. Then, the fit of the models to the earthquake data set of Turkey are examined.

We Know the Yin—But Where Is the Yang? Toward a Balanced Approach on Common Source Bias in Public Administration Scholarship

PubMed Central

George, Bert; Pandey, Sanjay K.

2017-01-01

Surveys have long been a dominant instrument for data collection in public administration. However, it has become widely accepted in the last decade that the usage of a self-reported instrument to measure both the independent and dependent variables results in common source bias (CSB). In turn, CSB is argued to inflate correlations between variables, resulting in biased findings. Subsequently, a narrow blinkered approach on the usage of surveys as single data source has emerged. In this article, we argue that this approach has resulted in an unbalanced perspective on CSB. We argue that claims on CSB are exaggerated, draw upon selective evidence, and project what should be tentative inferences as certainty over large domains of inquiry. We also discuss the perceptual nature of some variables and measurement validity concerns in using archival data. In conclusion, we present a flowchart that public administration scholars can use to analyze CSB concerns. PMID:29046599

We Know the Yin-But Where Is the Yang? Toward a Balanced Approach on Common Source Bias in Public Administration Scholarship.

PubMed

George, Bert; Pandey, Sanjay K

2017-06-01

Surveys have long been a dominant instrument for data collection in public administration. However, it has become widely accepted in the last decade that the usage of a self-reported instrument to measure both the independent and dependent variables results in common source bias (CSB). In turn, CSB is argued to inflate correlations between variables, resulting in biased findings. Subsequently, a narrow blinkered approach on the usage of surveys as single data source has emerged. In this article, we argue that this approach has resulted in an unbalanced perspective on CSB. We argue that claims on CSB are exaggerated, draw upon selective evidence, and project what should be tentative inferences as certainty over large domains of inquiry. We also discuss the perceptual nature of some variables and measurement validity concerns in using archival data. In conclusion, we present a flowchart that public administration scholars can use to analyze CSB concerns.

Reducing inherent biases introduced during DNA viral metagenome analyses of municipal wastewater

EPA Science Inventory

Metagenomics is a powerful tool for characterizing viral composition within environmental samples, but sample and molecular processing steps can bias the estimation of viral community structure. The objective of this study is to understand the inherent variability introduced when...

External quality-assurance results for the National Atmospheric Deposition Program / National Trends Network and Mercury Deposition Network, 2004

USGS Publications Warehouse

Wetherbee, Gregory A.; Latysh, Natalie E.; Greene, Shannon M.

2006-01-01

The U.S. Geological Survey (USGS) used five programs to provide external quality-assurance monitoring for the National Atmospheric Deposition Program/National Trends Network (NADP/NTN) and two programs to provide external quality-assurance monitoring for the NADP/Mercury Deposition Network (NADP/MDN) during 2004. An intersite-comparison program was used to estimate accuracy and precision of field-measured pH and specific-conductance. The variability and bias of NADP/NTN data attributed to field exposure, sample handling and shipping, and laboratory chemical analysis were estimated using the sample-handling evaluation (SHE), field-audit, and interlaboratory-comparison programs. Overall variability of NADP/NTN data was estimated using a collocated-sampler program. Variability and bias of NADP/MDN data attributed to field exposure, sample handling and shipping, and laboratory chemical analysis were estimated using a system-blank program and an interlaboratory-comparison program. In two intersite-comparison studies, approximately 89 percent of NADP/NTN site operators met the pH measurement accuracy goals, and 94.7 to 97.1 percent of NADP/NTN site operators met the accuracy goals for specific conductance. Field chemistry measurements were discontinued by NADP at the end of 2004. As a result, the USGS intersite-comparison program also was discontinued at the end of 2004. Variability and bias in NADP/NTN data due to sample handling and shipping were estimated from paired-sample concentration differences and specific conductance differences obtained for the SHE program. Median absolute errors (MAEs) equal to less than 3 percent were indicated for all measured analytes except potassium and hydrogen ion. Positive bias was indicated for most of the measured analytes except for calcium, hydrogen ion and specific conductance. Negative bias for hydrogen ion and specific conductance indicated loss of hydrogen ion and decreased specific conductance from contact of the sample with the collector bucket. Field-audit results for 2004 indicate dissolved analyte loss in more than one-half of NADP/NTN wet-deposition samples for all analytes except chloride. Concentrations of contaminants also were estimated from field-audit data. On the basis of 2004 field-audit results, at least 25 percent of the 2004 NADP/NTN concentrations for sodium, potassium, and chloride were lower than the maximum sodium, potassium, and chloride contamination likely to be found in 90 percent of the samples with 90-percent confidence. Variability and bias in NADP/NTN data attributed to chemical analysis by the NADP Central Analytical Laboratory (CAL) were comparable to the variability and bias estimated for other laboratories participating in the interlaboratory-comparison program for all analytes. Variability in NADP/NTN ammonium data evident in 2002-03 was reduced substantially during 2004. Sulfate, hydrogen-ion, and specific conductance data reported by CAL during 2004 were positively biased. A significant (a = 0.05) bias was identified for CAL sodium, potassium, ammonium, and nitrate data, but the absolute values of the median differences for these analytes were less than the method detection limits. No detections were reported for CAL analyses of deionized-water samples, indicating that contamination was not a problem for CAL. Control charts show that CAL data were within statistical control during at least 90 percent of 2004. Most 2004 CAL interlaboratory-comparison results for synthetic wet-deposition solutions were within ?10 percent of the most probable values (MPVs) for solution concentrations except for chloride, nitrate, sulfate, and specific conductance results from one sample in November and one specific conductance result in December. Overall variability of NADP/NTN wet-deposition measurements was estimated during water year 2004 by the median absolute errors for weekly wet-deposition sample concentrations and precipitation measurements for tw

Adaptive History Biases Result from Confidence-Weighted Accumulation of past Choices

PubMed Central

2018-01-01

Perceptual decision-making is biased by previous events, including the history of preceding choices: observers tend to repeat (or alternate) their judgments of the sensory environment more often than expected by chance. Computational models postulate that these so-called choice history biases result from the accumulation of internal decision signals across trials. Here, we provide psychophysical evidence for such a mechanism and its adaptive utility. Male and female human observers performed different variants of a challenging visual motion discrimination task near psychophysical threshold. In a first experiment, we decoupled categorical perceptual choices and motor responses on a trial-by-trial basis. Choice history bias was explained by previous perceptual choices, not motor responses, highlighting the importance of internal decision signals in action-independent formats. In a second experiment, observers performed the task in stimulus environments containing different levels of autocorrelation and providing no external feedback about choice correctness. Despite performing under overall high levels of uncertainty, observers adjusted both the strength and the sign of their choice history biases to these environments. When stimulus sequences were dominated by either repetitions or alternations, the individual degree of this adjustment of history bias was about as good a predictor of individual performance as individual perceptual sensitivity. The history bias adjustment scaled with two proxies for observers' confidence about their previous choices (accuracy and reaction time). Together, our results are consistent with the idea that action-independent, confidence-modulated decision variables are accumulated across choices in a flexible manner that depends on decision-makers' model of their environment. SIGNIFICANCE STATEMENT Decisions based on sensory input are often influenced by the history of one's preceding choices, manifesting as a bias to systematically repeat (or alternate) choices. We here provide support for the idea that such choice history biases arise from the context-dependent accumulation of a quantity referred to as the decision variable: the variable's sign dictates the choice and its magnitude the confidence about choice correctness. We show that choices are accumulated in an action-independent format and a context-dependent manner, weighted by the confidence about their correctness. This confidence-weighted accumulation of choices enables decision-makers to flexibly adjust their behavior to different sensory environments. The bias adjustment can be as important for optimizing performance as one's sensitivity to the momentary sensory input. PMID:29371318

Adaptive History Biases Result from Confidence-weighted Accumulation of Past Choices.

PubMed

Braun, Anke; Urai, Anne E; Donner, Tobias H

2018-01-25

Perceptual decision-making is biased by previous events, including the history of preceding choices: Observers tend to repeat (or alternate) their judgments of the sensory environment more often than expected by chance. Computational models postulate that these so-called choice history biases result from the accumulation of internal decision signals across trials. Here, we provide psychophysical evidence for such a mechanism and its adaptive utility. Male and female human observers performed different variants of a challenging visual motion discrimination task near psychophysical threshold. In a first experiment, we decoupled categorical perceptual choices and motor responses on a trial-by-trial basis. Choice history bias was explained by previous perceptual choices, not motor responses, highlighting the importance of internal decision signals in action-independent formats. In a second experiment, observers performed the task in stimulus environments containing different levels of auto-correlation and providing no external feedback about choice correctness. Despite performing under overall high levels of uncertainty, observers adjusted both the strength and the sign of their choice history biases to these environments. When stimulus sequences were dominated by either repetitions or alternations, the individual degree of this adjustment of history bias was about as good a predictor of individual performance as individual perceptual sensitivity. The history bias adjustment scaled with two proxies for observers' confidence about their previous choices (accuracy and reaction time). Taken together, our results are consistent with the idea that action-independent, confidence-modulated decision variables are accumulated across choices in a flexible manner that depends on decision-makers' model of their environment. Significance statement: Decisions based on sensory input are often influenced by the history of one's preceding choices, manifesting as a bias to systematically repeat (or alternate) choices. We here provide support for the idea that such choice history biases arise from the context-dependent accumulation of a quantity referred to as the decision variable: the variable's sign dictates the choice and its magnitude the confidence about choice correctness. We show that choices are accumulated in an action-independent format and a context-dependent manner, weighted by the confidence about their correctness. This confidence-weighted accumulation of choices enables decision-makers to flexibly adjust their behavior to different sensory environments. The bias adjustment can be as important for optimizing performance as one's sensitivity to the momentary sensory input. Copyright © 2018 Braun et al.

Implication of observed cloud variability for parameterizations of microphysical and radiative transfer processes in climate models

NASA Astrophysics Data System (ADS)

Huang, D.; Liu, Y.

2014-12-01

The effects of subgrid cloud variability on grid-average microphysical rates and radiative fluxes are examined by use of long-term retrieval products at the Tropical West Pacific (TWP), Southern Great Plains (SGP), and North Slope of Alaska (NSA) sites of the Department of Energy's Atmospheric Radiation Measurement (ARM) Program. Four commonly used distribution functions, the truncated Gaussian, Gamma, lognormal, and Weibull distributions, are constrained to have the same mean and standard deviation as observed cloud liquid water content. The PDFs are then used to upscale relevant physical processes to obtain grid-average process rates. It is found that the truncated Gaussian representation results in up to 30% mean bias in autoconversion rate whereas the mean bias for the lognormal representation is about 10%. The Gamma and Weibull distribution function performs the best for the grid-average autoconversion rate with the mean relative bias less than 5%. For radiative fluxes, the lognormal and truncated Gaussian representations perform better than the Gamma and Weibull representations. The results show that the optimal choice of subgrid cloud distribution function depends on the nonlinearity of the process of interest and thus there is no single distribution function that works best for all parameterizations. Examination of the scale (window size) dependence of the mean bias indicates that the bias in grid-average process rates monotonically increases with increasing window sizes, suggesting the increasing importance of subgrid variability with increasing grid sizes.

Characterizing energy budget variability at a Sahelian site: a test of NWP model behaviour

NASA Astrophysics Data System (ADS)

Mackie, Anna; Palmer, Paul I.; Brindley, Helen

2017-12-01

We use observations of surface and top-of-the-atmosphere (TOA) broadband radiation fluxes determined from the Atmospheric Radiation Measurement programme mobile facility, the Geostationary Earth Radiation Budget (GERB) and Spinning Enhanced Visible and Infrared Imager (SEVIRI) instruments and a range of meteorological variables at a site in the Sahel to test the ability of the ECMWF Integrated Forecasting System cycle 43r1 to describe energy budget variability. The model has daily average biases of -12 and 18 W m-2 for outgoing longwave and reflected shortwave TOA radiation fluxes, respectively. At the surface, the daily average bias is 12(13) W m-2 for the longwave downwelling (upwelling) radiation flux and -21(-13) W m-2 for the shortwave downwelling (upwelling) radiation flux. Using multivariate linear models of observation-model differences, we attribute radiation flux discrepancies to physical processes, and link surface and TOA fluxes. We find that model biases in surface radiation fluxes are mainly due to a low bias in ice water path (IWP), poor description of surface albedo and model-observation differences in surface temperature. We also attribute observed discrepancies in the radiation fluxes, particularly during the dry season, to the misrepresentation of aerosol fields in the model from use of a climatology instead of a dynamic approach. At the TOA, the low IWP impacts the amount of reflected shortwave radiation while biases in outgoing longwave radiation are additionally coupled to discrepancies in the surface upwelling longwave flux and atmospheric humidity.

Investigating Uncertainty in Predicting Carbon Dynamics in North American Biomes: Putting Support-Effect Bias in Perspective

NASA Technical Reports Server (NTRS)

Dungan, Jennifer L.; Brass, Jim (Technical Monitor)

2001-01-01

A fundamental strategy in NASA's Earth Observing System's (EOS) monitoring of vegetation and its contribution to the global carbon cycle is to rely on deterministic, process-based ecosystem models to make predictions of carbon flux over large regions. These models are parameterized (that is, the input variables are derived) using remotely sensed images such as those from the Moderate Resolution Imaging Spectroradiometer (MODIS), ground measurements and interpolated maps. Since early applications of these models, investigators have noted that results depend partly on the spatial support of the input variables. In general, the larger the support of the input data, the greater the chance that the effects of important components of the ecosystem will be averaged out. A review of previous work shows that using large supports can cause either positive or negative bias in carbon flux predictions. To put the magnitude and direction of these biases in perspective, we must quantify the range of uncertainty on our best measurements of carbon-related variables made on equivalent areas. In other words, support-effect bias should be placed in the context of prediction uncertainty from other sources. If the range of uncertainty at the smallest support is less than the support-effect bias, more research emphasis should probably be placed on support sizes that are intermediate between those of field measurements and MODIS. If the uncertainty range at the smallest support is larger than the support-effect bias, the accuracy of MODIS-based predictions will be difficult to quantify and more emphasis should be placed on field-scale characterization and sampling. This talk will describe methods to address these issues using a field measurement campaign in North America and "upscaling" using geostatistical estimation and simulation.

Inter-Annual Variability of Fledgling Sex Ratio in King Penguins.

PubMed

Bordier, Célia; Saraux, Claire; Viblanc, Vincent A; Gachot-Neveu, Hélène; Beaugey, Magali; Le Maho, Yvon; Le Bohec, Céline

2014-01-01

As the number of breeding pairs depends on the adult sex ratio in a monogamous species with biparental care, investigating sex-ratio variability in natural populations is essential to understand population dynamics. Using 10 years of data (2000-2009) in a seasonally monogamous seabird, the king penguin (Aptenodytes patagonicus), we investigated the annual sex ratio at fledging, and the potential environmental causes for its variation. Over more than 4000 birds, the annual sex ratio at fledging was highly variable (ranging from 44.4% to 58.3% of males), and on average slightly biased towards males (51.6%). Yearly variation in sex-ratio bias was neither related to density within the colony, nor to global or local oceanographic conditions known to affect both the productivity and accessibility of penguin foraging areas. However, rising sea surface temperature coincided with an increase in fledging sex-ratio variability. Fledging sex ratio was also correlated with difference in body condition between male and female fledglings. When more males were produced in a given year, their body condition was higher (and reciprocally), suggesting that parents might adopt a sex-biased allocation strategy depending on yearly environmental conditions and/or that the effect of environmental parameters on chick condition and survival may be sex-dependent. The initial bias in sex ratio observed at the juvenile stage tended to return to 1∶1 equilibrium upon first breeding attempts, as would be expected from Fisher's classic theory of offspring sex-ratio variation.

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.

Differential effects of weight bias experiences and internalization on exercise among women with overweight and obesity.

PubMed

Pearl, Rebecca L; Puhl, Rebecca M; Dovidio, John F

2015-12-01

This study investigated the effects of experiences with weight stigma and weight bias internalization on exercise. An online sample of 177 women with overweight and obesity (M(age) = 35.48 years, M(BMI) = 32.81) completed questionnaires assessing exercise behavior, self-efficacy, and motivation; experiences of weight stigmatization; weight bias internalization; and weight-stigmatizing attitudes toward others. Weight stigma experiences positively correlated with exercise behavior, but weight bias internalization was negatively associated with all exercise variables. Weight bias internalization was a partial mediator between weight stigma experiences and exercise behavior. The distinct effects of experiencing versus internalizing weight bias carry implications for clinical practice and public health. © The Author(s) 2014.

Military Readiness: DODs Readiness Rebuilding Efforts May Be at Risk without a Comprehensive Plan

DTIC Science & Technology

2016-09-01

Congressional Committees September 2016 GAO-16-841 United States Government Accountability Office United States Government Accountability ...omits information DOD identified as SECRET, which must be protected from public disclosure . GAO analyzed and reviewed data on reported readiness...protected from public disclosure . Therefore, this report omits SECRET information and data such as readiness trend data, deployment data, and selected

Survey on Inadequate and Omitted Citations in Manuscripts: A Precursory Study in Identification of Tasks for a Literature Review and Manuscript Writing Assistive System

ERIC Educational Resources Information Center

Raamkumar, Aravind Sesagiri; Foo, Schubert; Pang, Natalie

2016-01-01

Introduction: This paper looks at the issue of inadequate and omitted citations in manuscripts by collecting the experiential opinions of researchers from the dual perspectives of manuscript reviewers and authors. Method: An online survey was conducted with participation from 207 respondents who had experience of reviewing and authoring research…

APPLICATION OF BIAS AND ADJUSTMENT TECHNIQUES TO THE ETA-CMAQ AIR QUALITY FORECAST

EPA Science Inventory

The current air quality forecast system, based on linking NOAA's Eta meteorological model with EPA's Community Multiscale Air Quality (CMAQ) model, consistently overpredicts surface ozone concentrations, but simulates its day-to-day variability quite well. The ability of bias cor...

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.…

ENSO-driven energy budget perturbations in observations and CMIP models

DOE PAGES

Mayer, Michael; Fasullo, John T.; Trenberth, Kevin E.; ...

2016-03-19

Various observation-based datasets are employed to robustly quantify changes in ocean heat content (OHC), anomalous ocean–atmosphere energy exchanges and atmospheric energy transports during El Niño-Southern Oscillation (ENSO). These results are used as a benchmark to evaluate the energy pathways during ENSO as simulated by coupled climate model runs from the CMIP3 and CMIP5 archives. The models are able to qualitatively reproduce observed patterns of ENSO-related energy budget variability to some degree, but key aspects are seriously biased. Area-averaged tropical Pacific OHC variability associated with ENSO is greatly underestimated by all models because of strongly biased responses of net radiation atmore » top-of-the-atmosphere to ENSO. The latter are related to biases of mean convective activity in the models and project on surface energy fluxes in the eastern Pacific Intertropical Convergence Zone region. Moreover, models underestimate horizontal and vertical OHC redistribution in association with the generally too weak Bjerknes feedback, leading to a modeled ENSO affecting a too shallow layer of the Pacific. Vertical links between SST and OHC variability are too weak even in models driven with observed winds, indicating shortcomings of the ocean models. Furthermore, modeled teleconnections as measured by tropical Atlantic OHC variability are too weak and the tropical zonal mean ENSO signal is strongly underestimated or even completely missing in most of the considered models. In conclusion, results suggest that attempts to infer insight about climate sensitivity from ENSO-related variability are likely to be hampered by biases in ENSO in CMIP simulations that do not bear a clear link to future changes.« less

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.

Sampling bias in climate-conflict research

NASA Astrophysics Data System (ADS)

Adams, Courtland; Ide, Tobias; Barnett, Jon; Detges, Adrien

2018-03-01

Critics have argued that the evidence of an association between climate change and conflict is flawed because the research relies on a dependent variable sampling strategy1-4. Similarly, it has been hypothesized that convenience of access biases the sample of cases studied (the `streetlight effect'5). This also gives rise to claims that the climate-conflict literature stigmatizes some places as being more `naturally' violent6-8. Yet there has been no proof of such sampling patterns. Here we test whether climate-conflict research is based on such a biased sample through a systematic review of the literature. We demonstrate that research on climate change and violent conflict suffers from a streetlight effect. Further, studies which focus on a small number of cases in particular are strongly informed by cases where there has been conflict, do not sample on the independent variables (climate impact or risk), and hence tend to find some association between these two variables. These biases mean that research on climate change and conflict primarily focuses on a few accessible regions, overstates the links between both phenomena and cannot explain peaceful outcomes from climate change. This could result in maladaptive responses in those places that are stigmatized as being inherently more prone to climate-induced violence.

Overcoming ecologic bias using the two-phase study design.

PubMed

Wakefield, Jon; Haneuse, Sebastien J-P A

2008-04-15

Ecologic (aggregate) data are widely available and widely utilized in epidemiologic studies. However, ecologic bias, which arises because aggregate data cannot characterize within-group variability in exposure and confounder variables, can only be removed by supplementing ecologic data with individual-level data. Here the authors describe the two-phase study design as a framework for achieving this objective. In phase 1, outcomes are stratified by any combination of area, confounders, and error-prone (or discretized) versions of exposures of interest. Phase 2 data, sampled within each phase 1 stratum, provide accurate measures of exposure and possibly of additional confounders. The phase 1 aggregate-level data provide a high level of statistical power and a cross-classification by which individuals may be efficiently sampled in phase 2. The phase 2 individual-level data then provide a control for ecologic bias by characterizing the within-area variability in exposures and confounders. In this paper, the authors illustrate the two-phase study design by estimating the association between infant mortality and birth weight in several regions of North Carolina for 2000-2004, controlling for gender and race. This example shows that the two-phase design removes ecologic bias and produces gains in efficiency over the use of case-control data alone. The authors discuss the advantages and disadvantages of the approach.

Analysis of Observational Studies in the Presence of Treatment Selection Bias: Effects of Invasive Cardiac Management on AMI Survival Using Propensity Score and Instrumental Variable Methods

PubMed Central

Stukel, Thérèse A.; Fisher, Elliott S; Wennberg, David E.; Alter, David A.; Gottlieb, Daniel J.; Vermeulen, Marian J.

2007-01-01

Context Comparisons of outcomes between patients treated and untreated in observational studies may be biased due to differences in patient prognosis between groups, often because of unobserved treatment selection biases. Objective To compare 4 analytic methods for removing the effects of selection bias in observational studies: multivariable model risk adjustment, propensity score risk adjustment, propensity-based matching, and instrumental variable analysis. Design, Setting, and Patients A national cohort of 122 124 patients who were elderly (aged 65–84 years), receiving Medicare, and hospitalized with acute myocardial infarction (AMI) in 1994–1995, and who were eligible for cardiac catheterization. Baseline chart reviews were taken from the Cooperative Cardiovascular Project and linked to Medicare health administrative data to provide a rich set of prognostic variables. Patients were followed up for 7 years through December 31, 2001, to assess the association between long-term survival and cardiac catheterization within 30 days of hospital admission. Main Outcome Measure Risk-adjusted relative mortality rate using each of the analytic methods. Results Patients who received cardiac catheterization (n=73 238) were younger and had lower AMI severity than those who did not. After adjustment for prognostic factors by using standard statistical risk-adjustment methods, cardiac catheterization was associated with a 50% relative decrease in mortality (for multivariable model risk adjustment: adjusted relative risk [RR], 0.51; 95% confidence interval [CI], 0.50–0.52; for propensity score risk adjustment: adjusted RR, 0.54; 95% CI, 0.53–0.55; and for propensity-based matching: adjusted RR, 0.54; 95% CI, 0.52–0.56). Using regional catheterization rate as an instrument, instrumental variable analysis showed a 16% relative decrease in mortality (adjusted RR, 0.84; 95% CI, 0.79–0.90). The survival benefits of routine invasive care from randomized clinical trials are between 8% and 21 %. Conclusions Estimates of the observational association of cardiac catheterization with long-term AMI mortality are highly sensitive to analytic method. All standard risk-adjustment methods have the same limitations regarding removal of unmeasured treatment selection biases. Compared with standard modeling, instrumental variable analysis may produce less biased estimates of treatment effects, but is more suited to answering policy questions than specific clinical questions. PMID:17227979

Implicit racial bias as a moderator of the association between racial discrimination and hypertension: a study of Midlife African American men.

