Selecting the right statistical model for analysis of insect count data by using information theoretic measures.

PubMed

Sileshi, G

2006-10-01

Researchers and regulatory agencies often make statistical inferences from insect count data using modelling approaches that assume homogeneous variance. Such models do not allow for formal appraisal of variability which in its different forms is the subject of interest in ecology. Therefore, the objectives of this paper were to (i) compare models suitable for handling variance heterogeneity and (ii) select optimal models to ensure valid statistical inferences from insect count data. The log-normal, standard Poisson, Poisson corrected for overdispersion, zero-inflated Poisson, the negative binomial distribution and zero-inflated negative binomial models were compared using six count datasets on foliage-dwelling insects and five families of soil-dwelling insects. Akaike's and Schwarz Bayesian information criteria were used for comparing the various models. Over 50% of the counts were zeros even in locally abundant species such as Ootheca bennigseni Weise, Mesoplatys ochroptera Stål and Diaecoderus spp. The Poisson model after correction for overdispersion and the standard negative binomial distribution model provided better description of the probability distribution of seven out of the 11 insects than the log-normal, standard Poisson, zero-inflated Poisson or zero-inflated negative binomial models. It is concluded that excess zeros and variance heterogeneity are common data phenomena in insect counts. If not properly modelled, these properties can invalidate the normal distribution assumptions resulting in biased estimation of ecological effects and jeopardizing the integrity of the scientific inferences. Therefore, it is recommended that statistical models appropriate for handling these data properties be selected using objective criteria to ensure efficient statistical inference.

Modeling number of claims and prediction of total claim amount

NASA Astrophysics Data System (ADS)

Acar, Aslıhan Şentürk; Karabey, Uǧur

2017-07-01

In this study we focus on annual number of claims of a private health insurance data set which belongs to a local insurance company in Turkey. In addition to Poisson model and negative binomial model, zero-inflated Poisson model and zero-inflated negative binomial model are used to model the number of claims in order to take into account excess zeros. To investigate the impact of different distributional assumptions for the number of claims on the prediction of total claim amount, predictive performances of candidate models are compared by using root mean square error (RMSE) and mean absolute error (MAE) criteria.

Distribution pattern of public transport passenger in Yogyakarta, Indonesia

NASA Astrophysics Data System (ADS)

Narendra, Alfa; Malkhamah, Siti; Sopha, Bertha Maya

2018-03-01

The arrival and departure distribution pattern of Trans Jogja bus passenger is one of the fundamental model for simulation. The purpose of this paper is to build models of passengers flows. This research used passengers data from January to May 2014. There is no policy that change the operation system affecting the nature of this pattern nowadays. The roads, buses, land uses, schedule, and people are relatively still the same. The data then categorized based on the direction, days, and location. Moreover, each category was fitted into some well-known discrete distributions. Those distributions are compared based on its AIC value and BIC. The chosen distribution model has the smallest AIC and BIC value and the negative binomial distribution found has the smallest AIC and BIC value. Probability mass function (PMF) plots of those models were compared to draw generic model from each categorical negative binomial distribution models. The value of accepted generic negative binomial distribution is 0.7064 and 1.4504 of mu. The minimum and maximum passenger vector value of distribution are is 0 and 41.

Distribution-free Inference of Zero-inated Binomial Data for Longitudinal Studies.

PubMed

He, H; Wang, W J; Hu, J; Gallop, R; Crits-Christoph, P; Xia, Y L

2015-10-01

Count reponses with structural zeros are very common in medical and psychosocial research, especially in alcohol and HIV research, and the zero-inflated poisson (ZIP) and zero-inflated negative binomial (ZINB) models are widely used for modeling such outcomes. However, as alcohol drinking outcomes such as days of drinkings are counts within a given period, their distributions are bounded above by an upper limit (total days in the period) and thus inherently follow a binomial or zero-inflated binomial (ZIB) distribution, rather than a Poisson or zero-inflated Poisson (ZIP) distribution, in the presence of structural zeros. In this paper, we develop a new semiparametric approach for modeling zero-inflated binomial (ZIB)-like count responses for cross-sectional as well as longitudinal data. We illustrate this approach with both simulated and real study data.

A big data approach to the development of mixed-effects models for seizure count data.

PubMed

Tharayil, Joseph J; Chiang, Sharon; Moss, Robert; Stern, John M; Theodore, William H; Goldenholz, Daniel M

2017-05-01

Our objective was to develop a generalized linear mixed model for predicting seizure count that is useful in the design and analysis of clinical trials. This model also may benefit the design and interpretation of seizure-recording paradigms. Most existing seizure count models do not include children, and there is currently no consensus regarding the most suitable model that can be applied to children and adults. Therefore, an additional objective was to develop a model that accounts for both adult and pediatric epilepsy. Using data from SeizureTracker.com, a patient-reported seizure diary tool with >1.2 million recorded seizures across 8 years, we evaluated the appropriateness of Poisson, negative binomial, zero-inflated negative binomial, and modified negative binomial models for seizure count data based on minimization of the Bayesian information criterion. Generalized linear mixed-effects models were used to account for demographic and etiologic covariates and for autocorrelation structure. Holdout cross-validation was used to evaluate predictive accuracy in simulating seizure frequencies. For both adults and children, we found that a negative binomial model with autocorrelation over 1 day was optimal. Using holdout cross-validation, the proposed model was found to provide accurate simulation of seizure counts for patients with up to four seizures per day. The optimal model can be used to generate more realistic simulated patient data with very few input parameters. The availability of a parsimonious, realistic virtual patient model can be of great utility in simulations of phase II/III clinical trials, epilepsy monitoring units, outpatient biosensors, and mobile Health (mHealth) applications. Wiley Periodicals, Inc. © 2017 International League Against Epilepsy.

Fitting statistical distributions to sea duck count data: implications for survey design and abundance estimation

USGS Publications Warehouse

Zipkin, Elise F.; Leirness, Jeffery B.; Kinlan, Brian P.; O'Connell, Allan F.; Silverman, Emily D.

2014-01-01

Determining appropriate statistical distributions for modeling animal count data is important for accurate estimation of abundance, distribution, and trends. In the case of sea ducks along the U.S. Atlantic coast, managers want to estimate local and regional abundance to detect and track population declines, to define areas of high and low use, and to predict the impact of future habitat change on populations. In this paper, we used a modified marked point process to model survey data that recorded flock sizes of Common eiders, Long-tailed ducks, and Black, Surf, and White-winged scoters. The data come from an experimental aerial survey, conducted by the United States Fish & Wildlife Service (USFWS) Division of Migratory Bird Management, during which east-west transects were flown along the Atlantic Coast from Maine to Florida during the winters of 2009–2011. To model the number of flocks per transect (the points), we compared the fit of four statistical distributions (zero-inflated Poisson, zero-inflated geometric, zero-inflated negative binomial and negative binomial) to data on the number of species-specific sea duck flocks that were recorded for each transect flown. To model the flock sizes (the marks), we compared the fit of flock size data for each species to seven statistical distributions: positive Poisson, positive negative binomial, positive geometric, logarithmic, discretized lognormal, zeta and Yule–Simon. Akaike’s Information Criterion and Vuong’s closeness tests indicated that the negative binomial and discretized lognormal were the best distributions for all species for the points and marks, respectively. These findings have important implications for estimating sea duck abundances as the discretized lognormal is a more skewed distribution than the Poisson and negative binomial, which are frequently used to model avian counts; the lognormal is also less heavy-tailed than the power law distributions (e.g., zeta and Yule–Simon), which are becoming increasingly popular for group size modeling. Choosing appropriate statistical distributions for modeling flock size data is fundamental to accurately estimating population summaries, determining required survey effort, and assessing and propagating uncertainty through decision-making processes.

Identifiability in N-mixture models: a large-scale screening test with bird data.

PubMed

Kéry, Marc

2018-02-01

Binomial N-mixture models have proven very useful in ecology, conservation, and monitoring: they allow estimation and modeling of abundance separately from detection probability using simple counts. Recently, doubts about parameter identifiability have been voiced. I conducted a large-scale screening test with 137 bird data sets from 2,037 sites. I found virtually no identifiability problems for Poisson and zero-inflated Poisson (ZIP) binomial N-mixture models, but negative-binomial (NB) models had problems in 25% of all data sets. The corresponding multinomial N-mixture models had no problems. Parameter estimates under Poisson and ZIP binomial and multinomial N-mixture models were extremely similar. Identifiability problems became a little more frequent with smaller sample sizes (267 and 50 sites), but were unaffected by whether the models did or did not include covariates. Hence, binomial N-mixture model parameters with Poisson and ZIP mixtures typically appeared identifiable. In contrast, NB mixtures were often unidentifiable, which is worrying since these were often selected by Akaike's information criterion. Identifiability of binomial N-mixture models should always be checked. If problems are found, simpler models, integrated models that combine different observation models or the use of external information via informative priors or penalized likelihoods, may help. © 2017 by the Ecological Society of America.

Modelling parasite aggregation: disentangling statistical and ecological approaches.

PubMed

Yakob, Laith; Soares Magalhães, Ricardo J; Gray, Darren J; Milinovich, Gabriel; Wardrop, Nicola; Dunning, Rebecca; Barendregt, Jan; Bieri, Franziska; Williams, Gail M; Clements, Archie C A

2014-05-01

The overdispersion in macroparasite infection intensity among host populations is commonly simulated using a constant negative binomial aggregation parameter. We describe an alternative to utilising the negative binomial approach and demonstrate important disparities in intervention efficacy projections that can come about from opting for pattern-fitting models that are not process-explicit. We present model output in the context of the epidemiology and control of soil-transmitted helminths due to the significant public health burden imposed by these parasites, but our methods are applicable to other infections with demonstrable aggregation in parasite numbers among hosts. Copyright © 2014. Published by Elsevier Ltd.

Selecting a distributional assumption for modelling relative densities of benthic macroinvertebrates

USGS Publications Warehouse

Gray, B.R.

2005-01-01

The selection of a distributional assumption suitable for modelling macroinvertebrate density data is typically challenging. Macroinvertebrate data often exhibit substantially larger variances than expected under a standard count assumption, that of the Poisson distribution. Such overdispersion may derive from multiple sources, including heterogeneity of habitat (historically and spatially), differing life histories for organisms collected within a single collection in space and time, and autocorrelation. Taken to extreme, heterogeneity of habitat may be argued to explain the frequent large proportions of zero observations in macroinvertebrate data. Sampling locations may consist of habitats defined qualitatively as either suitable or unsuitable. The former category may yield random or stochastic zeroes and the latter structural zeroes. Heterogeneity among counts may be accommodated by treating the count mean itself as a random variable, while extra zeroes may be accommodated using zero-modified count assumptions, including zero-inflated and two-stage (or hurdle) approaches. These and linear assumptions (following log- and square root-transformations) were evaluated using 9 years of mayfly density data from a 52 km, ninth-order reach of the Upper Mississippi River (n = 959). The data exhibited substantial overdispersion relative to that expected under a Poisson assumption (i.e. variance:mean ratio = 23 ??? 1), and 43% of the sampling locations yielded zero mayflies. Based on the Akaike Information Criterion (AIC), count models were improved most by treating the count mean as a random variable (via a Poisson-gamma distributional assumption) and secondarily by zero modification (i.e. improvements in AIC values = 9184 units and 47-48 units, respectively). Zeroes were underestimated by the Poisson, log-transform and square root-transform models, slightly by the standard negative binomial model but not by the zero-modified models (61%, 24%, 32%, 7%, and 0%, respectively). However, the zero-modified Poisson models underestimated small counts (1 ??? y ??? 4) and overestimated intermediate counts (7 ??? y ??? 23). Counts greater than zero were estimated well by zero-modified negative binomial models, while counts greater than one were also estimated well by the standard negative binomial model. Based on AIC and percent zero estimation criteria, the two-stage and zero-inflated models performed similarly. The above inferences were largely confirmed when the models were used to predict values from a separate, evaluation data set (n = 110). An exception was that, using the evaluation data set, the standard negative binomial model appeared superior to its zero-modified counterparts using the AIC (but not percent zero criteria). This and other evidence suggest that a negative binomial distributional assumption should be routinely considered when modelling benthic macroinvertebrate data from low flow environments. Whether negative binomial models should themselves be routinely examined for extra zeroes requires, from a statistical perspective, more investigation. However, this question may best be answered by ecological arguments that may be specific to the sampled species and locations. ?? 2004 Elsevier B.V. All rights reserved.

An examination of sources of sensitivity of consumer surplus estimates in travel cost models.

PubMed

Blaine, Thomas W; Lichtkoppler, Frank R; Bader, Timothy J; Hartman, Travis J; Lucente, Joseph E

2015-03-15

We examine sensitivity of estimates of recreation demand using the Travel Cost Method (TCM) to four factors. Three of the four have been routinely and widely discussed in the TCM literature: a) Poisson verses negative binomial regression; b) application of Englin correction to account for endogenous stratification; c) truncation of the data set to eliminate outliers. A fourth issue we address has not been widely modeled: the potential effect on recreation demand of the interaction between income and travel cost. We provide a straightforward comparison of all four factors, analyzing the impact of each on regression parameters and consumer surplus estimates. Truncation has a modest effect on estimates obtained from the Poisson models but a radical effect on the estimates obtained by way of the negative binomial. Inclusion of an income-travel cost interaction term generally produces a more conservative but not a statistically significantly different estimate of consumer surplus in both Poisson and negative binomial models. It also generates broader confidence intervals. Application of truncation, the Englin correction and the income-travel cost interaction produced the most conservative estimates of consumer surplus and eliminated the statistical difference between the Poisson and the negative binomial. Use of the income-travel cost interaction term reveals that for visitors who face relatively low travel costs, the relationship between income and travel demand is negative, while it is positive for those who face high travel costs. This provides an explanation of the ambiguities on the findings regarding the role of income widely observed in the TCM literature. Our results suggest that policies that reduce access to publicly owned resources inordinately impact local low income recreationists and are contrary to environmental justice. Copyright © 2014 Elsevier Ltd. All rights reserved.

Distinguishing between Binomial, Hypergeometric and Negative Binomial Distributions

ERIC Educational Resources Information Center

Wroughton, Jacqueline; Cole, Tarah

2013-01-01

Recognizing the differences between three discrete distributions (Binomial, Hypergeometric and Negative Binomial) can be challenging for students. We present an activity designed to help students differentiate among these distributions. In addition, we present assessment results in the form of pre- and post-tests that were designed to assess the…

A methodology to design heuristics for model selection based on the characteristics of data: Application to investigate when the Negative Binomial Lindley (NB-L) is preferred over the Negative Binomial (NB).

PubMed

Shirazi, Mohammadali; Dhavala, Soma Sekhar; Lord, Dominique; Geedipally, Srinivas Reddy

2017-10-01

Safety analysts usually use post-modeling methods, such as the Goodness-of-Fit statistics or the Likelihood Ratio Test, to decide between two or more competitive distributions or models. Such metrics require all competitive distributions to be fitted to the data before any comparisons can be accomplished. Given the continuous growth in introducing new statistical distributions, choosing the best one using such post-modeling methods is not a trivial task, in addition to all theoretical or numerical issues the analyst may face during the analysis. Furthermore, and most importantly, these measures or tests do not provide any intuitions into why a specific distribution (or model) is preferred over another (Goodness-of-Logic). This paper ponders into these issues by proposing a methodology to design heuristics for Model Selection based on the characteristics of data, in terms of descriptive summary statistics, before fitting the models. The proposed methodology employs two analytic tools: (1) Monte-Carlo Simulations and (2) Machine Learning Classifiers, to design easy heuristics to predict the label of the 'most-likely-true' distribution for analyzing data. The proposed methodology was applied to investigate when the recently introduced Negative Binomial Lindley (NB-L) distribution is preferred over the Negative Binomial (NB) distribution. Heuristics were designed to select the 'most-likely-true' distribution between these two distributions, given a set of prescribed summary statistics of data. The proposed heuristics were successfully compared against classical tests for several real or observed datasets. Not only they are easy to use and do not need any post-modeling inputs, but also, using these heuristics, the analyst can attain useful information about why the NB-L is preferred over the NB - or vice versa- when modeling data. Copyright © 2017 Elsevier Ltd. All rights reserved.

Poisson and negative binomial item count techniques for surveys with sensitive question.

PubMed

Tian, Guo-Liang; Tang, Man-Lai; Wu, Qin; Liu, Yin

2017-04-01

Although the item count technique is useful in surveys with sensitive questions, privacy of those respondents who possess the sensitive characteristic of interest may not be well protected due to a defect in its original design. In this article, we propose two new survey designs (namely the Poisson item count technique and negative binomial item count technique) which replace several independent Bernoulli random variables required by the original item count technique with a single Poisson or negative binomial random variable, respectively. The proposed models not only provide closed form variance estimate and confidence interval within [0, 1] for the sensitive proportion, but also simplify the survey design of the original item count technique. Most importantly, the new designs do not leak respondents' privacy. Empirical results show that the proposed techniques perform satisfactorily in the sense that it yields accurate parameter estimate and confidence interval.

Spatiotemporal and random parameter panel data models of traffic crash fatalities in Vietnam.

PubMed

Truong, Long T; Kieu, Le-Minh; Vu, Tuan A

2016-09-01

This paper investigates factors associated with traffic crash fatalities in 63 provinces of Vietnam during the period from 2012 to 2014. Random effect negative binomial (RENB) and random parameter negative binomial (RPNB) panel data models are adopted to consider spatial heterogeneity across provinces. In addition, a spatiotemporal model with conditional autoregressive priors (ST-CAR) is utilised to account for spatiotemporal autocorrelation in the data. The statistical comparison indicates the ST-CAR model outperforms the RENB and RPNB models. Estimation results provide several significant findings. For example, traffic crash fatalities tend to be higher in provinces with greater numbers of level crossings. Passenger distance travelled and road lengths are also positively associated with fatalities. However, hospital densities are negatively associated with fatalities. The safety impact of the national highway 1A, the main transport corridor of the country, is also highlighted. Copyright © 2016 Elsevier Ltd. All rights reserved.

The analysis of incontinence episodes and other count data in patients with overactive bladder by Poisson and negative binomial regression.

PubMed

Martina, R; Kay, R; van Maanen, R; Ridder, A

2015-01-01

Clinical studies in overactive bladder have traditionally used analysis of covariance or nonparametric methods to analyse the number of incontinence episodes and other count data. It is known that if the underlying distributional assumptions of a particular parametric method do not hold, an alternative parametric method may be more efficient than a nonparametric one, which makes no assumptions regarding the underlying distribution of the data. Therefore, there are advantages in using methods based on the Poisson distribution or extensions of that method, which incorporate specific features that provide a modelling framework for count data. One challenge with count data is overdispersion, but methods are available that can account for this through the introduction of random effect terms in the modelling, and it is this modelling framework that leads to the negative binomial distribution. These models can also provide clinicians with a clearer and more appropriate interpretation of treatment effects in terms of rate ratios. In this paper, the previously used parametric and non-parametric approaches are contrasted with those based on Poisson regression and various extensions in trials evaluating solifenacin and mirabegron in patients with overactive bladder. In these applications, negative binomial models are seen to fit the data well. Copyright © 2014 John Wiley & Sons, Ltd.

Dispersion and sampling of adult Dermacentor andersoni in rangeland in Western North America.

PubMed

Rochon, K; Scoles, G A; Lysyk, T J

2012-03-01

A fixed precision sampling plan was developed for off-host populations of adult Rocky Mountain wood tick, Dermacentor andersoni (Stiles) based on data collected by dragging at 13 locations in Alberta, Canada; Washington; and Oregon. In total, 222 site-date combinations were sampled. Each site-date combination was considered a sample, and each sample ranged in size from 86 to 250 10 m2 quadrats. Analysis of simulated quadrats ranging in size from 10 to 50 m2 indicated that the most precise sample unit was the 10 m2 quadrat. Samples taken when abundance < 0.04 ticks per 10 m2 were more likely to not depart significantly from statistical randomness than samples taken when abundance was greater. Data were grouped into ten abundance classes and assessed for fit to the Poisson and negative binomial distributions. The Poisson distribution fit only data in abundance classes < 0.02 ticks per 10 m2, while the negative binomial distribution fit data from all abundance classes. A negative binomial distribution with common k = 0.3742 fit data in eight of the 10 abundance classes. Both the Taylor and Iwao mean-variance relationships were fit and used to predict sample sizes for a fixed level of precision. Sample sizes predicted using the Taylor model tended to underestimate actual sample sizes, while sample sizes estimated using the Iwao model tended to overestimate actual sample sizes. Using a negative binomial with common k provided estimates of required sample sizes closest to empirically calculated sample sizes.

Statistical inference involving binomial and negative binomial parameters.

PubMed

García-Pérez, Miguel A; Núñez-Antón, Vicente

2009-05-01

Statistical inference about two binomial parameters implies that they are both estimated by binomial sampling. There are occasions in which one aims at testing the equality of two binomial parameters before and after the occurrence of the first success along a sequence of Bernoulli trials. In these cases, the binomial parameter before the first success is estimated by negative binomial sampling whereas that after the first success is estimated by binomial sampling, and both estimates are related. This paper derives statistical tools to test two hypotheses, namely, that both binomial parameters equal some specified value and that both parameters are equal though unknown. Simulation studies are used to show that in small samples both tests are accurate in keeping the nominal Type-I error rates, and also to determine sample size requirements to detect large, medium, and small effects with adequate power. Additional simulations also show that the tests are sufficiently robust to certain violations of their assumptions.

Matching the Statistical Model to the Research Question for Dental Caries Indices with Many Zero Counts.

PubMed

Preisser, John S; Long, D Leann; Stamm, John W

2017-01-01

Marginalized zero-inflated count regression models have recently been introduced for the statistical analysis of dental caries indices and other zero-inflated count data as alternatives to traditional zero-inflated and hurdle models. Unlike the standard approaches, the marginalized models directly estimate overall exposure or treatment effects by relating covariates to the marginal mean count. This article discusses model interpretation and model class choice according to the research question being addressed in caries research. Two data sets, one consisting of fictional dmft counts in 2 groups and the other on DMFS among schoolchildren from a randomized clinical trial comparing 3 toothpaste formulations to prevent incident dental caries, are analyzed with negative binomial hurdle, zero-inflated negative binomial, and marginalized zero-inflated negative binomial models. In the first example, estimates of treatment effects vary according to the type of incidence rate ratio (IRR) estimated by the model. Estimates of IRRs in the analysis of the randomized clinical trial were similar despite their distinctive interpretations. The choice of statistical model class should match the study's purpose, while accounting for the broad decline in children's caries experience, such that dmft and DMFS indices more frequently generate zero counts. Marginalized (marginal mean) models for zero-inflated count data should be considered for direct assessment of exposure effects on the marginal mean dental caries count in the presence of high frequencies of zero counts. © 2017 S. Karger AG, Basel.

Matching the Statistical Model to the Research Question for Dental Caries Indices with Many Zero Counts

PubMed Central

Preisser, John S.; Long, D. Leann; Stamm, John W.

2017-01-01

Marginalized zero-inflated count regression models have recently been introduced for the statistical analysis of dental caries indices and other zero-inflated count data as alternatives to traditional zero-inflated and hurdle models. Unlike the standard approaches, the marginalized models directly estimate overall exposure or treatment effects by relating covariates to the marginal mean count. This article discusses model interpretation and model class choice according to the research question being addressed in caries research. Two datasets, one consisting of fictional dmft counts in two groups and the other on DMFS among schoolchildren from a randomized clinical trial (RCT) comparing three toothpaste formulations to prevent incident dental caries, are analysed with negative binomial hurdle (NBH), zero-inflated negative binomial (ZINB), and marginalized zero-inflated negative binomial (MZINB) models. In the first example, estimates of treatment effects vary according to the type of incidence rate ratio (IRR) estimated by the model. Estimates of IRRs in the analysis of the RCT were similar despite their distinctive interpretations. Choice of statistical model class should match the study’s purpose, while accounting for the broad decline in children’s caries experience, such that dmft and DMFS indices more frequently generate zero counts. Marginalized (marginal mean) models for zero-inflated count data should be considered for direct assessment of exposure effects on the marginal mean dental caries count in the presence of high frequencies of zero counts. PMID:28291962

Zero-state Markov switching count-data models: an empirical assessment.