PubMed

Chae, David H; Nuru-Jeter, Amani M; Adler, Nancy E

2012-01-01

Empirical findings on racial discrimination and hypertension risk have been inconsistent. Some studies have found no association between self-reported experiences of discrimination and cardiovascular health outcomes, whereas others have found moderated or curvilinear relationships. The current cross-sectional study examined whether the association between racial discrimination and hypertension is moderated by implicit racial bias among African American midlife men. This study examined the data on 91 African American men between 30 and 50 years of age. Primary variables were self-reported experiences of racial discrimination and unconscious racial bias as measured by the Black-White Implicit Association Test. Modified Poisson regression models were specified, examining hypertension, defined as a mean resting systolic level of at least 140 mm Hg or diastolic level of at least 90 mm Hg, or self-reported history of cardiovascular medication use with a physician diagnosis of hypertension. No main effects for discrimination or implicit racial bias were found, but the interaction of the two variables was significantly related to hypertension (χ(2)(1) = 4.89, p < .05). Among participants with an implicit antiblack bias, more frequent reports of discrimination were associated with a higher probability of hypertension, whereas among those with an implicit problack bias, it was associated with lower risk. The combination of experiencing racial discrimination and holding an antiblack bias may have particularly detrimental consequences on hypertension among African American midlife men, whereas holding an implicit problack bias may buffer the effects of racial discrimination. Efforts to address both internalized racial bias and racial discrimination may lower cardiovascular risk in this population.

Non-linear mixed effects modeling of antiretroviral drug response after administration of lopinavir, atazanavir and efavirenz containing regimens to treatment-naïve HIV-1 infected patients.

PubMed

Röshammar, Daniel; Simonsson, Ulrika S H; Ekvall, Håkan; Flamholc, Leo; Ormaasen, Vidar; Vesterbacka, Jan; Wallmark, Eva; Ashton, Michael; Gisslén, Magnus

2011-12-01

The objective of this analysis was to compare three methods of handling HIV-RNA data below the limit of quantification (LOQ) when describing the time-course of antiretroviral drug response using a drug-disease model. Treatment naïve Scandinavian HIV-positive patients (n = 242) were randomized to one of three study arms. Two nucleoside reverse transcriptase inhibitors were administrated in combination with 400/100 mg lopinavir/ritonavir twice daily, 300/100 mg atazanavir/ritonavir once a day or 600 mg efavirenz once a day. The viral response was monitored at screening, baseline and at 1, 2, 3, 4, 12, 24, 48, 96, 120, and 144 weeks after study initiation. Data up to 400 days was fitted using a viral dynamics non-linear mixed effects drug-disease model in NONMEM. HIV-RNA data below LOQ of 50 copies/ml plasma (39%) was omitted, replaced by LOQ/2 or included in the analysis using a likelihood-based method (M3 method). Including data below LOQ using the M3 method substantially improved the model fit. The drug response parameter expressing the fractional inhibition of viral replication was on average (95% CI) estimated to 0.787 (0.721-0.864) for lopinavir and atazanavir treatment arms and 0.868 (0.796-0.923) for the efavirenz containing regimen. At 400 days after treatment initiation 90% (76-100) of the lopinavir and atazanavir treated patients were predicted to have undetectable viral levels and 96% (89-100%) for the efavirenz containing treatment. Including viral data below the LOQ rather than omitting or replacing data provides advantages such as better model predictions and less biased parameter estimates which are of importance when quantifying antiretroviral drug response.

Statistical treatment for the wet bias in tree-ring chronologies: A case study from the InteriorWest, USA

Treesearch

Yan Sun; Matthew F. Bekker; R. Justin DeRose; Roger Kjelgren; S. -Y. Simon Wang

2017-01-01

Dendroclimatic research has long assumed a linear relationship between tree-ring increment and climate variables. However, ring width frequently underestimates extremely wet years, a phenomenon we refer to as ‘wet bias’. In this paper, we present statistical evidence for wet bias that is obscured by the assumption of linearity. To improve tree-ring-climate modeling, we...

Smoothed Biasing Forces Yield Unbiased Free Energies with the Extended-System Adaptive Biasing Force Method

PubMed Central

2016-01-01

We report a theoretical description and numerical tests of the extended-system adaptive biasing force method (eABF), together with an unbiased estimator of the free energy surface from eABF dynamics. Whereas the original ABF approach uses its running estimate of the free energy gradient as the adaptive biasing force, eABF is built on the idea that the exact free energy gradient is not necessary for efficient exploration, and that it is still possible to recover the exact free energy separately with an appropriate estimator. eABF does not directly bias the collective coordinates of interest, but rather fictitious variables that are harmonically coupled to them; therefore is does not require second derivative estimates, making it easily applicable to a wider range of problems than ABF. Furthermore, the extended variables present a smoother, coarse-grain-like sampling problem on a mollified free energy surface, leading to faster exploration and convergence. We also introduce CZAR, a simple, unbiased free energy estimator from eABF trajectories. eABF/CZAR converges to the physical free energy surface faster than standard ABF for a wide range of parameters. PMID:27959559

Studying Gender Bias in Physics Grading: The role of teaching experience and country

NASA Astrophysics Data System (ADS)

Hofer, Sarah I.

2015-11-01

The existence of gender-STEM (science, technology, engineering, and mathematics) stereotypes has been repeatedly documented. This article examines physics teachers' gender bias in grading and the influence of teaching experience in Switzerland, Austria, and Germany. In a 2 × 2 between-subjects design, with years of teaching experience included as moderating variable, physics teachers (N = 780) from Switzerland, Austria, and Germany graded a fictive student's answer to a physics test question. While the answer was exactly the same for each teacher, only the student's gender and specialization in languages vs. science were manipulated. Specialization was included to gauge the relative strength of potential gender bias effects. Multiple group regression analyses, with the grade that was awarded as the dependent variable, revealed only partial cross-border generalizability of the effect pattern. While the overall results in fact indicated the existence of a consistent and clear gender bias against girls in the first part of physics teachers' careers that disappeared with increasing teaching experience for Swiss teachers, Austrian teachers, and German female teachers, German male teachers showed no gender bias effects at all. The results are discussed regarding their relevance for educational practice and research.

[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.

Dealing with Omitted and Not-Reached Items in Competence Tests: Evaluating Approaches Accounting for Missing Responses in Item Response Theory Models

ERIC Educational Resources Information Center

Pohl, Steffi; Gräfe, Linda; Rose, Norman

2014-01-01

Data from competence tests usually show a number of missing responses on test items due to both omitted and not-reached items. Different approaches for dealing with missing responses exist, and there are no clear guidelines on which of those to use. While classical approaches rely on an ignorable missing data mechanism, the most recently developed…

The concurrent multiplicative-additive approach for gauge-radar/satellite multisensor precipitation estimates

NASA Astrophysics Data System (ADS)

Garcia-Pintado, J.; Barberá, G. G.; Erena Arrabal, M.; Castillo, V. M.

2010-12-01

Objective analysis schemes (OAS), also called ``succesive correction methods'' or ``observation nudging'', have been proposed for multisensor precipitation estimation combining remote sensing data (meteorological radar or satellite) with data from ground-based raingauge networks. However, opposite to the more complex geostatistical approaches, the OAS techniques for this use are not optimized. On the other hand, geostatistical techniques ideally require, at the least, modelling the covariance from the rain gauge data at every time step evaluated, which commonly cannot be soundly done. Here, we propose a new procedure (concurrent multiplicative-additive objective analysis scheme [CMA-OAS]) for operational rainfall estimation using rain gauges and meteorological radar, which does not require explicit modelling of spatial covariances. On the basis of a concurrent multiplicative-additive (CMA) decomposition of the spatially nonuniform radar bias, within-storm variability of rainfall and fractional coverage of rainfall are taken into account. Thus both spatially nonuniform radar bias, given that rainfall is detected, and bias in radar detection of rainfall are handled. The interpolation procedure of CMA-OAS is built on the OAS, whose purpose is to estimate a filtered spatial field of the variable of interest through a successive correction of residuals resulting from a Gaussian kernel smoother applied on spatial samples. The CMA-OAS, first, poses an optimization problem at each gauge-radar support point to obtain both a local multiplicative-additive radar bias decomposition and a regionalization parameter. Second, local biases and regionalization parameters are integrated into an OAS to estimate the multisensor rainfall at the ground level. The approach considers radar estimates as background a priori information (first guess), so that nudging to observations (gauges) may be relaxed smoothly to the first guess, and the relaxation shape is obtained from the sequential optimization. The procedure is suited to relatively sparse rain gauge networks. To show the procedure, six storms are analyzed at hourly steps over 10,663 km2. Results generally indicated an improved quality with respect to other methods evaluated: a standard mean-field bias adjustment, an OAS spatially variable adjustment with multiplicative factors, ordinary cokriging, and kriging with external drift. In theory, it could be equally applicable to gauge-satellite estimates and other hydrometeorological variables.

Bias and robustness of uncertainty components estimates in transient climate projections

NASA Astrophysics Data System (ADS)

Hingray, Benoit; Blanchet, Juliette; Jean-Philippe, Vidal

2016-04-01

A critical issue in climate change studies is the estimation of uncertainties in projections along with the contribution of the different uncertainty sources, including scenario uncertainty, the different components of model uncertainty and internal variability. Quantifying the different uncertainty sources faces actually different problems. For instance and for the sake of simplicity, an estimate of model uncertainty is classically obtained from the empirical variance of the climate responses obtained for the different modeling chains. These estimates are however biased. Another difficulty arises from the limited number of members that are classically available for most modeling chains. In this case, the climate response of one given chain and the effect of its internal variability may be actually difficult if not impossible to separate. The estimate of scenario uncertainty, model uncertainty and internal variability components are thus likely to be not really robust. We explore the importance of the bias and the robustness of the estimates for two classical Analysis of Variance (ANOVA) approaches: a Single Time approach (STANOVA), based on the only data available for the considered projection lead time and a time series based approach (QEANOVA), which assumes quasi-ergodicity of climate outputs over the whole available climate simulation period (Hingray and Saïd, 2014). We explore both issues for a simple but classical configuration where uncertainties in projections are composed of two single sources: model uncertainty and internal climate variability. The bias in model uncertainty estimates is explored from theoretical expressions of unbiased estimators developed for both ANOVA approaches. The robustness of uncertainty estimates is explored for multiple synthetic ensembles of time series projections generated with MonteCarlo simulations. For both ANOVA approaches, when the empirical variance of climate responses is used to estimate model uncertainty, the bias is always positive. It can be especially high with STANOVA. In the most critical configurations, when the number of members available for each modeling chain is small (< 3) and when internal variability explains most of total uncertainty variance (75% or more), the overestimation is higher than 100% of the true model uncertainty variance. The bias can be considerably reduced with a time series ANOVA approach, owing to the multiple time steps accounted for. The longer the transient time period used for the analysis, the larger the reduction. When a quasi-ergodic ANOVA approach is applied to decadal data for the whole 1980-2100 period, the bias is reduced by a factor 2.5 to 20 depending on the projection lead time. In all cases, the bias is likely to be not negligible for a large number of climate impact studies resulting in a likely large overestimation of the contribution of model uncertainty to total variance. For both approaches, the robustness of all uncertainty estimates is higher when more members are available, when internal variability is smaller and/or the response-to-uncertainty ratio is higher. QEANOVA estimates are much more robust than STANOVA ones: QEANOVA simulated confidence intervals are roughly 3 to 5 times smaller than STANOVA ones. Excepted for STANOVA when less than 3 members is available, the robustness is rather high for total uncertainty and moderate for internal variability estimates. For model uncertainty or response-to-uncertainty ratio estimates, the robustness is conversely low for QEANOVA to very low for STANOVA. In the most critical configurations (small number of member, large internal variability), large over- or underestimation of uncertainty components is very thus likely. To propose relevant uncertainty analyses and avoid misleading interpretations, estimates of uncertainty components should be therefore bias corrected and ideally come with estimates of their robustness. This work is part of the COMPLEX Project (European Collaborative Project FP7-ENV-2012 number: 308601; http://www.complex.ac.uk/). Hingray, B., Saïd, M., 2014. Partitioning internal variability and model uncertainty components in a multimodel multireplicate ensemble of climate projections. J.Climate. doi:10.1175/JCLI-D-13-00629.1 Hingray, B., Blanchet, J. (revision) Unbiased estimators for uncertainty components in transient climate projections. J. Climate Hingray, B., Blanchet, J., Vidal, J.P. (revision) Robustness of uncertainty components estimates in climate projections. J.Climate

Collaborative Research: Process-Resolving Decomposition of the Global Temperature Response to Modes of Low Frequency Variability in a Changing Climate

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

Deng, Yi

2014-11-24

DOE-GTRC-05596 11/24/2104 Collaborative Research: Process-Resolving Decomposition of the Global Temperature Response to Modes of Low Frequency Variability in a Changing Climate PI: Dr. Yi Deng (PI) School of Earth and Atmospheric Sciences Georgia Institute of Technology 404-385-1821, yi.deng@eas.gatech.edu El Niño-Southern Oscillation (ENSO) and Annular Modes (AMs) represent respectively the most important modes of low frequency variability in the tropical and extratropical circulations. The projection of future changes in the ENSO and AM variability, however, remains highly uncertain with the state-of-the-science climate models. This project conducted a process-resolving, quantitative evaluations of the ENSO and AM variability in the modern reanalysis observationsmore » and in climate model simulations. The goal is to identify and understand the sources of uncertainty and biases in models’ representation of ENSO and AM variability. Using a feedback analysis method originally formulated by one of the collaborative PIs, we partitioned the 3D atmospheric temperature anomalies and surface temperature anomalies associated with ENSO and AM variability into components linked to 1) radiation-related thermodynamic processes such as cloud and water vapor feedbacks, 2) local dynamical processes including convection and turbulent/diffusive energy transfer and 3) non-local dynamical processes such as the horizontal energy transport in the oceans and atmosphere. In the past 4 years, the research conducted at Georgia Tech under the support of this project has led to 15 peer-reviewed publications and 9 conference/workshop presentations. Two graduate students and one postdoctoral fellow also received research training through participating the project activities. This final technical report summarizes key scientific discoveries we made and provides also a list of all publications and conference presentations resulted from research activities at Georgia Tech. The main findings include: 1) the distinctly different roles played by atmospheric dynamical processes in establishing surface temperature response to ENSO at tropics and extratropics (i.e., atmospheric dynamics disperses energy out of tropics during ENSO warm events and modulate surface temperature at mid-, high-latitudes through controlling downward longwave radiation); 2) the representations of ENSO-related temperature response in climate models fail to converge at the process-level particularly over extratropics (i.e., models produce the right temperature responses to ENSO but with wrong reasons); 3) water vapor feedback contributes substantially to the temperature anomalies found over U.S. during different phases of the Northern Annular Mode (NAM), which adds new insight to the traditional picture that cold/warm advective processes are the main drivers of local temperature responses to the NAM; 4) the overall land surface temperature biases in the latest NCAR model (CESM1) are caused by biases in surface albedo while the surface temperature biases over ocean are related to multiple factors including biases in model albedo, cloud and oceanic dynamics, and the temperature biases over different ocean basins are also induced by different process biases. These results provide a detailed guidance for process-level model turning and improvement, and thus contribute directly to the overall goal of reducing model uncertainty in projecting future changes in the Earth’s climate system, especially in the ENSO and AM variability.« less

Schooling and Frailty among Seniors.

ERIC Educational Resources Information Center

Leigh, J. Paul; Dhir, Rachna

1997-01-01

Schooling has beneficial influences on health, even late in life. This paper's contributions to the schooling/health debate include removing unobserved variable bias and the bias from self-efficacy, time, and risk preferences; discovering no racial differences; finding strong correlations between schooling and measures of frailty for those aged 65…

Estimating and Modelling Bias of the Hierarchical Partitioning Public-Domain Software: Implications in Environmental Management and Conservation

PubMed Central

Olea, Pedro P.; Mateo-Tomás, Patricia; de Frutos, Ángel

2010-01-01

Background Hierarchical partitioning (HP) is an analytical method of multiple regression that identifies the most likely causal factors while alleviating multicollinearity problems. Its use is increasing in ecology and conservation by its usefulness for complementing multiple regression analysis. A public-domain software “hier.part package” has been developed for running HP in R software. Its authors highlight a “minor rounding error” for hierarchies constructed from >9 variables, however potential bias by using this module has not yet been examined. Knowing this bias is pivotal because, for example, the ranking obtained in HP is being used as a criterion for establishing priorities of conservation. Methodology/Principal Findings Using numerical simulations and two real examples, we assessed the robustness of this HP module in relation to the order the variables have in the analysis. Results indicated a considerable effect of the variable order on the amount of independent variance explained by predictors for models with >9 explanatory variables. For these models the nominal ranking of importance of the predictors changed with variable order, i.e. predictors declared important by its contribution in explaining the response variable frequently changed to be either most or less important with other variable orders. The probability of changing position of a variable was best explained by the difference in independent explanatory power between that variable and the previous one in the nominal ranking of importance. The lesser is this difference, the more likely is the change of position. Conclusions/Significance HP should be applied with caution when more than 9 explanatory variables are used to know ranking of covariate importance. The explained variance is not a useful parameter to use in models with more than 9 independent variables. The inconsistency in the results obtained by HP should be considered in future studies as well as in those already published. Some recommendations to improve the analysis with this HP module are given. PMID:20657734

Forecast Modelling via Variations in Binary Image-Encoded Information Exploited by Deep Learning Neural Networks.

PubMed

Liu, Da; Xu, Ming; Niu, Dongxiao; Wang, Shoukai; Liang, Sai

2016-01-01

Traditional forecasting models fit a function approximation from dependent invariables to independent variables. However, they usually get into trouble when date are presented in various formats, such as text, voice and image. This study proposes a novel image-encoded forecasting method that input and output binary digital two-dimensional (2D) images are transformed from decimal data. Omitting any data analysis or cleansing steps for simplicity, all raw variables were selected and converted to binary digital images as the input of a deep learning model, convolutional neural network (CNN). Using shared weights, pooling and multiple-layer back-propagation techniques, the CNN was adopted to locate the nexus among variations in local binary digital images. Due to the computing capability that was originally developed for binary digital bitmap manipulation, this model has significant potential for forecasting with vast volume of data. The model was validated by a power loads predicting dataset from the Global Energy Forecasting Competition 2012.

Forecast Modelling via Variations in Binary Image-Encoded Information Exploited by Deep Learning Neural Networks

PubMed Central

Xu, Ming; Niu, Dongxiao; Wang, Shoukai; Liang, Sai

2016-01-01

Traditional forecasting models fit a function approximation from dependent invariables to independent variables. However, they usually get into trouble when date are presented in various formats, such as text, voice and image. This study proposes a novel image-encoded forecasting method that input and output binary digital two-dimensional (2D) images are transformed from decimal data. Omitting any data analysis or cleansing steps for simplicity, all raw variables were selected and converted to binary digital images as the input of a deep learning model, convolutional neural network (CNN). Using shared weights, pooling and multiple-layer back-propagation techniques, the CNN was adopted to locate the nexus among variations in local binary digital images. Due to the computing capability that was originally developed for binary digital bitmap manipulation, this model has significant potential for forecasting with vast volume of data. The model was validated by a power loads predicting dataset from the Global Energy Forecasting Competition 2012. PMID:27281032

Fast State-Space Methods for Inferring Dendritic Synaptic Connectivity

DTIC Science & Technology

2013-08-08

the results of 100 simulations with the same parameters as in Figures 4 and 5. As expected, the LARS/LARS+ results are (downward) biased and have low...with a strength slightly biased toward lower values. To measure the variability of the results across the 20 simulations , we computed for each...are downward biased and have low variance, and the OLS results are unbiased but have high variance. Note that for LARS+ the values above the median are

Associations between mood instability and emotional processing in a large cohort of bipolar patients.

PubMed

Bilderbeck, A C; Reed, Z E; McMahon, H C; Atkinson, L Z; Price, J; Geddes, J R; Goodwin, G M; Harmer, C J

2016-11-01

Aberrant emotional biases have been reported in bipolar disorder (BD), but results are inconsistent. Despite the clinical relevance of chronic mood variability in BD, there is no previous research investigating how the extent of symptom fluctuations in bipolar disorder might relate to emotional biases. This exploratory study investigated, in a large cohort of bipolar patients, whether instability in weekly mood episode symptoms and other clinical and demographic factors were related to emotional bias as measured in a simple laboratory task. Participants (N = 271, BDI = 206, BDII = 121) completed an 'emotional categorization and memory' task. Weekly self-reported symptoms of depression and mania were collected prospectively. In linear regression analyses, associations between cognitive bias and mood variability were explored together with the influence of demographic and clinical factors, including current medication. Greater accuracy in the classification of negative words relative to positive words was associated with greater instability in depressive symptoms. Furthermore, greater negative bias in free recall was associated with higher instability in manic symptoms. Participants diagnosed with BDII, compared with BDI, showed overall better word recognition and recall. Current antipsychotic use was associated with reduced instability in manic symptoms but this did not impact on emotional processing performance. Emotional processing biases in bipolar disorder are related to instability in mood. These findings prompt further investigation into the underpinnings as well as clinical significance of mood instability.

Gravity dependence of the effect of optokinetic stimulation on the subjective visual vertical.