PubMed

Malyshkina, Nataliya V; Mannering, Fred L

2010-01-01

In this study, a two-state Markov switching count-data model is proposed as an alternative to zero-inflated models to account for the preponderance of zeros sometimes observed in transportation count data, such as the number of accidents occurring on a roadway segment over some period of time. For this accident-frequency case, zero-inflated models assume the existence of two states: one of the states is a zero-accident count state, which has accident probabilities that are so low that they cannot be statistically distinguished from zero, and the other state is a normal-count state, in which counts can be non-negative integers that are generated by some counting process, for example, a Poisson or negative binomial. While zero-inflated models have come under some criticism with regard to accident-frequency applications - one fact is undeniable - in many applications they provide a statistically superior fit to the data. The Markov switching approach we propose seeks to overcome some of the criticism associated with the zero-accident state of the zero-inflated model by allowing individual roadway segments to switch between zero and normal-count states over time. An important advantage of this Markov switching approach is that it allows for the direct statistical estimation of the specific roadway-segment state (i.e., zero-accident or normal-count state) whereas traditional zero-inflated models do not. To demonstrate the applicability of this approach, a two-state Markov switching negative binomial model (estimated with Bayesian inference) and standard zero-inflated negative binomial models are estimated using five-year accident frequencies on Indiana interstate highway segments. It is shown that the Markov switching model is a viable alternative and results in a superior statistical fit relative to the zero-inflated models.

Binomial test statistics using Psi functions

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

Bowman, Kimiko o

2007-01-01

For the negative binomial model (probability generating function (p + 1 - pt){sup -k}) a logarithmic derivative is the Psi function difference {psi}(k + x) - {psi}(k); this and its derivatives lead to a test statistic to decide on the validity of a specified model. The test statistic uses a data base so there exists a comparison available between theory and application. Note that the test function is not dominated by outliers. Applications to (i) Fisher's tick data, (ii) accidents data, (iii) Weldon's dice data are included.

A comparative study of count models: application to pedestrian-vehicle crashes along Malaysia federal roads.

PubMed

Hosseinpour, Mehdi; Pour, Mehdi Hossein; Prasetijo, Joewono; Yahaya, Ahmad Shukri; Ghadiri, Seyed Mohammad Reza

2013-01-01

The objective of this study was to examine the effects of various roadway characteristics on the incidence of pedestrian-vehicle crashes by developing a set of crash prediction models on 543 km of Malaysia federal roads over a 4-year time span between 2007 and 2010. Four count models including the Poisson, negative binomial (NB), hurdle Poisson (HP), and hurdle negative binomial (HNB) models were developed and compared to model the number of pedestrian crashes. The results indicated the presence of overdispersion in the pedestrian crashes (PCs) and showed that it is due to excess zero rather than variability in the crash data. To handle the issue, the hurdle Poisson model was found to be the best model among the considered models in terms of comparative measures. Moreover, the variables average daily traffic, heavy vehicle traffic, speed limit, land use, and area type were significantly associated with PCs.

Estimation of the cure rate in Iranian breast cancer patients.

PubMed

Rahimzadeh, Mitra; Baghestani, Ahmad Reza; Gohari, Mahmood Reza; Pourhoseingholi, Mohamad Amin

2014-01-01

Although the Cox's proportional hazard model is the popular approach for survival analysis to investigate significant risk factors of cancer patient survival, it is not appropriate in the case of log-term disease free survival. Recently, cure rate models have been introduced to distinguish between clinical determinants of cure and variables associated with the time to event of interest. The aim of this study was to use a cure rate model to determine the clinical associated factors for cure rates of patients with breast cancer (BC). This prospective cohort study covered 305 patients with BC, admitted at Shahid Faiazbakhsh Hospital, Tehran, during 2006 to 2008 and followed until April 2012. Cases of patient death were confirmed by telephone contact. For data analysis, a non-mixed cure rate model with Poisson distribution and negative binomial distribution were employed. All analyses were carried out using a developed Macro in WinBugs. Deviance information criteria (DIC) were employed to find the best model. The overall 1-year, 3-year and 5-year relative survival rates were 97%, 89% and 74%. Metastasis and stage of BC were the significant factors, but age was significant only in negative binomial model. The DIC also showed that the negative binomial model had a better fit. This study indicated that, metastasis and stage of BC were identified as the clinical criteria for cure rates. There are limited studies on BC survival which employed these cure rate models to identify the clinical factors associated with cure. These models are better than Cox, in the case of long-term survival.

Analysis of generalized negative binomial distributions attached to hyperbolic Landau levels

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

Chhaiba, Hassan, E-mail: chhaiba.hassan@gmail.com; Demni, Nizar, E-mail: nizar.demni@univ-rennes1.fr; Mouayn, Zouhair, E-mail: mouayn@fstbm.ac.ma

2016-07-15

To each hyperbolic Landau level of the Poincaré disc is attached a generalized negative binomial distribution. In this paper, we compute the moment generating function of this distribution and supply its atomic decomposition as a perturbation of the negative binomial distribution by a finitely supported measure. Using the Mandel parameter, we also discuss the nonclassical nature of the associated coherent states. Next, we derive a Lévy-Khintchine-type representation of its characteristic function when the latter does not vanish and deduce that it is quasi-infinitely divisible except for the lowest hyperbolic Landau level corresponding to the negative binomial distribution. By considering themore » total variation of the obtained quasi-Lévy measure, we introduce a new infinitely divisible distribution for which we derive the characteristic function.« less

Library Book Circulation and the Beta-Binomial Distribution.

ERIC Educational Resources Information Center

Gelman, E.; Sichel, H. S.

1987-01-01

Argues that library book circulation is a binomial rather than a Poisson process, and that individual book popularities are continuous beta distributions. Three examples demonstrate the superiority of beta over negative binomial distribution, and it is suggested that a bivariate-binomial process would be helpful in predicting future book…

Use of negative binomial distribution to describe the presence of Anisakis in Thyrsites atun.

PubMed

Peña-Rehbein, Patricio; De los Ríos-Escalante, Patricio

2012-01-01

Nematodes of the genus Anisakis have marine fishes as intermediate hosts. One of these hosts is Thyrsites atun, an important fishery resource in Chile between 38 and 41° S. This paper describes the frequency and number of Anisakis nematodes in the internal organs of Thyrsites atun. An analysis based on spatial distribution models showed that the parasites tend to be clustered. The variation in the number of parasites per host could be described by the negative binomial distribution. The maximum observed number of parasites was nine parasites per host. The environmental and zoonotic aspects of the study are also discussed.

Solving the problem of negative populations in approximate accelerated stochastic simulations using the representative reaction approach.

PubMed

Kadam, Shantanu; Vanka, Kumar

2013-02-15

Methods based on the stochastic formulation of chemical kinetics have the potential to accurately reproduce the dynamical behavior of various biochemical systems of interest. However, the computational expense makes them impractical for the study of real systems. Attempts to render these methods practical have led to the development of accelerated methods, where the reaction numbers are modeled by Poisson random numbers. However, for certain systems, such methods give rise to physically unrealistic negative numbers for species populations. The methods which make use of binomial variables, in place of Poisson random numbers, have since become popular, and have been partially successful in addressing this problem. In this manuscript, the development of two new computational methods, based on the representative reaction approach (RRA), has been discussed. The new methods endeavor to solve the problem of negative numbers, by making use of tools like the stochastic simulation algorithm and the binomial method, in conjunction with the RRA. It is found that these newly developed methods perform better than other binomial methods used for stochastic simulations, in resolving the problem of negative populations. Copyright © 2012 Wiley Periodicals, Inc.

A comparison of different ways of including baseline counts in negative binomial models for data from falls prevention trials.

PubMed

Zheng, Han; Kimber, Alan; Goodwin, Victoria A; Pickering, Ruth M

2018-01-01

A common design for a falls prevention trial is to assess falling at baseline, randomize participants into an intervention or control group, and ask them to record the number of falls they experience during a follow-up period of time. This paper addresses how best to include the baseline count in the analysis of the follow-up count of falls in negative binomial (NB) regression. We examine the performance of various approaches in simulated datasets where both counts are generated from a mixed Poisson distribution with shared random subject effect. Including the baseline count after log-transformation as a regressor in NB regression (NB-logged) or as an offset (NB-offset) resulted in greater power than including the untransformed baseline count (NB-unlogged). Cook and Wei's conditional negative binomial (CNB) model replicates the underlying process generating the data. In our motivating dataset, a statistically significant intervention effect resulted from the NB-logged, NB-offset, and CNB models, but not from NB-unlogged, and large, outlying baseline counts were overly influential in NB-unlogged but not in NB-logged. We conclude that there is little to lose by including the log-transformed baseline count in standard NB regression compared to CNB for moderate to larger sized datasets. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Analyzing crash frequency in freeway tunnels: A correlated random parameters approach.

PubMed

Hou, Qinzhong; Tarko, Andrew P; Meng, Xianghai

2018-02-01

The majority of past road safety studies focused on open road segments while only a few focused on tunnels. Moreover, the past tunnel studies produced some inconsistent results about the safety effects of the traffic patterns, the tunnel design, and the pavement conditions. The effects of these conditions therefore remain unknown, especially for freeway tunnels in China. The study presented in this paper investigated the safety effects of these various factors utilizing a four-year period (2009-2012) of data as well as three models: 1) a random effects negative binomial model (RENB), 2) an uncorrelated random parameters negative binomial model (URPNB), and 3) a correlated random parameters negative binomial model (CRPNB). Of these three, the results showed that the CRPNB model provided better goodness-of-fit and offered more insights into the factors that contribute to tunnel safety. The CRPNB was not only able to allocate the part of the otherwise unobserved heterogeneity to the individual model parameters but also was able to estimate the cross-correlations between these parameters. Furthermore, the study results showed that traffic volume, tunnel length, proportion of heavy trucks, curvature, and pavement rutting were associated with higher frequencies of traffic crashes, while the distance to the tunnel wall, distance to the adjacent tunnel, distress ratio, International Roughness Index (IRI), and friction coefficient were associated with lower crash frequencies. In addition, the effects of the heterogeneity of the proportion of heavy trucks, the curvature, the rutting depth, and the friction coefficient were identified and their inter-correlations were analyzed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Estimating spatial and temporal components of variation in count data using negative binomial mixed models

USGS Publications Warehouse

Irwin, Brian J.; Wagner, Tyler; Bence, James R.; Kepler, Megan V.; Liu, Weihai; Hayes, Daniel B.

2013-01-01

Partitioning total variability into its component temporal and spatial sources is a powerful way to better understand time series and elucidate trends. The data available for such analyses of fish and other populations are usually nonnegative integer counts of the number of organisms, often dominated by many low values with few observations of relatively high abundance. These characteristics are not well approximated by the Gaussian distribution. We present a detailed description of a negative binomial mixed-model framework that can be used to model count data and quantify temporal and spatial variability. We applied these models to data from four fishery-independent surveys of Walleyes Sander vitreus across the Great Lakes basin. Specifically, we fitted models to gill-net catches from Wisconsin waters of Lake Superior; Oneida Lake, New York; Saginaw Bay in Lake Huron, Michigan; and Ohio waters of Lake Erie. These long-term monitoring surveys varied in overall sampling intensity, the total catch of Walleyes, and the proportion of zero catches. Parameter estimation included the negative binomial scaling parameter, and we quantified the random effects as the variations among gill-net sampling sites, the variations among sampled years, and site × year interactions. This framework (i.e., the application of a mixed model appropriate for count data in a variance-partitioning context) represents a flexible approach that has implications for monitoring programs (e.g., trend detection) and for examining the potential of individual variance components to serve as response metrics to large-scale anthropogenic perturbations or ecological changes.

Evaluation of surrogate measures for pedestrian safety in various road and roadside environments.

DOT National Transportation Integrated Search

2012-10-01

This report presents an investigation of pedestrian conflicts and crash count models to learn which exposure measures and roadway or roadside characteristics significantly influence pedestrian safety at road crossings. Negative binomial models were e...

An analytical framework for estimating aquatic species density from environmental DNA

USGS Publications Warehouse

Chambert, Thierry; Pilliod, David S.; Goldberg, Caren S.; Doi, Hideyuki; Takahara, Teruhiko

2018-01-01

Environmental DNA (eDNA) analysis of water samples is on the brink of becoming a standard monitoring method for aquatic species. This method has improved detection rates over conventional survey methods and thus has demonstrated effectiveness for estimation of site occupancy and species distribution. The frontier of eDNA applications, however, is to infer species density. Building upon previous studies, we present and assess a modeling approach that aims at inferring animal density from eDNA. The modeling combines eDNA and animal count data from a subset of sites to estimate species density (and associated uncertainties) at other sites where only eDNA data are available. As a proof of concept, we first perform a cross-validation study using experimental data on carp in mesocosms. In these data, fish densities are known without error, which allows us to test the performance of the method with known data. We then evaluate the model using field data from a study on a stream salamander species to assess the potential of this method to work in natural settings, where density can never be known with absolute certainty. Two alternative distributions (Normal and Negative Binomial) to model variability in eDNA concentration data are assessed. Assessment based on the proof of concept data (carp) revealed that the Negative Binomial model provided much more accurate estimates than the model based on a Normal distribution, likely because eDNA data tend to be overdispersed. Greater imprecision was found when we applied the method to the field data, but the Negative Binomial model still provided useful density estimates. We call for further model development in this direction, as well as further research targeted at sampling design optimization. It will be important to assess these approaches on a broad range of study systems.

Development of enhanced pavement deterioration curves.

DOT National Transportation Integrated Search

2016-10-01

This report describes the research performed by the Center for Sustainable Transportation Infrastructure (CSTI) at the Virginia Tech Transportation Institute (VTTI) to develop a pavement condition prediction model, using (negative binomial) regressio...

Comparison of multiplicity distributions to the negative binomial distribution in muon-proton scattering

NASA Astrophysics Data System (ADS)

Arneodo, M.; Arvidson, A.; Aubert, J. J.; Badełek, B.; Beaufays, J.; Bee, C. P.; Benchouk, C.; Berghoff, G.; Bird, I.; Blum, D.; Böhm, E.; de Bouard, X.; Brasse, F. W.; Braun, H.; Broll, C.; Brown, S.; Brück, H.; Calen, H.; Chima, J. S.; Ciborowski, J.; Clifft, R.; Coignet, G.; Combley, F.; Coughlan, J.; D'Agostini, G.; Dahlgren, S.; Dengler, F.; Derado, I.; Dreyer, T.; Drees, J.; Düren, M.; Eckardt, V.; Edwards, A.; Edwards, M.; Ernst, T.; Eszes, G.; Favier, J.; Ferrero, M. I.; Figiel, J.; Flauger, W.; Foster, J.; Ftáčnik, J.; Gabathuler, E.; Gajewski, J.; Gamet, R.; Gayler, J.; Geddes, N.; Grafström, P.; Grard, F.; Haas, J.; Hagberg, E.; Hasert, F. J.; Hayman, P.; Heusse, P.; Jaffré, M.; Jachołkowska, A.; Janata, F.; Jancsó, G.; Johnson, A. S.; Kabuss, E. M.; Kellner, G.; Korbel, V.; Krüger, J.; Kullander, S.; Landgraf, U.; Lanske, D.; Loken, J.; Long, K.; Maire, M.; Malecki, P.; Manz, A.; Maselli, S.; Mohr, W.; Montanet, F.; Montgomery, H. E.; Nagy, E.; Nassalski, J.; Norton, P. R.; Oakham, F. G.; Osborne, A. M.; Pascaud, C.; Pawlik, B.; Payre, P.; Peroni, C.; Peschel, H.; Pessard, H.; Pettinghale, J.; Pietrzyk, B.; Pietrzyk, U.; Pönsgen, B.; Pötsch, M.; Renton, P.; Ribarics, P.; Rith, K.; Rondio, E.; Sandacz, A.; Scheer, M.; Schlagböhmer, A.; Schiemann, H.; Schmitz, N.; Schneegans, M.; Schneider, A.; Scholz, M.; Schröder, T.; Schultze, K.; Sloan, T.; Stier, H. E.; Studt, M.; Taylor, G. N.; Thénard, J. M.; Thompson, J. C.; de La Torre, A.; Toth, J.; Urban, L.; Urban, L.; Wallucks, W.; Whalley, M.; Wheeler, S.; Williams, W. S. C.; Wimpenny, S. J.; Windmolders, R.; Wolf, G.

1987-09-01

The multiplicity distributions of charged hadrons produced in the deep inelastic muon-proton scattering at 280 GeV are analysed in various rapidity intervals, as a function of the total hadronic centre of mass energy W ranging from 4 20 GeV. Multiplicity distributions for the backward and forward hemispheres are also analysed separately. The data can be well parameterized by binomial distributions, extending their range of applicability to the case of lepton-proton scattering. The energy and the rapidity dependence of the parameters is presented and a smooth transition from the negative binomial distribution via Poissonian to the ordinary binomial is observed.

Application of a hurdle negative binomial count data model to demand for bass fishing in the southeastern United States.

PubMed

Bilgic, Abdulbaki; Florkowski, Wojciech J

2007-06-01

This paper identifies factors that influence the demand for a bass fishing trip taken in the southeastern United States using a hurdle negative binomial count data model. The probability of fishing for a bass is estimated in the first stage and the fishing trip frequency is estimated in the second stage for individuals reporting bass fishing trips in the Southeast. The applied approach allows the decomposition of the effects of factors responsible for the decision to take a trip and the trip number. Calculated partial and total elasticities indicate a highly inelastic demand for the number of fishing trips as trip costs increase. However, the demand can be expected to increase if anglers experience a success measured by the number of caught fish or their size. Benefit estimates based on alternative estimation methods differ substantially, suggesting the need for testing each modeling approach applied in empirical studies.

Pricing American Asian options with higher moments in the underlying distribution

NASA Astrophysics Data System (ADS)

Lo, Keng-Hsin; Wang, Kehluh; Hsu, Ming-Feng

2009-01-01

We develop a modified Edgeworth binomial model with higher moment consideration for pricing American Asian options. With lognormal underlying distribution for benchmark comparison, our algorithm is as precise as that of Chalasani et al. [P. Chalasani, S. Jha, F. Egriboyun, A. Varikooty, A refined binomial lattice for pricing American Asian options, Rev. Derivatives Res. 3 (1) (1999) 85-105] if the number of the time steps increases. If the underlying distribution displays negative skewness and leptokurtosis as often observed for stock index returns, our estimates can work better than those in Chalasani et al. [P. Chalasani, S. Jha, F. Egriboyun, A. Varikooty, A refined binomial lattice for pricing American Asian options, Rev. Derivatives Res. 3 (1) (1999) 85-105] and are very similar to the benchmarks in Hull and White [J. Hull, A. White, Efficient procedures for valuing European and American path-dependent options, J. Derivatives 1 (Fall) (1993) 21-31]. The numerical analysis shows that our modified Edgeworth binomial model can value American Asian options with greater accuracy and speed given higher moments in their underlying distribution.

The Difference Calculus and The NEgative Binomial Distribution

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

Bowman, Kimiko o; Shenton, LR

2007-01-01

In a previous paper we state the dominant term in the third central moment of the maximum likelihood estimator k of the parameter k in the negative binomial probability function where the probability generating function is (p + 1 - pt){sup -k}. A partial sum of the series {Sigma}1/(k + x){sup 3} is involved, where x is a negative binomial random variate. In expectation this sum can only be found numerically using the computer. Here we give a simple definite integral in (0,1) for the generalized case. This means that now we do have a valid expression for {radical}{beta}{sub 11}(k)more » and {radical}{beta}{sub 11}(p). In addition we use the finite difference operator {Delta}, and E = 1 + {Delta} to set up formulas for low order moments. Other examples of the operators are quoted relating to the orthogonal set of polynomials associated with the negative binomial probability function used as a weight function.« less

Analysis of overdispersed count data: application to the Human Papillomavirus Infection in Men (HIM) Study.

PubMed

Lee, J-H; Han, G; Fulp, W J; Giuliano, A R

2012-06-01

The Poisson model can be applied to the count of events occurring within a specific time period. The main feature of the Poisson model is the assumption that the mean and variance of the count data are equal. However, this equal mean-variance relationship rarely occurs in observational data. In most cases, the observed variance is larger than the assumed variance, which is called overdispersion. Further, when the observed data involve excessive zero counts, the problem of overdispersion results in underestimating the variance of the estimated parameter, and thus produces a misleading conclusion. We illustrated the use of four models for overdispersed count data that may be attributed to excessive zeros. These are Poisson, negative binomial, zero-inflated Poisson and zero-inflated negative binomial models. The example data in this article deal with the number of incidents involving human papillomavirus infection. The four models resulted in differing statistical inferences. The Poisson model, which is widely used in epidemiology research, underestimated the standard errors and overstated the significance of some covariates.

Estimating abundance while accounting for rarity, correlated behavior, and other sources of variation in counts

USGS Publications Warehouse

Dorazio, Robert M.; Martin, Juulien; Edwards, Holly H.

2013-01-01

The class of N-mixture models allows abundance to be estimated from repeated, point count surveys while adjusting for imperfect detection of individuals. We developed an extension of N-mixture models to account for two commonly observed phenomena in point count surveys: rarity and lack of independence induced by unmeasurable sources of variation in the detectability of individuals. Rarity increases the number of locations with zero detections in excess of those expected under simple models of abundance (e.g., Poisson or negative binomial). Correlated behavior of individuals and other phenomena, though difficult to measure, increases the variation in detection probabilities among surveys. Our extension of N-mixture models includes a hurdle model of abundance and a beta-binomial model of detectability that accounts for additional (extra-binomial) sources of variation in detections among surveys. As an illustration, we fit this model to repeated point counts of the West Indian manatee, which was observed in a pilot study using aerial surveys. Our extension of N-mixture models provides increased flexibility. The effects of different sets of covariates may be estimated for the probability of occurrence of a species, for its mean abundance at occupied locations, and for its detectability.

Estimating abundance while accounting for rarity, correlated behavior, and other sources of variation in counts.

PubMed

Dorazio, Robert M; Martin, Julien; Edwards, Holly H

2013-07-01

The class of N-mixture models allows abundance to be estimated from repeated, point count surveys while adjusting for imperfect detection of individuals. We developed an extension of N-mixture models to account for two commonly observed phenomena in point count surveys: rarity and lack of independence induced by unmeasurable sources of variation in the detectability of individuals. Rarity increases the number of locations with zero detections in excess of those expected under simple models of abundance (e.g., Poisson or negative binomial). Correlated behavior of individuals and other phenomena, though difficult to measure, increases the variation in detection probabilities among surveys. Our extension of N-mixture models includes a hurdle model of abundance and a beta-binomial model of detectability that accounts for additional (extra-binomial) sources of variation in detections among surveys. As an illustration, we fit this model to repeated point counts of the West Indian manatee, which was observed in a pilot study using aerial surveys. Our extension of N-mixture models provides increased flexibility. The effects of different sets of covariates may be estimated for the probability of occurrence of a species, for its mean abundance at occupied locations, and for its detectability.

Modeling Zero-Inflated and Overdispersed Count Data: An Empirical Study of School Suspensions

ERIC Educational Resources Information Center

Desjardins, Christopher David

2016-01-01

The purpose of this article is to develop a statistical model that best explains variability in the number of school days suspended. Number of school days suspended is a count variable that may be zero-inflated and overdispersed relative to a Poisson model. Four models were examined: Poisson, negative binomial, Poisson hurdle, and negative…

Variable selection for zero-inflated and overdispersed data with application to health care demand in Germany

PubMed Central

Wang, Zhu; Shuangge, Ma; Wang, Ching-Yun

2017-01-01

In health services and outcome research, count outcomes are frequently encountered and often have a large proportion of zeros. The zero-inflated negative binomial (ZINB) regression model has important applications for this type of data. With many possible candidate risk factors, this paper proposes new variable selection methods for the ZINB model. We consider maximum likelihood function plus a penalty including the least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP). An EM (expectation-maximization) algorithm is proposed for estimating the model parameters and conducting variable selection simultaneously. This algorithm consists of estimating penalized weighted negative binomial models and penalized logistic models via the coordinated descent algorithm. Furthermore, statistical properties including the standard error formulae are provided. A simulation study shows that the new algorithm not only has more accurate or at least comparable estimation, also is more robust than the traditional stepwise variable selection. The proposed methods are applied to analyze the health care demand in Germany using an open-source R package mpath. PMID:26059498

Estimating safety effects of pavement management factors utilizing Bayesian random effect models.

PubMed

Jiang, Ximiao; Huang, Baoshan; Zaretzki, Russell L; Richards, Stephen; Yan, Xuedong

2013-01-01

Previous studies of pavement management factors that relate to the occurrence of traffic-related crashes are rare. Traditional research has mostly employed summary statistics of bidirectional pavement quality measurements in extended longitudinal road segments over a long time period, which may cause a loss of important information and result in biased parameter estimates. The research presented in this article focuses on crash risk of roadways with overall fair to good pavement quality. Real-time and location-specific data were employed to estimate the effects of pavement management factors on the occurrence of crashes. This research is based on the crash data and corresponding pavement quality data for the Tennessee state route highways from 2004 to 2009. The potential temporal and spatial correlations among observations caused by unobserved factors were considered. Overall 6 models were built accounting for no correlation, temporal correlation only, and both the temporal and spatial correlations. These models included Poisson, negative binomial (NB), one random effect Poisson and negative binomial (OREP, ORENB), and two random effect Poisson and negative binomial (TREP, TRENB) models. The Bayesian method was employed to construct these models. The inference is based on the posterior distribution from the Markov chain Monte Carlo (MCMC) simulation. These models were compared using the deviance information criterion. Analysis of the posterior distribution of parameter coefficients indicates that the pavement management factors indexed by Present Serviceability Index (PSI) and Pavement Distress Index (PDI) had significant impacts on the occurrence of crashes, whereas the variable rutting depth was not significant. Among other factors, lane width, median width, type of terrain, and posted speed limit were significant in affecting crash frequency. The findings of this study indicate that a reduction in pavement roughness would reduce the likelihood of traffic-related crashes. Hence, maintaining a low level of pavement roughness is strongly suggested. In addition, the results suggested that the temporal correlation among observations was significant and that the ORENB model outperformed all other models.

Sample size determination for a three-arm equivalence trial of Poisson and negative binomial responses.

PubMed

Chang, Yu-Wei; Tsong, Yi; Zhao, Zhigen

2017-01-01

Assessing equivalence or similarity has drawn much attention recently as many drug products have lost or will lose their patents in the next few years, especially certain best-selling biologics. To claim equivalence between the test treatment and the reference treatment when assay sensitivity is well established from historical data, one has to demonstrate both superiority of the test treatment over placebo and equivalence between the test treatment and the reference treatment. Thus, there is urgency for practitioners to derive a practical way to calculate sample size for a three-arm equivalence trial. The primary endpoints of a clinical trial may not always be continuous, but may be discrete. In this paper, the authors derive power function and discuss sample size requirement for a three-arm equivalence trial with Poisson and negative binomial clinical endpoints. In addition, the authors examine the effect of the dispersion parameter on the power and the sample size by varying its coefficient from small to large. In extensive numerical studies, the authors demonstrate that required sample size heavily depends on the dispersion parameter. Therefore, misusing a Poisson model for negative binomial data may easily lose power up to 20%, depending on the value of the dispersion parameter.

The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded.

PubMed

Nakagawa, Shinichi; Johnson, Paul C D; Schielzeth, Holger

2017-09-01

The coefficient of determination R 2 quantifies the proportion of variance explained by a statistical model and is an important summary statistic of biological interest. However, estimating R 2 for generalized linear mixed models (GLMMs) remains challenging. We have previously introduced a version of R 2 that we called [Formula: see text] for Poisson and binomial GLMMs, but not for other distributional families. Similarly, we earlier discussed how to estimate intra-class correlation coefficients (ICCs) using Poisson and binomial GLMMs. In this paper, we generalize our methods to all other non-Gaussian distributions, in particular to negative binomial and gamma distributions that are commonly used for modelling biological data. While expanding our approach, we highlight two useful concepts for biologists, Jensen's inequality and the delta method, both of which help us in understanding the properties of GLMMs. Jensen's inequality has important implications for biologically meaningful interpretation of GLMMs, whereas the delta method allows a general derivation of variance associated with non-Gaussian distributions. We also discuss some special considerations for binomial GLMMs with binary or proportion data. We illustrate the implementation of our extension by worked examples from the field of ecology and evolution in the R environment. However, our method can be used across disciplines and regardless of statistical environments. © 2017 The Author(s).

Football goal distributions and extremal statistics

NASA Astrophysics Data System (ADS)

Greenhough, J.; Birch, P. C.; Chapman, S. C.; Rowlands, G.

2002-12-01

We analyse the distributions of the number of goals scored by home teams, away teams, and the total scored in the match, in domestic football games from 169 countries between 1999 and 2001. The probability density functions (PDFs) of goals scored are too heavy-tailed to be fitted over their entire ranges by Poisson or negative binomial distributions which would be expected for uncorrelated processes. Log-normal distributions cannot include zero scores and here we find that the PDFs are consistent with those arising from extremal statistics. In addition, we show that it is sufficient to model English top division and FA Cup matches in the seasons of 1970/71-2000/01 on Poisson or negative binomial distributions, as reported in analyses of earlier seasons, and that these are not consistent with extremal statistics.

How time delay and network design shape response patterns in biochemical negative feedback systems.

PubMed

Börsch, Anastasiya; Schaber, Jörg

2016-08-24

Negative feedback in combination with time delay can bring about both sustained oscillations and adaptive behaviour in cellular networks. Here, we study which design features of systems with delayed negative feedback shape characteristic response patterns with special emphasis on the role of time delay. To this end, we analyse generic two-dimensional delay differential equations describing the dynamics of biochemical signal-response networks. We investigate the influence of several design features on the stability of the model equilibrium, i.e., presence of auto-inhibition and/or mass conservation and the kind and/or strength of the delayed negative feedback. We show that auto-inhibition and mass conservation have a stabilizing effect, whereas increasing abruptness and decreasing feedback threshold have a de-stabilizing effect on the model equilibrium. Moreover, applying our theoretical analysis to the mammalian p53 system we show that an auto-inhibitory feedback can decouple period and amplitude of an oscillatory response, whereas the delayed feedback can not. Our theoretical framework provides insight into how time delay and design features of biochemical networks act together to elicit specific characteristic response patterns. Such insight is useful for constructing synthetic networks and controlling their behaviour in response to external stimulation.

Spatiotemporal hurdle models for zero-inflated count data: Exploring trends in emergency department visits.

PubMed

Neelon, Brian; Chang, Howard H; Ling, Qiang; Hastings, Nicole S

2016-12-01

Motivated by a study exploring spatiotemporal trends in emergency department use, we develop a class of two-part hurdle models for the analysis of zero-inflated areal count data. The models consist of two components-one for the probability of any emergency department use and one for the number of emergency department visits given use. Through a hierarchical structure, the models incorporate both patient- and region-level predictors, as well as spatially and temporally correlated random effects for each model component. The random effects are assigned multivariate conditionally autoregressive priors, which induce dependence between the components and provide spatial and temporal smoothing across adjacent spatial units and time periods, resulting in improved inferences. To accommodate potential overdispersion, we consider a range of parametric specifications for the positive counts, including truncated negative binomial and generalized Poisson distributions. We adopt a Bayesian inferential approach, and posterior computation is handled conveniently within standard Bayesian software. Our results indicate that the negative binomial and generalized Poisson hurdle models vastly outperform the Poisson hurdle model, demonstrating that overdispersed hurdle models provide a useful approach to analyzing zero-inflated spatiotemporal data. © The Author(s) 2014.

Statistical models for RNA-seq data derived from a two-condition 48-replicate experiment.

PubMed

Gierliński, Marek; Cole, Christian; Schofield, Pietà; Schurch, Nicholas J; Sherstnev, Alexander; Singh, Vijender; Wrobel, Nicola; Gharbi, Karim; Simpson, Gordon; Owen-Hughes, Tom; Blaxter, Mark; Barton, Geoffrey J

2015-11-15

High-throughput RNA sequencing (RNA-seq) is now the standard method to determine differential gene expression. Identifying differentially expressed genes crucially depends on estimates of read-count variability. These estimates are typically based on statistical models such as the negative binomial distribution, which is employed by the tools edgeR, DESeq and cuffdiff. Until now, the validity of these models has usually been tested on either low-replicate RNA-seq data or simulations. A 48-replicate RNA-seq experiment in yeast was performed and data tested against theoretical models. The observed gene read counts were consistent with both log-normal and negative binomial distributions, while the mean-variance relation followed the line of constant dispersion parameter of ∼0.01. The high-replicate data also allowed for strict quality control and screening of 'bad' replicates, which can drastically affect the gene read-count distribution. RNA-seq data have been submitted to ENA archive with project ID PRJEB5348. g.j.barton@dundee.ac.uk. © The Author 2015. Published by Oxford University Press.

Statistical models for RNA-seq data derived from a two-condition 48-replicate experiment

PubMed Central

Cole, Christian; Schofield, Pietà; Schurch, Nicholas J.; Sherstnev, Alexander; Singh, Vijender; Wrobel, Nicola; Gharbi, Karim; Simpson, Gordon; Owen-Hughes, Tom; Blaxter, Mark; Barton, Geoffrey J.

2015-01-01

Motivation: High-throughput RNA sequencing (RNA-seq) is now the standard method to determine differential gene expression. Identifying differentially expressed genes crucially depends on estimates of read-count variability. These estimates are typically based on statistical models such as the negative binomial distribution, which is employed by the tools edgeR, DESeq and cuffdiff. Until now, the validity of these models has usually been tested on either low-replicate RNA-seq data or simulations. Results: A 48-replicate RNA-seq experiment in yeast was performed and data tested against theoretical models. The observed gene read counts were consistent with both log-normal and negative binomial distributions, while the mean-variance relation followed the line of constant dispersion parameter of ∼0.01. The high-replicate data also allowed for strict quality control and screening of ‘bad’ replicates, which can drastically affect the gene read-count distribution. Availability and implementation: RNA-seq data have been submitted to ENA archive with project ID PRJEB5348. Contact: g.j.barton@dundee.ac.uk PMID:26206307

Some considerations for excess zeroes in substance abuse research.

PubMed

Bandyopadhyay, Dipankar; DeSantis, Stacia M; Korte, Jeffrey E; Brady, Kathleen T

2011-09-01

Count data collected in substance abuse research often come with an excess of "zeroes," which are typically handled using zero-inflated regression models. However, there is a need to consider the design aspects of those studies before using such a statistical model to ascertain the sources of zeroes. We sought to illustrate hurdle models as alternatives to zero-inflated models to validate a two-stage decision-making process in situations of "excess zeroes." We use data from a study of 45 cocaine-dependent subjects where the primary scientific question was to evaluate whether study participation influences drug-seeking behavior. The outcome, "the frequency (count) of cocaine use days per week," is bounded (ranging from 0 to 7). We fit and compare binomial, Poisson, negative binomial, and the hurdle version of these models to study the effect of gender, age, time, and study participation on cocaine use. The hurdle binomial model provides the best fit. Gender and time are not predictive of use. Higher odds of use versus no use are associated with age; however once use is experienced, odds of further use decrease with increase in age. Participation was associated with higher odds of no-cocaine use; once there is use, participation reduced the odds of further use. Age and study participation are significantly predictive of cocaine-use behavior. The two-stage decision process as modeled by a hurdle binomial model (appropriate for bounded count data with excess zeroes) provides interesting insights into the study of covariate effects on count responses of substance use, when all enrolled subjects are believed to be "at-risk" of use.

A Time Series Analysis: Weather Factors, Human Migration and Malaria Cases in Endemic Area of Purworejo, Indonesia, 2005–2014

PubMed Central

REJEKI, Dwi Sarwani Sri; NURHAYATI, Nunung; AJI, Budi; MURHANDARWATI, E. Elsa Herdiana; KUSNANTO, Hari

2018-01-01

Background: Climatic and weather factors become important determinants of vector-borne diseases transmission like malaria. This study aimed to prove relationships between weather factors with considering human migration and previous case findings and malaria cases in endemic areas in Purworejo during 2005–2014. Methods: This study employed ecological time series analysis by using monthly data. The independent variables were the maximum temperature, minimum temperature, maximum humidity, minimum humidity, precipitation, human migration, and previous malaria cases, while the dependent variable was positive malaria cases. Three models of count data regression analysis i.e. Poisson model, quasi-Poisson model, and negative binomial model were applied to measure the relationship. The least Akaike Information Criteria (AIC) value was also performed to find the best model. Negative binomial regression analysis was considered as the best model. Results: The model showed that humidity (lag 2), precipitation (lag 3), precipitation (lag 12), migration (lag1) and previous malaria cases (lag 12) had a significant relationship with malaria cases. Conclusion: Weather, migration and previous malaria cases factors need to be considered as prominent indicators for the increase of malaria case projection. PMID:29900134

QNB: differential RNA methylation analysis for count-based small-sample sequencing data with a quad-negative binomial model.

PubMed

Liu, Lian; Zhang, Shao-Wu; Huang, Yufei; Meng, Jia

2017-08-31

As a newly emerged research area, RNA epigenetics has drawn increasing attention recently for the participation of RNA methylation and other modifications in a number of crucial biological processes. Thanks to high throughput sequencing techniques, such as, MeRIP-Seq, transcriptome-wide RNA methylation profile is now available in the form of count-based data, with which it is often of interests to study the dynamics at epitranscriptomic layer. However, the sample size of RNA methylation experiment is usually very small due to its costs; and additionally, there usually exist a large number of genes whose methylation level cannot be accurately estimated due to their low expression level, making differential RNA methylation analysis a difficult task. We present QNB, a statistical approach for differential RNA methylation analysis with count-based small-sample sequencing data. Compared with previous approaches such as DRME model based on a statistical test covering the IP samples only with 2 negative binomial distributions, QNB is based on 4 independent negative binomial distributions with their variances and means linked by local regressions, and in the way, the input control samples are also properly taken care of. In addition, different from DRME approach, which relies only the input control sample only for estimating the background, QNB uses a more robust estimator for gene expression by combining information from both input and IP samples, which could largely improve the testing performance for very lowly expressed genes. QNB showed improved performance on both simulated and real MeRIP-Seq datasets when compared with competing algorithms. And the QNB model is also applicable to other datasets related RNA modifications, including but not limited to RNA bisulfite sequencing, m 1 A-Seq, Par-CLIP, RIP-Seq, etc.

Regular exercise and related factors in patients with Parkinson's disease: Applying zero-inflated negative binomial modeling of exercise count data.

PubMed

Lee, JuHee; Park, Chang Gi; Choi, Moonki

2016-05-01

This study was conducted to identify risk factors that influence regular exercise among patients with Parkinson's disease in Korea. Parkinson's disease is prevalent in the elderly, and may lead to a sedentary lifestyle. Exercise can enhance physical and psychological health. However, patients with Parkinson's disease are less likely to exercise than are other populations due to physical disability. A secondary data analysis and cross-sectional descriptive study were conducted. A convenience sample of 106 patients with Parkinson's disease was recruited at an outpatient neurology clinic of a tertiary hospital in Korea. Demographic characteristics, disease-related characteristics (including disease duration and motor symptoms), self-efficacy for exercise, balance, and exercise level were investigated. Negative binomial regression and zero-inflated negative binomial regression for exercise count data were utilized to determine factors involved in exercise. The mean age of participants was 65.85 ± 8.77 years, and the mean duration of Parkinson's disease was 7.23 ± 6.02 years. Most participants indicated that they engaged in regular exercise (80.19%). Approximately half of participants exercised at least 5 days per week for 30 min, as recommended (51.9%). Motor symptoms were a significant predictor of exercise in the count model, and self-efficacy for exercise was a significant predictor of exercise in the zero model. Severity of motor symptoms was related to frequency of exercise. Self-efficacy contributed to the probability of exercise. Symptom management and improvement of self-efficacy for exercise are important to encourage regular exercise in patients with Parkinson's disease. Copyright © 2015 Elsevier Inc. All rights reserved.

A Model Comparison for Count Data with a Positively Skewed Distribution with an Application to the Number of University Mathematics Courses Completed

ERIC Educational Resources Information Center

Liou, Pey-Yan

2009-01-01

The current study examines three regression models: OLS (ordinary least square) linear regression, Poisson regression, and negative binomial regression for analyzing count data. Simulation results show that the OLS regression model performed better than the others, since it did not produce more false statistically significant relationships than…

A Negative Binomial Regression Model for Accuracy Tests

ERIC Educational Resources Information Center

Hung, Lai-Fa

2012-01-01

Rasch used a Poisson model to analyze errors and speed in reading tests. An important property of the Poisson distribution is that the mean and variance are equal. However, in social science research, it is very common for the variance to be greater than the mean (i.e., the data are overdispersed). This study embeds the Rasch model within an…

Forecasting asthma-related hospital admissions in London using negative binomial models.

PubMed

Soyiri, Ireneous N; Reidpath, Daniel D; Sarran, Christophe

2013-05-01

Health forecasting can improve health service provision and individual patient outcomes. Environmental factors are known to impact chronic respiratory conditions such as asthma, but little is known about the extent to which these factors can be used for forecasting. Using weather, air quality and hospital asthma admissions, in London (2005-2006), two related negative binomial models were developed and compared with a naive seasonal model. In the first approach, predictive forecasting models were fitted with 7-day averages of each potential predictor, and then a subsequent multivariable model is constructed. In the second strategy, an exhaustive search of the best fitting models between possible combinations of lags (0-14 days) of all the environmental effects on asthma admission was conducted. Three models were considered: a base model (seasonal effects), contrasted with a 7-day average model and a selected lags model (weather and air quality effects). Season is the best predictor of asthma admissions. The 7-day average and seasonal models were trivial to implement. The selected lags model was computationally intensive, but of no real value over much more easily implemented models. Seasonal factors can predict daily hospital asthma admissions in London, and there is a little evidence that additional weather and air quality information would add to forecast accuracy.

Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data

PubMed Central

Li, Jun; Tibshirani, Robert

2015-01-01

We discuss the identification of features that are associated with an outcome in RNA-Sequencing (RNA-Seq) and other sequencing-based comparative genomic experiments. RNA-Seq data takes the form of counts, so models based on the normal distribution are generally unsuitable. The problem is especially challenging because different sequencing experiments may generate quite different total numbers of reads, or ‘sequencing depths’. Existing methods for this problem are based on Poisson or negative binomial models: they are useful but can be heavily influenced by ‘outliers’ in the data. We introduce a simple, nonparametric method with resampling to account for the different sequencing depths. The new method is more robust than parametric methods. It can be applied to data with quantitative, survival, two-class or multiple-class outcomes. We compare our proposed method to Poisson and negative binomial-based methods in simulated and real data sets, and find that our method discovers more consistent patterns than competing methods. PMID:22127579

Meta-analysis of studies with bivariate binary outcomes: a marginal beta-binomial model approach

PubMed Central

Chen, Yong; Hong, Chuan; Ning, Yang; Su, Xiao

2018-01-01

When conducting a meta-analysis of studies with bivariate binary outcomes, challenges arise when the within-study correlation and between-study heterogeneity should be taken into account. In this paper, we propose a marginal beta-binomial model for the meta-analysis of studies with binary outcomes. This model is based on the composite likelihood approach, and has several attractive features compared to the existing models such as bivariate generalized linear mixed model (Chu and Cole, 2006) and Sarmanov beta-binomial model (Chen et al., 2012). The advantages of the proposed marginal model include modeling the probabilities in the original scale, not requiring any transformation of probabilities or any link function, having closed-form expression of likelihood function, and no constraints on the correlation parameter. More importantly, since the marginal beta-binomial model is only based on the marginal distributions, it does not suffer from potential misspecification of the joint distribution of bivariate study-specific probabilities. Such misspecification is difficult to detect and can lead to biased inference using currents methods. We compare the performance of the marginal beta-binomial model with the bivariate generalized linear mixed model and the Sarmanov beta-binomial model by simulation studies. Interestingly, the results show that the marginal beta-binomial model performs better than the Sarmanov beta-binomial model, whether or not the true model is Sarmanov beta-binomial, and the marginal beta-binomial model is more robust than the bivariate generalized linear mixed model under model misspecifications. Two meta-analyses of diagnostic accuracy studies and a meta-analysis of case-control studies are conducted for illustration. PMID:26303591

Socio-environmental predictors of Barmah forest virus transmission in coastal areas, Queensland, Australia.

PubMed

Naish, Suchithra; Hu, Wenbiao; Nicholls, Neville; Mackenzie, John S; Dale, Pat; McMichael, Anthony J; Tong, Shilu

2009-02-01

To assess the socio-environmental predictors of Barmah forest virus (BFV) transmission in coastal areas, Queensland, Australia. Data on BFV notified cases, climate, tidal levels and socioeconomic index for area (SEIFA) in six coastal cities, Queensland, for the period 1992-2001 were obtained from the relevant government agencies. Negative binomial regression models were used to assess the socio-environmental predictors of BFV transmission. The results show that maximum and minimum temperature, rainfall, relative humidity, high and low tide were statistically significantly associated with BFV incidence at lags 0-2 months. The fitted negative binomial regression models indicate a significant independent association of each of maximum temperature (beta = 0.139, P = 0.000), high tide (beta = 0.005, P = 0.000) and SEIFA index (beta = -0.010, P = 0.000) with BFV transmission after adjustment for confounding variables. The transmission of BFV disease in Queensland coastal areas seemed to be determined by a combination of local social and environmental factors. The model developed in this study may have applications in the control and prevention of BFV disease in these areas.

Mental health status and healthcare utilization among community dwelling older adults.

PubMed

Adepoju, Omolola; Lin, Szu-Hsuan; Mileski, Michael; Kruse, Clemens Scott; Mask, Andrew

2018-04-27

Shifts in mental health utilization patterns are necessary to allow for meaningful access to care for vulnerable populations. There have been long standing issues in how mental health is provided, which has caused problems in that care being efficacious for those seeking it. To assess the relationship between mental health status and healthcare utilization among adults ≥65 years. A negative binomial regression model was used to assess the relationship between mental health status and healthcare utilization related to office-based physician visits, while a two-part model, consisting of logistic regression and negative binomial regression, was used to separately model emergency visits and inpatient services. The receipt of care in office-based settings were marginally higher for subjects with mental health difficulties. Both probabilities and counts of inpatient hospitalizations were similar across mental health categories. The count of ER visits was similar across mental health categories; however, the probability of having an emergency department visit was marginally higher for older adults who reported mental health difficulties in 2012. These findings are encouraging and lend promise to the recent initiatives on addressing gaps in mental healthcare services.