PubMed

Ward, Bryan K; Bockisch, Christopher J; Caramia, Nicoletta; Bertolini, Giovanni; Tarnutzer, Alexander Andrea

2017-05-01

Accurate and precise estimates of direction of gravity are essential for spatial orientation. According to Bayesian theory, multisensory vestibular, visual, and proprioceptive input is centrally integrated in a weighted fashion based on the reliability of the component sensory signals. For otolithic input, a decreasing signal-to-noise ratio was demonstrated with increasing roll angle. We hypothesized that the weights of vestibular (otolithic) and extravestibular (visual/proprioceptive) sensors are roll-angle dependent and predicted an increased weight of extravestibular cues with increasing roll angle, potentially following the Bayesian hypothesis. To probe this concept, the subjective visual vertical (SVV) was assessed in different roll positions (≤ ± 120°, steps = 30°, n = 10) with/without presenting an optokinetic stimulus (velocity = ± 60°/s). The optokinetic stimulus biased the SVV toward the direction of stimulus rotation for roll angles ≥ ± 30° ( P < 0.005). Offsets grew from 3.9 ± 1.8° (upright) to 22.1 ± 11.8° (±120° roll tilt, P < 0.001). Trial-to-trial variability increased with roll angle, demonstrating a nonsignificant increase when providing optokinetic stimulation. Variability and optokinetic bias were correlated ( R 2 = 0.71, slope = 0.71, 95% confidence interval = 0.57-0.86). An optimal-observer model combining an optokinetic bias with vestibular input reproduced measured errors closely. These findings support the hypothesis of a weighted multisensory integration when estimating direction of gravity with optokinetic stimulation. Visual input was weighted more when vestibular input became less reliable, i.e., at larger roll-tilt angles. However, according to Bayesian theory, the variability of combined cues is always lower than the variability of each source cue. If the observed increase in variability, although nonsignificant, is true, either it must depend on an additional source of variability, added after SVV computation, or it would conflict with the Bayesian hypothesis. NEW & NOTEWORTHY Applying a rotating optokinetic stimulus while recording the subjective visual vertical in different whole body roll angles, we noted the optokinetic-induced bias to correlate with the roll angle. These findings allow the hypothesis that the established optimal weighting of single-sensory cues depending on their reliability to estimate direction of gravity could be extended to a bias caused by visual self-motion stimuli. Copyright © 2017 the American Physiological Society.

Accounting for interannual variability: A comparison of options for water resources climate change impact assessments

NASA Astrophysics Data System (ADS)

Johnson, Fiona; Sharma, Ashish

2011-04-01

Empirical scaling approaches for constructing rainfall scenarios from general circulation model (GCM) simulations are commonly used in water resources climate change impact assessments. However, these approaches have a number of limitations, not the least of which is that they cannot account for changes in variability or persistence at annual and longer time scales. Bias correction of GCM rainfall projections offers an attractive alternative to scaling methods as it has similar advantages to scaling in that it is computationally simple, can consider multiple GCM outputs, and can be easily applied to different regions or climatic regimes. In addition, it also allows for interannual variability to evolve according to the GCM simulations, which provides additional scenarios for risk assessments. This paper compares two scaling and four bias correction approaches for estimating changes in future rainfall over Australia and for a case study for water supply from the Warragamba catchment, located near Sydney, Australia. A validation of the various rainfall estimation procedures is conducted on the basis of the latter half of the observational rainfall record. It was found that the method leading to the lowest prediction errors varies depending on the rainfall statistic of interest. The flexibility of bias correction approaches in matching rainfall parameters at different frequencies is demonstrated. The results also indicate that for Australia, the scaling approaches lead to smaller estimates of uncertainty associated with changes to interannual variability for the period 2070-2099 compared to the bias correction approaches. These changes are also highlighted using the case study for the Warragamba Dam catchment.

Moderator Variables as Bias in Testing Black Children

ERIC Educational Resources Information Center

Williams, Robert L.

1975-01-01

The claim that tests of intelligence and abilities are the best predictors of academic success fails to examine closely the important moderator variable as test and criterion characteristics rather than as person characteristics. (EH)

Enhanced Conformational Sampling Using Replica Exchange with Collective-Variable Tempering.

PubMed

Gil-Ley, Alejandro; Bussi, Giovanni

2015-03-10

The computational study of conformational transitions in RNA and proteins with atomistic molecular dynamics often requires suitable enhanced sampling techniques. We here introduce a novel method where concurrent metadynamics are integrated in a Hamiltonian replica-exchange scheme. The ladder of replicas is built with different strengths of the bias potential exploiting the tunability of well-tempered metadynamics. Using this method, free-energy barriers of individual collective variables are significantly reduced compared with simple force-field scaling. The introduced methodology is flexible and allows adaptive bias potentials to be self-consistently constructed for a large number of simple collective variables, such as distances and dihedral angles. The method is tested on alanine dipeptide and applied to the difficult problem of conformational sampling in a tetranucleotide.

Compliance-Effect Correlation Bias in Instrumental Variables Estimators

ERIC Educational Resources Information Center

Reardon, Sean F.

2010-01-01

Instrumental variable estimators hold the promise of enabling researchers to estimate the effects of educational treatments that are not (or cannot be) randomly assigned but that may be affected by randomly assigned interventions. Examples of the use of instrumental variables in such cases are increasingly common in educational and social science…

Measurement variability error for estimates of volume change

Treesearch

James A. Westfall; Paul L. Patterson

2007-01-01

Using quality assurance data, measurement variability distributions were developed for attributes that affect tree volume prediction. Random deviations from the measurement variability distributions were applied to 19381 remeasured sample trees in Maine. The additional error due to measurement variation and measurement bias was estimated via a simulation study for...

Optimal Asteroid Mass Determination from Planetary Range Observations: A Study of a Simplified Test Model

NASA Technical Reports Server (NTRS)

Kuchynka, P.; Laskar, J.; Fienga, A.

2011-01-01

Mars ranging observations are available over the past 10 years with an accuracy of a few meters. Such precise measurements of the Earth-Mars distance provide valuable constraints on the masses of the asteroids perturbing both planets. Today more than 30 asteroid masses have thus been estimated from planetary ranging data (see [1] and [2]). Obtaining unbiased mass estimations is nevertheless difficult. Various systematic errors can be introduced by imperfect reduction of spacecraft tracking observations to planetary ranging data. The large number of asteroids and the limited a priori knowledge of their masses is also an obstacle for parameter selection. Fitting in a model a mass of a negligible perturber, or on the contrary omitting a significant perturber, will induce important bias in determined asteroid masses. In this communication, we investigate a simplified version of the mass determination problem. Instead of planetary ranging observations from spacecraft or radar data, we consider synthetic ranging observations generated with the INPOP [2] ephemeris for a test model containing 25000 asteroids. We then suggest a method for optimal parameter selection and estimation in this simplified framework.

Does job satisfaction improve the health of workers? New evidence using panel data and objective measures of health.

PubMed

Fischer, Justina A V; Sousa-Poza, Alfonso

2009-01-01

This paper evaluates the relationship between job satisfaction and measures of health of workers using the German Socio-Economic Panel. Methodologically, it addresses two important design problems encountered frequently in the literature: (a) cross-sectional causality problems and (b) the absence of objective measures of physical health that complement self-reported measures of health status. Not only does using the panel structure with individual fixed effects mitigate the bias from omitting unobservable personal psycho-social characteristics, but employing more objective health measures such as health-system contacts and disability addresses such measurement problems relating to self-report assessments of health status.We find a positive link between job satisfaction (and changes over time therein) and subjective health measures (and changes therein); that is, employees with higher or improved job satisfaction levels feel healthier and are more satisfied with their health. This observation also holds true for more objective measures of health. Particularly, improvements in job satisfaction over time appear to prevent workers from (further) health deterioration. Copyright (c) 2008 John Wiley & Sons, Ltd.

Politics of energy. [Conflict between producing and consuming states

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

Copulos, M.R.

1978-01-01

Conflict between energy-consuming and energy-producing states is predicted to be the issue of the future rather than the traditional ''guns and butter'' division in Congress. Regional divisions based on energy status have brought together new alliances of moderates and conservatives of opposing parties. A combination of intense lobbying and some serious flaws in the original Carter energy proposals have made it difficult for Congress to develop a satisfactory policy. The National Energy Plan's major flaw, a disregard for developing new energy supplies and a refusal to compromise, resulted in a Senate bill that omitted proposed tax provisions. Policymaking within themore » White House is judged to lack objectivity because of the environmental bias of White House staff, whose positions are viewed as both contradictory and counter-productive. The energy plan could have been withdrawn for revision when its goals were declared to be unattainable in the areas of conservation and reduced imports. The foreign policy implications inherent in a reliance on Persian Gulf oil and in opposition to nuclear fuel reprocessing will affect U.S. leadership in the balance of power.« less

Correcting power and p-value calculations for bias in diffusion tensor imaging.

PubMed

Lauzon, Carolyn B; Landman, Bennett A

2013-07-01

Diffusion tensor imaging (DTI) provides quantitative parametric maps sensitive to tissue microarchitecture (e.g., fractional anisotropy, FA). These maps are estimated through computational processes and subject to random distortions including variance and bias. Traditional statistical procedures commonly used for study planning (including power analyses and p-value/alpha-rate thresholds) specifically model variability, but neglect potential impacts of bias. Herein, we quantitatively investigate the impacts of bias in DTI on hypothesis test properties (power and alpha-rate) using a two-sided hypothesis testing framework. We present theoretical evaluation of bias on hypothesis test properties, evaluate the bias estimation technique SIMEX for DTI hypothesis testing using simulated data, and evaluate the impacts of bias on spatially varying power and alpha rates in an empirical study of 21 subjects. Bias is shown to inflame alpha rates, distort the power curve, and cause significant power loss even in empirical settings where the expected difference in bias between groups is zero. These adverse effects can be attenuated by properly accounting for bias in the calculation of power and p-values. Copyright © 2013 Elsevier Inc. All rights reserved.

[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.

Judgments of Omitted BE and DO in Questions as Extended Finiteness Clinical Markers of Specific Language Impairment (SLI) to 15 Years: A Study of Growth and Asymptote

ERIC Educational Resources Information Center

Rice, Mabel L.; Hoffman, Lesa; Wexler, Ken

2009-01-01

Purpose: Clinical grammar markers are needed for children with SLI older than 8 years. This study followed children who were previously studied on sentences with omitted finiteness to determine if affected children continue to perform at low levels and to examine possible predictors of low performance. This is the first longitudinal report of…

The Signs B [Image Omitted] and B-Bent [Image Omitted] in Israeli Sign Language According to the Theory of Phonology as Human Behavior

ERIC Educational Resources Information Center

Fuks, Orit; Tobin, Yishai

2008-01-01

The purpose of the present research is to examine which of the two factors: (1) the iconic-semiotic factor; or (2) the human-phonetic factor is more relevant in explaining the appearance and distribution of the hand shape B-bent in Israeli Sign Language (ISL). The B-bent shape has been the subject of much attention in sign language research…

Thalamic regulation of sucrose-seeking during unexpected reward omission

PubMed Central

Do-Monte, Fabricio H.; Minier-Toribio, Angélica; Quiñones-Laracuente, Kelvin; Medina-Colón, Estefanía M.; Quirk, Gregory J.

2017-01-01

SUMMARY The paraventricular nucleus of the thalamus (PVT) is thought to regulate behavioral responses under emotionally arousing conditions. Reward-associated cues activate PVT neurons, however, the specific PVT efferents regulating reward-seeking remain elusive. Using a cued sucrose-seeking task, we manipulated PVT activity under two emotionally distinct conditions: 1) when reward was available during the cue as expected, or 2) when reward was unexpectedly omitted during the cue. Pharmacological inactivation of the anterior PVT (aPVT), but not the posterior PVT, increased sucrose-seeking only when reward was omitted. Consistent with this, photoactivation of aPVT neurons abolished sucrose-seeking, and the firing of aPVT neurons differentiated reward availability. Photoinhibition of aPVT projections to the nucleus accumbens or to the amygdala increased or decreased, respectively, sucrose-seeking only when reward was omitted. Our findings suggest that PVT bidirectionally modulates sucrose-seeking under the negative (frustrative) conditions of reward omission. PMID:28426970

Optomechanically-induced transparency in parity-time-symmetric microresonators

PubMed Central

Jing, H.; Özdemir, Şahin K.; Geng, Z.; Zhang, Jing; Lü, Xin-You; Peng, Bo; Yang, Lan; Nori, Franco

2015-01-01

Optomechanically-induced transparency (OMIT) and the associated slowing of light provide the basis for storing photons in nanoscale devices. Here we study OMIT in parity-time (PT)-symmetric microresonators with a tunable gain-to-loss ratio. This system features a sideband-reversed, non-amplifying transparency , i.e., an inverted-OMIT. When the gain-to-loss ratio is varied, the system exhibits a transition from a PT-symmetric phase to a broken-PT-symmetric phase. This PT-phase transition results in the reversal of the pump and gain dependence of the transmission rates. Moreover, we show that by tuning the pump power at a fixed gain-to-loss ratio, or the gain-to-loss ratio at a fixed pump power, one can switch from slow to fast light and vice versa. These findings provide new tools for controlling light propagation using nanofabricated phononic devices. PMID:26169253

Combined Uncertainty and A-Posteriori Error Bound Estimates for General CFD Calculations: Theory and Software Implementation

NASA Technical Reports Server (NTRS)

Barth, Timothy J.

2014-01-01

This workshop presentation discusses the design and implementation of numerical methods for the quantification of statistical uncertainty, including a-posteriori error bounds, for output quantities computed using CFD methods. Hydrodynamic realizations often contain numerical error arising from finite-dimensional approximation (e.g. numerical methods using grids, basis functions, particles) and statistical uncertainty arising from incomplete information and/or statistical characterization of model parameters and random fields. The first task at hand is to derive formal error bounds for statistics given realizations containing finite-dimensional numerical error [1]. The error in computed output statistics contains contributions from both realization error and the error resulting from the calculation of statistics integrals using a numerical method. A second task is to devise computable a-posteriori error bounds by numerically approximating all terms arising in the error bound estimates. For the same reason that CFD calculations including error bounds but omitting uncertainty modeling are only of limited value, CFD calculations including uncertainty modeling but omitting error bounds are only of limited value. To gain maximum value from CFD calculations, a general software package for uncertainty quantification with quantified error bounds has been developed at NASA. The package provides implementations for a suite of numerical methods used in uncertainty quantification: Dense tensorization basis methods [3] and a subscale recovery variant [1] for non-smooth data, Sparse tensorization methods[2] utilizing node-nested hierarchies, Sampling methods[4] for high-dimensional random variable spaces.

Nutritional Status Associated to Skipping Breakfast in Brazilian Health Service Patients.

PubMed

Batista-Jorge, Gislaine Cândida; Barcala-Jorge, Antônio Sérgio; Oliveira Dias, Anderson Frederico; Silveira, Marise Fagundes; de Farias Lelis, Deborah; Oliveira Andrade, João Marcus; Claro, Rafael Moreira; de Paula, Alfredo Mauricio Batista; Guimaraes, Andre Luiz Sena; Ferreira, Adaliene Versiane; Santos, Sérgio Henrique Sousa

2016-01-01

Recent studies show that skipping breakfast is associated with an increased risk of obesity, diabetes and cardiovascular diseases. In this context, this study evaluated 400 patients from the Brazilian health service who had their nutritional status defined based on the body mass index and were classified as physically active or insufficient active. The energy intake and macronutrients was also assessed by a 24-hour dietary recall where the association of overweight/obesity with the investigated variables was evaluated using chi-square, Student's t test and multivariate analysis (p < 0.05). The main results showed that more than half of the studied population have the habit of omitting breakfast (55.8%), and among those, 81.2% were overweight/obese (p < 0.0001). Almost three-fourths of these individuals consumed no more than 4 meals a day (73.0%), and regarding this meal frequency/day, 78.8% of the individuals who reported having 4 meals or less a day were overweight/obese compared with 57.8% who reported as having 5-6 meals/day (p < 0.0001). The individuals who reported to omit breakfast had a higher chance of being overweight compared with those who had this habit (OR 2.20; 95% CI 1.40-3.60) and the chance of the physically insufficient active individuals to be overweight/obese was 2.9 times higher when compared to the active individuals (p < 0.0001). Our findings suggest that regular breakfast consumption may decrease overweight and obesity risk. © 2016 S. Karger AG, Basel.

Empirical Recommendations for Improving the Stability of the Dot-Probe Task in Clinical Research

PubMed Central

Price, Rebecca B.; Kuckertz, Jennie M.; Siegle, Greg J.; Ladouceur, Cecile D.; Silk, Jennifer S.; Ryan, Neal D.; Dahl, Ronald E.; Amir, Nader

2014-01-01

The dot-probe task has been widely used in research to produce an index of biased attention based on reaction times (RTs). Despite its popularity, very few published studies have examined psychometric properties of the task, including test-retest reliability, and no previous study has examined reliability in clinically anxious samples or systematically explored the effects of task design and analysis decisions on reliability. In the current analysis, we utilized dot-probe data from three studies where attention bias towards threat-related faces was assessed at multiple (≥5) timepoints. Two of the studies were similar (adults with Social Anxiety Disorder, similar design features) while one was much more disparate (pediatric healthy volunteers, distinct task design). We explored the effects of analysis choices (e.g., bias score calculation formula, methods for outlier handling) on reliability and searched for convergence of findings across the three studies. We found that, when considering the three studies concurrently, the most reliable RT bias index utilized data from dot-bottom trials, comparing congruent to incongruent trials, with rescaled outliers, particularly after averaging across more than one assessment point. Although reliability of RT bias indices was moderate to low under most circumstances, within-session variability in bias (attention bias variability; ABV), a recently proposed RT index, was more reliable across sessions. Several eyetracking-based indices of attention bias (available in the pediatric healthy sample only) showed reliability that matched the optimal RT index (ABV). On the basis of these findings, we make specific recommendations to researchers using the dot probe, particularly those wishing to investigate individual differences and/or single-patient applications. PMID:25419646

Alternative Regression Equations for Estimation of Annual Peak-Streamflow Frequency for Undeveloped Watersheds in Texas using PRESS Minimization

USGS Publications Warehouse

Asquith, William H.; Thompson, David B.

2008-01-01

The U.S. Geological Survey, in cooperation with the Texas Department of Transportation and in partnership with Texas Tech University, investigated a refinement of the regional regression method and developed alternative equations for estimation of peak-streamflow frequency for undeveloped watersheds in Texas. A common model for estimation of peak-streamflow frequency is based on the regional regression method. The current (2008) regional regression equations for 11 regions of Texas are based on log10 transformations of all regression variables (drainage area, main-channel slope, and watershed shape). Exclusive use of log10-transformation does not fully linearize the relations between the variables. As a result, some systematic bias remains in the current equations. The bias results in overestimation of peak streamflow for both the smallest and largest watersheds. The bias increases with increasing recurrence interval. The primary source of the bias is the discernible curvilinear relation in log10 space between peak streamflow and drainage area. Bias is demonstrated by selected residual plots with superimposed LOWESS trend lines. To address the bias, a statistical framework based on minimization of the PRESS statistic through power transformation of drainage area is described and implemented, and the resulting regression equations are reported. Compared to log10-exclusive equations, the equations derived from PRESS minimization have PRESS statistics and residual standard errors less than the log10 exclusive equations. Selected residual plots for the PRESS-minimized equations are presented to demonstrate that systematic bias in regional regression equations for peak-streamflow frequency estimation in Texas can be reduced. Because the overall error is similar to the error associated with previous equations and because the bias is reduced, the PRESS-minimized equations reported here provide alternative equations for peak-streamflow frequency estimation.

A simple technique investigating baseline heterogeneity helped to eliminate potential bias in meta-analyses.

PubMed

Hicks, Amy; Fairhurst, Caroline; Torgerson, David J

2018-03-01

To perform a worked example of an approach that can be used to identify and remove potentially biased trials from meta-analyses via the analysis of baseline variables. True randomisation produces treatment groups that differ only by chance; therefore, a meta-analysis of a baseline measurement should produce no overall difference and zero heterogeneity. A meta-analysis from the British Medical Journal, known to contain significant heterogeneity and imbalance in baseline age, was chosen. Meta-analyses of baseline variables were performed and trials systematically removed, starting with those with the largest t-statistic, until the I 2 measure of heterogeneity became 0%, then the outcome meta-analysis repeated with only the remaining trials as a sensitivity check. We argue that heterogeneity in a meta-analysis of baseline variables should not exist, and therefore removing trials which contribute to heterogeneity from a meta-analysis will produce a more valid result. In our example none of the overall outcomes changed when studies contributing to heterogeneity were removed. We recommend routine use of this technique, using age and a second baseline variable predictive of outcome for the particular study chosen, to help eliminate potential bias in meta-analyses. Copyright © 2017 Elsevier Inc. All rights reserved.

A systematic review and meta-analysis of cognitive bias to food stimuli in people with disordered eating behaviour.

PubMed

Brooks, Samantha; Prince, Alexis; Stahl, Daniel; Campbell, Iain C; Treasure, Janet

2011-02-01

Maladaptive cognitions about food, weight and shape bias attention, memory and judgment and may be linked to disordered eating behaviour. This paper reviews information processing of food stimuli (words, pictures) in people with eating disorders (ED). PubMed, Ovid, ScienceDirect, PsychInfo, Web of Science, Cochrane Library and Google Scholar were searched to December 2009. 63 studies measured attention, memory and judgment bias towards food stimuli in women with ED. Stroop tasks had sufficient sample size for a meta-analyses and effects ranged from small to medium. Other studies of attention bias had variable effects (e.g. the Dot-Probe task, distracter tasks and Startle Eyeblink Modulation). A meta-analysis of memory bias studies in ED and RE yielded insignificant effect. Effect sizes for judgment bias ranged from negligible to large. People with ED have greater attentional bias to food stimuli than healthy controls (HC). Evidence for a memory and judgment bias in ED is limited. Copyright © 2010 Elsevier Ltd. All rights reserved.

Just tell me what to do: bringing back experimenter control in active contingency tasks with the command-performance procedure and finding cue density effects along the way.

PubMed

Hannah, Samuel D; Beneteau, Jennifer L

2009-03-01

Active contingency tasks, such as those used to explore judgments of control, suffer from variability in the actual values of critical variables. The authors debut a new, easily implemented procedure that restores control over these variables to the experimenter simply by telling participants when to respond, and when to withhold responding. This command-performance procedure not only restores control over critical variables such as actual contingency, it also allows response frequency to be manipulated independently of contingency or outcome frequency. This yields the first demonstration, to our knowledge, of the equivalent of a cue density effect in an active contingency task. Judgments of control are biased by response frequency outcome frequency, just as they are also biased by outcome frequency. (c) 2009 APA, all rights reserved

Avoiding and Correcting Bias in Score-Based Latent Variable Regression with Discrete Manifest Items

ERIC Educational Resources Information Center

Lu, Irene R. R.; Thomas, D. Roland

2008-01-01

This article considers models involving a single structural equation with latent explanatory and/or latent dependent variables where discrete items are used to measure the latent variables. Our primary focus is the use of scores as proxies for the latent variables and carrying out ordinary least squares (OLS) regression on such scores to estimate…

`Been There Done That': Disentangling Option Value Effects from User Heterogeneity When Valuing Natural Resources with a Use Component

NASA Astrophysics Data System (ADS)

Lyssenko, Nikita; Martínez-Espiñeira, Roberto

2012-11-01

Endogeneity bias arises in contingent valuation studies when the error term in the willingness to pay (WTP) equation is correlated with explanatory variables because observable and unobservable characteristics of the respondents affect both their WTP and the value of those variables. We correct for the endogeneity of variables that capture previous experience with the resource valued, humpback whales, and with the geographic area of study. We consider several endogenous behavioral variables. Therefore, we apply a multivariate Probit approach to jointly model them with WTP. In this case, correcting for endogeneity increases econometric efficiency and substantially corrects the bias affecting the estimated coefficients of the experience variables, by isolating the decreasing effect on option value caused by having already experienced the resource. Stark differences are unveiled between the marginal effects on WTP of previous experience of the resource in an alternative location versus experience in the location studied, Newfoundland and Labrador (Canada).

'Been there done that': disentangling option value effects from user heterogeneity when valuing natural resources with a use component.