Modeling left-turn crash occurrence at signalized intersections by conflicting patterns.

PubMed

Wang, Xuesong; Abdel-Aty, Mohamed

2008-01-01

In order to better understand the underlying crash mechanisms, left-turn crashes occurring at 197 four-legged signalized intersections over 6 years were classified into nine patterns based on vehicle maneuvers and then were assigned to intersection approaches. Crash frequency of each pattern was modeled at the approach level by mainly using Generalized Estimating Equations (GEE) with the Negative Binomial as the link function to account for the correlation among the crash data. GEE with a binomial logit link function was also applied for patterns with fewer crashes. The Cumulative Residuals test shows that, for correlated left-turn crashes, GEE models usually outperformed basic Negative Binomial models. The estimation results show that there are obvious differences in the factors that cause the occurrence of different left-turn collision patterns. For example, for each pattern, the traffic flows to which the colliding vehicles belong are identified to be significant. The width of the crossing distance (represented by the number of through lanes on the opposing approach of the left-turning traffic) is associated with more left-turn traffic colliding with opposing through traffic (Pattern 5), but with less left-turning traffic colliding with near-side crossing through traffic (Pattern 8). The safety effectiveness of the left-turning signal is not consistent for different crash patterns; "protected" phasing is correlated with fewer Pattern 5 crashes, but with more Pattern 8 crashes. The study indicates that in order to develop efficient countermeasures for left-turn crashes and improve safety at signalized intersections, left-turn crashes should be considered in different patterns.

Variable selection for zero-inflated and overdispersed data with application to health care demand in Germany.

PubMed

Wang, Zhu; Ma, Shuangge; Wang, Ching-Yun

2015-09-01

In health services and outcome research, count outcomes are frequently encountered and often have a large proportion of zeros. The zero-inflated negative binomial (ZINB) regression model has important applications for this type of data. With many possible candidate risk factors, this paper proposes new variable selection methods for the ZINB model. We consider maximum likelihood function plus a penalty including the least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD), and minimax concave penalty (MCP). An EM (expectation-maximization) algorithm is proposed for estimating the model parameters and conducting variable selection simultaneously. This algorithm consists of estimating penalized weighted negative binomial models and penalized logistic models via the coordinated descent algorithm. Furthermore, statistical properties including the standard error formulae are provided. A simulation study shows that the new algorithm not only has more accurate or at least comparable estimation, but also is more robust than the traditional stepwise variable selection. The proposed methods are applied to analyze the health care demand in Germany using the open-source R package mpath. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Yes, the GIGP Really Does Work--And Is Workable!

ERIC Educational Resources Information Center

Burrell, Quentin L.; Fenton, Michael R.

1993-01-01

Discusses the generalized inverse Gaussian-Poisson (GIGP) process for informetric modeling. Negative binomial distribution is discussed, construction of the GIGP process is explained, zero-truncated GIGP is considered, and applications of the process with journals, library circulation statistics, and database index terms are described. (50…

Negative Binomial Process Count and Mixture Modeling.

PubMed

Zhou, Mingyuan; Carin, Lawrence

2015-02-01

The seemingly disjoint problems of count and mixture modeling are united under the negative binomial (NB) process. A gamma process is employed to model the rate measure of a Poisson process, whose normalization provides a random probability measure for mixture modeling and whose marginalization leads to an NB process for count modeling. A draw from the NB process consists of a Poisson distributed finite number of distinct atoms, each of which is associated with a logarithmic distributed number of data samples. We reveal relationships between various count- and mixture-modeling distributions and construct a Poisson-logarithmic bivariate distribution that connects the NB and Chinese restaurant table distributions. Fundamental properties of the models are developed, and we derive efficient Bayesian inference. It is shown that with augmentation and normalization, the NB process and gamma-NB process can be reduced to the Dirichlet process and hierarchical Dirichlet process, respectively. These relationships highlight theoretical, structural, and computational advantages of the NB process. A variety of NB processes, including the beta-geometric, beta-NB, marked-beta-NB, marked-gamma-NB and zero-inflated-NB processes, with distinct sharing mechanisms, are also constructed. These models are applied to topic modeling, with connections made to existing algorithms under Poisson factor analysis. Example results show the importance of inferring both the NB dispersion and probability parameters.

Goodness-of-fit tests and model diagnostics for negative binomial regression of RNA sequencing data.

PubMed

Mi, Gu; Di, Yanming; Schafer, Daniel W

2015-01-01

This work is about assessing model adequacy for negative binomial (NB) regression, particularly (1) assessing the adequacy of the NB assumption, and (2) assessing the appropriateness of models for NB dispersion parameters. Tools for the first are appropriate for NB regression generally; those for the second are primarily intended for RNA sequencing (RNA-Seq) data analysis. The typically small number of biological samples and large number of genes in RNA-Seq analysis motivate us to address the trade-offs between robustness and statistical power using NB regression models. One widely-used power-saving strategy, for example, is to assume some commonalities of NB dispersion parameters across genes via simple models relating them to mean expression rates, and many such models have been proposed. As RNA-Seq analysis is becoming ever more popular, it is appropriate to make more thorough investigations into power and robustness of the resulting methods, and into practical tools for model assessment. In this article, we propose simulation-based statistical tests and diagnostic graphics to address model adequacy. We provide simulated and real data examples to illustrate that our proposed methods are effective for detecting the misspecification of the NB mean-variance relationship as well as judging the adequacy of fit of several NB dispersion models.

Meta-analysis of studies with bivariate binary outcomes: a marginal beta-binomial model approach.

PubMed

Chen, Yong; Hong, Chuan; Ning, Yang; Su, Xiao

2016-01-15

When conducting a meta-analysis of studies with bivariate binary outcomes, challenges arise when the within-study correlation and between-study heterogeneity should be taken into account. In this paper, we propose a marginal beta-binomial model for the meta-analysis of studies with binary outcomes. This model is based on the composite likelihood approach and has several attractive features compared with the existing models such as bivariate generalized linear mixed model (Chu and Cole, 2006) and Sarmanov beta-binomial model (Chen et al., 2012). The advantages of the proposed marginal model include modeling the probabilities in the original scale, not requiring any transformation of probabilities or any link function, having closed-form expression of likelihood function, and no constraints on the correlation parameter. More importantly, because the marginal beta-binomial model is only based on the marginal distributions, it does not suffer from potential misspecification of the joint distribution of bivariate study-specific probabilities. Such misspecification is difficult to detect and can lead to biased inference using currents methods. We compare the performance of the marginal beta-binomial model with the bivariate generalized linear mixed model and the Sarmanov beta-binomial model by simulation studies. Interestingly, the results show that the marginal beta-binomial model performs better than the Sarmanov beta-binomial model, whether or not the true model is Sarmanov beta-binomial, and the marginal beta-binomial model is more robust than the bivariate generalized linear mixed model under model misspecifications. Two meta-analyses of diagnostic accuracy studies and a meta-analysis of case-control studies are conducted for illustration. Copyright © 2015 John Wiley & Sons, Ltd.

Accounting for non-independent detection when estimating abundance of organisms with a Bayesian approach

USGS Publications Warehouse

Martin, Julien; Royle, J. Andrew; MacKenzie, Darryl I.; Edwards, Holly H.; Kery, Marc; Gardner, Beth

2011-01-01

Summary 1. Binomial mixture models use repeated count data to estimate abundance. They are becoming increasingly popular because they provide a simple and cost-effective way to account for imperfect detection. However, these models assume that individuals are detected independently of each other. This assumption may often be violated in the field. For instance, manatees (Trichechus manatus latirostris) may surface in turbid water (i.e. become available for detection during aerial surveys) in a correlated manner (i.e. in groups). However, correlated behaviour, affecting the non-independence of individual detections, may also be relevant in other systems (e.g. correlated patterns of singing in birds and amphibians). 2. We extend binomial mixture models to account for correlated behaviour and therefore to account for non-independent detection of individuals. We simulated correlated behaviour using beta-binomial random variables. Our approach can be used to simultaneously estimate abundance, detection probability and a correlation parameter. 3. Fitting binomial mixture models to data that followed a beta-binomial distribution resulted in an overestimation of abundance even for moderate levels of correlation. In contrast, the beta-binomial mixture model performed considerably better in our simulation scenarios. We also present a goodness-of-fit procedure to evaluate the fit of beta-binomial mixture models. 4. We illustrate our approach by fitting both binomial and beta-binomial mixture models to aerial survey data of manatees in Florida. We found that the binomial mixture model did not fit the data, whereas there was no evidence of lack of fit for the beta-binomial mixture model. This example helps illustrate the importance of using simulations and assessing goodness-of-fit when analysing ecological data with N-mixture models. Indeed, both the simulations and the goodness-of-fit procedure highlighted the limitations of the standard binomial mixture model for aerial manatee surveys. 5. Overestimation of abundance by binomial mixture models owing to non-independent detections is problematic for ecological studies, but also for conservation. For example, in the case of endangered species, it could lead to inappropriate management decisions, such as downlisting. These issues will be increasingly relevant as more ecologists apply flexible N-mixture models to ecological data.

[Evaluation of estimation of prevalence ratio using bayesian log-binomial regression model].

PubMed

Gao, W L; Lin, H; Liu, X N; Ren, X W; Li, J S; Shen, X P; Zhu, S L

2017-03-10

To evaluate the estimation of prevalence ratio ( PR ) by using bayesian log-binomial regression model and its application, we estimated the PR of medical care-seeking prevalence to caregivers' recognition of risk signs of diarrhea in their infants by using bayesian log-binomial regression model in Openbugs software. The results showed that caregivers' recognition of infant' s risk signs of diarrhea was associated significantly with a 13% increase of medical care-seeking. Meanwhile, we compared the differences in PR 's point estimation and its interval estimation of medical care-seeking prevalence to caregivers' recognition of risk signs of diarrhea and convergence of three models (model 1: not adjusting for the covariates; model 2: adjusting for duration of caregivers' education, model 3: adjusting for distance between village and township and child month-age based on model 2) between bayesian log-binomial regression model and conventional log-binomial regression model. The results showed that all three bayesian log-binomial regression models were convergence and the estimated PRs were 1.130(95 %CI : 1.005-1.265), 1.128(95 %CI : 1.001-1.264) and 1.132(95 %CI : 1.004-1.267), respectively. Conventional log-binomial regression model 1 and model 2 were convergence and their PRs were 1.130(95 % CI : 1.055-1.206) and 1.126(95 % CI : 1.051-1.203), respectively, but the model 3 was misconvergence, so COPY method was used to estimate PR , which was 1.125 (95 %CI : 1.051-1.200). In addition, the point estimation and interval estimation of PRs from three bayesian log-binomial regression models differed slightly from those of PRs from conventional log-binomial regression model, but they had a good consistency in estimating PR . Therefore, bayesian log-binomial regression model can effectively estimate PR with less misconvergence and have more advantages in application compared with conventional log-binomial regression model.

Predicting Children's Asthma Hospitalizations: Rural and Urban Differences in Texas

ERIC Educational Resources Information Center

Grineski, Sara E.

2009-01-01

Asthma is the number one chronic health condition facing children today; however, little is known about rural-urban inequalities in asthma. This "area effects on health" study examines rural-urban differences in childhood asthma hospitalizations within the state of Texas using negative binomial regression models. Effects associated with…

Transportation safety data and analysis : Volume 2, Calibration of the highway safety manual and development of new safety performance functions.

DOT National Transportation Integrated Search

2011-03-01

This report documents the calibration of the Highway Safety Manual (HSM) safety performance function (SPF) : for rural two-lane two-way roadway segments in Utah and the development of new models using negative : binomial and hierarchical Bayesian mod...

Predictive accuracy of particle filtering in dynamic models supporting outbreak projections.

PubMed

Safarishahrbijari, Anahita; Teyhouee, Aydin; Waldner, Cheryl; Liu, Juxin; Osgood, Nathaniel D

2017-09-26

While a new generation of computational statistics algorithms and availability of data streams raises the potential for recurrently regrounding dynamic models with incoming observations, the effectiveness of such arrangements can be highly subject to specifics of the configuration (e.g., frequency of sampling and representation of behaviour change), and there has been little attempt to identify effective configurations. Combining dynamic models with particle filtering, we explored a solution focusing on creating quickly formulated models regrounded automatically and recurrently as new data becomes available. Given a latent underlying case count, we assumed that observed incident case counts followed a negative binomial distribution. In accordance with the condensation algorithm, each such observation led to updating of particle weights. We evaluated the effectiveness of various particle filtering configurations against each other and against an approach without particle filtering according to the accuracy of the model in predicting future prevalence, given data to a certain point and a norm-based discrepancy metric. We examined the effectiveness of particle filtering under varying times between observations, negative binomial dispersion parameters, and rates with which the contact rate could evolve. We observed that more frequent observations of empirical data yielded super-linearly improved accuracy in model predictions. We further found that for the data studied here, the most favourable assumptions to make regarding the parameters associated with the negative binomial distribution and changes in contact rate were robust across observation frequency and the observation point in the outbreak. Combining dynamic models with particle filtering can perform well in projecting future evolution of an outbreak. Most importantly, the remarkable improvements in predictive accuracy resulting from more frequent sampling suggest that investments to achieve efficient reporting mechanisms may be more than paid back by improved planning capacity. The robustness of the results on particle filter configuration in this case study suggests that it may be possible to formulate effective standard guidelines and regularized approaches for such techniques in particular epidemiological contexts. Most importantly, the work tentatively suggests potential for health decision makers to secure strong guidance when anticipating outbreak evolution for emerging infectious diseases by combining even very rough models with particle filtering method.

Population heterogeneity in the salience of multiple risk factors for adolescent delinquency.

PubMed

Lanza, Stephanie T; Cooper, Brittany R; Bray, Bethany C

2014-03-01

To present mixture regression analysis as an alternative to more standard regression analysis for predicting adolescent delinquency. We demonstrate how mixture regression analysis allows for the identification of population subgroups defined by the salience of multiple risk factors. We identified population subgroups (i.e., latent classes) of individuals based on their coefficients in a regression model predicting adolescent delinquency from eight previously established risk indices drawn from the community, school, family, peer, and individual levels. The study included N = 37,763 10th-grade adolescents who participated in the Communities That Care Youth Survey. Standard, zero-inflated, and mixture Poisson and negative binomial regression models were considered. Standard and mixture negative binomial regression models were selected as optimal. The five-class regression model was interpreted based on the class-specific regression coefficients, indicating that risk factors had varying salience across classes of adolescents. Standard regression showed that all risk factors were significantly associated with delinquency. Mixture regression provided more nuanced information, suggesting a unique set of risk factors that were salient for different subgroups of adolescents. Implications for the design of subgroup-specific interventions are discussed. Copyright © 2014 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Finite mixture modeling approach for developing crash modification factors in highway safety analysis.

PubMed

Park, Byung-Jung; Lord, Dominique; Wu, Lingtao

2016-10-28

This study aimed to investigate the relative performance of two models (negative binomial (NB) model and two-component finite mixture of negative binomial models (FMNB-2)) in terms of developing crash modification factors (CMFs). Crash data on rural multilane divided highways in California and Texas were modeled with the two models, and crash modification functions (CMFunctions) were derived. The resultant CMFunction estimated from the FMNB-2 model showed several good properties over that from the NB model. First, the safety effect of a covariate was better reflected by the CMFunction developed using the FMNB-2 model, since the model takes into account the differential responsiveness of crash frequency to the covariate. Second, the CMFunction derived from the FMNB-2 model is able to capture nonlinear relationships between covariate and safety. Finally, following the same concept as those for NB models, the combined CMFs of multiple treatments were estimated using the FMNB-2 model. The results indicated that they are not the simple multiplicative of single ones (i.e., their safety effects are not independent under FMNB-2 models). Adjustment Factors (AFs) were then developed. It is revealed that current Highway Safety Manual's method could over- or under-estimate the combined CMFs under particular combination of covariates. Safety analysts are encouraged to consider using the FMNB-2 models for developing CMFs and AFs. Copyright © 2016 Elsevier Ltd. All rights reserved.

A comparison of observation-level random effect and Beta-Binomial models for modelling overdispersion in Binomial data in ecology & evolution.

PubMed

Harrison, Xavier A

2015-01-01

Overdispersion is a common feature of models of biological data, but researchers often fail to model the excess variation driving the overdispersion, resulting in biased parameter estimates and standard errors. Quantifying and modeling overdispersion when it is present is therefore critical for robust biological inference. One means to account for overdispersion is to add an observation-level random effect (OLRE) to a model, where each data point receives a unique level of a random effect that can absorb the extra-parametric variation in the data. Although some studies have investigated the utility of OLRE to model overdispersion in Poisson count data, studies doing so for Binomial proportion data are scarce. Here I use a simulation approach to investigate the ability of both OLRE models and Beta-Binomial models to recover unbiased parameter estimates in mixed effects models of Binomial data under various degrees of overdispersion. In addition, as ecologists often fit random intercept terms to models when the random effect sample size is low (<5 levels), I investigate the performance of both model types under a range of random effect sample sizes when overdispersion is present. Simulation results revealed that the efficacy of OLRE depends on the process that generated the overdispersion; OLRE failed to cope with overdispersion generated from a Beta-Binomial mixture model, leading to biased slope and intercept estimates, but performed well for overdispersion generated by adding random noise to the linear predictor. Comparison of parameter estimates from an OLRE model with those from its corresponding Beta-Binomial model readily identified when OLRE were performing poorly due to disagreement between effect sizes, and this strategy should be employed whenever OLRE are used for Binomial data to assess their reliability. Beta-Binomial models performed well across all contexts, but showed a tendency to underestimate effect sizes when modelling non-Beta-Binomial data. Finally, both OLRE and Beta-Binomial models performed poorly when models contained <5 levels of the random intercept term, especially for estimating variance components, and this effect appeared independent of total sample size. These results suggest that OLRE are a useful tool for modelling overdispersion in Binomial data, but that they do not perform well in all circumstances and researchers should take care to verify the robustness of parameter estimates of OLRE models.

Accident prediction model for public highway-rail grade crossings.

PubMed

Lu, Pan; Tolliver, Denver

2016-05-01

Considerable research has focused on roadway accident frequency analysis, but relatively little research has examined safety evaluation at highway-rail grade crossings. Highway-rail grade crossings are critical spatial locations of utmost importance for transportation safety because traffic crashes at highway-rail grade crossings are often catastrophic with serious consequences. The Poisson regression model has been employed to analyze vehicle accident frequency as a good starting point for many years. The most commonly applied variations of Poisson including negative binomial, and zero-inflated Poisson. These models are used to deal with common crash data issues such as over-dispersion (sample variance is larger than the sample mean) and preponderance of zeros (low sample mean and small sample size). On rare occasions traffic crash data have been shown to be under-dispersed (sample variance is smaller than the sample mean) and traditional distributions such as Poisson or negative binomial cannot handle under-dispersion well. The objective of this study is to investigate and compare various alternate highway-rail grade crossing accident frequency models that can handle the under-dispersion issue. The contributions of the paper are two-fold: (1) application of probability models to deal with under-dispersion issues and (2) obtain insights regarding to vehicle crashes at public highway-rail grade crossings. Copyright © 2016 Elsevier Ltd. All rights reserved.

Technical and biological variance structure in mRNA-Seq data: life in the real world

PubMed Central

2012-01-01

Background mRNA expression data from next generation sequencing platforms is obtained in the form of counts per gene or exon. Counts have classically been assumed to follow a Poisson distribution in which the variance is equal to the mean. The Negative Binomial distribution which allows for over-dispersion, i.e., for the variance to be greater than the mean, is commonly used to model count data as well. Results In mRNA-Seq data from 25 subjects, we found technical variation to generally follow a Poisson distribution as has been reported previously and biological variability was over-dispersed relative to the Poisson model. The mean-variance relationship across all genes was quadratic, in keeping with a Negative Binomial (NB) distribution. Over-dispersed Poisson and NB distributional assumptions demonstrated marked improvements in goodness-of-fit (GOF) over the standard Poisson model assumptions, but with evidence of over-fitting in some genes. Modeling of experimental effects improved GOF for high variance genes but increased the over-fitting problem. Conclusions These conclusions will guide development of analytical strategies for accurate modeling of variance structure in these data and sample size determination which in turn will aid in the identification of true biological signals that inform our understanding of biological systems. PMID:22769017

The effect of a major cigarette price change on smoking behavior in california: a zero-inflated negative binomial model.

PubMed

Sheu, Mei-Ling; Hu, Teh-Wei; Keeler, Theodore E; Ong, Michael; Sung, Hai-Yen

2004-08-01

The objective of this paper is to determine the price sensitivity of smokers in their consumption of cigarettes, using evidence from a major increase in California cigarette prices due to Proposition 10 and the Tobacco Settlement. The study sample consists of individual survey data from Behavioral Risk Factor Survey (BRFS) and price data from the Bureau of Labor Statistics between 1996 and 1999. A zero-inflated negative binomial (ZINB) regression model was applied for the statistical analysis. The statistical model showed that price did not have an effect on reducing the estimated prevalence of smoking. However, it indicated that among smokers the price elasticity was at the level of -0.46 and statistically significant. Since smoking prevalence is significantly lower than it was a decade ago, price increases are becoming less effective as an inducement for hard-core smokers to quit, although they may respond by decreasing consumption. For those who only smoke occasionally (many of them being young adults) price increases alone may not be an effective inducement to quit smoking. Additional underlying behavioral factors need to be identified so that more effective anti-smoking strategies can be developed.

Multi-level Bayesian safety analysis with unprocessed Automatic Vehicle Identification data for an urban expressway.

PubMed

Shi, Qi; Abdel-Aty, Mohamed; Yu, Rongjie

2016-03-01

In traffic safety studies, crash frequency modeling of total crashes is the cornerstone before proceeding to more detailed safety evaluation. The relationship between crash occurrence and factors such as traffic flow and roadway geometric characteristics has been extensively explored for a better understanding of crash mechanisms. In this study, a multi-level Bayesian framework has been developed in an effort to identify the crash contributing factors on an urban expressway in the Central Florida area. Two types of traffic data from the Automatic Vehicle Identification system, which are the processed data capped at speed limit and the unprocessed data retaining the original speed were incorporated in the analysis along with road geometric information. The model framework was proposed to account for the hierarchical data structure and the heterogeneity among the traffic and roadway geometric data. Multi-level and random parameters models were constructed and compared with the Negative Binomial model under the Bayesian inference framework. Results showed that the unprocessed traffic data was superior. Both multi-level models and random parameters models outperformed the Negative Binomial model and the models with random parameters achieved the best model fitting. The contributing factors identified imply that on the urban expressway lower speed and higher speed variation could significantly increase the crash likelihood. Other geometric factors were significant including auxiliary lanes and horizontal curvature. Copyright © 2015 Elsevier Ltd. All rights reserved.

A time series model: First-order integer-valued autoregressive (INAR(1))

NASA Astrophysics Data System (ADS)

Simarmata, D. M.; Novkaniza, F.; Widyaningsih, Y.

2017-07-01

Nonnegative integer-valued time series arises in many applications. A time series model: first-order Integer-valued AutoRegressive (INAR(1)) is constructed by binomial thinning operator to model nonnegative integer-valued time series. INAR (1) depends on one period from the process before. The parameter of the model can be estimated by Conditional Least Squares (CLS). Specification of INAR(1) is following the specification of (AR(1)). Forecasting in INAR(1) uses median or Bayesian forecasting methodology. Median forecasting methodology obtains integer s, which is cumulative density function (CDF) until s, is more than or equal to 0.5. Bayesian forecasting methodology forecasts h-step-ahead of generating the parameter of the model and parameter of innovation term using Adaptive Rejection Metropolis Sampling within Gibbs sampling (ARMS), then finding the least integer s, where CDF until s is more than or equal to u . u is a value taken from the Uniform(0,1) distribution. INAR(1) is applied on pneumonia case in Penjaringan, Jakarta Utara, January 2008 until April 2016 monthly.