PubMed

Lyssenko, Nikita; Martínez-Espiñeira, Roberto

2012-11-01

Endogeneity bias arises in contingent valuation studies when the error term in the willingness to pay (WTP) equation is correlated with explanatory variables because observable and unobservable characteristics of the respondents affect both their WTP and the value of those variables. We correct for the endogeneity of variables that capture previous experience with the resource valued, humpback whales, and with the geographic area of study. We consider several endogenous behavioral variables. Therefore, we apply a multivariate Probit approach to jointly model them with WTP. In this case, correcting for endogeneity increases econometric efficiency and substantially corrects the bias affecting the estimated coefficients of the experience variables, by isolating the decreasing effect on option value caused by having already experienced the resource. Stark differences are unveiled between the marginal effects on WTP of previous experience of the resource in an alternative location versus experience in the location studied, Newfoundland and Labrador (Canada).

Automated quantification of myocardial infarction from MR images by accounting for partial volume effects: animal, phantom, and human study.

PubMed

Heiberg, Einar; Ugander, Martin; Engblom, Henrik; Götberg, Matthias; Olivecrona, Göran K; Erlinge, David; Arheden, Håkan

2008-02-01

Ethics committees approved human and animal study components; informed written consent was provided (prospective human study [20 men; mean age, 62 years]) or waived (retrospective human study [16 men, four women; mean age, 59 years]). The purpose of this study was to prospectively evaluate a clinically applicable method, accounting for the partial volume effect, to automatically quantify myocardial infarction from delayed contrast material-enhanced magnetic resonance images. Pixels were weighted according to signal intensity to calculate infarct fraction for each pixel. Mean bias +/- variability (or standard deviation), expressed as percentage left ventricular myocardium (%LVM), were -0.3 +/- 1.3 (animals), -1.2 +/- 1.7 (phantoms), and 0.3 +/- 2.7 (patients), respectively. Algorithm had lower variability than dichotomous approach (2.7 vs 7.7 %LVM, P < .01) and did not differ from interobserver variability for bias (P = .31) or variability (P = .38). The weighted approach provides automatic quantification of myocardial infarction with higher accuracy and lower variability than a dichotomous algorithm. (c) RSNA, 2007.

Cognitive biases to healthy and unhealthy food words predict change in BMI.

PubMed

Calitri, Raff; Pothos, Emmanuel M; Tapper, Katy; Brunstrom, Jeffrey M; Rogers, Peter J

2010-12-01

The current study explored the predictive value of cognitive biases to food cues (assessed by emotional Stroop and dot probe tasks) on weight change over a 1-year period. This was a longitudinal study with undergraduate students (N = 102) living in shared student accommodation. After controlling for the effects of variables associated with weight (e.g., physical activity, stress, restrained eating, external eating, and emotional eating), no effects of cognitive bias were found with the dot probe. However, for the emotional Stroop, cognitive bias to unhealthy foods predicted an increase in BMI whereas cognitive bias to healthy foods was associated with a decrease in BMI. Results parallel findings in substance abuse research; cognitive biases appear to predict behavior change. Accordingly, future research should consider strategies for attentional retraining, encouraging individuals to reorient attention away from unhealthy eating cues.

The Use of Propensity Scores and Observational Data to Estimate Randomized Controlled Trial Generalizability Bias

PubMed Central

Pressler, Taylor R.; Kaizar, Eloise E.

2014-01-01

While randomized controlled trials (RCT) are considered the “gold standard” for clinical studies, the use of exclusion criteria may impact the external validity of the results. It is unknown whether estimators of effect size are biased by excluding a portion of the target population from enrollment. We propose to use observational data to estimate the bias due to enrollment restrictions, which we term generalizability bias. In this paper we introduce a class of estimators for the generalizability bias and use simulation to study its properties in the presence of non-constant treatment effects. We find the surprising result that our estimators can be unbiased for the true generalizability bias even when all potentially confounding variables are not measured. In addition, our proposed doubly robust estimator performs well even for mis-specified models. PMID:23553373

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.

Compatibility check of measured aircraft responses using kinematic equations and extended Kalman filter

NASA Technical Reports Server (NTRS)

Klein, V.; Schiess, J. R.

1977-01-01

An extended Kalman filter smoother and a fixed point smoother were used for estimation of the state variables in the six degree of freedom kinematic equations relating measured aircraft responses and for estimation of unknown constant bias and scale factor errors in measured data. The computing algorithm includes an analysis of residuals which can improve the filter performance and provide estimates of measurement noise characteristics for some aircraft output variables. The technique developed was demonstrated using simulated and real flight test data. Improved accuracy of measured data was obtained when the data were corrected for estimated bias errors.

On Rater Agreement and Rater Training

ERIC Educational Resources Information Center

Wang, Binhong

2010-01-01

This paper first analyzed two studies on rater factors and rating criteria to raise the problem of rater agreement. After that the author reveals the causes of discrepencies in rating administration by discussing rater variability and rater bias. The author argues that rater bias can not be eliminated completely, we can only reduce the error to a…

Heaping-Induced Bias in Regression-Discontinuity Designs. NBER Working Paper No. 17408

ERIC Educational Resources Information Center

Barreca, Alan I.; Lindo, Jason M.; Waddell, Glen R.

2011-01-01

This study uses Monte Carlo simulations to demonstrate that regression-discontinuity designs arrive at biased estimates when attributes related to outcomes predict heaping in the running variable. After showing that our usual diagnostics are poorly suited to identifying this type of problem, we provide alternatives. We also demonstrate how the…

Reducing Bias Due to Systematic Attrition in Longitudinal Studies: The Benefits of Multiple Imputation

ERIC Educational Resources Information Center

Asendorpf, Jens B.; van de Schoot, Rens; Denissen, Jaap J. A.; Hutteman, Roos

2014-01-01

Most longitudinal studies are plagued by drop-out related to variables at earlier assessments (systematic attrition). Although systematic attrition is often analysed in longitudinal studies, surprisingly few researchers attempt to reduce biases due to systematic attrition, even though this is possible and nowadays technically easy. This is…

Bias in Student Survey Findings from Active Parental Consent Procedures

ERIC Educational Resources Information Center

Shaw, Thérèse; Cross, Donna; Thomas, Laura T.; Zubrick, Stephen R.

2015-01-01

Increasingly, researchers are required to obtain active (explicit) parental consent prior to surveying children and adolescents in schools. This study assessed the potential bias present in a sample of actively consented students, and in the estimates of associations between variables obtained from this sample. Students (n = 3496) from 36…

Effects of Perceived School Well-Being and Negative Emotionality on Students' Attentional Bias for Academic Stressors

ERIC Educational Resources Information Center

Scrimin, Sara; Moscardino, Ughetta; Altoè, Gianmarco; Mason, Lucia

2016-01-01

Background: Previous research indicates that children can display different attention allocation patterns in response to threat. However, data are lacking on the possible existence of an attentional bias in response to academic stressors, and whether variables related to school well-being (SWB) and students' individual characteristics may…

Internalized weight bias mediates the relationship between depressive symptoms and disordered eating behavior among women who think they are overweight.

PubMed

Sienko, Rachel M; Saules, Karen K; Carr, Meagan M

2016-08-01

This study tested the potential mediating role of Internalized Weight Bias (IWB) in the relationship between depressive symptoms (DEP-SX) and disordered eating behavior. In particular, we hypothesized that IWB may be an intervening variable in the well documented association between depression and disordered eating. College women (N=172) who were taking undergraduate psychology courses and who endorsed thinking they were overweight completed the Patient Health Questionnaire depression screener (PHQ-9), the Weight Bias Internalization Scale (WBIS), and the Eating Disorder Examination Questionnaire (EDE-Q). Bootstrapping mediation analyses were conducted to explore the relationships between these variables. IWB was significantly correlated with eating disorder symptoms and DEP-SX, but not Body Mass Index. Mediation analyses supported a model in which IWB mediated the relationship between DEP-SX and disordered eating behavior. Results indicate that individuals with elevated DEP-SX may be likely to internalize weight bias, which may in turn lead to maladaptive approaches to eating and weight control, regardless of one's actual weight status. Copyright © 2016 Elsevier Ltd. All rights reserved.

Inference for binomial probability based on dependent Bernoulli random variables with applications to meta‐analysis and group level studies

PubMed Central

Bakbergenuly, Ilyas; Morgenthaler, Stephan

2016-01-01

We study bias arising as a result of nonlinear transformations of random variables in random or mixed effects models and its effect on inference in group‐level studies or in meta‐analysis. The findings are illustrated on the example of overdispersed binomial distributions, where we demonstrate considerable biases arising from standard log‐odds and arcsine transformations of the estimated probability p^, both for single‐group studies and in combining results from several groups or studies in meta‐analysis. Our simulations confirm that these biases are linear in ρ, for small values of ρ, the intracluster correlation coefficient. These biases do not depend on the sample sizes or the number of studies K in a meta‐analysis and result in abysmal coverage of the combined effect for large K. We also propose bias‐correction for the arcsine transformation. Our simulations demonstrate that this bias‐correction works well for small values of the intraclass correlation. The methods are applied to two examples of meta‐analyses of prevalence. PMID:27192062

A Projection of Changes in Landfilling Atmospheric River Frequency and Extreme Precipitation over Western North America from the Large Ensemble CESM Simulations

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

Hagos, Samson M.; Leung, Lai-Yung R.; Yoon, Jin-Ho

Simulations from the Community Earth System Model Large Ensemble project are analyzed to investigate the impact of global warming on atmospheric rivers (ARs). The model has notable biases in simulating the subtropical jet position and the relationship between extreme precipitation and moisture transport. After accounting for these biases, the model projects an ensemble mean increase of 35% in the number of landfalling AR days between the last twenty years of the 20th and 21st centuries. However, the number of AR associated extreme precipitation days increases only by 28% because the moisture transport required to produce extreme precipitation also increases withmore » warming. Internal variability introduces an uncertainty of ±8% and ±7% in the projected changes in AR days and associated extreme precipitation days. In contrast, accountings for model biases only change the projected changes by about 1%. The significantly larger mean changes compared to internal variability and to the effects of model biases highlight the robustness of AR responses to global warming.« less

Comment on “Models of stochastic, spatially varying stress in the crust compatible with focal‐mechanism data, and how stress inversions can be biased toward the stress rate” by Deborah Elaine Smith and Thomas H. Heaton

USGS Publications Warehouse

Hardebeck, Jeanne L.

2015-01-01

This model makes specific predictions about the orientations and heterogeneity of earthquake focal mechanisms. Smith and Heaton (2011) attempt to validate this heterogeneous stress model using observations of earthquake focal‐mechanism variability from Hardebeck (2006). They then demonstrate that the model predicts a bias in the orientations of earthquake focal mechanisms, which are biased away from the background stress and toward the stressing rate. They suggest the focal‐mechanism bias in this model invalidates the large body of work over the last several decades, that has inferred stress orientations from the inversion of earthquake focal mechanisms. The question of whether or not the Smith and Heaton (2011) model is applicable to the real Earth is therefore important not only for understanding spatial stress variability but also for evaluating the numerous studies that have inferred crustal stress orientations from earthquake focal mechanisms (e.g., as compiled by Heidbach et al., 2008).

The social transmission of metacontrol policies: Mechanisms underlying the interpersonal transfer of persistence and flexibility.

PubMed

Hommel, Bernhard; Colzato, Lorenza S

2017-10-01

Humans often face binary cognitive-control dilemmas, with the choice between persistence and flexibility being a crucial one. Tackling these dilemmas requires metacontrol, i.e., the control of the current cognitive-control policy. As predicted from functional, psychometric, neuroscientific, and modeling approaches, interindividual variability in metacontrol biases towards persistence or flexibility could be demonstrated in metacontrol-sensitive tasks. These biases covary systematically with genetic predispositions regarding mesofrontal and nigrostriatal dopaminergic functioning and the individualistic or collectivistic nature of the cultural background. However, there is also evidence for mood- and meditation-induced intraindividual variability (with negative mood and focused-attention meditation being associated with a bias towards persistence, and positive mood and open-monitoring meditation being associated with a bias towards flexibility), suggesting that genetic and cultural factors do not determine metacontrol settings entirely. We suggest a theoretical framework that explains how genetic predisposition and cultural learning can lead to the implementation of metacontrol defaults, which however can be shifted towards persistence or flexibility by situational factors. Copyright © 2017 Elsevier Ltd. All rights reserved.

Leveraging organismal biology to forecast the effects of climate change.

PubMed

Buckley, Lauren B; Cannistra, Anthony F; John, Aji

2018-04-26

Despite the pressing need for accurate forecasts of ecological and evolutionary responses to environmental change, commonly used modelling approaches exhibit mixed performance because they omit many important aspects of how organisms respond to spatially and temporally variable environments. Integrating models based on organismal phenotypes at the physiological, performance and fitness levels can improve model performance. We summarize current limitations of environmental data and models and discuss potential remedies. The paper reviews emerging techniques for sensing environments at fine spatial and temporal scales, accounting for environmental extremes, and capturing how organisms experience the environment. Intertidal mussel data illustrate biologically important aspects of environmental variability. We then discuss key challenges in translating environmental conditions into organismal performance including accounting for the varied timescales of physiological processes, for responses to environmental fluctuations including the onset of stress and other thresholds, and for how environmental sensitivities vary across lifecycles. We call for the creation of phenotypic databases to parameterize forecasting models and advocate for improved sharing of model code and data for model testing. We conclude with challenges in organismal biology that must be solved to improve forecasts over the next decade.acclimation, biophysical models, ecological forecasting, extremes, microclimate, spatial and temporal variability.

Heteroscedasticity as a Basis of Direction Dependence in Reversible Linear Regression Models.

PubMed

Wiedermann, Wolfgang; Artner, Richard; von Eye, Alexander

2017-01-01

Heteroscedasticity is a well-known issue in linear regression modeling. When heteroscedasticity is observed, researchers are advised to remedy possible model misspecification of the explanatory part of the model (e.g., considering alternative functional forms and/or omitted variables). The present contribution discusses another source of heteroscedasticity in observational data: Directional model misspecifications in the case of nonnormal variables. Directional misspecification refers to situations where alternative models are equally likely to explain the data-generating process (e.g., x → y versus y → x). It is shown that the homoscedasticity assumption is likely to be violated in models that erroneously treat true nonnormal predictors as response variables. Recently, Direction Dependence Analysis (DDA) has been proposed as a framework to empirically evaluate the direction of effects in linear models. The present study links the phenomenon of heteroscedasticity with DDA and describes visual diagnostics and nine homoscedasticity tests that can be used to make decisions concerning the direction of effects in linear models. Results of a Monte Carlo simulation that demonstrate the adequacy of the approach are presented. An empirical example is provided, and applicability of the methodology in cases of violated assumptions is discussed.

Adaptive enhanced sampling with a path-variable for the simulation of protein folding and aggregation

NASA Astrophysics Data System (ADS)

Peter, Emanuel K.

2017-12-01

In this article, we present a novel adaptive enhanced sampling molecular dynamics (MD) method for the accelerated simulation of protein folding and aggregation. We introduce a path-variable L based on the un-biased momenta p and displacements dq for the definition of the bias s applied to the system and derive 3 algorithms: general adaptive bias MD, adaptive path-sampling, and a hybrid method which combines the first 2 methodologies. Through the analysis of the correlations between the bias and the un-biased gradient in the system, we find that the hybrid methodology leads to an improved force correlation and acceleration in the sampling of the phase space. We apply our method on SPC/E water, where we find a conservation of the average water structure. We then use our method to sample dialanine and the folding of TrpCage, where we find a good agreement with simulation data reported in the literature. Finally, we apply our methodologies on the initial stages of aggregation of a hexamer of Alzheimer's amyloid β fragment 25-35 (Aβ 25-35) and find that transitions within the hexameric aggregate are dominated by entropic barriers, while we speculate that especially the conformation entropy plays a major role in the formation of the fibril as a rate limiting factor.

Comparative risk assessment and cessation information seeking among smokeless tobacco users.

PubMed

Jun, Jungmi; Nan, Xiaoli

2018-05-01

This research examined (1) smokeless tobacco users' comparative optimism in assessing the health and addiction risks of their own product in comparison with cigarettes, and (2) the effects of comparative optimism on cessation information-seeking. A nationally-representative sample from the 2015 Health Information National Trends Survey (HINTS)-FDA was employed. The analyses revealed the presence of comparative optimism in assessing both health and addiction risks among smokeless tobacco users. Comparative optimism was negatively correlated with most cessation information-seeking variables. Health bias (the health risk rating gap between the subject's own tobacco product and cigarettes) was associated with decreased intent to use cessation support. However, the health bias and addiction bias (the addiction risk rating gap between the subject's own tobacco product and cigarettes) were not consistent predictors of all cessation information-seeking variables, when covariates of socio-demographics and tobacco use status were included. In addition, positive correlations between health bias and past/recent cessation-information searches were observed. Optimisic biases may negatively influence cessation behaviors not only directly but also indirectly by influencing an important moderator, cessation information-seeking. Future interventions should prioritize dispelling the comparative optimism in perceiving risks of smokeless tobacco use, as well as provide more reliable cessation information specific to smokeless tobacco users. Copyright © 2018 Elsevier Ltd. All rights reserved.

Adaptive enhanced sampling with a path-variable for the simulation of protein folding and aggregation.

PubMed

Peter, Emanuel K

2017-12-07

In this article, we present a novel adaptive enhanced sampling molecular dynamics (MD) method for the accelerated simulation of protein folding and aggregation. We introduce a path-variable L based on the un-biased momenta p and displacements dq for the definition of the bias s applied to the system and derive 3 algorithms: general adaptive bias MD, adaptive path-sampling, and a hybrid method which combines the first 2 methodologies. Through the analysis of the correlations between the bias and the un-biased gradient in the system, we find that the hybrid methodology leads to an improved force correlation and acceleration in the sampling of the phase space. We apply our method on SPC/E water, where we find a conservation of the average water structure. We then use our method to sample dialanine and the folding of TrpCage, where we find a good agreement with simulation data reported in the literature. Finally, we apply our methodologies on the initial stages of aggregation of a hexamer of Alzheimer's amyloid β fragment 25-35 (Aβ 25-35) and find that transitions within the hexameric aggregate are dominated by entropic barriers, while we speculate that especially the conformation entropy plays a major role in the formation of the fibril as a rate limiting factor.

Implicit and Explicit Representations of Hand Position in Tool Use

PubMed Central

Rand, Miya K.; Heuer, Herbert

2013-01-01

Understanding the interactions of visual and proprioceptive information in tool use is important as it is the basis for learning of the tool's kinematic transformation and thus skilled performance. This study investigated how the CNS combines seen cursor positions and felt hand positions under a visuo-motor rotation paradigm. Young and older adult participants performed aiming movements on a digitizer while looking at rotated visual feedback on a monitor. After each movement, they judged either the proprioceptively sensed hand direction or the visually sensed cursor direction. We identified asymmetric mutual biases with a strong visual dominance. Furthermore, we found a number of differences between explicit and implicit judgments of hand directions. The explicit judgments had considerably larger variability than the implicit judgments. The bias toward the cursor direction for the explicit judgments was about twice as strong as for the implicit judgments. The individual biases of explicit and implicit judgments were uncorrelated. Biases of these judgments exhibited opposite sequential effects. Moreover, age-related changes were also different between these judgments. The judgment variability was decreased and the bias toward the cursor direction was increased with increasing age only for the explicit judgments. These results indicate distinct explicit and implicit neural representations of hand direction, similar to the notion of distinct visual systems. PMID:23894307

On the use of nudging techniques for regional climate modeling: application for tropical convection

NASA Astrophysics Data System (ADS)

Pohl, Benjamin; Crétat, Julien

2014-09-01

Using a large set of WRF ensemble simulations at 70-km horizontal resolution over a domain encompassing the Warm Pool region and its surroundings [45°N-45°S, 10°E-240°E], this study aims at quantifying how nudging techniques can modify the simulation of deep atmospheric convection. Both seasonal mean climate, transient variability at intraseasonal timescales, and the respective weight of internal (stochastic) and forced (reproducible) variability are considered. Sensitivity to a large variety of nudging settings (nudged variables and layers and nudging strength) and to the model physics (using 3 convective parameterizations) is addressed. Integrations are carried out during a 7-month season characterized by neutral background conditions and strong intraseasonal variability. Results show that (1) the model responds differently to the nudging from one parameterization to another. Biases are decreased by ~50 % for Betts-Miller-Janjic convection against 17 % only for Grell-Dévényi, the scheme producing yet the largest biases; (2) relaxing air temperature is the most efficient way to reduce biases, while nudging the wind increases most co-variability with daily observations; (3) the model's internal variability is drastically reduced and mostly depends on the nudging strength and nudged variables; (4) interrupting the relaxation before the end of the simulations leads to an abrupt convergence towards the model's natural solution, with no clear effects on the simulated climate after a few days. The usefulness and limitations of the approach are finally discussed through the example of the Madden-Julian Oscillation, that the model fails at simulating and that can be artificially and still imperfectly reproduced in relaxation experiments.

Impact of Embedded Military Metal Alloys on Skeletal Physiology in an Animal Model

DTIC Science & Technology

2017-04-04

turnover were completed and statistical comparison performed for each time point. Each ELISA was performed according to the instructions within each kit...expectations for controls. Results of osteocalcin ELISA were evaluated and any results with a coefficient of variation greater than 25% were omitted...Results of TRAP5b ELISA were evaluated and any results with a coefficient of variation greater than 25% were omitted from analysis. Measures of TRAP5b

Multi-Agent Technology for Air Space Deconfliction

DTIC Science & Technology

2008-01-01

previously accepted limit of the runway count is also omitted although in the case study used in the reported research the JFK airport is considered...La Guardia, Republic. Fig. 2.3 depicts the approach zone of JFK airport . In general words, the airport airspace topology is divided into two zones: (i...be omitted when necessary or if the customer believes they are too hard. Fig.2.3. Approach zone of JFK airport 11 4. Development of a realistic

U.S. Military Action Against the Islamic State: Answers to Frequently Asked Legal Questions

DTIC Science & Technology

2014-09-09

and exclusive power of the President as the sole organ of the federal government in the field of international relations”). U.S. Military Action...with relevant statutory authority,26 there has been little jurisprudence concerning the scope of presidential authority to order the use of force...29 Dames & Moore, 453 U.S. 678-679 ( internal citations omitted). 30 Medellin v. Texas, 552 U.S. 491, 531-532 (2008) ( internal citations omitted

Selective neck irradiation for supraglottic cancer: focus on Sublevel IIb omission.

PubMed

Kanayama, Naoyuki; Nishiyama, Kinji; Kawaguchi, Yoshifumi; Konishi, Koji; Ogawa, Kazuhiko; Suzuki, Motoyuki; Yoshii, Tadashi; Fujii, Takashi; Yoshino, Kunitoshi; Teshima, Teruki

2016-01-01

To estimate selective neck irradiation omitting surgical Sublevel IIb. Bilateral necks of 47 patients (94 necks) were subjected to definitive radiotherapy for supraglottic cancer. Sixty-nine and 25 necks were clinically node negative (cN-) and clinically node positive (cN+), respectively. We subdivided Sublevel IIb by the international consensus guideline for radiotherapy into Sublevel IIb/a, directly posterior to the internal jugular vein, and Sublevel IIb/b, which was behind Sublevel IIb/a and coincided with surgical Sublevel IIb. Bilateral (Sub)levels IIa, III, IV and IIb/a were routinely irradiated, whereas Sublevel IIb/b was omitted from the elective clinical target volume in 73/94 treated necks (78%). Two patients presented with ipsilateral Sublevel IIb/a metastases. No Sublevel IIb/b metastasis was observed. Five patients experienced cervical lymph node recurrence; Sublevel IIb/a recurrence developed in two patients, whereas no Sublevel IIb/b recurrence occurred even in the cN- necks of cN+ patients or cN0 patients. The 5-year regional control rates were 91.5% for Sublevel IIb/b-omitted patients and 77.8% for Sublevel IIb/b treated patients. Selective neck irradiation omitting Sublevel IIb/b did not compromise regional control and could be indicated for cN- neck of supraglottic cancer. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Agent-patient similarity affects sentence structure in language production: evidence from subject omissions in Mandarin

PubMed Central

Hsiao, Yaling; Gao, Yannan; MacDonald, Maryellen C.