[Spatial epidemiological study on malaria epidemics in Hainan province].

PubMed

Wen, Liang; Shi, Run-He; Fang, Li-Qun; Xu, De-Zhong; Li, Cheng-Yi; Wang, Yong; Yuan, Zheng-Quan; Zhang, Hui

2008-06-01

To better understand the characteristics of spatial distribution of malaria epidemics in Hainan province and to explore the relationship between malaria epidemics and environmental factors, as well to develop prediction model on malaria epidemics. Data on Malaria and meteorological factors were collected in all 19 counties in Hainan province from May to Oct., 2000, and the proportion of land use types of these counties in this period were extracted from digital map of land use in Hainan province. Land surface temperatures (LST) were extracted from MODIS images and elevations of these counties were extracted from DEM of Hainan province. The coefficients of correlation of malaria incidences and these environmental factors were then calculated with SPSS 13.0, and negative binomial regression analysis were done using SAS 9.0. The incidence of malaria showed (1) positive correlations to elevation, proportion of forest land area and grassland area; (2) negative correlations to the proportion of cultivated area, urban and rural residents and to industrial enterprise area, LST; (3) no correlations to meteorological factors, proportion of water area, and unemployed land area. The prediction model of malaria which came from negative binomial regression analysis was: I (monthly, unit: 1/1,000,000) = exp (-1.672-0.399xLST). Spatial distribution of malaria epidemics was associated with some environmental factors, and prediction model of malaria epidemic could be developed with indexes which extracted from satellite remote sensing images.

Modeling the distribution of colonial species to improve estimation of plankton concentration in ballast water

NASA Astrophysics Data System (ADS)

Rajakaruna, Harshana; VandenByllaardt, Julie; Kydd, Jocelyn; Bailey, Sarah

2018-03-01

The International Maritime Organization (IMO) has set limits on allowable plankton concentrations in ballast water discharge to minimize aquatic invasions globally. Previous guidance on ballast water sampling and compliance decision thresholds was based on the assumption that probability distributions of plankton are Poisson when spatially homogenous, or negative binomial when heterogeneous. We propose a hierarchical probability model, which incorporates distributions at the level of particles (i.e., discrete individuals plus colonies per unit volume) and also within particles (i.e., individuals per particle) to estimate the average plankton concentration in ballast water. We examined the performance of the models using data for plankton in the size class ≥ 10 μm and < 50 μm, collected from five different depths of a ballast tank of a commercial ship in three independent surveys. We show that the data fit to the negative binomial and the hierarchical probability models equally well, with both models performing better than the Poisson model at the scale of our sampling. The hierarchical probability model, which accounts for both the individuals and the colonies in a sample, reduces the uncertainty associated with the concentration estimation, and improves the power of rejecting the decision on ship's compliance when a ship does not truly comply with the standard. We show examples of how to test ballast water compliance using the above models.

Application of the Hyper-Poisson Generalized Linear Model for Analyzing Motor Vehicle Crashes.

PubMed

Khazraee, S Hadi; Sáez-Castillo, Antonio Jose; Geedipally, Srinivas Reddy; Lord, Dominique

2015-05-01

The hyper-Poisson distribution can handle both over- and underdispersion, and its generalized linear model formulation allows the dispersion of the distribution to be observation-specific and dependent on model covariates. This study's objective is to examine the potential applicability of a newly proposed generalized linear model framework for the hyper-Poisson distribution in analyzing motor vehicle crash count data. The hyper-Poisson generalized linear model was first fitted to intersection crash data from Toronto, characterized by overdispersion, and then to crash data from railway-highway crossings in Korea, characterized by underdispersion. The results of this study are promising. When fitted to the Toronto data set, the goodness-of-fit measures indicated that the hyper-Poisson model with a variable dispersion parameter provided a statistical fit as good as the traditional negative binomial model. The hyper-Poisson model was also successful in handling the underdispersed data from Korea; the model performed as well as the gamma probability model and the Conway-Maxwell-Poisson model previously developed for the same data set. The advantages of the hyper-Poisson model studied in this article are noteworthy. Unlike the negative binomial model, which has difficulties in handling underdispersed data, the hyper-Poisson model can handle both over- and underdispersed crash data. Although not a major issue for the Conway-Maxwell-Poisson model, the effect of each variable on the expected mean of crashes is easily interpretable in the case of this new model. © 2014 Society for Risk Analysis.

Modeling avian abundance from replicated counts using binomial mixture models

USGS Publications Warehouse

Kery, Marc; Royle, J. Andrew; Schmid, Hans

2005-01-01

Abundance estimation in ecology is usually accomplished by capture–recapture, removal, or distance sampling methods. These may be hard to implement at large spatial scales. In contrast, binomial mixture models enable abundance estimation without individual identification, based simply on temporally and spatially replicated counts. Here, we evaluate mixture models using data from the national breeding bird monitoring program in Switzerland, where some 250 1-km2 quadrats are surveyed using the territory mapping method three times during each breeding season. We chose eight species with contrasting distribution (wide–narrow), abundance (high–low), and detectability (easy–difficult). Abundance was modeled as a random effect with a Poisson or negative binomial distribution, with mean affected by forest cover, elevation, and route length. Detectability was a logit-linear function of survey date, survey date-by-elevation, and sampling effort (time per transect unit). Resulting covariate effects and parameter estimates were consistent with expectations. Detectability per territory (for three surveys) ranged from 0.66 to 0.94 (mean 0.84) for easy species, and from 0.16 to 0.83 (mean 0.53) for difficult species, depended on survey effort for two easy and all four difficult species, and changed seasonally for three easy and three difficult species. Abundance was positively related to route length in three high-abundance and one low-abundance (one easy and three difficult) species, and increased with forest cover in five forest species, decreased for two nonforest species, and was unaffected for a generalist species. Abundance estimates under the most parsimonious mixture models were between 1.1 and 8.9 (median 1.8) times greater than estimates based on territory mapping; hence, three surveys were insufficient to detect all territories for each species. We conclude that binomial mixture models are an important new approach for estimating abundance corrected for detectability when only repeated-count data are available. Future developments envisioned include estimation of trend, occupancy, and total regional abundance.

Togetherness among Plasmodium falciparum gametocytes: interpretation through simulation and consequences for malaria transmission.

PubMed

Gaillard, F O; Boudin, C; Chau, N P; Robert, V; Pichon, G

2003-11-01

Previous experimental gametocyte infections of Anopheles arabiensis on 3 volunteers naturally infected with Plasmodium falciparum were conducted in Senegal. They showed that gametocyte counts in the mosquitoes are, like macroparasite intakes, heterogeneous (overdispersed). They followed a negative binomial distribution, the overdispersion coefficient seeming constant (k = 3.1). To try to explain this heterogeneity, we used an individual-based model (IBM), simulating the behaviour of gametocytes in the human blood circulation and their ingestion by mosquitoes. The hypothesis was that there exists a clustering of the gametocytes in the capillaries. From a series of simulations, in the case of clustering the following results were obtained: (i) the distribution of the gametocytes ingested by the mosquitoes followed a negative binomial, (ii) the k coefficient significantly increased with the density of circulating gametocytes. To validate this model result, 2 more experiments were conducted in Cameroon. Pooled experiments showed a distinct density dependency of the k-values. The simulation results and the experimental results were thus in agreement and suggested that an aggregation process at the microscopic level might produce the density-dependent overdispersion at the macroscopic level. Simulations also suggested that the clustering of gametocytes might facilitate fertilization of gametes.

Effectiveness on Early Childhood Caries of an Oral Health Promotion Program for Medical Providers

PubMed Central

Widmer-Racich, Katina; Sevick, Carter; Starzyk, Erin J.; Mauritson, Katya; Hambidge, Simon J.

2017-01-01

Objectives. To assess an oral health promotion (OHP) intervention for medical providers’ impact on early childhood caries (ECC). Methods. We implemented a quasiexperimental OHP intervention in 8 federally qualified health centers that trained medical providers on ECC risk assessment, oral examination and instruction, dental referral, and fluoride varnish applications (FVAs). We measured OHP delivery by FVA count at medical visits. We measured the intervention’s impact on ECC in 3 unique cohorts of children aged 3 to 4 years in 2009 (preintervention; n = 202), 2011 (midintervention; n = 420), and 2015 (≥ 4 FVAs; n = 153). We compared numbers of decayed, missing, and filled tooth surfaces using adjusted zero-inflated negative binomial models. Results. Across 3 unique cohorts, the FVA mean (range) count was 0.0 (0), 1.1 (0–7), and 4.5 (4–7) in 2009, 2011, and 2015, respectively. In adjusted zero-inflated negative binomial models analyses, children in the 2015 cohort had significantly fewer decayed, missing, and filled tooth surfaces than did children in previous cohorts. Conclusions. An OHP intervention targeting medical providers reduced ECC when children received 4 or more FVAs at a medical visit by age 3 years. PMID:28661802

Single-vehicle crashes along rural mountainous highways in Malaysia: An application of random parameters negative binomial model.

PubMed

Rusli, Rusdi; Haque, Md Mazharul; King, Mark; Voon, Wong Shaw

2017-05-01

Mountainous highways generally associate with complex driving environment because of constrained road geometries, limited cross-section elements, inappropriate roadside features, and adverse weather conditions. As a result, single-vehicle (SV) crashes are overrepresented along mountainous roads, particularly in developing countries, but little attention is known about the roadway geometric, traffic and weather factors contributing to these SV crashes. As such, the main objective of the present study is to investigate SV crashes using detailed data obtained from a rigorous site survey and existing databases. The final dataset included a total of 56 variables representing road geometries including horizontal and vertical alignment, traffic characteristics, real-time weather condition, cross-sectional elements, roadside features, and spatial characteristics. To account for structured heterogeneities resulting from multiple observations within a site and other unobserved heterogeneities, the study applied a random parameters negative binomial model. Results suggest that rainfall during the crash is positively associated with SV crashes, but real-time visibility is negatively associated. The presence of a road shoulder, particularly a bitumen shoulder or wider shoulders, along mountainous highways is associated with less SV crashes. While speeding along downgrade slopes increases the likelihood of SV crashes, proper delineation decreases the likelihood. Findings of this study have significant implications for designing safer highways in mountainous areas, particularly in the context of a developing country. Copyright © 2017 Elsevier Ltd. All rights reserved.

FluBreaks: early epidemic detection from Google flu trends.

PubMed

Pervaiz, Fahad; Pervaiz, Mansoor; Abdur Rehman, Nabeel; Saif, Umar

2012-10-04

The Google Flu Trends service was launched in 2008 to track changes in the volume of online search queries related to flu-like symptoms. Over the last few years, the trend data produced by this service has shown a consistent relationship with the actual number of flu reports collected by the US Centers for Disease Control and Prevention (CDC), often identifying increases in flu cases weeks in advance of CDC records. However, contrary to popular belief, Google Flu Trends is not an early epidemic detection system. Instead, it is designed as a baseline indicator of the trend, or changes, in the number of disease cases. To evaluate whether these trends can be used as a basis for an early warning system for epidemics. We present the first detailed algorithmic analysis of how Google Flu Trends can be used as a basis for building a fully automated system for early warning of epidemics in advance of methods used by the CDC. Based on our work, we present a novel early epidemic detection system, called FluBreaks (dritte.org/flubreaks), based on Google Flu Trends data. We compared the accuracy and practicality of three types of algorithms: normal distribution algorithms, Poisson distribution algorithms, and negative binomial distribution algorithms. We explored the relative merits of these methods, and related our findings to changes in Internet penetration and population size for the regions in Google Flu Trends providing data. Across our performance metrics of percentage true-positives (RTP), percentage false-positives (RFP), percentage overlap (OT), and percentage early alarms (EA), Poisson- and negative binomial-based algorithms performed better in all except RFP. Poisson-based algorithms had average values of 99%, 28%, 71%, and 76% for RTP, RFP, OT, and EA, respectively, whereas negative binomial-based algorithms had average values of 97.8%, 17.8%, 60%, and 55% for RTP, RFP, OT, and EA, respectively. Moreover, the EA was also affected by the region's population size. Regions with larger populations (regions 4 and 6) had higher values of EA than region 10 (which had the smallest population) for negative binomial- and Poisson-based algorithms. The difference was 12.5% and 13.5% on average in negative binomial- and Poisson-based algorithms, respectively. We present the first detailed comparative analysis of popular early epidemic detection algorithms on Google Flu Trends data. We note that realizing this opportunity requires moving beyond the cumulative sum and historical limits method-based normal distribution approaches, traditionally employed by the CDC, to negative binomial- and Poisson-based algorithms to deal with potentially noisy search query data from regions with varying population and Internet penetrations. Based on our work, we have developed FluBreaks, an early warning system for flu epidemics using Google Flu Trends.

Magnitude of virologic blips is associated with a higher risk for virologic rebound in HIV-infected individuals: a recurrent events analysis.

PubMed

Grennan, J Troy; Loutfy, Mona R; Su, DeSheng; Harrigan, P Richard; Cooper, Curtis; Klein, Marina; Machouf, Nima; Montaner, Julio S G; Rourke, Sean; Tsoukas, Christos; Hogg, Bob; Raboud, Janet

2012-04-15

The importance of human immunodeficiency virus (HIV) blip magnitude on virologic rebound has been raised in clinical guidelines relating to viral load assays. Antiretroviral-naive individuals initiating combination antiretroviral therapy (cART) after 1 January 2000 and achieving virologic suppression were studied. Negative binomial models were used to identify blip correlates. Recurrent event models were used to determine the association between blips and rebound by incorporating multiple periods of virologic suppression per individual. 3550 participants (82% male; median age, 40 years) were included. In a multivariable negative binomial regression model, the Amplicor assay was associated with a lower blip rate than branched DNA (rate ratio, 0.69; P < .01), controlling for age, sex, region, baseline HIV-1 RNA and CD4 count, AIDS-defining illnesses, year of cART initiation, cART type, and HIV-1 RNA testing frequency. In a multivariable recurrent event model controlling for age, sex, intravenous drug use, cART start year, cART type, assay type, and HIV-1 RNA testing frequency, blips of 500-999 copies/mL were associated with virologic rebound (hazard ratio, 2.70; P = .002), whereas blips of 50-499 were not. HIV-1 RNA assay was an important determinant of blip rates and should be considered in clinical guidelines. Blips ≥500 copies/mL were associated with increased rebound risk.

A statistical model to estimate the impact of a hepatitis A vaccination programme.

PubMed

Oviedo, Manuel; Pilar Muñoz, M; Domínguez, Angela; Borras, Eva; Carmona, Gloria

2008-11-11

A program of routine hepatitis A+B vaccination in preadolescents was introduced in 1998 in Catalonia, a region situated in the northeast of Spain. The objective of this study was to quantify the reduction in the incidence of hepatitis A in order to differentiate the natural reduction of the incidence of hepatitis A from that produced due to the vaccination programme and to predict the evolution of the disease in forthcoming years. A generalized linear model (GLM) using negative binomial regression was used to estimate the incidence rates of hepatitis A in Catalonia by year, age group and vaccination. Introduction of the vaccine reduced cases by 5.5 by year (p-value<0.001), but there was a significant interaction between the year of report and vaccination that smoothed this reduction (p-value<0.001). The reduction was not equal in all age groups, being greater in the 12-18 years age group, which fell from a mean rate of 8.15 per 100,000 person/years in the pre-vaccination period (1992-1998) to 1.4 in the vaccination period (1999-2005). The model predicts the evolution accurately for the group of vaccinated subjects. Negative binomial regression is more appropriate than Poisson regression when observed variance exceeds the observed mean (overdispersed count data), can cause a variable apparently contribute more on the model of what really makes it.

75 FR 65524 - United Auto Workers Local 1999, Oklahoma City, OK; Notice of Negative Determination Regarding...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-10-25

... DEPARTMENT OF LABOR Employment and Training Administration [TA-W-71,863] United Auto Workers Local... workers and former workers of United Auto Workers Local 1999, Oklahoma City, Oklahoma (the subject firm... Auto Workers Local 1999, Oklahoma City, Oklahoma, was based on the findings that the workers at the...

Generalized seasonal autoregressive integrated moving average models for count data with application to malaria time series with low case numbers.

PubMed

Briët, Olivier J T; Amerasinghe, Priyanie H; Vounatsou, Penelope

2013-01-01

With the renewed drive towards malaria elimination, there is a need for improved surveillance tools. While time series analysis is an important tool for surveillance, prediction and for measuring interventions' impact, approximations by commonly used Gaussian methods are prone to inaccuracies when case counts are low. Therefore, statistical methods appropriate for count data are required, especially during "consolidation" and "pre-elimination" phases. Generalized autoregressive moving average (GARMA) models were extended to generalized seasonal autoregressive integrated moving average (GSARIMA) models for parsimonious observation-driven modelling of non Gaussian, non stationary and/or seasonal time series of count data. The models were applied to monthly malaria case time series in a district in Sri Lanka, where malaria has decreased dramatically in recent years. The malaria series showed long-term changes in the mean, unstable variance and seasonality. After fitting negative-binomial Bayesian models, both a GSARIMA and a GARIMA deterministic seasonality model were selected based on different criteria. Posterior predictive distributions indicated that negative-binomial models provided better predictions than Gaussian models, especially when counts were low. The G(S)ARIMA models were able to capture the autocorrelation in the series. G(S)ARIMA models may be particularly useful in the drive towards malaria elimination, since episode count series are often seasonal and non-stationary, especially when control is increased. Although building and fitting GSARIMA models is laborious, they may provide more realistic prediction distributions than do Gaussian methods and may be more suitable when counts are low.

Generalized Seasonal Autoregressive Integrated Moving Average Models for Count Data with Application to Malaria Time Series with Low Case Numbers

PubMed Central

Briët, Olivier J. T.; Amerasinghe, Priyanie H.; Vounatsou, Penelope

2013-01-01

Introduction With the renewed drive towards malaria elimination, there is a need for improved surveillance tools. While time series analysis is an important tool for surveillance, prediction and for measuring interventions’ impact, approximations by commonly used Gaussian methods are prone to inaccuracies when case counts are low. Therefore, statistical methods appropriate for count data are required, especially during “consolidation” and “pre-elimination” phases. Methods Generalized autoregressive moving average (GARMA) models were extended to generalized seasonal autoregressive integrated moving average (GSARIMA) models for parsimonious observation-driven modelling of non Gaussian, non stationary and/or seasonal time series of count data. The models were applied to monthly malaria case time series in a district in Sri Lanka, where malaria has decreased dramatically in recent years. Results The malaria series showed long-term changes in the mean, unstable variance and seasonality. After fitting negative-binomial Bayesian models, both a GSARIMA and a GARIMA deterministic seasonality model were selected based on different criteria. Posterior predictive distributions indicated that negative-binomial models provided better predictions than Gaussian models, especially when counts were low. The G(S)ARIMA models were able to capture the autocorrelation in the series. Conclusions G(S)ARIMA models may be particularly useful in the drive towards malaria elimination, since episode count series are often seasonal and non-stationary, especially when control is increased. Although building and fitting GSARIMA models is laborious, they may provide more realistic prediction distributions than do Gaussian methods and may be more suitable when counts are low. PMID:23785448

Do Barriers to Crime Prevention Moderate the Effects of Situational Crime Prevention Policies on Violent Crime in High Schools?

ERIC Educational Resources Information Center

Sevigny, Eric L.; Zhang, Gary

2018-01-01

This study investigates how barriers to school-based crime prevention programming moderate the effects of situational crime prevention (SCP) policies on levels of violent crime in U.S. public high schools. Using data from the 2008 School Survey on Crime and Safety, we estimate a series of negative binomial regression models with interactions to…

Evaluation of the Johne's disease risk assessment and management plan on dairy farms in Ontario, Canada.

PubMed

Pieper, Laura; Sorge, Ulrike S; DeVries, Trevor J; Godkin, Ann; Lissemore, Kerry; Kelton, David F

2015-10-01

Johne's disease (JD) is a production-limiting gastrointestinal disease in cattle. To minimize the effects of JD, the Ontario dairy industry launched the Ontario Johne's Education and Management Assistance Program in 2010. As part of the program, trained veterinarians conducted a risk assessment and management plan (RAMP), an on-farm questionnaire where high RAMP scores are associated with high risk of JD transmission. Subsequently, veterinarians recommended farm-specific management practices for JD prevention. Milk or serum ELISA results from the milking herd were used to determine the herd ELISA status (HES) and within-herd prevalence. After 3.5 yr of implementation of the program, the aim of this study was to evaluate the associations among RAMP scores, HES, and recommendations. Data from 2,103 herds were available for the analyses. A zero-inflated negative binomial model for the prediction of the number of ELISA-positive animals per farm was built. The model included individual RAMP questions about purchasing animals in the logistic portion, indicating risks for between-herd transmission, and purchasing bulls, birth of calves outside the designated calving area, colostrum and milk feeding management, and adult cow environmental hygiene in the negative binomial portion, indicating risk factors for within-herd transmission. However, farms which fed low-risk milk compared with milk replacer had fewer seropositive animals. The model additionally included the JD herd history in the negative binomial and the logistic portion, indicating that herds with a JD herd history were more likely to have at least 1 positive animal and to have a higher number of positive animals. Generally, a positive association was noted between RAMP scores and the odds of receiving a recommendation for the respective risk area; however, the relationship was not always linear. For general JD risk and calving area risk, seropositive herds had higher odds of receiving recommendations compared with seronegative herds if the section scores were low. This study suggests that the RAMP is a valuable tool to assess the risk for JD transmission within and between herds and to determine farm-specific recommendations for JD prevention. Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Comparison of robustness to outliers between robust poisson models and log-binomial models when estimating relative risks for common binary outcomes: a simulation study.

PubMed

Chen, Wansu; Shi, Jiaxiao; Qian, Lei; Azen, Stanley P

2014-06-26

To estimate relative risks or risk ratios for common binary outcomes, the most popular model-based methods are the robust (also known as modified) Poisson and the log-binomial regression. Of the two methods, it is believed that the log-binomial regression yields more efficient estimators because it is maximum likelihood based, while the robust Poisson model may be less affected by outliers. Evidence to support the robustness of robust Poisson models in comparison with log-binomial models is very limited. In this study a simulation was conducted to evaluate the performance of the two methods in several scenarios where outliers existed. The findings indicate that for data coming from a population where the relationship between the outcome and the covariate was in a simple form (e.g. log-linear), the two models yielded comparable biases and mean square errors. However, if the true relationship contained a higher order term, the robust Poisson models consistently outperformed the log-binomial models even when the level of contamination is low. The robust Poisson models are more robust (or less sensitive) to outliers compared to the log-binomial models when estimating relative risks or risk ratios for common binary outcomes. Users should be aware of the limitations when choosing appropriate models to estimate relative risks or risk ratios.

Development and validation of an automated delirium risk assessment system (Auto-DelRAS) implemented in the electronic health record system.