2014-01-01

Interference effects from semantically similar items are well-known in studies of single word production, where the presence of semantically similar distractor words slows picture naming. This article examines the consequences of this interference in sentence production and tests the hypothesis that in situations of high similarity-based interference, producers are more likely to omit one of the interfering elements than when there is low semantic similarity and thus low interference. This work investigated language production in Mandarin, which allows subject noun phrases to be omitted in discourse contexts in which the subject entity has been previously mentioned in the discourse. We hypothesize that Mandarin speakers omit the subject more often when the subject and the object entities are conceptually similar. A corpus analysis of simple transitive sentences found higher rates of subject omission when both the subject and object were animate (potentially yielding similarity-based interference) than when the subject was animate and object was inanimate. A second study manipulated subject-object animacy in a picture description task and replicated this result: participants omitted the animate subject more often when the object was also animate than when it was inanimate. These results suggest that similarity-based interference affects sentence forms, particularly when the agent of the action is mentioned in the sentence. Alternatives and mechanisms for this effect are discussed. PMID:25278915

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.

Regression Methods for Categorical Dependent Variables: Effects on a Model of Student College Choice

ERIC Educational Resources Information Center

Rapp, Kelly E.

2012-01-01

The use of categorical dependent variables with the classical linear regression model (CLRM) violates many of the model's assumptions and may result in biased estimates (Long, 1997; O'Connell, Goldstein, Rogers, & Peng, 2008). Many dependent variables of interest to educational researchers (e.g., professorial rank, educational attainment) are…

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.

A two centre observational study of simultaneous pulse oximetry and arterial oxygen saturation recordings in intensive care unit patients.

PubMed

Ebmeier, S J; Barker, M; Bacon, M; Beasley, R C; Bellomo, R; Knee Chong, C; Eastwood, G M; Gilchrist, J; Kagaya, H; Pilcher, J; Reddy, S K; Ridgeon, E; Sarma, N; Sprogis, S; Tanaka, A; Tweedie, M; Weatherall, M; Young, P J

2018-05-01

The influence of variables that might affect the accuracy of pulse oximetry (SpO₂) recordings in critically ill patients is not well established. We sought to describe the relationship between paired SpO₂/SaO₂ (oxygen saturation via arterial blood gas analysis) in adult intensive care unit (ICU) patients and to describe the diagnostic performance of SpO₂ in detecting low SaO₂ and PaO₂. A paired SpO₂/SaO₂ measurement was obtained from 404 adults in ICU. Measurements were used to calculate bias, precision, and limits of agreement. Associations between bias and variables including vasopressor and inotrope use, capillary refill time, hand temperature, pulse pressure, body temperature, oximeter model, and skin colour were estimated. There was no overall statistically significant bias in paired SpO₂/SaO₂ measurements; observed limits of agreement were +/-4.4%. However, body temperature, oximeter model, and skin colour, were statistically significantly associated with the degree of bias. SpO₂ <89% had a sensitivity of 3/7 (42.9%; 95% confidence intervals, CI, 9.9% to 81.6%) and a specificity of 344/384 (89.6%; 95% CI 86.1% to 92.5%) for detecting SaO₂ <89%. The absence of statistically significant bias in paired SpO₂/SaO₂ in adult ICU patients provides support for the use of pulse oximetry to titrate oxygen therapy. However, SpO₂ recordings alone should be used cautiously when SaO₂ recordings of 4.4% higher or lower than the observed SpO₂ would be of concern. A range of variables relevant to the critically ill had little or no effect on bias.

SU-F-I-80: Correction for Bias in a Channelized Hotelling Model Observer Caused by Temporally Variable Non-Stationary Noise

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

Favazza, C; Fetterly, K

2016-06-15

Purpose: Application of a channelized Hotelling model observer (CHO) over a wide range of x-ray angiography detector target dose (DTD) levels demonstrated substantial bias for conditions yielding low detectability indices (d’), including low DTD and small test objects. The purpose of this work was to develop theory and methods to correct this bias. Methods: A hypothesis was developed wherein the measured detectability index (d’b) for a known test object is positively biased by temporally variable non-stationary noise in the images. Hotelling’s T2 test statistic provided the foundation for a mathematical theory which accounts for independent contributions to the measured d’bmore » value from both the test object (d’o) and non-stationary noise (d’ns). Experimental methods were developed to directly estimate d’o by determining d’ns and subtracting it from d’b, in accordance with the theory. Specifically, d’ns was determined from two sets of images from which the traditional test object was withheld. This method was applied to angiography images with DTD levels in the range 0 to 240 nGy and for disk-shaped iodine-based contrast targets with diameters 0.5 to 4.0 mm. Results: Bias in d’ was evidenced by d’b values which exceeded values expected from a quantum limited imaging system and decreasing object size and DTD. d’ns increased with decreasing DTD, reaching a maximum of 2.6 for DTD = 0. Bias-corrected d’o estimates demonstrated sub-quantum limited performance of the x-ray angiography for low DTD. Findings demonstrated that the source of non-stationary noise was detector electronic readout noise. Conclusion: Theory and methods to estimate and correct bias in CHO measurements from temporally variable non-stationary noise were presented. The temporal non-stationary noise was shown to be due to electronic readout noise. This method facilitates accurate estimates of d’ values over a large range of object size and detector target dose.« less

Variable-bias coin tossing

NASA Astrophysics Data System (ADS)

Colbeck, Roger; Kent, Adrian

2006-03-01

Alice is a charismatic quantum cryptographer who believes her parties are unmissable; Bob is a (relatively) glamorous string theorist who believes he is an indispensable guest. To prevent possibly traumatic collisions of self-perception and reality, their social code requires that decisions about invitation or acceptance be made via a cryptographically secure variable-bias coin toss (VBCT). This generates a shared random bit by the toss of a coin whose bias is secretly chosen, within a stipulated range, by one of the parties; the other party learns only the random bit. Thus one party can secretly influence the outcome, while both can save face by blaming any negative decisions on bad luck. We describe here some cryptographic VBCT protocols whose security is guaranteed by quantum theory and the impossibility of superluminal signaling, setting our results in the context of a general discussion of secure two-party computation. We also briefly discuss other cryptographic applications of VBCT.

Enhanced Conformational Sampling Using Replica Exchange with Collective-Variable Tempering

PubMed Central

2015-01-01

The computational study of conformational transitions in RNA and proteins with atomistic molecular dynamics often requires suitable enhanced sampling techniques. We here introduce a novel method where concurrent metadynamics are integrated in a Hamiltonian replica-exchange scheme. The ladder of replicas is built with different strengths of the bias potential exploiting the tunability of well-tempered metadynamics. Using this method, free-energy barriers of individual collective variables are significantly reduced compared with simple force-field scaling. The introduced methodology is flexible and allows adaptive bias potentials to be self-consistently constructed for a large number of simple collective variables, such as distances and dihedral angles. The method is tested on alanine dipeptide and applied to the difficult problem of conformational sampling in a tetranucleotide. PMID:25838811

Positive bias is a defining characteristic of aging to the same extent as declining performance.

PubMed

Simón, Teresa; Suengas, Aurora G; Ruiz-Gallego-Largo, Trinidad; Bandrés, Javier

2013-01-01

The aim of this study was to analyze whether one of the supposed gains of aging--positive bias--discriminates between young and older participants to the same extent as some of the losses in cognitive performance--recall and source monitoring--that come with age. Two age groups (N = 120)--young (M = 22.08, SD = 3.30) and older (M = 72.78, SD = 6.57)--carried out three tasks with varying levels of difficulty that included recall, recognition, and source monitoring using pictures, faces, and personal descriptors exchanged in a conversation as stimuli. The results of the discriminant analysis performed on 20 dependent variables indicated that six of them were key in discriminating between young and older participants. Younger participants outperformed older participants in recalling pictures, and in recognizing the descriptors exchanged in a conversation, as well as in monitoring their source. Just as important in discriminating between the two groups were the ability to recognize previously seen pictures, the likability rating they produced, and the recognition of faces with positive expressions--all superior in older participants. Thus, variables related to a positive bias--likability ratings and recognition of positive expressions--characterize the differences as a function of age as well as variables related to cognitive performance, such as recall and source monitoring. In addition, the likability ratings evoked by both pictures and faces were also significantly higher in the older participants with better cognitive performance than in those who performed poorly. This effect was not present in younger participants. The results are interpreted within the framework of socioemotional selectivity theory as evidence for a positive bias in old age. The connection between a positive bias and the maintenance of cognitive performance is also discussed.

Quality requirements for veterinary hematology analyzers in small animals-a survey about veterinary experts' requirements and objective evaluation of analyzer performance based on a meta-analysis of method validation studies: bench top hematology analyzer.

PubMed

Cook, Andrea M; Moritz, Andreas; Freeman, Kathleen P; Bauer, Natali

2016-09-01

Scarce information exists about quality requirements and objective evaluation of performance of large veterinary bench top hematology analyzers. The study was aimed at comparing the observed total error (TEobs ) derived from meta-analysis of published method validation data to the total allowable error (TEa ) for veterinary hematology variables in small animals based on experts' opinions. Ideally, TEobs should be < TEa . An online survey was sent to veterinary experts in clinical pathology and small animal internal medicine for providing the maximal allowable deviation from a given result for each variable. Percent of TEa = (allowable median deviation/clinical threshold) * 100%. Second, TEobs for 3 laser-based bench top hematology analyzers (ADVIA 2120; Sysmex XT2000iV, and CellDyn 3500) was calculated based on method validation studies published between 2005 and 2013 (n = 4). The percent TEobs = 2 * CV (%) + bias (%). The CV was derived from published studies except for the ADVIA 2120 (internal data), and bias was estimated from the regression equation. A total of 41 veterinary experts (19 diplomates, 8 residents, 10 postgraduate students, 4 anonymous specialists) responded. The proposed range of TEa was wide, but generally ≤ 20%. The TEobs was < TEa for all variables and analyzers except for canine and feline HGB (high bias, low CV) and platelet counts (high bias, high CV). Overall, veterinary bench top analyzers fulfilled experts' requirements except for HGB due to method-related bias, and platelet counts due to known preanalytic/analytic issues. © 2016 American Society for Veterinary Clinical Pathology.

Quantification of both the area-at-risk and acute myocardial infarct size in ST-segment elevation myocardial infarction using T1-mapping.

PubMed

Bulluck, Heerajnarain; Hammond-Haley, Matthew; Fontana, Marianna; Knight, Daniel S; Sirker, Alex; Herrey, Anna S; Manisty, Charlotte; Kellman, Peter; Moon, James C; Hausenloy, Derek J

2017-08-01

A comprehensive cardiovascular magnetic resonance (CMR) in reperfused ST-segment myocardial infarction (STEMI) patients can be challenging to perform and can be time-consuming. We aimed to investigate whether native T1-mapping can accurately delineate the edema-based area-at-risk (AAR) and post-contrast T1-mapping and synthetic late gadolinium (LGE) images can quantify MI size at 1.5 T. Conventional LGE imaging and T2-mapping could then be omitted, thereby shortening the scan duration. Twenty-eight STEMI patients underwent a CMR scan at 1.5 T, 3 ± 1 days following primary percutaneous coronary intervention. The AAR was quantified using both native T1 and T2-mapping. MI size was quantified using conventional LGE, post-contrast T1-mapping and synthetic magnitude-reconstructed inversion recovery (MagIR) LGE and synthetic phase-sensitive inversion recovery (PSIR) LGE, derived from the post-contrast T1 maps. Native T1-mapping performed as well as T2-mapping in delineating the AAR (41.6 ± 11.9% of the left ventricle [% LV] versus 41.7 ± 12.2% LV, P = 0.72; R 2 0.97; ICC 0.986 (0.969-0.993); bias -0.1 ± 4.2% LV). There were excellent correlation and inter-method agreement with no bias, between MI size by conventional LGE, synthetic MagIR LGE (bias 0.2 ± 2.2%LV, P = 0.35), synthetic PSIR LGE (bias 0.4 ± 2.2% LV, P = 0.060) and post-contrast T1-mapping (bias 0.3 ± 1.8% LV, P = 0.10). The mean scan duration was 58 ± 4 min. Not performing T2 mapping (6 ± 1 min) and conventional LGE (10 ± 1 min) would shorten the CMR study by 15-20 min. T1-mapping can accurately quantify both the edema-based AAR (using native T1 maps) and acute MI size (using post-contrast T1 maps) in STEMI patients without major cardiovascular risk factors. This approach would shorten the duration of a comprehensive CMR study without significantly compromising on data acquisition and would obviate the need to perform T2 maps and LGE imaging.

Global longitudinal strain software upgrade: Implications for intervendor consistency and longitudinal imaging studies.

PubMed

Castel, Anne-Laure; Menet, Aymeric; Ennezat, Pierre-Vladimir; Delelis, François; Le Goffic, Caroline; Binda, Camille; Guerbaai, Raphaëlle-Ashley; Levy, Franck; Graux, Pierre; Tribouilloy, Christophe; Maréchaux, Sylvestre

2016-01-01

Speckle tracking can be used to measure left ventricular global longitudinal strain (GLS). To study the effect of speckle tracking software product upgrades on GLS values and intervendor consistency. Subjects (patients or healthy volunteers) underwent systematic echocardiography with equipment from Philips and GE, without a change in their position. Off-line post-processing for GLS assessment was performed with the former and most recent upgrades from these two vendors (Philips QLAB 9.0 and 10.2; GE EchoPAC 12.1 and 13.1.1). GLS was obtained in three myocardial layers with EchoPAC 13.1.1. Intersoftware and intervendor consistency was assessed. Interobserver variability was tested in a subset of patients. Among 73 subjects (65 patients and 8 healthy volunteers), absolute values of GLS were higher with QLAB 10.2 compared with 9.0 (intraclass correlation coefficient [ICC]: 0.88; bias: 2.2%). Agreement between EchoPAC 13.1.1 and 12.1 varied by myocardial layer (13.1.1 only): midwall (ICC: 0.95; bias: -1.1%), endocardium (ICC: 0.93; bias: 1.6%) and epicardial (ICC: 0.80; bias: -3.3%). Although GLS was comparable for QLAB 9.0 versus EchoPAC 12.1 (ICC: 0.95; bias: 0.5%), the agreement was lower between QLAB 10.2 and EchoPAC 13.1.1 endocardial (ICC: 0.91; bias: 1.1%), midwall (ICC: 0.73; bias: 3.9%) and epicardial (ICC: 0.54; bias: 6.0%). Interobserver variability of all software products in a subset of 20 patients was excellent (ICC: 0.97-0.99; bias: -0.8 to 1.0%). Upgrades of speckle tracking software may be associated with significant changes in GLS values, which could affect intersoftware and intervendor consistency. This finding has important clinical implications for the longitudinal follow-up of patients with speckle tracking echocardiography. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

North Tropical Atlantic Climate Variability and Model Biases

NASA Astrophysics Data System (ADS)

Yang, Y.

2017-12-01

Remote forcing from El Niño-Southern Oscillation (ENSO) and local ocean-atmosphere feedback are important for climate variability over the North Tropical Atlantic. These two factors are extracted by the ensemble mean and inter-member difference of a 10-member Pacific Ocean-Global Atmosphere (POGA) experiment, in which sea surface temperatures (SSTs) are restored to the observed anomalies over the tropical Pacific but fully coupled to the atmosphere elsewhere. POGA reasonably captures main features of observed North Tropical Atlantic variability. ENSO forced and local North Tropical Atlantic modes (NTAMs) develop with wind-evaporation-SST feedback, explaining one third and two thirds of total variance respectively. Notable biases, however, exist. The seasonality of the simulated NTAM is delayed by one month, due to the late development of the North Atlantic Oscillation (NAO) in the model. A spurious band of enhanced sea surface temperature (SST) variance (SBEV) is identified over the northern equatorial Atlantic in POGA and 14 out of 23 CMIP5 models. The SBEV is especially pronounced in boreal spring and due to the combined effect of both anomalous atmospheric thermal forcing and oceanic vertical upwelling. While the tropical North Atlantic variability is only weakly correlated with the Atlantic Zonal Mode (AZM) in observations, the SBEV in CMIP5 produces conditions that drive and intensify the AZM variability via triggering the Bjerknes feedback. This partially explains why AZM is strong in some CMIP5 models even though the equatorial cold tongue and easterly trades are biased low.

Patient views and correlates of radiotherapy omission in a population-based sample of older women with favorable-prognosis breast cancer.

PubMed

Shumway, Dean A; Griffith, Kent A; Hawley, Sarah T; Wallner, Lauren P; Ward, Kevin C; Hamilton, Ann S; Morrow, Monica; Katz, Steven J; Jagsi, Reshma

2018-04-18

The omission of radiotherapy (RT) after lumpectomy is a reasonable option for many older women with favorable-prognosis breast cancer. In the current study, we sought to evaluate patient perspectives regarding decision making about RT. Women aged 65 to 79 years with AJCC 7th edition stage I and II breast cancer who were reported to the Georgia and Los Angeles County Surveillance, Epidemiology, and End Results registries were surveyed (response rate, 70%) regarding RT decisions, the rationale for omitting RT, decision-making values, and understanding of disease recurrence risk. We also surveyed their corresponding surgeons (response rate, 77%). Patient characteristics associated with the omission of RT were evaluated using multilevel, multivariable logistic regression, accounting for patient clustering within surgeons. Of 999 patients, 135 omitted RT (14%). Older age, lower tumor grade, and having estrogen receptor-positive disease each were found to be strongly associated with omission of RT in multivariable analyses, whereas the number of comorbidities was not. Non-English speakers were more likely to omit RT (adjusted odds ratio, 5.9; 95% confidence interval, 1.4-24.5). The most commonly reported reasons for RT omission were that a physician advised the patient that it was not needed (54% of patients who omitted RT) and patient choice (41%). Risk of local disease recurrence was overestimated by all patients: by approximately 2-fold among those who omitted RT and by approximately 8-fold among those who received RT. The risk of distant disease recurrence was overestimated by approximately 3-fold on average. To some extent, decisions regarding RT omission are appropriately influenced by patient age, tumor grade, and estrogen receptor status, but do not appear to be optimally tailored according to competing comorbidities. Many women who are candidates for RT omission overestimate their risk of disease recurrence. Cancer 2018. © 2018 American Cancer Society. © 2018 American Cancer Society.

Real-time measurement system for evaluation of the carotid intima-media thickness with a robust edge operator.

PubMed

Faita, Francesco; Gemignani, Vincenzo; Bianchini, Elisabetta; Giannarelli, Chiara; Ghiadoni, Lorenzo; Demi, Marcello

2008-09-01

The purpose of this report is to describe an automatic real-time system for evaluation of the carotid intima-media thickness (CIMT) characterized by 3 main features: minimal interobserver and intraobserver variability, real-time capabilities, and great robustness against noise. One hundred fifty carotid B-mode ultrasound images were used to validate the system. Two skilled operators were involved in the analysis. Agreement with the gold standard, defined as the mean of 2 manual measurements of a skilled operator, and the interobserver and intraobserver variability were quantitatively evaluated by regression analysis and Bland-Altman statistics. The automatic measure of the CIMT showed a mean bias +/- SD of 0.001 +/- 0.035 mm toward the manual measurement. The intraobserver variability, evaluated with Bland-Altman plots, showed a bias that was not significantly different from 0, whereas the SD of the differences was greater in the manual analysis (0.038 mm) than in the automatic analysis (0.006 mm). For interobserver variability, the automatic measurement had a bias that was not significantly different from 0, with a satisfactory SD of the differences (0.01 mm), whereas in the manual measurement, a little bias was present (0.012 mm), and the SD of the differences was noticeably greater (0.044 mm). The CIMT has been accepted as a noninvasive marker of early vascular alteration. At present, the manual approach is largely used to estimate CIMT values. However, that method is highly operator dependent and time-consuming. For these reasons, we developed a new system for the CIMT measurement that conjugates precision with real-time analysis, thus providing considerable advantages in clinical practice.

Using Propensity Score Methods to Approximate Factorial Experimental Designs to Analyze the Relationship between Two Variables and an Outcome

ERIC Educational Resources Information Center

Dong, Nianbo

2015-01-01

Researchers have become increasingly interested in programs' main and interaction effects of two variables (A and B, e.g., two treatment variables or one treatment variable and one moderator) on outcomes. A challenge for estimating main and interaction effects is to eliminate selection bias across A-by-B groups. I introduce Rubin's causal model to…

A Framework for Integrating Implicit Bias Recognition Into Health Professions Education.

PubMed

Sukhera, Javeed; Watling, Chris

2018-01-01

Existing literature on implicit bias is fragmented and comes from a variety of fields like cognitive psychology, business ethics, and higher education, but implicit-bias-informed educational approaches have been underexplored in health professions education and are difficult to evaluate using existing tools. Despite increasing attention to implicit bias recognition and management in health professions education, many programs struggle to meaningfully integrate these topics into curricula. The authors propose a six-point actionable framework for integrating implicit bias recognition and management into health professions education that draws on the work of previous researchers and includes practical tools to guide curriculum developers. The six key features of this framework are creating a safe and nonthreatening learning context, increasing knowledge about the science of implicit bias, emphasizing how implicit bias influences behaviors and patient outcomes, increasing self-awareness of existing implicit biases, improving conscious efforts to overcome implicit bias, and enhancing awareness of how implicit bias influences others. Important considerations for designing implicit-bias-informed curricula-such as individual and contextual variables, as well as formal and informal cultural influences-are discussed. The authors also outline assessment and evaluation approaches that consider outcomes at individual, organizational, community, and societal levels. The proposed framework may facilitate future research and exploration regarding the use of implicit bias in health professions education.

Implementation of Instrumental Variable Bounds for Data Missing Not at Random.

PubMed

Marden, Jessica R; Wang, Linbo; Tchetgen, Eric J Tchetgen; Walter, Stefan; Glymour, M Maria; Wirth, Kathleen E

2018-05-01

Instrumental variables are routinely used to recover a consistent estimator of an exposure causal effect in the presence of unmeasured confounding. Instrumental variable approaches to account for nonignorable missing data also exist but are less familiar to epidemiologists. Like instrumental variables for exposure causal effects, instrumental variables for missing data rely on exclusion restriction and instrumental variable relevance assumptions. Yet these two conditions alone are insufficient for point identification. For estimation, researchers have invoked a third assumption, typically involving fairly restrictive parametric constraints. Inferences can be sensitive to these parametric assumptions, which are typically not empirically testable. The purpose of our article is to discuss another approach for leveraging a valid instrumental variable. Although the approach is insufficient for nonparametric identification, it can nonetheless provide informative inferences about the presence, direction, and magnitude of selection bias, without invoking a third untestable parametric assumption. An important contribution of this article is an Excel spreadsheet tool that can be used to obtain empirical evidence of selection bias and calculate bounds and corresponding Bayesian 95% credible intervals for a nonidentifiable population proportion. For illustrative purposes, we used the spreadsheet tool to analyze HIV prevalence data collected by the 2007 Zambia Demographic and Health Survey (DHS).

Estimation and applications of size-biased distributions in forestry

Treesearch

Jeffrey H. Gove

2003-01-01

Size-biased distributions arise naturally in several contexts in forestry and ecology. Simple power relationships (e.g. basal area and diameter at breast height) between variables are one such area of interest arising from a modelling perspective. Another, probability proportional to size PPS) sampling, is found in the most widely used methods for sampling standing or...

Accounting for measurement error in log regression models with applications to accelerated testing.

PubMed

Richardson, Robert; Tolley, H Dennis; Evenson, William E; Lunt, Barry M

2018-01-01

In regression settings, parameter estimates will be biased when the explanatory variables are measured with error. This bias can significantly affect modeling goals. In particular, accelerated lifetime testing involves an extrapolation of the fitted model, and a small amount of bias in parameter estimates may result in a significant increase in the bias of the extrapolated predictions. Additionally, bias may arise when the stochastic component of a log regression model is assumed to be multiplicative when the actual underlying stochastic component is additive. To account for these possible sources of bias, a log regression model with measurement error and additive error is approximated by a weighted regression model which can be estimated using Iteratively Re-weighted Least Squares. Using the reduced Eyring equation in an accelerated testing setting, the model is compared to previously accepted approaches to modeling accelerated testing data with both simulations and real data.

A laboratory study of the electromagnetic bias of rough surface scattering by water waves

NASA Technical Reports Server (NTRS)

Parsons, Chester L.; Miller, Lee S.

1990-01-01

The design, development, and use of a focused-beam radar to measure the electromagnetic bias introduced by the scattering of radar waves by a roughened water surface are discussed. The bias measurements were made over wide ranges of environmental conditions in a wavetank laboratory. Wave-elevation data were provided by standard laboratory capacitance probes. Backscattered radar power measurements coincident in time and space with the elevation data were produced by the radar. The two data sets are histogrammed to produce probability density functions for elevation and radar reflectivity, from which the electromagnetic bias is computed. The experimental results demonstrate that the electromagnetic bias is quite variable over the wide range of environmental conditions that can be produced in the laboratory. The data suggest that the bias is dependent upon the local wind field and on the amplitude and frequency of any background wave field that is present.

Associations among selective attention, memory bias, cognitive errors and symptoms of anxiety in youth.

PubMed

Watts, Sarah E; Weems, Carl F

2006-12-01

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 measures of the child's anxiety disorder symptoms. Youth completed assessments measuring selective attention, memory bias, and cognitive errors. Results indicated that selective attention, memory bias, and cognitive errors were each correlated with childhood anxiety problems and provide support for a cognitive model of anxiety which posits that these three biases are associated with childhood anxiety problems. Only limited support for significant interrelations among selective attention, memory bias, and cognitive errors was found. Finally, results point towards an effective strategy for moving the assessment of selective attention to younger and community samples of youth.

Nonlinear vs. linear biasing in Trp-cage folding simulations

NASA Astrophysics Data System (ADS)

Spiwok, Vojtěch; Oborský, Pavel; Pazúriková, Jana; Křenek, Aleš; Králová, Blanka

2015-03-01

Biased simulations have great potential for the study of slow processes, including protein folding. Atomic motions in molecules are nonlinear, which suggests that simulations with enhanced sampling of collective motions traced by nonlinear dimensionality reduction methods may perform better than linear ones. In this study, we compare an unbiased folding simulation of the Trp-cage miniprotein with metadynamics simulations using both linear (principle component analysis) and nonlinear (Isomap) low dimensional embeddings as collective variables. Folding of the mini-protein was successfully simulated in 200 ns simulation with linear biasing and non-linear motion biasing. The folded state was correctly predicted as the free energy minimum in both simulations. We found that the advantage of linear motion biasing is that it can sample a larger conformational space, whereas the advantage of nonlinear motion biasing lies in slightly better resolution of the resulting free energy surface. In terms of sampling efficiency, both methods are comparable.

Nonlinear vs. linear biasing in Trp-cage folding simulations.

PubMed

Spiwok, Vojtěch; Oborský, Pavel; Pazúriková, Jana; Křenek, Aleš; Králová, Blanka

2015-03-21

Biased simulations have great potential for the study of slow processes, including protein folding. Atomic motions in molecules are nonlinear, which suggests that simulations with enhanced sampling of collective motions traced by nonlinear dimensionality reduction methods may perform better than linear ones. In this study, we compare an unbiased folding simulation of the Trp-cage miniprotein with metadynamics simulations using both linear (principle component analysis) and nonlinear (Isomap) low dimensional embeddings as collective variables. Folding of the mini-protein was successfully simulated in 200 ns simulation with linear biasing and non-linear motion biasing. The folded state was correctly predicted as the free energy minimum in both simulations. We found that the advantage of linear motion biasing is that it can sample a larger conformational space, whereas the advantage of nonlinear motion biasing lies in slightly better resolution of the resulting free energy surface. In terms of sampling efficiency, both methods are comparable.

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.

Improving RNA-Seq expression estimation by modeling isoform- and exon-specific read sequencing rate.

PubMed

Liu, Xuejun; Shi, Xinxin; Chen, Chunlin; Zhang, Li

2015-10-16

The high-throughput sequencing technology, RNA-Seq, has been widely used to quantify gene and isoform expression in the study of transcriptome in recent years. Accurate expression measurement from the millions or billions of short generated reads is obstructed by difficulties. One is ambiguous mapping of reads to reference transcriptome caused by alternative splicing. This increases the uncertainty in estimating isoform expression. The other is non-uniformity of read distribution along the reference transcriptome due to positional, sequencing, mappability and other undiscovered sources of biases. This violates the uniform assumption of read distribution for many expression calculation approaches, such as the direct RPKM calculation and Poisson-based models. Many methods have been proposed to address these difficulties. Some approaches employ latent variable models to discover the underlying pattern of read sequencing. However, most of these methods make bias correction based on surrounding sequence contents and share the bias models by all genes. They therefore cannot estimate gene- and isoform-specific biases as revealed by recent studies. We propose a latent variable model, NLDMseq, to estimate gene and isoform expression. Our method adopts latent variables to model the unknown isoforms, from which reads originate, and the underlying percentage of multiple spliced variants. The isoform- and exon-specific read sequencing biases are modeled to account for the non-uniformity of read distribution, and are identified by utilizing the replicate information of multiple lanes of a single library run. We employ simulation and real data to verify the performance of our method in terms of accuracy in the calculation of gene and isoform expression. Results show that NLDMseq obtains competitive gene and isoform expression compared to popular alternatives. Finally, the proposed method is applied to the detection of differential expression (DE) to show its usefulness in the downstream analysis. The proposed NLDMseq method provides an approach to accurately estimate gene and isoform expression from RNA-Seq data by modeling the isoform- and exon-specific read sequencing biases. It makes use of a latent variable model to discover the hidden pattern of read sequencing. We have shown that it works well in both simulations and real datasets, and has competitive performance compared to popular methods. The method has been implemented as a freely available software which can be found at https://github.com/PUGEA/NLDMseq.

Cognitive determinants of affective forecasting errors

PubMed Central

Hoerger, Michael; Quirk, Stuart W.; Lucas, Richard E.; Carr, Thomas H.

2011-01-01

Often to the detriment of human decision making, people are prone to an impact bias when making affective forecasts, overestimating the emotional consequences of future events. The cognitive processes underlying the impact bias, and methods for correcting it, have been debated and warrant further exploration. In the present investigation, we examined both individual differences and contextual variables associated with cognitive processing in affective forecasting for an election. Results showed that the perceived importance of the event and working memory capacity were both associated with an increased impact bias for some participants, whereas retrieval interference had no relationship with bias. Additionally, an experimental manipulation effectively reduced biased forecasts, particularly among participants who were most distracted thinking about peripheral life events. These findings have direct theoretical implications for understanding the impact bias, highlight the importance of individual differences in affective forecasting, and have ramifications for future decision making research. The possible functional role of the impact bias is discussed within the context of evolutionary psychology. PMID:21912580

A systematic review of electronic audit and feedback: intervention effectiveness and use of behaviour change theory.

PubMed

Tuti, Timothy; Nzinga, Jacinta; Njoroge, Martin; Brown, Benjamin; Peek, Niels; English, Mike; Paton, Chris; van der Veer, Sabine N

2017-05-12

Audit and feedback is a common intervention for supporting clinical behaviour change. Increasingly, health data are available in electronic format. Yet, little is known regarding if and how electronic audit and feedback (e-A&F) improves quality of care in practice. The study aimed to assess the effectiveness of e-A&F interventions in a primary care and hospital context and to identify theoretical mechanisms of behaviour change underlying these interventions. In August 2016, we searched five electronic databases, including MEDLINE and EMBASE via Ovid, and the Cochrane Central Register of Controlled Trials for published randomised controlled trials. We included studies that evaluated e-A&F interventions, defined as a summary of clinical performance delivered through an interactive computer interface to healthcare providers. Data on feedback characteristics, underlying theoretical domains, effect size and risk of bias were extracted by two independent review authors, who determined the domains within the Theoretical Domains Framework (TDF). We performed a meta-analysis of e-A&F effectiveness, and a narrative analysis of the nature and patterns of TDF domains and potential links with the intervention effect. We included seven studies comprising of 81,700 patients being cared for by 329 healthcare professionals/primary care facilities. Given the extremely high heterogeneity of the e-A&F interventions and five studies having a medium or high risk of bias, the average effect was deemed unreliable. Only two studies explicitly used theory to guide intervention design. The most frequent theoretical domains targeted by the e-A&F interventions included 'knowledge', 'social influences', 'goals' and 'behaviour regulation', with each intervention targeting a combination of at least three. None of the interventions addressed the domains 'social/professional role and identity' or 'emotion'. Analyses identified the number of different domains coded in control arm to have the biggest role in heterogeneity in e-A&F effect size. Given the high heterogeneity of identified studies, the effects of e-A&F were found to be highly variable. Additionally, e-A&F interventions tend to implicitly target only a fraction of known theoretical domains, even after omitting domains presumed not to be linked to e-A&F. Also, little evaluation of comparative effectiveness across trial arms was conducted. Future research should seek to further unpack the theoretical domains essential for effective e-A&F in order to better support strategic individual and team goals.

Bias correction of surface downwelling longwave and shortwave radiation for the EWEMBI dataset

NASA Astrophysics Data System (ADS)

Lange, Stefan

2018-05-01

Many meteorological forcing datasets include bias-corrected surface downwelling longwave and shortwave radiation (rlds and rsds). Methods used for such bias corrections range from multi-year monthly mean value scaling to quantile mapping at the daily timescale. An additional downscaling is necessary if the data to be corrected have a higher spatial resolution than the observational data used to determine the biases. This was the case when EartH2Observe (E2OBS; Calton et al., 2016) rlds and rsds were bias-corrected using more coarsely resolved Surface Radiation Budget (SRB; Stackhouse Jr. et al., 2011) data for the production of the meteorological forcing dataset EWEMBI (Lange, 2016). This article systematically compares various parametric quantile mapping methods designed specifically for this purpose, including those used for the production of EWEMBI rlds and rsds. The methods vary in the timescale at which they operate, in their way of accounting for physical upper radiation limits, and in their approach to bridging the spatial resolution gap between E2OBS and SRB. It is shown how temporal and spatial variability deflation related to bilinear interpolation and other deterministic downscaling approaches can be overcome by downscaling the target statistics of quantile mapping from the SRB to the E2OBS grid such that the sub-SRB-grid-scale spatial variability present in the original E2OBS data is retained. Cross validations at the daily and monthly timescales reveal that it is worthwhile to take empirical estimates of physical upper limits into account when adjusting either radiation component and that, overall, bias correction at the daily timescale is more effective than bias correction at the monthly timescale if sampling errors are taken into account.

A bias-corrected CMIP5 dataset for Africa using the CDF-t method - a contribution to agricultural impact studies

NASA Astrophysics Data System (ADS)

Moise Famien, Adjoua; Janicot, Serge; Delfin Ochou, Abe; Vrac, Mathieu; Defrance, Dimitri; Sultan, Benjamin; Noël, Thomas

2018-03-01

The objective of this paper is to present a new dataset of bias-corrected CMIP5 global climate model (GCM) daily data over Africa. This dataset was obtained using the cumulative distribution function transform (CDF-t) method, a method that has been applied to several regions and contexts but never to Africa. Here CDF-t has been applied over the period 1950-2099 combining Historical runs and climate change scenarios for six variables: precipitation, mean near-surface air temperature, near-surface maximum air temperature, near-surface minimum air temperature, surface downwelling shortwave radiation, and wind speed, which are critical variables for agricultural purposes. WFDEI has been used as the reference dataset to correct the GCMs. Evaluation of the results over West Africa has been carried out on a list of priority user-based metrics that were discussed and selected with stakeholders. It includes simulated yield using a crop model simulating maize growth. These bias-corrected GCM data have been compared with another available dataset of bias-corrected GCMs using WATCH Forcing Data as the reference dataset. The impact of WFD, WFDEI, and also EWEMBI reference datasets has been also examined in detail. It is shown that CDF-t is very effective at removing the biases and reducing the high inter-GCM scattering. Differences with other bias-corrected GCM data are mainly due to the differences among the reference datasets. This is particularly true for surface downwelling shortwave radiation, which has a significant impact in terms of simulated maize yields. Projections of future yields over West Africa are quite different, depending on the bias-correction method used. However all these projections show a similar relative decreasing trend over the 21st century.

Adjusted Analyses in Studies Addressing Therapy and Harm: Users' Guides to the Medical Literature.

PubMed

Agoritsas, Thomas; Merglen, Arnaud; Shah, Nilay D; O'Donnell, Martin; Guyatt, Gordon H

2017-02-21

Observational studies almost always have bias because prognostic factors are unequally distributed between patients exposed or not exposed to an intervention. The standard approach to dealing with this problem is adjusted or stratified analysis. Its principle is to use measurement of risk factors to create prognostically homogeneous groups and to combine effect estimates across groups.The purpose of this Users' Guide is to introduce readers to fundamental concepts underlying adjustment as a way of dealing with prognostic imbalance and to the basic principles and relative trustworthiness of various adjustment strategies.One alternative to the standard approach is propensity analysis, in which groups are matched according to the likelihood of membership in exposed or unexposed groups. Propensity methods can deal with multiple prognostic factors, even if there are relatively few patients having outcome events. However, propensity methods do not address other limitations of traditional adjustment: investigators may not have measured all relevant prognostic factors (or not accurately), and unknown factors may bias the results.A second approach, instrumental variable analysis, relies on identifying a variable associated with the likelihood of receiving the intervention but not associated with any prognostic factor or with the outcome (other than through the intervention); this could mimic randomization. However, as with assumptions of other adjustment approaches, it is never certain if an instrumental variable analysis eliminates bias.Although all these approaches can reduce the risk of bias in observational studies, none replace the balance of both known and unknown prognostic factors offered by randomization.

Instrumental variables as bias amplifiers with general outcome and confounding.

PubMed

Ding, P; VanderWeele, T J; Robins, J M

2017-06-01

Drawing causal inference with observational studies is the central pillar of many disciplines. One sufficient condition for identifying the causal effect is that the treatment-outcome relationship is unconfounded conditional on the observed covariates. It is often believed that the more covariates we condition on, the more plausible this unconfoundedness assumption is. This belief has had a huge impact on practical causal inference, suggesting that we should adjust for all pretreatment covariates. However, when there is unmeasured confounding between the treatment and outcome, estimators adjusting for some pretreatment covariate might have greater bias than estimators without adjusting for this covariate. This kind of covariate is called a bias amplifier, and includes instrumental variables that are independent of the confounder, and affect the outcome only through the treatment. Previously, theoretical results for this phenomenon have been established only for linear models. We fill in this gap in the literature by providing a general theory, showing that this phenomenon happens under a wide class of models satisfying certain monotonicity assumptions. We further show that when the treatment follows an additive or multiplicative model conditional on the instrumental variable and the confounder, these monotonicity assumptions can be interpreted as the signs of the arrows of the causal diagrams.

Two-range magnetoelectric sensor

NASA Astrophysics Data System (ADS)

Bichurin, M.; Petrov, V.; Leontyev, V.; Saplev, A.

2017-01-01

In this study, we present a two-range magnetoelectric ME sensor design comprising of permendur (alloy of Fe-Co-V), nickel, and lead zirconate titanate (PZT) laminate composite. A systematic study was conducted to clarify the contribution of magnetostrictive layers variables to the ME response over the applied range of magnetic bias field. The two-range behavior was characterized by opposite sign of the ME response when magnetic dc bias is in different sub-ranges. The ME coefficient as a function of magnetic bias field was found to be dependent on the laminate composite structure.

Bias correction for rainrate retrievals from satellite passive microwave sensors

NASA Technical Reports Server (NTRS)

Short, David A.

1990-01-01

Rainrates retrieved from past and present satellite-borne microwave sensors are affected by a fundamental remote sensing problem. Sensor fields-of-view are typically large enough to encompass substantial rainrate variability, whereas the retrieval algorithms, based on radiative transfer calculations, show a non-linear relationship between rainrate and microwave brightness temperature. Retrieved rainrates are systematically too low. A statistical model of the bias problem shows that bias correction factors depend on the probability distribution of instantaneous rainrate and on the average thickness of the rain layer.

Accuracy and Completeness of Clinical Coding Using ICD-10 for Ambulatory Visits

PubMed Central

Horsky, Jan; Drucker, Elizabeth A.; Ramelson, Harley Z.

2017-01-01

This study describes a simulation of diagnostic coding using an EHR. Twenty-three ambulatory clinicians were asked to enter appropriate codes for six standardized scenarios with two different EHRs. Their interactions with the query interface were analyzed for patterns and variations in search strategies and the resulting sets of entered codes for accuracy and completeness. Just over a half of entered codes were appropriate for a given scenario and about a quarter were omitted. Crohn’s disease and diabetes scenarios had the highest rate of inappropriate coding and code variation. The omission rate was higher for secondary than for primary visit diagnoses. Codes for immunization, dialysis dependence and nicotine dependence were the most often omitted. We also found a high rate of variation in the search terms used to query the EHR for the same diagnoses. Changes to the training of clinicians and improved design of EHR query modules may lower the rate of inappropriate and omitted codes. PMID:29854158

Optomechanically induced transparency with Bose–Einstein condensate in double-cavity optomechanical system

NASA Astrophysics Data System (ADS)

Liu, Li-Wei; Gengzang, Duo-Jie; An, Xiu-Jia; Wang, Pei-Yu

2018-03-01

We propose a novel technique of generating multiple optomechanically induced transparency (OMIT) of a weak probe field in hybrid optomechanical system. This system consists of a cigar-shaped Bose–Einstein condensate (BEC), trapped inside each high finesse Fabry-Pérot cavity. In the resolved sideband regime, the analytic solutions of the absorption and the dispersion spectrum are given. The tunneling strength of the two resonators and the coupling parameters of the each BEC in combination with the cavity field have the appearance of three distinct OMIT windows in the absorption spectrum. Furthermore, whether there is BEC in each cavity is a key factor in the number of OMIT windows determination. The technique presented may have potential applications in quantum engineering and quantum information networks. Project supported by the National Natural Science Foundation of China (Grant Nos. 11564034, 11105062, and 21663026) and the Scientific Research Funds of College of Electrical Engineering, Northwest University, China (Grant No. xbmuyjrc201115).

Forms of attrition in a longitudinal study of religion and health in older adults and implications for sample bias

PubMed Central

Hayward, R. David; Krause, Neal

2014-01-01

The use of longitudinal designs in the field of religion and health makes it important to understand how attrition bias may affect findings in this area. This study examines attrition in a 4-wave, 8-year study of older adults. Attrition resulted in a sample biased towards more educated and more religiously-involved individuals. Conditional linear growth curve models found that trajectories of change for some variables differed among attrition categories. Ineligibles had worsening depression, declining control, and declining attendance. Mortality was associated with worsening religious coping styles. Refusers experienced worsening depression. Nevertheless, there was no evidence of bias in the key religion and health results. PMID:25257794

Forms of Attrition in a Longitudinal Study of Religion and Health in Older Adults and Implications for Sample Bias.

PubMed

Hayward, R David; Krause, Neal

2016-02-01

The use of longitudinal designs in the field of religion and health makes it important to understand how attrition bias may affect findings in this area. This study examines attrition in a 4-wave, 8-year study of older adults. Attrition resulted in a sample biased toward more educated and more religiously involved individuals. Conditional linear growth curve models found that trajectories of change for some variables differed among attrition categories. Ineligibles had worsening depression, declining control, and declining attendance. Mortality was associated with worsening religious coping styles. Refusers experienced worsening depression. Nevertheless, there was no evidence of bias in the key religion and health results.

Parametric mapping of [18F]fluoromisonidazole positron emission tomography using basis functions.

PubMed

Hong, Young T; Beech, John S; Smith, Rob; Baron, Jean-Claude; Fryer, Tim D

2011-02-01

In this study, we show a basis function method (BAFPIC) for voxelwise calculation of kinetic parameters (K(1), k(2), k(3), K(i)) and blood volume using an irreversible two-tissue compartment model. BAFPIC was applied to rat ischaemic stroke micro-positron emission tomography data acquired with the hypoxia tracer [(18)F]fluoromisonidazole because irreversible two-tissue compartmental modelling provided good fits to data from both hypoxic and normoxic tissues. Simulated data show that BAFPIC produces kinetic parameters with significantly lower variability and bias than nonlinear least squares (NLLS) modelling in hypoxic tissue. The advantage of BAFPIC over NLLS is less pronounced in normoxic tissue. K(i) determined from BAFPIC has lower variability than that from the Patlak-Gjedde graphical analysis (PGA) by up to 40% and lower bias, except for normoxic tissue at mid-high noise levels. Consistent with the simulation results, BAFPIC parametric maps of real data suffer less noise-induced variability than do NLLS and PGA. Delineation of hypoxia on BAFPIC k(3) maps is aided by low variability in normoxic tissue, which matches that in K(i) maps. BAFPIC produces K(i) values that correlate well with those from PGA (r(2)=0.93 to 0.97; slope 0.99 to 1.05, absolute intercept <0.00002 mL/g per min). BAFPIC is a computationally efficient method of determining parametric maps with low bias and variance.