PubMed

Moon, Kyoung-Ja; Jin, Yinji; Jin, Taixian; Lee, Sun-Mi

2018-01-01

A key component of the delirium management is prevention and early detection. To develop an automated delirium risk assessment system (Auto-DelRAS) that automatically alerts health care providers of an intensive care unit (ICU) patient's delirium risk based only on data collected in an electronic health record (EHR) system, and to evaluate the clinical validity of this system. Cohort and system development designs were used. Medical and surgical ICUs in two university hospitals in Seoul, Korea. A total of 3284 patients for the development of Auto-DelRAS, 325 for external validation, 694 for validation after clinical applications. The 4211 data items were extracted from the EHR system and delirium was measured using CAM-ICU (Confusion Assessment Method for Intensive Care Unit). The potential predictors were selected and a logistic regression model was established to create a delirium risk scoring algorithm to construct the Auto-DelRAS. The Auto-DelRAS was evaluated at three months and one year after its application to clinical practice to establish the predictive validity of the system. Eleven predictors were finally included in the logistic regression model. The results of the Auto-DelRAS risk assessment were shown as high/moderate/low risk on a Kardex screen. The predictive validity, analyzed after the clinical application of Auto-DelRAS after one year, showed a sensitivity of 0.88, specificity of 0.72, positive predictive value of 0.53, negative predictive value of 0.94, and a Youden index of 0.59. A relatively high level of predictive validity was maintained with the Auto-DelRAS system, even one year after it was applied to clinical practice. Copyright © 2017. Published by Elsevier Ltd.

On performance of parametric and distribution-free models for zero-inflated and over-dispersed count responses.

PubMed

Tang, Wan; Lu, Naiji; Chen, Tian; Wang, Wenjuan; Gunzler, Douglas David; Han, Yu; Tu, Xin M

2015-10-30

Zero-inflated Poisson (ZIP) and negative binomial (ZINB) models are widely used to model zero-inflated count responses. These models extend the Poisson and negative binomial (NB) to address excessive zeros in the count response. By adding a degenerate distribution centered at 0 and interpreting it as describing a non-risk group in the population, the ZIP (ZINB) models a two-component population mixture. As in applications of Poisson and NB, the key difference between ZIP and ZINB is the allowance for overdispersion by the ZINB in its NB component in modeling the count response for the at-risk group. Overdispersion arising in practice too often does not follow the NB, and applications of ZINB to such data yield invalid inference. If sources of overdispersion are known, other parametric models may be used to directly model the overdispersion. Such models too are subject to assumed distributions. Further, this approach may not be applicable if information about the sources of overdispersion is unavailable. In this paper, we propose a distribution-free alternative and compare its performance with these popular parametric models as well as a moment-based approach proposed by Yu et al. [Statistics in Medicine 2013; 32: 2390-2405]. Like the generalized estimating equations, the proposed approach requires no elaborate distribution assumptions. Compared with the approach of Yu et al., it is more robust to overdispersed zero-inflated responses. We illustrate our approach with both simulated and real study data. Copyright © 2015 John Wiley & Sons, Ltd.

A Three-Parameter Generalisation of the Beta-Binomial Distribution with Applications

DTIC Science & Technology

1987-07-01

York. Rust, R.T. and Klompmaker, J.E. (1981). Improving the estimation procedure for the beta binomial t.v. exposure model. Journal of Marketing ... Research . 18, 442-448. Sabavala, D.J. and Morrison, D.G. (1977). Television show loyalty: a beta- binomial model using recall data. Journal of Advertiuing

Comparisons of fully automated syphilis tests with conventional VDRL and FTA-ABS tests.

PubMed

Choi, Seung Jun; Park, Yongjung; Lee, Eun Young; Kim, Sinyoung; Kim, Hyon-Suk

2013-06-01

Serologic tests are widely used for the diagnosis of syphilis. However, conventional methods require well-trained technicians to produce reliable results. We compared automated nontreponemal and treponemal tests with conventional methods. The HiSens Auto Rapid Plasma Reagin (AutoRPR) and Treponema Pallidum particle agglutination (AutoTPPA) tests, which utilize latex turbidimetric immunoassay, were assessed. A total of 504 sera were assayed by AutoRPR, AutoTPPA, conventional VDRL and FTA-ABS. Among them, 250 samples were also tested by conventional TPPA. The concordance rate between the results of VDRL and AutoRPR was 67.5%, and 164 discrepant cases were all VDRL reactive but AutoRPR negative. In the 164 cases, 133 showed FTA-ABS reactivity. Medical records of 106 among the 133 cases were reviewed, and 82 among 106 specimens were found to be collected from patients already treated for syphilis. The concordance rate between the results of AutoTPPA and FTA-ABS was 97.8%. The results of conventional TPPA and AutoTPPA for 250 samples were concordant in 241 cases (96.4%). AutoRPR showed higher specificity than that of VDRL, while VDRL demonstrated higher sensitivity than that of AutoRPR regardless of whether the patients had been already treated for syphilis or not. Both FTA-ABS and AutoTPPA showed high sensitivities and specificities greater than 98.0%. Automated RPR and TPPA tests could be alternatives to conventional syphilis tests, and AutoRPR would be particularly suitable in treatment monitoring, since results by AutoRPR in cases after treatment became negative more rapidly than by VDRL. Copyright © 2013. Published by Elsevier Inc.

Bayesian inference for disease prevalence using negative binomial group testing

PubMed Central

Pritchard, Nicholas A.; Tebbs, Joshua M.

2011-01-01

Group testing, also known as pooled testing, and inverse sampling are both widely used methods of data collection when the goal is to estimate a small proportion. Taking a Bayesian approach, we consider the new problem of estimating disease prevalence from group testing when inverse (negative binomial) sampling is used. Using different distributions to incorporate prior knowledge of disease incidence and different loss functions, we derive closed form expressions for posterior distributions and resulting point and credible interval estimators. We then evaluate our new estimators, on Bayesian and classical grounds, and apply our methods to a West Nile Virus data set. PMID:21259308

Measurement error and outcome distributions: Methodological issues in regression analyses of behavioral coding data.

PubMed

Holsclaw, Tracy; Hallgren, Kevin A; Steyvers, Mark; Smyth, Padhraic; Atkins, David C

2015-12-01

Behavioral coding is increasingly used for studying mechanisms of change in psychosocial treatments for substance use disorders (SUDs). However, behavioral coding data typically include features that can be problematic in regression analyses, including measurement error in independent variables, non normal distributions of count outcome variables, and conflation of predictor and outcome variables with third variables, such as session length. Methodological research in econometrics has shown that these issues can lead to biased parameter estimates, inaccurate standard errors, and increased Type I and Type II error rates, yet these statistical issues are not widely known within SUD treatment research, or more generally, within psychotherapy coding research. Using minimally technical language intended for a broad audience of SUD treatment researchers, the present paper illustrates the nature in which these data issues are problematic. We draw on real-world data and simulation-based examples to illustrate how these data features can bias estimation of parameters and interpretation of models. A weighted negative binomial regression is introduced as an alternative to ordinary linear regression that appropriately addresses the data characteristics common to SUD treatment behavioral coding data. We conclude by demonstrating how to use and interpret these models with data from a study of motivational interviewing. SPSS and R syntax for weighted negative binomial regression models is included in online supplemental materials. (c) 2016 APA, all rights reserved).

Measurement error and outcome distributions: Methodological issues in regression analyses of behavioral coding data

PubMed Central

Holsclaw, Tracy; Hallgren, Kevin A.; Steyvers, Mark; Smyth, Padhraic; Atkins, David C.

2015-01-01

Behavioral coding is increasingly used for studying mechanisms of change in psychosocial treatments for substance use disorders (SUDs). However, behavioral coding data typically include features that can be problematic in regression analyses, including measurement error in independent variables, non-normal distributions of count outcome variables, and conflation of predictor and outcome variables with third variables, such as session length. Methodological research in econometrics has shown that these issues can lead to biased parameter estimates, inaccurate standard errors, and increased type-I and type-II error rates, yet these statistical issues are not widely known within SUD treatment research, or more generally, within psychotherapy coding research. Using minimally-technical language intended for a broad audience of SUD treatment researchers, the present paper illustrates the nature in which these data issues are problematic. We draw on real-world data and simulation-based examples to illustrate how these data features can bias estimation of parameters and interpretation of models. A weighted negative binomial regression is introduced as an alternative to ordinary linear regression that appropriately addresses the data characteristics common to SUD treatment behavioral coding data. We conclude by demonstrating how to use and interpret these models with data from a study of motivational interviewing. SPSS and R syntax for weighted negative binomial regression models is included in supplementary materials. PMID:26098126

nbCNV: a multi-constrained optimization model for discovering copy number variants in single-cell sequencing data.

PubMed

Zhang, Changsheng; Cai, Hongmin; Huang, Jingying; Song, Yan

2016-09-17

Variations in DNA copy number have an important contribution to the development of several diseases, including autism, schizophrenia and cancer. Single-cell sequencing technology allows the dissection of genomic heterogeneity at the single-cell level, thereby providing important evolutionary information about cancer cells. In contrast to traditional bulk sequencing, single-cell sequencing requires the amplification of the whole genome of a single cell to accumulate enough samples for sequencing. However, the amplification process inevitably introduces amplification bias, resulting in an over-dispersing portion of the sequencing data. Recent study has manifested that the over-dispersed portion of the single-cell sequencing data could be well modelled by negative binomial distributions. We developed a read-depth based method, nbCNV to detect the copy number variants (CNVs). The nbCNV method uses two constraints-sparsity and smoothness to fit the CNV patterns under the assumption that the read signals are negatively binomially distributed. The problem of CNV detection was formulated as a quadratic optimization problem, and was solved by an efficient numerical solution based on the classical alternating direction minimization method. Extensive experiments to compare nbCNV with existing benchmark models were conducted on both simulated data and empirical single-cell sequencing data. The results of those experiments demonstrate that nbCNV achieves superior performance and high robustness for the detection of CNVs in single-cell sequencing data.

Logistic quantile regression provides improved estimates for bounded avian counts: a case study of California Spotted Owl fledgling production

Treesearch

Brian S. Cade; Barry R. Noon; Rick D. Scherer; John J. Keane

2017-01-01

Counts of avian fledglings, nestlings, or clutch size that are bounded below by zero and above by some small integer form a discrete random variable distribution that is not approximated well by conventional parametric count distributions such as the Poisson or negative binomial. We developed a logistic quantile regression model to provide estimates of the empirical...

Oral health of schoolchildren in Western Australia.

PubMed

Arrow, P

2016-09-01

The West Australian School Dental Service (SDS) provides free, statewide, primary dental care to schoolchildren aged 5-17 years. This study reports on an evaluation of the oral health of children examined during the 2014 calendar year. Children were sampled, based on their date of birth, and SDS clinicians collected the clinical information. Weighted mean values of caries experience were presented. Negative binomial regression modelling was undertaken to test for factors of significance in the rate of caries occurrence. Data from children aged 5-15 years were used (girls = 4616, boys = 4900). Mean dmft (5-10-year-olds), 1.42 SE 0.03; mean DMFT (6-15-year-olds), 0.51 SE 0.01. Negative binomial regression model of permanent tooth caries found higher rates of caries in children who were from non-fluoridated areas (RR 2.1); Aboriginal (RR 2.4); had gingival inflammation (RR 1.5); lower ICSEA level (RR 1.4); and recalled at more than 24-month interval (RR 1.8). The study highlighted poor dental health associated with living in non-fluoridated areas, Aboriginal identity, poor oral hygiene, lower socioeconomic level and having extended intervals between dental checkups. Timely assessments and preventive measures targeted at groups, including extending community water fluoridation, may assist in further improving the oral health of children in Western Australia. © 2015 Australian Dental Association.

Characterizing environmental risk factors for West Nile virus in Quebec, Canada, using clinical data in humans and serology in pet dogs.

PubMed

Rocheleau, J P; Michel, P; Lindsay, L R; Drebot, M; Dibernardo, A; Ogden, N H; Fortin, A; Arsenault, J

2017-10-01

The identification of specific environments sustaining emerging arbovirus amplification and transmission to humans is a key component of public health intervention planning. This study aimed at identifying environmental factors associated with West Nile virus (WNV) infections in southern Quebec, Canada, by modelling and jointly interpreting aggregated clinical data in humans and serological data in pet dogs. Environmental risk factors were estimated in humans by negative binomial regression based on a dataset of 191 human WNV clinical cases reported in the study area between 2011 and 2014. Risk factors for infection in dogs were evaluated by logistic and negative binomial models based on a dataset including WNV serological results from 1442 dogs sampled from the same geographical area in 2013. Forested lands were identified as low-risk environments in humans. Agricultural lands represented higher risk environments for dogs. Environments identified as impacting risk in the current study were somewhat different from those identified in other studies conducted in north-eastern USA, which reported higher risk in suburban environments. In the context of the current study, combining human and animal data allowed a more comprehensive and possibly a more accurate view of environmental WNV risk factors to be obtained than by studying aggregated human data alone.

Not in My Back Yard: A Comparative Analysis of Crime Around Publicly Funded Drug Treatment Centers, Liquor Stores, Convenience Stores, and Corner Stores in One Mid-Atlantic City.

PubMed

Furr-Holden, C Debra M; Milam, Adam J; Nesoff, Elizabeth D; Johnson, Renee M; Fakunle, David O; Jennings, Jacky M; Thorpe, Roland J

2016-01-01

This research examined whether publicly funded drug treatment centers (DTCs) were associated with violent crime in excess of the violence happening around other commercial businesses. Violent crime data and locations of community entities were geocoded and mapped. DTCs and other retail outlets were matched based on a Neighborhood Disadvantage score at the census tract level. Street network buffers ranging from 100 to 1,400 feet were placed around each location. Negative binomial regression models were used to estimate the relationship between the count of violent crimes and the distance from each business type. Compared with the mean count of violent crime around drug treatment centers, the mean count of violent crime (M = 2.87) was significantly higher around liquor stores (M = 3.98; t test; p < .01) and corner stores (M = 3.78; t test; p < .01), and there was no statistically significant difference between the count around convenience stores (M = 2.65; t test; p = .32). In the adjusted negative binomial regression models, there was a negative and significant relationship between the count of violent crime and the distance from drug treatment centers (β = -.069, p < .01), liquor stores (β = -.081, p < .01), corner stores (β = -.116, p < .01), and convenience stores (β = -.154, p < .01). Violent crime associated with drug treatment centers is similar to that associated with liquor stores and is less frequent than that associated with convenience stores and corner stores.

On the p, q-binomial distribution and the Ising model

NASA Astrophysics Data System (ADS)

Lundow, P. H.; Rosengren, A.

2010-08-01

We employ p, q-binomial coefficients, a generalisation of the binomial coefficients, to describe the magnetisation distributions of the Ising model. For the complete graph this distribution corresponds exactly to the limit case p = q. We apply our investigation to the simple d-dimensional lattices for d = 1, 2, 3, 4, 5 and fit p, q-binomial distributions to our data, some of which are exact but most are sampled. For d = 1 and d = 5, the magnetisation distributions are remarkably well-fitted by p,q-binomial distributions. For d = 4 we are only slightly less successful, while for d = 2, 3 we see some deviations (with exceptions!) between the p, q-binomial and the Ising distribution. However, at certain temperatures near T c the statistical moments of the fitted distribution agree with the moments of the sampled data within the precision of sampling. We begin the paper by giving results of the behaviour of the p, q-distribution and its moment growth exponents given a certain parameterisation of p, q. Since the moment exponents are known for the Ising model (or at least approximately for d = 3) we can predict how p, q should behave and compare this to our measured p, q. The results speak in favour of the p, q-binomial distribution's correctness regarding its general behaviour in comparison to the Ising model. The full extent to which they correctly model the Ising distribution, however, is not settled.

The five-factor model of impulsivity-like traits and emotional lability in aggressive behavior.

PubMed

Dvorak, Robert D; Pearson, Matthew R; Kuvaas, Nicholas J

2013-01-01

Factors that increase automatic psychological processes may result in impulsive action and, consequently, aggressive behavior. The current cross-sectional study examined the association between the five-factor model of impulsivity-like traits (negative urgency, positive urgency, premeditation, perseverance, and sensation seeking), emotional lability, and physically aggressive behaviors among college students (n = 481) in a negative binomial hurdle model. In the logistic portion of the model, emotional lability was related to a higher likelihood of engaging in aggressive acts in the past 6 months. The association between emotional lability and the likelihood of aggressive behavior was moderated by two impulsivity-like traits: negative urgency and positive urgency. Specifically, emotional lability was related to engaging in aggressive acts among those with high negative urgency, and among those with low positive urgency. In the count portion of the model, emotional lability was uniquely related to the number of aggressive acts in the past 6 months. Our results indicate that emotional lability and facets of impulsivity interactively relate to engagement in aggressive behavior, suggesting that these variables be integrated into models of aggression. © 2013 Wiley Periodicals, Inc.

Robust inference in the negative binomial regression model with an application to falls data.

PubMed

Aeberhard, William H; Cantoni, Eva; Heritier, Stephane

2014-12-01

A popular way to model overdispersed count data, such as the number of falls reported during intervention studies, is by means of the negative binomial (NB) distribution. Classical estimating methods are well-known to be sensitive to model misspecifications, taking the form of patients falling much more than expected in such intervention studies where the NB regression model is used. We extend in this article two approaches for building robust M-estimators of the regression parameters in the class of generalized linear models to the NB distribution. The first approach achieves robustness in the response by applying a bounded function on the Pearson residuals arising in the maximum likelihood estimating equations, while the second approach achieves robustness by bounding the unscaled deviance components. For both approaches, we explore different choices for the bounding functions. Through a unified notation, we show how close these approaches may actually be as long as the bounding functions are chosen and tuned appropriately, and provide the asymptotic distributions of the resulting estimators. Moreover, we introduce a robust weighted maximum likelihood estimator for the overdispersion parameter, specific to the NB distribution. Simulations under various settings show that redescending bounding functions yield estimates with smaller biases under contamination while keeping high efficiency at the assumed model, and this for both approaches. We present an application to a recent randomized controlled trial measuring the effectiveness of an exercise program at reducing the number of falls among people suffering from Parkinsons disease to illustrate the diagnostic use of such robust procedures and their need for reliable inference. © 2014, The International Biometric Society.

Dispersion models and sampling of cacao mirid bug Sahlbergella singularis (Hemiptera: Miridae) on Theobroma Cacao in southern Cameroon.

PubMed

Bisseleua, D H B; Vidal, Stefan

2011-02-01

The spatio-temporal distribution of Sahlbergella singularis Haglung, a major pest of cacao trees (Theobroma cacao) (Malvaceae), was studied for 2 yr in traditional cacao forest gardens in the humid forest area of southern Cameroon. The first objective was to analyze the dispersion of this insect on cacao trees. The second objective was to develop sampling plans based on fixed levels of precision for estimating S. singularis populations. The following models were used to analyze the data: Taylor's power law, Iwao's patchiness regression, the Nachman model, and the negative binomial distribution. Our results document that Taylor's power law was a better fit for the data than the Iwao and Nachman models. Taylor's b and Iwao's β were both significantly >1, indicating that S. singularis aggregated on specific trees. This result was further supported by the calculated common k of 1.75444. Iwao's α was significantly <0, indicating that the basic distribution component of S. singularis was the individual insect. Comparison of negative binomial (NBD) and Nachman models indicated that the NBD model was appropriate for studying S. singularis distribution. Optimal sample sizes for fixed precision levels of 0.10, 0.15, and 0.25 were estimated with Taylor's regression coefficients. Required sample sizes increased dramatically with increasing levels of precision. This is the first study on S. singularis dispersion in cacao plantations. Sampling plans, presented here, should be a tool for research on population dynamics and pest management decisions of mirid bugs on cacao. © 2011 Entomological Society of America

Multivariate random-parameters zero-inflated negative binomial regression model: an application to estimate crash frequencies at intersections.

PubMed

Dong, Chunjiao; Clarke, David B; Yan, Xuedong; Khattak, Asad; Huang, Baoshan

2014-09-01

Crash data are collected through police reports and integrated with road inventory data for further analysis. Integrated police reports and inventory data yield correlated multivariate data for roadway entities (e.g., segments or intersections). Analysis of such data reveals important relationships that can help focus on high-risk situations and coming up with safety countermeasures. To understand relationships between crash frequencies and associated variables, while taking full advantage of the available data, multivariate random-parameters models are appropriate since they can simultaneously consider the correlation among the specific crash types and account for unobserved heterogeneity. However, a key issue that arises with correlated multivariate data is the number of crash-free samples increases, as crash counts have many categories. In this paper, we describe a multivariate random-parameters zero-inflated negative binomial (MRZINB) regression model for jointly modeling crash counts. The full Bayesian method is employed to estimate the model parameters. Crash frequencies at urban signalized intersections in Tennessee are analyzed. The paper investigates the performance of MZINB and MRZINB regression models in establishing the relationship between crash frequencies, pavement conditions, traffic factors, and geometric design features of roadway intersections. Compared to the MZINB model, the MRZINB model identifies additional statistically significant factors and provides better goodness of fit in developing the relationships. The empirical results show that MRZINB model possesses most of the desirable statistical properties in terms of its ability to accommodate unobserved heterogeneity and excess zero counts in correlated data. Notably, in the random-parameters MZINB model, the estimated parameters vary significantly across intersections for different crash types. Copyright © 2014 Elsevier Ltd. All rights reserved.

Distribution pattern of phthirapterans infesting certain common Indian birds.

PubMed

Saxena, A K; Kumar, Sandeep; Gupta, Nidhi; Mitra, J D; Ali, S A; Srivastava, Roshni

2007-08-01

The prevalence and frequency distribution patterns of 10 phthirapteran species infesting house sparrows, Indian parakeets, common mynas, and white breasted kingfishers were recorded in the district of Rampur, India, during 2004-05. The sample mean abundances, mean intensities, range of infestations, variance to mean ratios, values of the exponent of the negative binomial distribution, and the indices of discrepancy were also computed. Frequency distribution patterns of all phthirapteran species were skewed, but the observed frequencies did not correspond to the negative binomial distribution. Thus, adult-nymph ratios varied in different species from 1:0.53 to 1:1.25. Sex ratios of different phthirapteran species ranged from 1:1.10 to 1:1.65 and were female biased.

Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory.

PubMed

Lord, Dominique; Washington, Simon P; Ivan, John N

2005-01-01

There has been considerable research conducted over the last 20 years focused on predicting motor vehicle crashes on transportation facilities. The range of statistical models commonly applied includes binomial, Poisson, Poisson-gamma (or negative binomial), zero-inflated Poisson and negative binomial models (ZIP and ZINB), and multinomial probability models. Given the range of possible modeling approaches and the host of assumptions with each modeling approach, making an intelligent choice for modeling motor vehicle crash data is difficult. There is little discussion in the literature comparing different statistical modeling approaches, identifying which statistical models are most appropriate for modeling crash data, and providing a strong justification from basic crash principles. In the recent literature, it has been suggested that the motor vehicle crash process can successfully be modeled by assuming a dual-state data-generating process, which implies that entities (e.g., intersections, road segments, pedestrian crossings, etc.) exist in one of two states-perfectly safe and unsafe. As a result, the ZIP and ZINB are two models that have been applied to account for the preponderance of "excess" zeros frequently observed in crash count data. The objective of this study is to provide defensible guidance on how to appropriate model crash data. We first examine the motor vehicle crash process using theoretical principles and a basic understanding of the crash process. It is shown that the fundamental crash process follows a Bernoulli trial with unequal probability of independent events, also known as Poisson trials. We examine the evolution of statistical models as they apply to the motor vehicle crash process, and indicate how well they statistically approximate the crash process. We also present the theory behind dual-state process count models, and note why they have become popular for modeling crash data. A simulation experiment is then conducted to demonstrate how crash data give rise to "excess" zeros frequently observed in crash data. It is shown that the Poisson and other mixed probabilistic structures are approximations assumed for modeling the motor vehicle crash process. Furthermore, it is demonstrated that under certain (fairly common) circumstances excess zeros are observed-and that these circumstances arise from low exposure and/or inappropriate selection of time/space scales and not an underlying dual state process. In conclusion, carefully selecting the time/space scales for analysis, including an improved set of explanatory variables and/or unobserved heterogeneity effects in count regression models, or applying small-area statistical methods (observations with low exposure) represent the most defensible modeling approaches for datasets with a preponderance of zeros.