Systematic Biases in Weak Lensing Cosmology with the Dark Energy Survey

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

Samuroff, Simon

This thesis sets out a practical guide to applying shear measurements as a cosmological tool. We first present one of two science-ready galaxy shape catalogues from Year 1 of the Dark Energy Survey (DES Y1), which covers 1500 square degrees in four bandsmore » $griz$, with a median redshift of $0.59$. We describe the shape measurement process implemented by the DES Y1 imshape catalogue, which contains 21.9 million high-quality $r$-band bulge/disc fits. In Chapter 3 a new suite of image simulations, referred to as Hoopoe, are presented. The Hoopoe dataset is tailored to DES Y1 and includes realistic blending, spatial masks and variation in the point spread function. We derive shear corrections, which we show are robust to changes in calibration method, galaxy binning and variance within the simulated dataset. Sources of systematic uncertainty in the simulation-based shear calibration are discussed, leading to a final estimate of the $$1\\sigma$$ uncertainties in the residual multiplica tive bias after calibration of 0.025. Chapter 4 describes an extension of the analysis on the Hoopoe simulations into a detailed investigation of the impact of galaxy neighbours on shape measurement and shear cosmology. Four mechanisms by which neighbours can have a non-negligible influence on shear measurement are identified. These effects, if ignored, would contribute a net multiplicative bias of $$m \\sim 0.03 - 0.09$$ in DES Y1, though the precise impact will depend on both the measurement code and the selection cuts applied. We use the cosmological inference pipeline of DES Y1 to explore the cosmological implications of neighbour bias and show that omitting blending from the calibration simulation for DES Y1 would bias the inferred clustering amplitude $$S_8 \\equiv \\sigma_8 (\\omegam /0.3)^{0.5}$$ by $$1.5 \\sigma$$ towards low values. Finally, we use the Hoopoe simulations to test the effect of neighbour-induced spatial correlations in the multiplicative bias. We find the cosmo logical impact to be subdominant to statistical error at the! current level of precision. Another major uncertainity in shear cosmology is the accuracy of our ensemble redshift distributions. Chapter 5 presents a numerical investigation into the combined constraining power of cosmic shear, galaxy clustering and their cross-correlation in DES Y1, and the potential for internal calibration of redshift errors. Introducing a moderate uniform bias into the redshift distributions used to model the weak lensing (WL) galaxies is shown to produce a $$> 2\\sigma$$ bias in $$S_8$$. We demonstrate that this cosmological bias can be eliminated by marginalising over redshift error nuisance parameters. Strikingly, the cosmological constraint of the combined dataset is largely undiminished by the loss of prior information on the WL distributions. We demonstrate that this implicit self-calibration is the result of complementary degeneracy directions in the combined data. In Chapter 6 we present the preliminary results of an investigation into galaxy intrin sic alignments. Using the DES Y1 data, we show a clear dependence in alignment amplitude on galaxy type, in agreement with previous results. We subject these findings to a series of initial robustness tests. We conclude with a short overview of the work presented, and discuss prospects for the future.« less

Errors and uncertainties in regional climate simulations of rainfall variability over Tunisia: a multi-model and multi-member approach

NASA Astrophysics Data System (ADS)

Fathalli, Bilel; Pohl, Benjamin; Castel, Thierry; Safi, Mohamed Jomâa

2018-02-01

Temporal and spatial variability of rainfall over Tunisia (at 12 km spatial resolution) is analyzed in a multi-year (1992-2011) ten-member ensemble simulation performed using the WRF model, and a sample of regional climate hindcast simulations from Euro-CORDEX. RCM errors and skills are evaluated against a dense network of local rain gauges. Uncertainties arising, on the one hand, from the different model configurations and, on the other hand, from internal variability are furthermore quantified and ranked at different timescales using simple spread metrics. Overall, the WRF simulation shows good skill for simulating spatial patterns of rainfall amounts over Tunisia, marked by strong altitudinal and latitudinal gradients, as well as the rainfall interannual variability, in spite of systematic errors. Mean rainfall biases are wet in both DJF and JJA seasons for the WRF ensemble, while they are dry in winter and wet in summer for most of the used Euro-CORDEX models. The sign of mean annual rainfall biases over Tunisia can also change from one member of the WRF ensemble to another. Skills in regionalizing precipitation over Tunisia are season dependent, with better correlations and weaker biases in winter. Larger inter-member spreads are observed in summer, likely because of (1) an attenuated large-scale control on Mediterranean and Tunisian climate, and (2) a larger contribution of local convective rainfall to the seasonal amounts. Inter-model uncertainties are globally stronger than those attributed to model's internal variability. However, inter-member spreads can be of the same magnitude in summer, emphasizing the important stochastic nature of the summertime rainfall variability over Tunisia.

Effect of correlated observation error on parameters, predictions, and uncertainty

USGS Publications Warehouse

Tiedeman, Claire; Green, Christopher T.

2013-01-01

Correlations among observation errors are typically omitted when calculating observation weights for model calibration by inverse methods. We explore the effects of omitting these correlations on estimates of parameters, predictions, and uncertainties. First, we develop a new analytical expression for the difference in parameter variance estimated with and without error correlations for a simple one-parameter two-observation inverse model. Results indicate that omitting error correlations from both the weight matrix and the variance calculation can either increase or decrease the parameter variance, depending on the values of error correlation (ρ) and the ratio of dimensionless scaled sensitivities (rdss). For small ρ, the difference in variance is always small, but for large ρ, the difference varies widely depending on the sign and magnitude of rdss. Next, we consider a groundwater reactive transport model of denitrification with four parameters and correlated geochemical observation errors that are computed by an error-propagation approach that is new for hydrogeologic studies. We compare parameter estimates, predictions, and uncertainties obtained with and without the error correlations. Omitting the correlations modestly to substantially changes parameter estimates, and causes both increases and decreases of parameter variances, consistent with the analytical expression. Differences in predictions for the models calibrated with and without error correlations can be greater than parameter differences when both are considered relative to their respective confidence intervals. These results indicate that including observation error correlations in weighting for nonlinear regression can have important effects on parameter estimates, predictions, and their respective uncertainties.

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.

Testing the cultural theory of risk in France

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

Brenot, J.; Bonnefous, S.; Marris, C.

1998-12-01

Cultural Theory, as developed by Mary Douglas, argues that differing risk perceptions can be explained by reference to four distinct cultural biases: hierarchy, egalitarianism, individualism, and fatalism. This paper presents empirical results from a quantitative survey based on a questionnaire devised by Karl Dake to measure these cultural biases. A large representative sample was used to test this instrument in the French social context. Correlations between cultural biases and perceptions of 20 social and environmental risks were examined. These correlations were very weak, but were statistically significant: cultural biases explained 6%, at most, of the variance in risk perceptions. Standardmore » socio-demographic variables were also weakly related to risk perceptions (especially gender, social class, and education), and cultural biases and socio-demographic variables were themselves intercorrelated (especially with age, social class, and political outlook). The authors compare these results with surveys conducted in other countries using the same instrument and conclude that new methods, more qualitative and contextual, still need to be developed to investigate the cultural dimensions of risk perceptions. The paper also discusses relationships between perceptions of personal and residual risk, and between perceived risk and demand for additional safety measures. These three dimensions were generally closely related, but interesting differences were observed for some risk issues. Included in the list of risk perceptions were pollution, hazardous materials, and radioactive wastes.« less

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…

Retransformation bias in a stem profile model

Treesearch

Raymond L. Czaplewski; David Bruce

1990-01-01

An unbiased profile model, fit to diameter divided by diameter at breast height, overestimated volume of 5.3-m log sections by 0.5 to 3.5%. Another unbiased profile model, fit to squared diameter divided by squared diameter at breast height, underestimated bole diameters by 0.2 to 2.1%. These biases are caused by retransformation of the predicted dependent variable;...

Snapshots of Children's Changing Biases during Language Development: Differential Weighting of Perceptual and Linguistic Factors Predicts Noun Age of Acquisition

ERIC Educational Resources Information Center

Ramey, Christopher H.; Chrysikou, Evangelia G.; Reilly, Jamie

2013-01-01

Word learning is a lifelong activity constrained by cognitive biases that people possess at particular points in development. Age of acquisition (AoA) is a psycholinguistic variable that may prove useful toward gauging the relative weighting of different phonological, semantic, and morphological factors at different phases of language acquisition…

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.

The consequences of ignoring measurement invariance for path coefficients in structural equation models

PubMed Central

Guenole, Nigel; Brown, Anna

2014-01-01

We report a Monte Carlo study examining the effects of two strategies for handling measurement non-invariance – modeling and ignoring non-invariant items – on structural regression coefficients between latent variables measured with item response theory models for categorical indicators. These strategies were examined across four levels and three types of non-invariance – non-invariant loadings, non-invariant thresholds, and combined non-invariance on loadings and thresholds – in simple, partial, mediated and moderated regression models where the non-invariant latent variable occupied predictor, mediator, and criterion positions in the structural regression models. When non-invariance is ignored in the latent predictor, the focal group regression parameters are biased in the opposite direction to the difference in loadings and thresholds relative to the referent group (i.e., lower loadings and thresholds for the focal group lead to overestimated regression parameters). With criterion non-invariance, the focal group regression parameters are biased in the same direction as the difference in loadings and thresholds relative to the referent group. While unacceptable levels of parameter bias were confined to the focal group, bias occurred at considerably lower levels of ignored non-invariance than was previously recognized in referent and focal groups. PMID:25278911

The role of observer bias in the North American Breeding Bird Survey

USGS Publications Warehouse

Faanes, C.A.; Bystrak, D.

1981-01-01

Ornithologists sampling breeding bird populations are subject to a number of biases in bird recognition and identification. Using Breeding Bird Survey data, these biases are examined qualitatively and quantitatively, and their effects on counts are evaluated. Differences in hearing ability and degree of expertise are the major observer biases considered. Other, more subtle influences are also discussed, including unfamiliar species, resolution, imagination, similar songs and attitude and condition of observers. In most cases, welltrained observers are comparable in ability and their differences contribute little beyond sampling error. However, just as hearing loss can affect results, so can an unprepared observer. These biases are important because they can reduce the credibility of any bird population sampling effort. Care is advised in choosing observers and in interpreting and using results when observers of variable competence are involved.

Characterization of an active metasurface using terahertz ellipsometry

DOE PAGES

Karl, Nicholas; Heimbeck, Martin S.; Everitt, Henry O.; ...

2017-11-06

Switchable metasurfaces fabricated on a doped epi-layer have become an important platform for developing techniques to control terahertz (THz) radiation, as a DC bias can modulate the transmission characteristics of the metasurface. To model and understand this performance in new device configurations accurately, a quantitative understanding of the bias-dependent surface characteristics is required. In this work, we perform THz variable angle spectroscopic ellipsometry on a switchable metasurface as a function of DC bias. By comparing these data with numerical simulations, we extract a model for the response of the metasurface at any bias value. Using this model, we predict amore » giant bias-induced phase modulation in a guided wave configuration. Lastly, these predictions are in qualitative agreement with our measurements, offering a route to efficient modulation of THz signals.« less

Trends and interannual variability of mass and steric sea level in the Tropical Asian Seas

NASA Astrophysics Data System (ADS)

Kleinherenbrink, Marcel; Riva, Riccardo; Frederikse, Thomas; Merrifield, Mark; Wada, Yoshihide

2017-08-01

The mass and steric components of sea level changes have been separated in the Tropical Asian Seas (TAS) using a statistically optimal combination of Jason satellite altimetry, GRACE satellite gravimetry, and ocean reanalyses. Using observational uncertainties, statistically optimally weighted time series for both components have been obtained in four regions within the TAS over the period January 2005 to December 2012. The mass and steric sea level variability is regressed with the first two principal components (PC1&2) of Pacific equatorial wind stress and the Dipole Mode Index (DMI). Sea level in the South China Sea is not affected by any of the indices. Steric variability in the TAS is largest in the deep Banda and Celebes seas and is affected by both PCs and the DMI. Mass variability is largest on the continental shelves, which is primarily controlled by PC1. We argue that a water flux from the Western Tropical Pacific Ocean is the cause for mass variability in the TAS. The steric trends are about 2 mm yr-1 larger than the mass trends in the TAS. A significant part of the mass trend can be explained by the aforementioned indices and the nodal cycle. Trends obtained from fingerprints of mass redistribution are statistically equal to mass trends after subtracting the nodal cycle and the indices. Ultimately, the effect of omitting the TAS in global sea level budgets is estimated to be 0.3 mm yr-1.

Canine Sense and Sensibility: Tipping Points and Response Latency Variability as an Optimism Index in a Canine Judgement Bias Assessment

PubMed Central

Starling, Melissa J.; Branson, Nicholas; Cody, Denis; Starling, Timothy R.; McGreevy, Paul D.

2014-01-01

Recent advances in animal welfare science used judgement bias, a type of cognitive bias, as a means to objectively measure an animal's affective state. It is postulated that animals showing heightened expectation of positive outcomes may be categorised optimistic, while those showing heightened expectations of negative outcomes may be considered pessimistic. This study pioneers the use of a portable, automated apparatus to train and test the judgement bias of dogs. Dogs were trained in a discrimination task in which they learned to touch a target after a tone associated with a lactose-free milk reward and abstain from touching the target after a tone associated with water. Their judgement bias was then probed by presenting tones between those learned in the discrimination task and measuring their latency to respond by touching the target. A Cox's Proportional Hazards model was used to analyse censored response latency data. Dog and Cue both had a highly significant effect on latency and risk of touching a target. This indicates that judgement bias both exists in dogs and differs between dogs. Test number also had a significant effect, indicating that dogs were less likely to touch the target over successive tests. Detailed examination of the response latencies revealed tipping points where average latency increased by 100% or more, giving an indication of where dogs began to treat ambiguous cues as predicting more negative outcomes than positive ones. Variability scores were calculated to provide an index of optimism using average latency and standard deviation at cues after the tipping point. The use of a mathematical approach to assessing judgement bias data in animal studies offers a more detailed interpretation than traditional statistical analyses. This study provides proof of concept for the use of an automated apparatus for measuring cognitive bias in dogs. PMID:25229458

A general theoretical framework for interpreting patient-reported outcomes estimated from ordinally scaled item responses.

PubMed

Massof, Robert W

2014-10-01

A simple theoretical framework explains patient responses to items in rating scale questionnaires. Fixed latent variables position each patient and each item on the same linear scale. Item responses are governed by a set of fixed category thresholds, one for each ordinal response category. A patient's item responses are magnitude estimates of the difference between the patient variable and the patient's estimate of the item variable, relative to his/her personally defined response category thresholds. Differences between patients in their personal estimates of the item variable and in their personal choices of category thresholds are represented by random variables added to the corresponding fixed variables. Effects of intervention correspond to changes in the patient variable, the patient's response bias, and/or latent item variables for a subset of items. Intervention effects on patients' item responses were simulated by assuming the random variables are normally distributed with a constant scalar covariance matrix. Rasch analysis was used to estimate latent variables from the simulated responses. The simulations demonstrate that changes in the patient variable and changes in response bias produce indistinguishable effects on item responses and manifest as changes only in the estimated patient variable. Changes in a subset of item variables manifest as intervention-specific differential item functioning and as changes in the estimated person variable that equals the average of changes in the item variables. Simulations demonstrate that intervention-specific differential item functioning produces inefficiencies and inaccuracies in computer adaptive testing. © The Author(s) 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Efficiency versus bias: the role of distributional parameters in count contingent behaviour models

Treesearch

Joseph Englin; Arwin Pang; Thomas Holmes

2011-01-01

One of the challenges facing many applications of non-market valuations is to find data with enough variation in the variable(s) of interest to estimate econometrically their effects on the quantity demanded. A solution to this problem was the introduction of stated preference surveys. These surveys can introduce variation into variables where there is no natural...

All Effects of Psychophysical Variables on Color Attributes: A Classification System

PubMed Central

Pridmore, Ralph W.; Melgosa, Manuel

2015-01-01

This paper reports the research and structuring of a classification system for the effects of psychophysical variables on the color attributes. A basic role of color science is to psychophysically specify color appearance. An early stage is to specify the effects of the psychophysical variables (as singles, pairs, etc) on the color attributes (as singles, pairs, etc), for example to model color appearance. Current data on effects are often scarce or conflicting. Few effects are well understood, and the practice of naming effects after their discoverer(s) is inadequate and can be confusing. The number and types of possible effects have never been systematically analyzed and categorized. We propose a simple and rigorous system of classification including nomenclature. The total range of effects is computed from the possible combinations of three psychophysical variables (luminance, dominant wavelength, purity) and six color attributes (lightness, brightness, hue, chroma, colorfulness, saturation) in all modes of appearance. Omitting those effects that are normally impossible to perceive at any one time (such as four- or five-dimensional colors), the total number perceivable is 161 types of effects for all modes of appearance. The type of effect is named after the psychophysical stimulus (or stimuli) and the relevant color attribute(s), e.g., Luminance-on-hue effect (traditionally known as Bezold-Brucke effect). Each type of effect may include slightly different effects with infinite variations depending on experimental parameters. PMID:25859845

A study of some fundamental physicochemical variables on the morphology of mesoporous silica nanoparticles MCM-41 type

NASA Astrophysics Data System (ADS)

Beltrán-Osuna, Ángela A.; Gómez Ribelles, José L.; Perilla, Jairo E.

2017-12-01

All variables affecting the morphology of mesoporous silica nanoparticles (MSN) should be carefully analyzed in order to truly tailored design their mesoporous structure according to their final use. Although complete control on MCM-41 synthesis has been already claimed, reproducibility and repeatability of results remain a big issue due to the lack of information reported in literature. Stirring rate, reaction volume, and system configuration (i.e., opened or closed reactor) are three variables that are usually omitted, making the comparison of product characteristics difficult. Specifically, the rate of solvent evaporation is seldom disclosed, and its influence has not been previously analyzed. These variables were systematically studied in this work, and they were proven to have a fundamental impact on final particle morphology. Hence, a high degree of circularity ( C = 0.97) and monodispersed particle size distributions were only achieved when a stirring speed of 500 rpm and a reaction scale of 500 mL were used in a partially opened system, for a 2 h reaction at 80 °C. Well-shaped spherical mesoporous silica nanoparticles with a diameter of 95 nm, a pore size of 2.8 nm, and a total surface area of 954 m2 g-1 were obtained. Final characteristics made this product suitable to be used in biomedicine and nanopharmaceutics, especially for the design of drug delivery systems.

Performance of the Cockcroft-Gault, MDRD, and New CKD-EPI Formulas in Relation to GFR, Age, and Body Size

PubMed Central

Grootendorst, Diana Carina; Verduijn, Marion; Elliott, Elise Grace; Dekker, Friedo Wilhelm; Krediet, Raymond Theodorus

2010-01-01

Background and objectives: We compared the estimations of Cockcroft-Gault, Modification of Diet in Renal Disease (MDRD), and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations to a gold standard GFR measurement using 125I-iothalamate, within strata of GFR, gender, age, body weight, and body mass index (BMI). Design, setting, participants, & measurements: For people who previously underwent a GFR measurement, bias, precision, and accuracies between measured and estimated kidney functions were calculated within strata of the variables. The relation between the absolute bias and the variables was tested with linear regression analysis. Results: Overall (n = 271, 44% male, mean measured GFR 72.6 ml/min per 1.73 m2 [SD 30.4 ml/min per 1.73 m2]), mean bias was smallest for MDRD (P < 0.01). CKD-EPI had highest accuracy (P < 0.01 compared with Cockcroft-Gault), which did not differ from MDRD (P = 0.14). The absolute bias of all formulas was related to age. For MDRD and CKD-EPI, absolute bias was also related to the GFR; for Cockcroft-Gault, it was related to body weight and BMI as well. In all extreme subgroups, MDRD and CKD-EPI provided highest accuracies. Conclusions: The absolute bias of all formulas is influenced by age; CKD-EPI and MDRD are also influenced by GFR. Cockcroft-Gault is additionally influenced by body weight and BMI. In general, CKD-EPI gives the best estimation of GFR, although its accuracy is close to that of the MDRD. PMID:20299365

Gaussian-based routines to impute categorical variables in health surveys.

PubMed

Yucel, Recai M; He, Yulei; Zaslavsky, Alan M

2011-12-20

The multivariate normal (MVN) distribution is arguably the most popular parametric model used in imputation and is available in most software packages (e.g., SAS PROC MI, R package norm). When it is applied to categorical variables as an approximation, practitioners often either apply simple rounding techniques for ordinal variables or create a distinct 'missing' category and/or disregard the nominal variable from the imputation phase. All of these practices can potentially lead to biased and/or uninterpretable inferences. In this work, we develop a new rounding methodology calibrated to preserve observed distributions to multiply impute missing categorical covariates. The major attractiveness of this method is its flexibility to use any 'working' imputation software, particularly those based on MVN, allowing practitioners to obtain usable imputations with small biases. A simulation study demonstrates the clear advantage of the proposed method in rounding ordinal variables and, in some scenarios, its plausibility in imputing nominal variables. We illustrate our methods on a widely used National Survey of Children with Special Health Care Needs where incomplete values on race posed a valid threat on inferences pertaining to disparities. Copyright © 2011 John Wiley & Sons, Ltd.

Selecting a Variable for Predicting the Diagnosis of PTB Patients From Comparison of Chest X-ray Images

NASA Astrophysics Data System (ADS)

Mohd. Rijal, Omar; Mohd. Noor, Norliza; Teng, Shee Lee

A statistical method of comparing two digital chest radiographs for Pulmonary Tuberculosis (PTB) patients has been proposed. After applying appropriate image registration procedures, a selected subset of each image is converted to an image histogram (or box plot). Comparing two chest X-ray images is equivalent to the direct comparison of the two corresponding histograms. From each histogram, eleven percentiles (of image intensity) are calculated. The number of percentiles that shift to the left (NLSP) when second image is compared to the first has been shown to be an indicator of patients` progress. In this study, the values of NLSP is to be compared with the actual diagnosis (Y) of several medical practitioners. A logistic regression model is used to study the relationship between NLSP and Y. This study showed that NLSP may be used as an alternative or second opinion for Y. The proposed regression model also show that important explanatory variables such as outcomes of sputum test (Z) and degree of image registration (W) may be omitted when estimating Y-values.

Searching for a two-factor model of marriage duration: commentary on Gottman and Levenson.

PubMed

DeKay, Michael L; Greeno, Catherine G; Houck, Patricia R

2002-01-01

Gottman and Levenson (2002) report a number of post hoc ordinary least squares regressions to "predict" the length of marriage, given that divorce has occurred. We argue that the type of statistical model they use is inappropriate for answering clinically relevant questions about the causes and timing of divorce, and present several reasons why an alternative family of models called duration models would be more appropriate. The distribution of marriage length is not bimodal, as Gottman and Levenson suggest, and their search for a two-factor model for explaining marriage length is misguided. Their regression models omit many variables known to affect marriage length, and instead use variables that were pre-screened for their predictive ability. Their final model is based on data for only 15 cases, including one unusual case that has undue influence on the results. For these and other technical reasons presented in the text, we believe that Gottman and Levenson's results are not replicable, and that they should not be used to guide interventions for couples in clinical settings.

The Importance of Conditioned Stimuli in Cigarette and E-Cigarette Craving Reduction by E-Cigarettes.

PubMed

Van Heel, Martijn; Van Gucht, Dinska; Vanbrabant, Koen; Baeyens, Frank

2017-02-15

This study examined the impact of four variables pertaining to the use of e-cigarettes (e-cigs) on cravings for tobacco cigarettes and for e-cigs after an overnight abstinence period. The four variables were the nicotine level, the sensorimotor component, the visual aspect, and the aroma of the e-cig. In an experimental study, 81 participants without prior vaping experience first got acquainted with using e-cigs in a one-week tryout period, after which they participated in a lab session assessing the effect of five minutes of vaping following an abstinence period of 12 h. A mixed-effects model clearly showed the importance of nicotine in craving reduction. However, also non-nicotine factors, in particular the sensorimotor component, were shown to contribute to craving reduction. Handling cues interacted with the level (presence/absence) of nicotine: it was only when the standard hand-to-mouth action cues were omitted that the craving reducing effects of nicotine were observed. Effects of aroma or visual cues were not observed, or weak and difficult to interpret, respectively.