Statistical methods for the beta-binomial model in teratology.

PubMed Central

Yamamoto, E; Yanagimoto, T

1994-01-01

The beta-binomial model is widely used for analyzing teratological data involving littermates. Recent developments in statistical analyses of teratological data are briefly reviewed with emphasis on the model. For statistical inference of the parameters in the beta-binomial distribution, separation of the likelihood introduces an likelihood inference. This leads to reducing biases of estimators and also to improving accuracy of empirical significance levels of tests. Separate inference of the parameters can be conducted in a unified way. PMID:8187716

Evaluating the Auto-MODS Assay, a Novel Tool for Tuberculosis Diagnosis for Use in Resource-Limited Settings

PubMed Central

Wang, Linwei; Mohammad, Sohaib H.; Li, Qiaozhi; Rienthong, Somsak; Rienthong, Dhanida; Nedsuwan, Supalert; Mahasirimongkol, Surakameth; Yasui, Yutaka

2014-01-01

There is an urgent need for simple, rapid, and affordable diagnostic tests for tuberculosis (TB) to combat the great burden of the disease in developing countries. The microscopic observation drug susceptibility assay (MODS) is a promising tool to fill this need, but it is not widely used due to concerns regarding its biosafety and efficiency. This study evaluated the automated MODS (Auto-MODS), which operates on principles similar to those of MODS but with several key modifications, making it an appealing alternative to MODS in resource-limited settings. In the operational setting of Chiang Rai, Thailand, we compared the performance of Auto-MODS with the gold standard liquid culture method in Thailand, mycobacterial growth indicator tube (MGIT) 960 plus the SD Bioline TB Ag MPT64 test, in terms of accuracy and efficiency in differentiating TB and non-TB samples as well as distinguishing TB and multidrug-resistant (MDR) TB samples. Sputum samples from clinically diagnosed TB and non-TB subjects across 17 hospitals in Chiang Rai were consecutively collected from May 2011 to September 2012. A total of 360 samples were available for evaluation, of which 221 (61.4%) were positive and 139 (38.6%) were negative for mycobacterial cultures according to MGIT 960. Of the 221 true-positive samples, Auto-MODS identified 212 as positive and 9 as negative (sensitivity, 95.9%; 95% confidence interval [CI], 92.4% to 98.1%). Of the 139 true-negative samples, Auto-MODS identified 135 as negative and 4 as positive (specificity, 97.1%; 95% CI, 92.8% to 99.2%). The median time to culture positivity was 10 days, with an interquartile range of 8 to 13 days for Auto-MODS. Auto-MODS is an effective and cost-sensitive alternative diagnostic tool for TB diagnosis in resource-limited settings. PMID:25378569

Predicting length of stay from an electronic patient record system: a primary total knee replacement example.

PubMed

Carter, Evelene M; Potts, Henry W W

2014-04-04

To investigate whether factors can be identified that significantly affect hospital length of stay from those available in an electronic patient record system, using primary total knee replacements as an example. To investigate whether a model can be produced to predict the length of stay based on these factors to help resource planning and patient expectations on their length of stay. Data were extracted from the electronic patient record system for discharges from primary total knee operations from January 2007 to December 2011 (n=2,130) at one UK hospital and analysed for their effect on length of stay using Mann-Whitney and Kruskal-Wallis tests for discrete data and Spearman's correlation coefficient for continuous data. Models for predicting length of stay for primary total knee replacements were tested using the Poisson regression and the negative binomial modelling techniques. Factors found to have a significant effect on length of stay were age, gender, consultant, discharge destination, deprivation and ethnicity. Applying a negative binomial model to these variables was successful. The model predicted the length of stay of those patients who stayed 4-6 days (~50% of admissions) with 75% accuracy within 2 days (model data). Overall, the model predicted the total days stayed over 5 years to be only 88 days more than actual, a 6.9% uplift (test data). Valuable information can be found about length of stay from the analysis of variables easily extracted from an electronic patient record system. Models can be successfully created to help improve resource planning and from which a simple decision support system can be produced to help patient expectation on their length of stay.

Tobit analysis of vehicle accident rates on interstate highways.

PubMed

Anastasopoulos, Panagiotis Ch; Tarko, Andrew P; Mannering, Fred L

2008-03-01

There has been an abundance of research that has used Poisson models and its variants (negative binomial and zero-inflated models) to improve our understanding of the factors that affect accident frequencies on roadway segments. This study explores the application of an alternate method, tobit regression, by viewing vehicle accident rates directly (instead of frequencies) as a continuous variable that is left-censored at zero. Using data from vehicle accidents on Indiana interstates, the estimation results show that many factors relating to pavement condition, roadway geometrics and traffic characteristics significantly affect vehicle accident rates.

Probabilistic assessment of precipitation-triggered landslides using historical records of landslide occurence, Seattle, Washington

USGS Publications Warehouse

Coe, J.A.; Michael, J.A.; Crovelli, R.A.; Savage, W.Z.; Laprade, W.T.; Nashem, W.D.

2004-01-01

Ninety years of historical landslide records were used as input to the Poisson and binomial probability models. Results from these models show that, for precipitation-triggered landslides, approximately 9 percent of the area of Seattle has annual exceedance probabilities of 1 percent or greater. Application of the Poisson model for estimating the future occurrence of individual landslides results in a worst-case scenario map, with a maximum annual exceedance probability of 25 percent on a hillslope near Duwamish Head in West Seattle. Application of the binomial model for estimating the future occurrence of a year with one or more landslides results in a map with a maximum annual exceedance probability of 17 percent (also near Duwamish Head). Slope and geology both play a role in localizing the occurrence of landslides in Seattle. A positive correlation exists between slope and mean exceedance probability, with probability tending to increase as slope increases. Sixty-four percent of all historical landslide locations are within 150 m (500 ft, horizontal distance) of the Esperance Sand/Lawton Clay contact, but within this zone, no positive or negative correlation exists between exceedance probability and distance to the contact.

Anti-neuropeptide Y plasma immunoglobulins in relation to mood and appetite in depressive disorder.

PubMed

Garcia, Frederico D; Coquerel, Quentin; do Rego, Jean-Claude; Cravezic, Aurore; Bole-Feysot, Christine; Kiive, Evelyn; Déchelotte, Pierre; Harro, Jaanus; Fetissov, Sergueï O

2012-09-01

Depression and eating disorders are frequently associated, but the molecular pathways responsible for co-occurrence of altered mood, appetite and body weight are not yet fully understood. Neuropeptide Y (NPY) has potent antidepressant and orexigenic properties and low central NPY levels have been reported in major depression. In the present study, we hypothesized that in patients with major depression alteration of mood, appetite and body weight may be related to NPY-reactive autoantibodies (autoAbs). To test this hypothesis, we compared plasma levels and affinities of NPY-reactive autoAbs between patients with major depression and healthy controls. Then, to evaluate if changes of NPY autoAb properties can be causally related to altered mood and appetite, we developed central and peripheral passive transfer models of human autoAbs in mice and studied depressive-like behavior in forced-swim test and food intake. We found that plasma levels of NPY IgG autoAbs were lower in patients with moderate but not with mild depression correlating negatively with the Montgomery-Åsberg Depression Rating Scale scores and with immobility time of the forced-swim test in mice after peripheral injection of autoAbs. No significant differences in NPY IgG autoAb affinities between patients with depression and controls were found, but higher affinity of IgG autoAbs for NPY was associated with lower body mass index and prevented NPY-induced orexigenic response in mice after their central injection. These data suggest that changes of plasma levels of anti-NPY autoAbs are relevant to altered mood, while changes of their affinity may participate in altered appetite and body weight in patients with depressive disorder. Copyright © 2012 Elsevier Ltd. All rights reserved.

[A study on the relationship between postmortem interval and the changes of DNA content in the kidney cellule of rat].

PubMed

Liu, L; Peng, D B; Liu, Y; Deng, W N; Liu, Y L; Li, J J

2001-05-01

To study changes of DNA content in the kidney cellule of rats and relationship with the postmortem interval. This experiment chose seven parameter of cell nuclear, including the area and integral optical density, determined the changes of DNA content in the kidney cellule of 15 rats at different intervals between 0 and 48 h postmortem with auto-TV-image system. The degradation rate of DNA in nuclear has a certainty relationship to early PMI(in 48 h) of rat, and get binomial regress equation. Determining the quantity of DNA in nuclear should be an objective and exact way to estimate the PMI.

The magnetisation distribution of the Ising model - a new approach

NASA Astrophysics Data System (ADS)

Hakan Lundow, Per; Rosengren, Anders

2010-03-01

A completely new approach to the Ising model in 1 to 5 dimensions is developed. We employ a generalisation of the binomial coefficients to describe the magnetisation distributions of the Ising model. For the complete graph this distribution is exact. For simple lattices of dimensions d=1 and d=5 the magnetisation distributions are remarkably well-fitted by the generalized binomial distributions. For d=4 we are only slightly less successful, while for d=2,3 we see some deviations (with exceptions!) between the generalized binomial and the Ising distribution. The results speak in favour of the generalized binomial distribution's correctness regarding their general behaviour in comparison to the Ising model. A theoretical analysis of the distribution's moments also lends support their being correct asymptotically, including the logarithmic corrections in d=4. The full extent to which they correctly model the Ising distribution, and for which graph families, is not settled though.

Negative Urgency, Distress Tolerance, and Substance Abuse Among College Students

PubMed Central

Kaiser, Alison J.; Milich, Richard; Lynam, Donald R.; Charnigo, Richard J.

2012-01-01

Objective Negative affect has been consistently linked with substance use/problems in prior research. The present study sought to build upon these findings by exploring how an individual’s characteristic responding to negative affect impacts substance abuse risk. Trait negative affect was examined in relation to substance abuse outcomes along with two variables tapping into response to negative affect: Distress Tolerance, an individual’s perceived ability to tolerate negative affect, and Negative Urgency, the tendency to act rashly while experiencing distress. Method Participants were 525 first-year college students (48.1% male, 81.1% Caucasian), who completed self-report measures assessing personality traits and alcohol-related problems, and a structured interview assessing past and current substance use. Relations were tested using Zero-Inflated Negative Binomial regression models, and each of the personality variables was tested in a model on its own, and in a model where all three traits were accounted for. Results Negative Urgency emerged as the best predictor, relating to every one of the substance use outcome variables even when trait negative affect and Distress Tolerance were accounted for. Conclusions These findings suggest that Negative Urgency is an important factor to consider in developing prevention and intervention efforts aimed at reducing substance use and problems. PMID:22698894

3D Microperfusion Model of ADPKD

DTIC Science & Technology

2015-10-01

Stratasys 3D printer. PDMS was cast in the negative molds in order to create permanent biocompatible plastic masters (SmoothCast 310). All goals of task...fabrication was accomplished using a custom multistep fabrication process. A negative mold of the bioreactor, designed in AutoCAD, was created using a...immortalized renal cortical epithelial cells (NKi-2). A range of doxycycline concentrations were dosed on the cells for 48 hours to test for induction of

Relating river discharge and water temperature to the recruitment of age‐0 White Sturgeon (Acipenser transmontanus Richardson, 1836) in the Columbia River using over‐dispersed catch data

USGS Publications Warehouse

Counihan, Timothy D.; Chapman, Colin G.

2018-01-01

The goals were to (i) determine if river discharge and water temperature during various early life history stages were predictors of age‐0 White Sturgeon, Acipenser transmontanus, recruitment, and (ii) provide an example of how over‐dispersed catch data, including data with many zero observations, can be used to better understand the effects of regulated rivers on the productivity of depressed sturgeon populations. An information theoretic approach was used to develop and select negative binomial and zero‐inflated negative binomial models that model the relation of age‐0 White Sturgeon survey data from three contiguous Columbia River reservoirs to river discharge and water temperature during spawning, egg incubation, larval, and post‐larval phases. Age‐0 White Sturgeon were collected with small mesh gill nets in The Dalles and John Day reservoirs from 1997 to 2014 and a bottom trawl in Bonneville Reservoir from 1989 to 2006. Results suggest that seasonal river discharge was positively correlated with age‐0 recruitment; notably that discharge, 16 June–31 July was positively correlated to age‐0 recruitment in all three reservoirs. The best approximating models for two of the three reservoirs also suggest that seasonal water temperature may be a determinant of age‐0 recruitment. Our research demonstrates how over‐dispersed catch data can be used to better understand the effects of environmental conditions on sturgeon populations caused by the construction and operation of dams.

Not in My Back Yard: A Comparative Analysis of Crime Around Publicly Funded Drug Treatment Centers, Liquor Stores, Convenience Stores, and Corner Stores in One Mid-Atlantic City

PubMed Central

Furr-Holden, C. Debra M.; Milam, Adam J.; Nesoff, Elizabeth D.; Johnson, Renee M.; Fakunle, David O.; Jennings, Jacky M.; Thorpe, Roland J.

2016-01-01

Objective: This research examined whether publicly funded drug treatment centers (DTCs) were associated with violent crime in excess of the violence happening around other commercial businesses. Method: Violent crime data and locations of community entities were geocoded and mapped. DTCs and other retail outlets were matched based on a Neighborhood Disadvantage score at the census tract level. Street network buffers ranging from 100 to 1,400 feet were placed around each location. Negative binomial regression models were used to estimate the relationship between the count of violent crimes and the distance from each business type. Results: Compared with the mean count of violent crime around drug treatment centers, the mean count of violent crime (M = 2.87) was significantly higher around liquor stores (M = 3.98; t test; p < .01) and corner stores (M = 3.78; t test; p < .01), and there was no statistically significant difference between the count around convenience stores (M = 2.65; t test; p = .32). In the adjusted negative binomial regression models, there was a negative and significant relationship between the count of violent crime and the distance from drug treatment centers (β = -.069, p < .01), liquor stores (β = -.081, p < .01), corner stores (β = -.116, p < .01), and convenience stores (β = -.154, p < .01). Conclusions: Violent crime associated with drug treatment centers is similar to that associated with liquor stores and is less frequent than that associated with convenience stores and corner stores. PMID:26751351

Novel formulation of the ℳ model through the Generalized-K distribution for atmospheric optical channels.

PubMed

Garrido-Balsells, José María; Jurado-Navas, Antonio; Paris, José Francisco; Castillo-Vazquez, Miguel; Puerta-Notario, Antonio

2015-03-09

In this paper, a novel and deeper physical interpretation on the recently published Málaga or ℳ statistical distribution is provided. This distribution, which is having a wide acceptance by the scientific community, models the optical irradiance scintillation induced by the atmospheric turbulence. Here, the analytical expressions previously published are modified in order to express them by a mixture of the known Generalized-K and discrete Binomial and Negative Binomial distributions. In particular, the probability density function (pdf) of the ℳ model is now obtained as a linear combination of these Generalized-K pdf, in which the coefficients depend directly on the parameters of the ℳ distribution. In this way, the Málaga model can be physically interpreted as a superposition of different optical sub-channels each of them described by the corresponding Generalized-K fading model and weighted by the ℳ dependent coefficients. The expressions here proposed are simpler than the equations of the original ℳ model and are validated by means of numerical simulations by generating ℳ -distributed random sequences and their associated histogram. This novel interpretation of the Málaga statistical distribution provides a valuable tool for analyzing the performance of atmospheric optical channels for every turbulence condition.

Speech-discrimination scores modeled as a binomial variable.

PubMed

Thornton, A R; Raffin, M J

1978-09-01

Many studies have reported variability data for tests of speech discrimination, and the disparate results of these studies have not been given a simple explanation. Arguments over the relative merits of 25- vs 50-word tests have ignored the basic mathematical properties inherent in the use of percentage scores. The present study models performance on clinical tests of speech discrimination as a binomial variable. A binomial model was developed, and some of its characteristics were tested against data from 4120 scores obtained on the CID Auditory Test W-22. A table for determining significant deviations between scores was generated and compared to observed differences in half-list scores for the W-22 tests. Good agreement was found between predicted and observed values. Implications of the binomial characteristics of speech-discrimination scores are discussed.

Comparison and Field Validation of Binomial Sampling Plans for Oligonychus perseae (Acari: Tetranychidae) on Hass Avocado in Southern California.

PubMed

Lara, Jesus R; Hoddle, Mark S

2015-08-01

Oligonychus perseae Tuttle, Baker, & Abatiello is a foliar pest of 'Hass' avocados [Persea americana Miller (Lauraceae)]. The recommended action threshold is 50-100 motile mites per leaf, but this count range and other ecological factors associated with O. perseae infestations limit the application of enumerative sampling plans in the field. Consequently, a comprehensive modeling approach was implemented to compare the practical application of various binomial sampling models for decision-making of O. perseae in California. An initial set of sequential binomial sampling models were developed using three mean-proportion modeling techniques (i.e., Taylor's power law, maximum likelihood, and an empirical model) in combination with two-leaf infestation tally thresholds of either one or two mites. Model performance was evaluated using a robust mite count database consisting of >20,000 Hass avocado leaves infested with varying densities of O. perseae and collected from multiple locations. Operating characteristic and average sample number results for sequential binomial models were used as the basis to develop and validate a standardized fixed-size binomial sampling model with guidelines on sample tree and leaf selection within blocks of avocado trees. This final validated model requires a leaf sampling cost of 30 leaves and takes into account the spatial dynamics of O. perseae to make reliable mite density classifications for a 50-mite action threshold. Recommendations for implementing this fixed-size binomial sampling plan to assess densities of O. perseae in commercial California avocado orchards are discussed. © The Authors 2015. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Multilevel Models for Binary Data

ERIC Educational Resources Information Center

Powers, Daniel A.

2012-01-01

The methods and models for categorical data analysis cover considerable ground, ranging from regression-type models for binary and binomial data, count data, to ordered and unordered polytomous variables, as well as regression models that mix qualitative and continuous data. This article focuses on methods for binary or binomial data, which are…

Estimating relative risks in multicenter studies with a small number of centers - which methods to use? A simulation study.

PubMed

Pedroza, Claudia; Truong, Van Thi Thanh

2017-11-02

Analyses of multicenter studies often need to account for center clustering to ensure valid inference. For binary outcomes, it is particularly challenging to properly adjust for center when the number of centers or total sample size is small, or when there are few events per center. Our objective was to evaluate the performance of generalized estimating equation (GEE) log-binomial and Poisson models, generalized linear mixed models (GLMMs) assuming binomial and Poisson distributions, and a Bayesian binomial GLMM to account for center effect in these scenarios. We conducted a simulation study with few centers (≤30) and 50 or fewer subjects per center, using both a randomized controlled trial and an observational study design to estimate relative risk. We compared the GEE and GLMM models with a log-binomial model without adjustment for clustering in terms of bias, root mean square error (RMSE), and coverage. For the Bayesian GLMM, we used informative neutral priors that are skeptical of large treatment effects that are almost never observed in studies of medical interventions. All frequentist methods exhibited little bias, and the RMSE was very similar across the models. The binomial GLMM had poor convergence rates, ranging from 27% to 85%, but performed well otherwise. The results show that both GEE models need to use small sample corrections for robust SEs to achieve proper coverage of 95% CIs. The Bayesian GLMM had similar convergence rates but resulted in slightly more biased estimates for the smallest sample sizes. However, it had the smallest RMSE and good coverage across all scenarios. These results were very similar for both study designs. For the analyses of multicenter studies with a binary outcome and few centers, we recommend adjustment for center with either a GEE log-binomial or Poisson model with appropriate small sample corrections or a Bayesian binomial GLMM with informative priors.

Dental Caries and Enamel Defects in Very Low Birth Weight Adolescents

PubMed Central

Nelson, S.; Albert, J.M.; Lombardi, G.; Wishnek, S.; Asaad, G.; Kirchner, H.L.; Singer, L.T.

2011-01-01

Objectives The purpose of this study was to examine developmental enamel defects and dental caries in very low birth weight adolescents with high risk (HR-VLBW) and low risk (LR-VLBW) compared to full-term (term) adolescents. Methods The sample consisted of 224 subjects (80 HR-VLBW, 59 LR-VLBW, 85 term adolescents) recruited from an ongoing longitudinal study. Sociodemographic and medical information was available from birth. Dental examination of the adolescent at the 14-year visit included: enamel defects (opacity and hypoplasia); decayed, missing, filled teeth of incisors and molars (DMFT-IM) and of overall permanent teeth (DMFT); Simplified Oral Hygiene Index for debris/calculus on teeth, and sealant presence. A caregiver questionnaire completed simultaneously assessed dental behavior, access, insurance status and prevention factors. Hierarchical analysis utilized the zero-inflated negative binomial model and zero-inflated Poisson model. Results The zero-inflated negative binomial model controlling for sociodemographic variables indicated that the LR-VLBW group had an estimated 75% increase (p < 0.05) in number of demarcated opacities in the incisors and first molar teeth compared to the term group. Hierarchical modeling indicated that demarcated opacities were a significant predictor of DMFT-IM after control for relevant covariates. The term adolescents had significantly increased DMFT-IM and DMFT scores compared to the LR-VLBW adolescents. Conclusion LR-VLBW was a significant risk factor for increased enamel defects in the permanent incisors and first molars. Term children had increased caries compared to the LR-VLBW group. The effect of birth group and enamel defects on caries has to be investigated longitudinally from birth. PMID:20975268

Linking parasite populations in hosts to parasite populations in space through Taylor's law and the negative binomial distribution

PubMed Central

Poulin, Robert; Lagrue, Clément

2017-01-01

The spatial distribution of individuals of any species is a basic concern of ecology. The spatial distribution of parasites matters to control and conservation of parasites that affect human and nonhuman populations. This paper develops a quantitative theory to predict the spatial distribution of parasites based on the distribution of parasites in hosts and the spatial distribution of hosts. Four models are tested against observations of metazoan hosts and their parasites in littoral zones of four lakes in Otago, New Zealand. These models differ in two dichotomous assumptions, constituting a 2 × 2 theoretical design. One assumption specifies whether the variance function of the number of parasites per host individual is described by Taylor's law (TL) or the negative binomial distribution (NBD). The other assumption specifies whether the numbers of parasite individuals within each host in a square meter of habitat are independent or perfectly correlated among host individuals. We find empirically that the variance–mean relationship of the numbers of parasites per square meter is very well described by TL but is not well described by NBD. Two models that posit perfect correlation of the parasite loads of hosts in a square meter of habitat approximate observations much better than two models that posit independence of parasite loads of hosts in a square meter, regardless of whether the variance–mean relationship of parasites per host individual obeys TL or NBD. We infer that high local interhost correlations in parasite load strongly influence the spatial distribution of parasites. Local hotspots could influence control and conservation of parasites. PMID:27994156

Four Bootstrap Confidence Intervals for the Binomial-Error Model.

ERIC Educational Resources Information Center

Lin, Miao-Hsiang; Hsiung, Chao A.

1992-01-01

Four bootstrap methods are identified for constructing confidence intervals for the binomial-error model. The extent to which similar results are obtained and the theoretical foundation of each method and its relevance and ranges of modeling the true score uncertainty are discussed. (SLD)

EM Adaptive LASSO—A Multilocus Modeling Strategy for Detecting SNPs Associated with Zero-inflated Count Phenotypes

PubMed Central

Mallick, Himel; Tiwari, Hemant K.