The Importance of Conditioned Stimuli in Cigarette and E-Cigarette Craving Reduction by E-Cigarettes

PubMed Central

Van Heel, Martijn; Van Gucht, Dinska; Vanbrabant, Koen; Baeyens, Frank

2017-01-01

This study examined the impact of four variables pertaining to the use of e-cigarettes (e-cigs) on cravings for tobacco cigarettes and for e-cigs after an overnight abstinence period. The four variables were the nicotine level, the sensorimotor component, the visual aspect, and the aroma of the e-cig. In an experimental study, 81 participants without prior vaping experience first got acquainted with using e-cigs in a one-week tryout period, after which they participated in a lab session assessing the effect of five minutes of vaping following an abstinence period of 12 h. A mixed-effects model clearly showed the importance of nicotine in craving reduction. However, also non-nicotine factors, in particular the sensorimotor component, were shown to contribute to craving reduction. Handling cues interacted with the level (presence/absence) of nicotine: it was only when the standard hand-to-mouth action cues were omitted that the craving reducing effects of nicotine were observed. Effects of aroma or visual cues were not observed, or weak and difficult to interpret, respectively. PMID:28212302

Varied representation of the West Pacific pattern in multiple dynamical seasonal predictions of APCC-MME

NASA Astrophysics Data System (ADS)

Lee, Yun-Young

2017-04-01

West Pacific (WP) teleconnection pattern is one of the well-known primary modes of boreal winter low-frequency variability (LFV) resolved in 500 hPa geopotential height and its phase and amplitude strongly influence regional weather conditions including temperature and rainfall extremes [Baxter and Nigam, 2015; Hsu and Wallace, 1985; Linkin and Nigam, 2008; Mo and Livezey, 1986; Thompson and Wallace, 1998; Wallace and Gutzler, 1981]. This study primary aims to evaluate individual 11 GCMs seasonal hindcasts employed as members of multi-model ensemble (MME) produced in APEC Climate Center (APCC) in representing WP. For the extensive and comprehensive evaluation, this study applied seven verification metrics in three scopes: (a) temporal representation of observed indices, (b) spatial mode separation in the Northern Hemisphere (NH), and (c) regional mode isolated in the preset longitudinal domain. Verification results display quite large inter-model spread. Some models mimic observed index variability while others display large bias of index variability compared to climatology. Basic north-south dipole pattern is mostly well reproduced in both rotated and unrotated loading modes. However, each individual seasonal forecast model exhibits slightly different behavior (e.g. amplification/weakening, zonal and meridional shift, downstream extension and so forth) in representing spatial structure of WP. When taking all 7 metrics into account, one Europe (CMCC) model, one Oceania (POAMA) model and two North America (NASA and NCEP) models are classified as relatively good performers while PNU is classified as a matchless poor performer out of 11. Least WP representing skill of PNU is sort of consistent with the largest bias of NH total variability. This study further tries to examine winter mean biases of individual models and figure out how mean bias is linked to WP representation in model world. Model bias of winter climatology is investigated focusing on six large scale phenomena: East Asian winter monsoon (EAWM), Atlantic dipole, Pacific/Atlantic jets and Pacific/Atlantic Hadley circulations. Changes in structure and amplitude of them are diagnosed in terms of root mean square error, pattern correlation, intensity bias, zonal displacement and/or downstream extension. There is consistent strengthening/downstream extension of Atlantic jet and absence of southern divergence cell of Atlantic Hadley in most seasonal prediction models. It is demonstrated that WP representation has something to do with bias of Atlantic winter climatology (Atlantic dipole and Atlantic jet) from scatter plot and regression analysis. This implies the importance of realistic simulation of winter climatology further upstream for better WP representation. A fundamental conclusion of this study is that the representation of primary WP features varies among individual models of APCC-MME and it is significantly dependent on the deficiencies of some winter mean climatological patterns.

Dynamic Time Expansion and Compression Using Nonlinear Waveguides

DOEpatents

Findikoglu, Alp T.; Hahn, Sangkoo F.; Jia, Quanxi

2004-06-22

Dynamic time expansion or compression of a small amplitude input signal generated with an initial scale is performed using a nonlinear waveguide. A nonlinear waveguide having a variable refractive index is connected to a bias voltage source having a bias signal amplitude that is large relative to the input signal to vary the reflective index and concomitant speed of propagation of the nonlinear waveguide and an electrical circuit for applying the small amplitude signal and the large amplitude bias signal simultaneously to the nonlinear waveguide. The large amplitude bias signal with the input signal alters the speed of propagation of the small-amplitude signal with time in the nonlinear waveguide to expand or contract the initial time scale of the small-amplitude input signal.

Dynamic time expansion and compression using nonlinear waveguides

DOEpatents

Findikoglu, Alp T [Los Alamos, NM; Hahn, Sangkoo F [Los Alamos, NM; Jia, Quanxi [Los Alamos, NM

2004-06-22

Dynamic time expansion or compression of a small-amplitude input signal generated with an initial scale is performed using a nonlinear waveguide. A nonlinear waveguide having a variable refractive index is connected to a bias voltage source having a bias signal amplitude that is large relative to the input signal to vary the reflective index and concomitant speed of propagation of the nonlinear waveguide and an electrical circuit for applying the small-amplitude signal and the large amplitude bias signal simultaneously to the nonlinear waveguide. The large amplitude bias signal with the input signal alters the speed of propagation of the small-amplitude signal with time in the nonlinear waveguide to expand or contract the initial time scale of the small-amplitude input signal.

Applying signal-detection theory to the study of observer accuracy and bias in behavioral assessment.

PubMed

Lerman, Dorothea C; Tetreault, Allison; Hovanetz, Alyson; Bellaci, Emily; Miller, Jonathan; Karp, Hilary; Mahmood, Angela; Strobel, Maggie; Mullen, Shelley; Keyl, Alice; Toupard, Alexis

2010-01-01

We evaluated the feasibility and utility of a laboratory model for examining observer accuracy within the framework of signal-detection theory (SDT). Sixty-one individuals collected data on aggression while viewing videotaped segments of simulated teacher-child interactions. The purpose of Experiment 1 was to determine if brief feedback and contingencies for scoring accurately would bias responding reliably. Experiment 2 focused on one variable (specificity of the operational definition) that we hypothesized might decrease the likelihood of bias. The effects of social consequences and information about expected behavior change were examined in Experiment 3. Results indicated that feedback and contingencies reliably biased responding and that the clarity of the definition only moderately affected this outcome.

Developmental precursors of young school-age children's hostile attribution bias.

PubMed

Choe, Daniel Ewon; Lane, Jonathan D; Grabell, Adam S; Olson, Sheryl L

2013-12-01

This prospective longitudinal study provides evidence of preschool-age precursors of hostile attribution bias in young school-age children, a topic that has received little empirical attention. We examined multiple risk domains, including laboratory and observational assessments of children's social-cognition, general cognitive functioning, effortful control, and peer aggression. Preschoolers (N = 231) with a more advanced theory-of-mind, better emotion understanding, and higher IQ made fewer hostile attributions of intent in the early school years. Further exploration of these significant predictors revealed that only certain components of these capacities (i.e., nonstereotypical emotion understanding, false-belief explanation, and verbal IQ) were robust predictors of a hostile attribution bias in young school-age children and were especially strong predictors among children with more advanced effortful control. These relations were prospective in nature-the effects of preschool variables persisted after accounting for similar variables at school age. We conclude by discussing the implications of our findings for future research and prevention. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Variationally Optimized Free-Energy Flooding for Rate Calculation.

PubMed

McCarty, James; Valsson, Omar; Tiwary, Pratyush; Parrinello, Michele

2015-08-14

We propose a new method to obtain kinetic properties of infrequent events from molecular dynamics simulation. The procedure employs a recently introduced variational approach [Valsson and Parrinello, Phys. Rev. Lett. 113, 090601 (2014)] to construct a bias potential as a function of several collective variables that is designed to flood the associated free energy surface up to a predefined level. The resulting bias potential effectively accelerates transitions between metastable free energy minima while ensuring bias-free transition states, thus allowing accurate kinetic rates to be obtained. We test the method on a few illustrative systems for which we obtain an order of magnitude improvement in efficiency relative to previous approaches and several orders of magnitude relative to unbiased molecular dynamics. We expect an even larger improvement in more complex systems. This and the ability of the variational approach to deal efficiently with a large number of collective variables will greatly enhance the scope of these calculations. This work is a vindication of the potential that the variational principle has if applied in innovative ways.

Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures.

PubMed

Munro, Sarah A; Lund, Steven P; Pine, P Scott; Binder, Hans; Clevert, Djork-Arné; Conesa, Ana; Dopazo, Joaquin; Fasold, Mario; Hochreiter, Sepp; Hong, Huixiao; Jafari, Nadereh; Kreil, David P; Łabaj, Paweł P; Li, Sheng; Liao, Yang; Lin, Simon M; Meehan, Joseph; Mason, Christopher E; Santoyo-Lopez, Javier; Setterquist, Robert A; Shi, Leming; Shi, Wei; Smyth, Gordon K; Stralis-Pavese, Nancy; Su, Zhenqiang; Tong, Weida; Wang, Charles; Wang, Jian; Xu, Joshua; Ye, Zhan; Yang, Yong; Yu, Ying; Salit, Marc

2014-09-25

There is a critical need for standard approaches to assess, report and compare the technical performance of genome-scale differential gene expression experiments. Here we assess technical performance with a proposed standard 'dashboard' of metrics derived from analysis of external spike-in RNA control ratio mixtures. These control ratio mixtures with defined abundance ratios enable assessment of diagnostic performance of differentially expressed transcript lists, limit of detection of ratio (LODR) estimates and expression ratio variability and measurement bias. The performance metrics suite is applicable to analysis of a typical experiment, and here we also apply these metrics to evaluate technical performance among laboratories. An interlaboratory study using identical samples shared among 12 laboratories with three different measurement processes demonstrates generally consistent diagnostic power across 11 laboratories. Ratio measurement variability and bias are also comparable among laboratories for the same measurement process. We observe different biases for measurement processes using different mRNA-enrichment protocols.

A Statistical Analysis of Reviewer Agreement and Bias in Evaluating Medical Abstracts 1

PubMed Central

Cicchetti, Domenic V.; Conn, Harold O.

1976-01-01

Observer variability affects virtually all aspects of clinical medicine and investigation. One important aspect, not previously examined, is the selection of abstracts for presentation at national medical meetings. In the present study, 109 abstracts, submitted to the American Association for the Study of Liver Disease, were evaluated by three “blind” reviewers for originality, design-execution, importance, and overall scientific merit. Of the 77 abstracts rated for all parameters by all observers, interobserver agreement ranged between 81 and 88%. However, corresponding intraclass correlations varied between 0.16 (approaching statistical significance) and 0.37 (p < 0.01). Specific tests of systematic differences in scoring revealed statistically significant levels of observer bias on most of the abstract components. Moreover, the mean differences in interobserver ratings were quite small compared to the standard deviations of these differences. These results emphasize the importance of evaluating the simple percentage of rater agreement within the broader context of observer variability and systematic bias. PMID:997596

On obtaining neutron-star mass and radius constraints from quiescent low-mass X-ray binaries in the Galactic plane

NASA Astrophysics Data System (ADS)

Marino, Alessio; Degenaar, N.; Di Salvo, T.; Wijnands, R.; Burderi, L.; Iaria, R.

2018-06-01

X-ray spectral analysis of quiescent low-mass X-ray binaries (LMXBs) has been one of the most common tools to measure the radius of neutron stars (NSs) for over a decade. So far, this method has been mainly applied to NSs in globular clusters, primarily because of their well-constrained distances. Here, we study Chandra data of seven transient LMXBs in the Galactic plane in quiescence to investigate the potential of constraining the radius (and mass) of the NSs inhabiting these systems. We find that only two of these objects had X-ray spectra of sufficient quality to obtain reasonable constraints on the radius, with the most stringent being an upper limit of R ≲14.5 km for EXO 0748-676 (for assumed ranges for mass and distance). Using these seven sources, we also investigate systematic biases on the mass/radius determination; for Aql X-1 we find that omitting a power-law spectral component when it does not seem to be required by the data, results in peculiar trends in the obtained radius with changing mass and distance. For EXO 0748-676 we find that a slight variation in the lower limit of the energy range chosen for the fit leads to systematically different masses and radii. Finally, we simulated Athena spectra and found that some of the biases can be lifted when higher quality spectra are available and that, in general, the search for constraints on the equation of state of ultra-dense matter via NS radius and mass measurements may receive a considerable boost in the future.

Randomized trials published in Chinese or Western journals: comparative empirical analysis.

PubMed

Purgato, Marianna; Cipriani, Andrea; Barbui, Corrado

2012-06-01

A major concern to the inclusion in systematic reviews of studies originating in China and published in Chinese journals refers to the quality of study reporting. In this systematic survey of randomized trials, we compared the characteristics of studies published in Chinese journals with those of studies published in Western journals. We included 69 studies comparing citalopram with other antidepressant drugs in the treatment of major depression. Of these, 37 (54%) were published in Chinese journals. The standard of reporting was generally poor in both Western and Chinese studies. In some Chinese studies, the generation of the randomization sequence raised concern about their experimental nature, and in almost all included studies, the concealment of allocation was not properly described. Blinding was seldom adopted in Chinese studies, and the risk of sponsorship bias was uncertain because Chinese studies did not report any financial support. In most Western studies, outcome data were selectively and incompletely reported. Pooling together all trials revealed that citalopram was similarly effective in comparison with all other antidepressant drugs both in Western studies (standardized mean difference, -0.04; 95% confidence interval, -0.15 to 0.06) and in Chinese studies (standardized mean difference, -0.08, 95% confidence interval, -0.18 to 0.02). Randomized controlled trials published in Chinese journals represent most of the studies included in this review. This suggests that omitting to search biomedical databases originating from China would systematically exclude a relevant proportion of randomized trials published in Chinese journals, with a risk of random error or bias. The increasing inclusion of Chinese studies in systematic reviews reinforces the need to check the quality of randomized trials that are meta-analyzed.

Reporting clinical outcomes of breast reconstruction: a systematic review.

PubMed

Potter, S; Brigic, A; Whiting, P F; Cawthorn, S J; Avery, K N L; Donovan, J L; Blazeby, J M

2011-01-05

Breast reconstruction after mastectomy for cancer requires accurate evaluation to inform evidence-based participatory decision making, but the standards of outcome reporting after breast reconstruction have not previously been considered. We used extensive searches to identify articles reporting surgical outcomes of breast reconstruction. We extracted data using published criteria for complication reporting modified to reflect reconstructive practice. Study designs included randomized controlled trials, cohort studies, and case series. The Cochrane Risk of Bias tool was used to critically appraise all study designs. Other criteria used to assess the studies were selection and funding bias, statistical power calculations, and institutional review board approval. Wilcoxon signed rank tests were used to compare the breadth and frequency of study outcomes, and χ² tests were used to compare the number of studies in each group reporting each of the published criteria. All statistical tests were two-sided. Surgical complications following breast reconstruction in 42,146 women were evaluated in 134 studies. These included 11 (8.2%) randomized trials, 74 (55.2%) cohort studies, and 49 (36.6%) case series. Fifty-three percent of studies demonstrated a disparity between methods and results in the numbers of complications reported. Complications were defined by 87 (64.9%) studies and graded by 78 (58.2%). Details such as the duration of follow-up and risk factors for adverse outcomes were omitted from 47 (35.1%) and 58 (43.3%) studies, respectively. Overall, the studies defined fewer than 20% of the complications they reported, and the definitions were largely inconsistent. The results of this systematic review suggest that outcome reporting in breast reconstruction is inconsistent and lacks methodological rigor. The development of a standardized core outcome set is recommended to improve outcome reporting in breast reconstruction.

Analysis of the Quality of Clinical Trials Published in Spanish-Language Dermatology Journals Between 1997 and 2012.

PubMed

Sanclemente, G; Pardo, H; Sánchez, S; Bonfill, X

2016-01-01

The value of randomized clinical trials (RCTs) undertaken to identify an association between an intervention and an outcome is determined by their quality and scientific rigor. To assess the methodological quality of RCTs published in Spanish-language dermatology journals. By way of a systematic manual search, we identified all the RCTs in journals published in Spain and Latin America between 1997 (the year in which the CONSORT statement was published) and 2012. Risk of bias was evaluated for each RCT by assessing the following domains: randomization sequence generation, allocation concealment, blinding of patients and those assessing outcomes, missing data, and patient follow-up. Source of funding and conflict of interest statements, if any, were recorded for each study. The search identified 70 RCTs published in 21 journals. Most of the RCTs had a high risk of bias, primarily because of gaps in the reporting of important methodological aspects. The source of funding was reported in only 15 studies. In spite of the considerable number of Spanish and Latin American journals, few RCTs have been published in the 15 years analyzed. Most of the RCTs published had serious defects in that the authors omitted methodological information essential to any evaluation of the quality of the trial and failed to report sources of funding or possible conflicts of interest for the authors involved. Authors of experimental clinical research in dermatology published in Spain and Latin America need to substantially improve both the design of their trials and the reporting of results. Copyright © 2015 Elsevier España, S.L.U. and AEDV. All rights reserved.

Decadal climate simulations using the Climate Forecast System (CFS) coupled to the SSiB2 land surface model

NASA Astrophysics Data System (ADS)

De Sales, F.; Xue, Y.; Marx, L.; Ek, M. B.

2016-12-01

The Simplified Simple Biophysical version 2 (SSiB2) model was implemented in the NCEP Climate Forecast System (CFS) for two 30-yr simulations. One simulation was initialized from CFS reanalysis data (EXP1), and the other from a 10-yr spin-up run (EXP2), in which the ocean model was allowed to run freely while the atmosphere and land surface were maintained constant to adjust inconsistencies in the initial conditions. EXP2 also includes an update in the SSiB2's average soil water potential calculation. The material presented highlights the model's performance in predicting spatial and temporal variability of monthly precipitation and surface temperature and aims at determining the optimum configuration for longer simulations. In general, the model is able to reproduce the main features of large-scale precipitation, with spatial correlation (scorr) and RMSE of 0.8 and 1.4 mm day-1, respectively. A split ITCZ pattern is observed in the Pacific and Indian oceans, which results in dry biases along the equator and wet-bias bands to its north and south. Positive biases are also observed in the Atlantic ITCZ. The model generates consistent surface temperature climatology (scorr > 0.9, RMSE= 2.3°C). Warm biases are observed especially over southern Asia during summer. Both experiments produce similar precipitation climatology patterns with similar biases. EXP2, however, improves the temperature simulation by reducing the global bias by 48% and 26% during boreal winter and summer, respectively; and improves the temperature decadal variability for many areas. Moreover, EXP2 generates a better continental surface air warming trend. In the attempt to improve the precipitation decadal variability in the simulations, remotely-sensed LAI and vegetation cover fraction have been implemented in the CFS/SSiB2 to substitute the look-up table originally used in EXP1 and 2. The satellite vegetation data has been processed into global monthly maps which are continuous updated throughout the simulation. Results from this experiment will also be presented.

Relaxing the rule of ten events per variable in logistic and Cox regression.

PubMed

Vittinghoff, Eric; McCulloch, Charles E

2007-03-15

The rule of thumb that logistic and Cox models should be used with a minimum of 10 outcome events per predictor variable (EPV), based on two simulation studies, may be too conservative. The authors conducted a large simulation study of other influences on confidence interval coverage, type I error, relative bias, and other model performance measures. They found a range of circumstances in which coverage and bias were within acceptable levels despite less than 10 EPV, as well as other factors that were as influential as or more influential than EPV. They conclude that this rule can be relaxed, in particular for sensitivity analyses undertaken to demonstrate adequate control of confounding.

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.

The South American Land Data Assimilation System (SALDAS) 5-Year Retrospective Atmospheric Forcing Datasets

NASA Technical Reports Server (NTRS)

deGoncalves, Luis Gustavo G.; Shuttleworth, William J.; Vila, Daniel; Larroza, Elaine; Bottino, Marcus J.; Herdies, Dirceu L.; Aravequia, Jose A.; De Mattos, Joao G. Z.; Toll, David L.; Rodell, Matthew;

Nonlinear vs. linear biasing in Trp-cage folding simulations

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

Spiwok, Vojtěch, E-mail: spiwokv@vscht.cz; Oborský, Pavel; Králová, Blanka

2015-03-21

Biased simulations have great potential for the study of slow processes, including protein folding. Atomic motions in molecules are nonlinear, which suggests that simulations with enhanced sampling of collective motions traced by nonlinear dimensionality reduction methods may perform better than linear ones. In this study, we compare an unbiased folding simulation of the Trp-cage miniprotein with metadynamics simulations using both linear (principle component analysis) and nonlinear (Isomap) low dimensional embeddings as collective variables. Folding of the mini-protein was successfully simulated in 200 ns simulation with linear biasing and non-linear motion biasing. The folded state was correctly predicted as the free energymore » minimum in both simulations. We found that the advantage of linear motion biasing is that it can sample a larger conformational space, whereas the advantage of nonlinear motion biasing lies in slightly better resolution of the resulting free energy surface. In terms of sampling efficiency, both methods are comparable.« less

Improve projections of changes in southern African summer rainfall through comprehensive multi-timescale empirical statistical downscaling

NASA Astrophysics Data System (ADS)

Dieppois, B.; Pohl, B.; Eden, J.; Crétat, J.; Rouault, M.; Keenlyside, N.; New, M. G.

2017-12-01

The water management community has hitherto neglected or underestimated many of the uncertainties in climate impact scenarios, in particular, uncertainties associated with decadal climate variability. Uncertainty in the state-of-the-art global climate models (GCMs) is time-scale-dependant, e.g. stronger at decadal than at interannual timescales, in response to the different parameterizations and to internal climate variability. In addition, non-stationarity in statistical downscaling is widely recognized as a key problem, in which time-scale dependency of predictors plays an important role. As with global climate modelling, therefore, the selection of downscaling methods must proceed with caution to avoid unintended consequences of over-correcting the noise in GCMs (e.g. interpreting internal climate variability as a model bias). GCM outputs from the Coupled Model Intercomparison Project 5 (CMIP5) have therefore first been selected based on their ability to reproduce southern African summer rainfall variability and their teleconnections with Pacific sea-surface temperature across the dominant timescales. In observations, southern African summer rainfall has recently been shown to exhibit significant periodicities at the interannual timescale (2-8 years), quasi-decadal (8-13 years) and inter-decadal (15-28 years) timescales, which can be interpret as the signature of ENSO, the IPO, and the PDO over the region. Most of CMIP5 GCMs underestimate southern African summer rainfall variability and their teleconnections with Pacific SSTs at these three timescales. In addition, according to a more in-depth analysis of historical and pi-control runs, this bias is might result from internal climate variability in some of the CMIP5 GCMs, suggesting potential for bias-corrected prediction based empirical statistical downscaling. A multi-timescale regression based downscaling procedure, which determines the predictors across the different timescales, has thus been used to simulate southern African summer rainfall. This multi-timescale procedure shows much better skills in simulating decadal timescales of variability compared to commonly used statistical downscaling approaches.