2016-01-01

Count data are increasingly ubiquitous in genetic association studies, where it is possible to observe excess zero counts as compared to what is expected based on standard assumptions. For instance, in rheumatology, data are usually collected in multiple joints within a person or multiple sub-regions of a joint, and it is not uncommon that the phenotypes contain enormous number of zeroes due to the presence of excessive zero counts in majority of patients. Most existing statistical methods assume that the count phenotypes follow one of these four distributions with appropriate dispersion-handling mechanisms: Poisson, Zero-inflated Poisson (ZIP), Negative Binomial, and Zero-inflated Negative Binomial (ZINB). However, little is known about their implications in genetic association studies. Also, there is a relative paucity of literature on their usefulness with respect to model misspecification and variable selection. In this article, we have investigated the performance of several state-of-the-art approaches for handling zero-inflated count data along with a novel penalized regression approach with an adaptive LASSO penalty, by simulating data under a variety of disease models and linkage disequilibrium patterns. By taking into account data-adaptive weights in the estimation procedure, the proposed method provides greater flexibility in multi-SNP modeling of zero-inflated count phenotypes. A fast coordinate descent algorithm nested within an EM (expectation-maximization) algorithm is implemented for estimating the model parameters and conducting variable selection simultaneously. Results show that the proposed method has optimal performance in the presence of multicollinearity, as measured by both prediction accuracy and empirical power, which is especially apparent as the sample size increases. Moreover, the Type I error rates become more or less uncontrollable for the competing methods when a model is misspecified, a phenomenon routinely encountered in practice. PMID:27066062

EM Adaptive LASSO-A Multilocus Modeling Strategy for Detecting SNPs Associated with Zero-inflated Count Phenotypes.

PubMed

Mallick, Himel; Tiwari, Hemant K

2016-01-01

Count data are increasingly ubiquitous in genetic association studies, where it is possible to observe excess zero counts as compared to what is expected based on standard assumptions. For instance, in rheumatology, data are usually collected in multiple joints within a person or multiple sub-regions of a joint, and it is not uncommon that the phenotypes contain enormous number of zeroes due to the presence of excessive zero counts in majority of patients. Most existing statistical methods assume that the count phenotypes follow one of these four distributions with appropriate dispersion-handling mechanisms: Poisson, Zero-inflated Poisson (ZIP), Negative Binomial, and Zero-inflated Negative Binomial (ZINB). However, little is known about their implications in genetic association studies. Also, there is a relative paucity of literature on their usefulness with respect to model misspecification and variable selection. In this article, we have investigated the performance of several state-of-the-art approaches for handling zero-inflated count data along with a novel penalized regression approach with an adaptive LASSO penalty, by simulating data under a variety of disease models and linkage disequilibrium patterns. By taking into account data-adaptive weights in the estimation procedure, the proposed method provides greater flexibility in multi-SNP modeling of zero-inflated count phenotypes. A fast coordinate descent algorithm nested within an EM (expectation-maximization) algorithm is implemented for estimating the model parameters and conducting variable selection simultaneously. Results show that the proposed method has optimal performance in the presence of multicollinearity, as measured by both prediction accuracy and empirical power, which is especially apparent as the sample size increases. Moreover, the Type I error rates become more or less uncontrollable for the competing methods when a model is misspecified, a phenomenon routinely encountered in practice.

Phase transition and information cascade in a voting model

NASA Astrophysics Data System (ADS)

Hisakado, M.; Mori, S.

2010-08-01

In this paper, we introduce a voting model that is similar to a Keynesian beauty contest and analyse it from a mathematical point of view. There are two types of voters—copycat and independent—and two candidates. Our voting model is a binomial distribution (independent voters) doped in a beta binomial distribution (copycat voters). We find that the phase transition in this system is at the upper limit of t, where t is the time (or the number of the votes). Our model contains three phases. If copycats constitute a majority or even half of the total voters, the voting rate converges more slowly than it would in a binomial distribution. If independents constitute the majority of voters, the voting rate converges at the same rate as it would in a binomial distribution. We also study why it is difficult to estimate the conclusion of a Keynesian beauty contest when there is an information cascade.

On Models for Binomial Data with Random Numbers of Trials

PubMed Central

Comulada, W. Scott; Weiss, Robert E.

2010-01-01

Summary A binomial outcome is a count s of the number of successes out of the total number of independent trials n = s + f, where f is a count of the failures. The n are random variables not fixed by design in many studies. Joint modeling of (s, f) can provide additional insight into the science and into the probability π of success that cannot be directly incorporated by the logistic regression model. Observations where n = 0 are excluded from the binomial analysis yet may be important to understanding how π is influenced by covariates. Correlation between s and f may exist and be of direct interest. We propose Bayesian multivariate Poisson models for the bivariate response (s, f), correlated through random effects. We extend our models to the analysis of longitudinal and multivariate longitudinal binomial outcomes. Our methodology was motivated by two disparate examples, one from teratology and one from an HIV tertiary intervention study. PMID:17688514

Distribution of chewing lice upon the polygynous peacock Pavo cristatus.

PubMed

Stewart, I R; Clark, F; Petrie, M

1996-04-01

An opportunistic survey of louse distribution upon the peacock Pavo cristatus was undertaken following a cull of 23 birds from an English zoo. After complete skin and feather dissolution, 2 species of lice were retrieved, Goniodes pavonis and Amyrsidea minuta. The distribution of both louse species could be described by a negative binomial model. The significance of this is discussed in relation to transmission dynamics of lice in the atypical avian mating system found in the peacock, which involves no male parental care.

On extinction time of a generalized endemic chain-binomial model.

PubMed

Aydogmus, Ozgur

2016-09-01

We considered a chain-binomial epidemic model not conferring immunity after infection. Mean field dynamics of the model has been analyzed and conditions for the existence of a stable endemic equilibrium are determined. The behavior of the chain-binomial process is probabilistically linked to the mean field equation. As a result of this link, we were able to show that the mean extinction time of the epidemic increases at least exponentially as the population size grows. We also present simulation results for the process to validate our analytical findings. Copyright © 2016 Elsevier Inc. All rights reserved.

Binomial leap methods for simulating stochastic chemical kinetics.

PubMed

Tian, Tianhai; Burrage, Kevin

2004-12-01

This paper discusses efficient simulation methods for stochastic chemical kinetics. Based on the tau-leap and midpoint tau-leap methods of Gillespie [D. T. Gillespie, J. Chem. Phys. 115, 1716 (2001)], binomial random variables are used in these leap methods rather than Poisson random variables. The motivation for this approach is to improve the efficiency of the Poisson leap methods by using larger stepsizes. Unlike Poisson random variables whose range of sample values is from zero to infinity, binomial random variables have a finite range of sample values. This probabilistic property has been used to restrict possible reaction numbers and to avoid negative molecular numbers in stochastic simulations when larger stepsize is used. In this approach a binomial random variable is defined for a single reaction channel in order to keep the reaction number of this channel below the numbers of molecules that undergo this reaction channel. A sampling technique is also designed for the total reaction number of a reactant species that undergoes two or more reaction channels. Samples for the total reaction number are not greater than the molecular number of this species. In addition, probability properties of the binomial random variables provide stepsize conditions for restricting reaction numbers in a chosen time interval. These stepsize conditions are important properties of robust leap control strategies. Numerical results indicate that the proposed binomial leap methods can be applied to a wide range of chemical reaction systems with very good accuracy and significant improvement on efficiency over existing approaches. (c) 2004 American Institute of Physics.

Impact of early childhood caries on oral health-related quality of life of preschool children.

PubMed

Li, M Y; Zhi, Q H; Zhou, Y; Qiu, R M; Lin, H C

2015-03-01

Child oral health-related quality of life (COHRQoL) has been assessed in developed areas; however, it remains unstudied in mainland China. Studies on COHRQoL would benefit a large number of children in China suffering from oral health problems such as dental caries. This study explored the relationship between COHRQoL and early childhood caries, adjusted by socioeconomic factors, in 3- to 4-year-old children in a region of southern China. In this study, 1062 children aged 3-4 years were recruited by cluster sampling and their oral health statuses were examined by a trained dentist. The Chinese version of the Early Childhood Oral Health Impact Scale (ECOHIS) and questions about the children's socioeconomic conditions were completed by the children's parents. A negative binomial regression analysis was used to assess the prevalence of early childhood caries among the children and its influence on COHRQoL. The total ECOHIS scores of the returned scale sets ranged from 0 to 31, and their average scores was 3.1±5.1. The negative binomial analysis showed that the dmfs indices were significantly associated with the ECOHIS score and subscale scores (P<0.05). The multivariate adjusted model showed that a higher dmft index was associated with greater negative impact on COHRQoL (RR = 1.10; 95% CI = 1.07, 1.13; P < 0.05). However, demographic and socioeconomic factors were not associated with COHRQoL (P>0.05). The severity of early childhood caries has a negative impact on the oral health-related quality of life of preschool children and their parents.

Discrimination of numerical proportions: A comparison of binomial and Gaussian models.

PubMed

Raidvee, Aire; Lember, Jüri; Allik, Jüri

2017-01-01

Observers discriminated the numerical proportion of two sets of elements (N = 9, 13, 33, and 65) that differed either by color or orientation. According to the standard Thurstonian approach, the accuracy of proportion discrimination is determined by irreducible noise in the nervous system that stochastically transforms the number of presented visual elements onto a continuum of psychological states representing numerosity. As an alternative to this customary approach, we propose a Thurstonian-binomial model, which assumes discrete perceptual states, each of which is associated with a certain visual element. It is shown that the probability β with which each visual element can be noticed and registered by the perceptual system can explain data of numerical proportion discrimination at least as well as the continuous Thurstonian-Gaussian model, and better, if the greater parsimony of the Thurstonian-binomial model is taken into account using AIC model selection. We conclude that Gaussian and binomial models represent two different fundamental principles-internal noise vs. using only a fraction of available information-which are both plausible descriptions of visual perception.

Analyzing hospitalization data: potential limitations of Poisson regression.

PubMed

Weaver, Colin G; Ravani, Pietro; Oliver, Matthew J; Austin, Peter C; Quinn, Robert R

2015-08-01

Poisson regression is commonly used to analyze hospitalization data when outcomes are expressed as counts (e.g. number of days in hospital). However, data often violate the assumptions on which Poisson regression is based. More appropriate extensions of this model, while available, are rarely used. We compared hospitalization data between 206 patients treated with hemodialysis (HD) and 107 treated with peritoneal dialysis (PD) using Poisson regression and compared results from standard Poisson regression with those obtained using three other approaches for modeling count data: negative binomial (NB) regression, zero-inflated Poisson (ZIP) regression and zero-inflated negative binomial (ZINB) regression. We examined the appropriateness of each model and compared the results obtained with each approach. During a mean 1.9 years of follow-up, 183 of 313 patients (58%) were never hospitalized (indicating an excess of 'zeros'). The data also displayed overdispersion (variance greater than mean), violating another assumption of the Poisson model. Using four criteria, we determined that the NB and ZINB models performed best. According to these two models, patients treated with HD experienced similar hospitalization rates as those receiving PD {NB rate ratio (RR): 1.04 [bootstrapped 95% confidence interval (CI): 0.49-2.20]; ZINB summary RR: 1.21 (bootstrapped 95% CI 0.60-2.46)}. Poisson and ZIP models fit the data poorly and had much larger point estimates than the NB and ZINB models [Poisson RR: 1.93 (bootstrapped 95% CI 0.88-4.23); ZIP summary RR: 1.84 (bootstrapped 95% CI 0.88-3.84)]. We found substantially different results when modeling hospitalization data, depending on the approach used. Our results argue strongly for a sound model selection process and improved reporting around statistical methods used for modeling count data. © The Author 2015. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

Traumatic Brain Injury among US Active Duty Military Personnel and Negative Drinking-Related Consequences

PubMed Central

Adams, Rachel Sayko; Larson, Mary Jo; Corrigan, John D.; Ritter, Grant A.; Williams, Thomas V.

2013-01-01

This study used the 2008 Department of Defense Survey of Health Related Behaviors among Active Duty Military Personnel to determine whether traumatic brain injury (TBI) is associated with past year drinking-related consequences. The study sample included currently-drinking personnel who had a combat deployment in the past year and were home for ≥6 months (N = 3,350). Negative binomial regression models were used to assess the incidence rate ratios of consequences, by TBI-level. Experiencing a TBI with a loss of consciousness >20 minutes was significantly associated with consequences independent of demographics, combat exposure, posttraumatic stress disorder, and binge drinking. The study’s limitations are noted. PMID:23869456

The Effect of Sharrows, Painted Bicycle Lanes and Physically Protected Paths on the Severity of Bicycle Injuries Caused by Motor Vehicles.

PubMed

Wall, Stephen P; Lee, David C; Frangos, Spiros G; Sethi, Monica; Heyer, Jessica H; Ayoung-Chee, Patricia; DiMaggio, Charles J

2016-01-01

We conducted individual and ecologic analyses of prospectively collected data from 839 injured bicyclists who collided with motorized vehicles and presented to Bellevue Hospital, an urban Level-1 trauma center in New York City, from December 2008 to August 2014. Variables included demographics, scene information, rider behaviors, bicycle route availability, and whether the collision occurred before the road segment was converted to a bicycle route. We used negative binomial modeling to assess the risk of injury occurrence following bicycle path or lane implementation. We dichotomized U.S. National Trauma Data Bank Injury Severity Scores (ISS) into none/mild (0-8) versus moderate, severe, or critical (>8) and used adjusted multivariable logistic regression to model the association of ISS with collision proximity to sharrows (i.e., bicycle lanes designated for sharing with cars), painted bicycle lanes, or physically protected paths. Negative binomial modeling of monthly counts, while adjusting for pedestrian activity, revealed that physically protected paths were associated with 23% fewer injuries. Painted bicycle lanes reduced injury risk by nearly 90% (IDR 0.09, 95% CI 0.02-0.33). Holding all else equal, compared to no bicycle route, a bicycle injury nearby sharrows was nearly twice as likely to be moderate, severe, or critical (adjusted odds ratio 1.94; 95% confidence interval (CI) 0.91-4.15). Painted bicycle lanes and physically protected paths were 1.52 (95% CI 0.85-2.71) and 1.66 (95% CI 0.85-3.22) times as likely to be associated with more than mild injury respectively.

Sickness absence and psychosocial job quality: an analysis from a longitudinal survey of working Australians, 2005-2012.

PubMed

Milner, Allison; Butterworth, Peter; Bentley, Rebecca; Kavanagh, Anne M; LaMontagne, Anthony D

2015-05-15

Sickness absence is associated with adverse health, organizational, and societal outcomes. Using data from a longitudinal cohort study of working Australians (the Household, Income and Labour Dynamics in Australia (HILDA) Survey), we examined the relationship between changes in individuals' overall psychosocial job quality and variation in sickness absence. The outcome variables were paid sickness absence (yes/no) and number of days of paid sickness absence in the past year (2005-2012). The main exposure variable was psychosocial job quality, measured using a psychosocial job quality index (levels of job control, demands and complexity, insecurity, and perceptions of unfair pay). Analysis was conducted using longitudinal fixed-effects logistic regression models and negative binomial regression models. There was a dose-response relationship between the number of psychosocial job stressors reported by an individual and the odds of paid sickness absence (1 adversity: odds ratio (OR) = 1.26, 95% confidence interval (CI): 1.09, 1.45 (P = 0.002); 2 adversities: OR = 1.28, 95% CI: 1.09, 1.51 (P = 0.002); ≥3 adversities: OR = 1.58, 95% CI: 1.29, 1.94 (P < 0.001)). The negative binomial regression models also indicated that respondents reported a greater number of days of sickness absence in response to worsening psychosocial job quality. These results suggest that workplace interventions aiming to improve the quality of work could help reduce sickness absence. © The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

A review on models for count data with extra zeros

NASA Astrophysics Data System (ADS)

Zamri, Nik Sarah Nik; Zamzuri, Zamira Hasanah

2017-04-01

Typically, the zero inflated models are usually used in modelling count data with excess zeros. The existence of the extra zeros could be structural zeros or random which occur by chance. These types of data are commonly found in various disciplines such as finance, insurance, biomedical, econometrical, ecology, and health sciences. As found in the literature, the most popular zero inflated models used are zero inflated Poisson and zero inflated negative binomial. Recently, more complex models have been developed to account for overdispersion and unobserved heterogeneity. In addition, more extended distributions are also considered in modelling data with this feature. In this paper, we review related literature, provide a recent development and summary on models for count data with extra zeros.

Community covariates of malnutrition based mortality among older adults.

PubMed

Lee, Matthew R; Berthelot, Emily R

2010-05-01

The purpose of this study was to identify community level covariates of malnutrition-based mortality among older adults. A community level framework was delineated which explains rates of malnutrition-related mortality among older adults as a function of community levels of socioeconomic disadvantage, disability, and social isolation among members of this group. County level data on malnutrition mortality of people 65 years of age and older for the period 2000-2003 were drawn from the CDC WONDER system databases. County level measures of older adult socioeconomic disadvantage, disability, and social isolation were derived from the 2000 US Census of Population and Housing. Negative binomial regression models adjusting for the size of the population at risk, racial composition, urbanism, and region were estimated to assess the relationships among these indicators. Results from negative binomial regression analysis yielded the following: a standard deviation increase in socioeconomic/physical disadvantage was associated with a 12% increase in the rate of malnutrition mortality among older adults (p < 0.001), whereas a standard deviation increase in social isolation was associated with a 5% increase in malnutrition mortality among older adults (p < 0.05). Community patterns of malnutrition based mortality among older adults are partly a function of levels of socioeconomic and physical disadvantage and social isolation among older adults. 2010 Elsevier Inc. All rights reserved.

Association between adherence to physical activity guidelines and health-related quality of life among individuals with physician-diagnosed arthritis.

PubMed

Austin, Shamly; Qu, Haiyan; Shewchuk, Richard M

2012-10-01

To examine the association between adherence to physical activity guidelines and health-related quality of life (HRQOL) among individuals with arthritis. A cross-sectional sample with 33,071 US adults, 45 years or older with physician-diagnosed arthritis was obtained from 2007 Behavioral Risk Factor Surveillance System survey. We conducted negative binomial regression analysis to examine HRQOL as a function of adherence to physical activity guidelines controlling for physicians' recommendations for physical activity, age, sex, race, education, marital status, employment, annual income, health insurance, personal physician, emotional support, body mass index, activity limitations, health status, and co-morbidities based on Behavioral Model of Health Services Utilization. Descriptive statistics showed that 60% adults with arthritis did not adhere to physical activity guidelines, mean physically and mentally unhealthy days were 7.7 and 4.4 days, respectively. Results from negative binomial regression indicated that individuals who did not adhere to physical activity guidelines had 1.14 days more physically unhealthy days and 1.12 days more mentally unhealthy days than those who adhered controlling for covariates. Adherence to physical activity is important to improve HRQOL for individuals with arthritis. However, adherence is low among this population. Interventions are required to engage individuals with arthritis in physical activity.

Void probability as a function of the void's shape and scale-invariant models

NASA Technical Reports Server (NTRS)

Elizalde, E.; Gaztanaga, E.

1991-01-01

The dependence of counts in cells on the shape of the cell for the large scale galaxy distribution is studied. A very concrete prediction can be done concerning the void distribution for scale invariant models. The prediction is tested on a sample of the CfA catalog, and good agreement is found. It is observed that the probability of a cell to be occupied is bigger for some elongated cells. A phenomenological scale invariant model for the observed distribution of the counts in cells, an extension of the negative binomial distribution, is presented in order to illustrate how this dependence can be quantitatively determined. An original, intuitive derivation of this model is presented.

Low level exposure to inorganic mercury interferes with B cell receptor signaling in transitional type 1 B cells.

PubMed

Gill, R; McCabe, M J; Rosenspire, A J

2017-09-01

Mercury (Hg) has been implicated as a factor contributing to autoimmune disease in animal models and humans. However the mechanism by which this occurs has remained elusive. Since the discovery of B cells it has been appreciated by immunologists that during the normal course of B cell development, some immature B cells must be generated that produce immunoglobulin reactive to self-antigens (auto-antibodies). However in the course of normal development, the vast majority of immature auto-reactive B cells are prevented from maturing by processes collectively known as tolerance. Autoimmune disease arises when these mechanisms of tolerance are disrupted. In the B cell compartment, it is firmly established that tolerance depends in part upon negative selection of self-reactive immature (transitional type 1) B cells. In these cells negative selection depends upon signals generated by the B Cell Receptor (BCR), in the sense that those T1 B cells who's BCRs most strongly bind to, and so generate the strongest signals to self-antigens are neutralized. In this report we have utilized multicolor phosphoflow cytometry to show that in immature T1 B cells Hg attenuates signal generation by the BCR through mechanisms that may involve Lyn, a key tyrosine kinase in the BCR signal transduction pathway. We suggest that exposure to low, environmentally relevant levels of Hg, disrupts tolerance by interfering with BCR signaling in immature B cells, potentially leading to the appearance of mature auto-reactive B cells which have the ability to contribute to auto-immune disease. Copyright © 2017 Elsevier Inc. All rights reserved.

Blood screening in a southern Nigeria City: a case study with SAVAN.

PubMed

Ehikhamenor, Edeaghe; Azodo, Clement; Chinedu, Ekeh; Festus, Eghieme

2009-10-01

Commercial motorcycle transportation and motorbike riding in Nigeria is prevalent, and road traffic accidents often result. Characteristic of such accidents is massive blood loss, thus exerting extreme pressure on the blood bank for replenishment and screening. The need to galvanize the system to design a blood bank with minimal bureaucracy and easy access led to screening for blood group. A delay in accessing blood for the victims leads to higher mortality. Our approach was to establish a pre-crash blood data for all auto-bike riders who participated in Save Accident Victims Association of Nigeria (SAVAN, an indigenous, nongovernmental organization) training program. Data used were obtained from 1250 auto-bike riders who volunteered at our workshop. Tile grouping method was used for the screening. Blood group O positive (54.3%) was the most common blood group type among the auto-bike riders studied, with A positive following at 20.3 percent, B positive at 18.8 percent, O negative at 3.7 percent, AB positive at 1.3 percent, B negative at 1.1 percent, and A negative at 0.5 percent. It was observed that none of the volunteers grouped AB negative. Blood group of auto-bike riders, pedestrians, passengers, and all potential victims should be documented in their identification card to facilitate blood transfusion during major crisis or disasters where the facilities for typing are not available.

[Epidemiology of scrub typhus and influencing factors in Yunnan province, 2006-2013].

PubMed

Sun, Y; Shi, C; Li, X L; Fang, L Q; Cao, W C

2018-01-10

Objective: To understand the epidemiological characteristics of scrub typhu s and influencing factors in Yunnan province, and provide further information for the prevention and control of scrub typhus. Methods: Based on the incidence data of scrub typhus reported in Yunnan from 2006 to 2013, the epidemiological characteristics of scrub typhus were analyzed and related environmental factors were identified with panel negative binomial regression model. Results: A total of 8 980 scrub typhus cases were reported during 2006-2013 in Yunnan. The average annual incidence was 2.46/100 000, with an uptrend observed. Natural focus expansion was found, affecting 71.3% of the counties in 2013. The epidemic mainly occurred in summer and autumn with the incidence peak during July-October. The annual incidence was higher in females than in males. More cases occurred in children and farmers, the proportions of cases in farmers and pre-school aged children showed an obvious increase. Panel negative binomial regression model indicated that the transmission risk of scrub typhus was positive associated with monthly temperature and monthly relative humidity. Furthermore, an "U" pattern between the risk and the increased coverage of cropland and grassland as well as an "inverted-U" pattern between the risk and increased coverage of shrub were observed. Conclusion: It is necessary to strengthen the scrub typhus surveillance in warm and moist areas as well as the areas with high coverage of cropland and grassland in Yunnan, and the health education in children and farmers who are at high risk.

GENE-Counter: A Computational Pipeline for the Analysis of RNA-Seq Data for Gene Expression Differences

PubMed Central

Di, Yanming; Schafer, Daniel W.; Wilhelm, Larry J.; Fox, Samuel E.; Sullivan, Christopher M.; Curzon, Aron D.; Carrington, James C.; Mockler, Todd C.; Chang, Jeff H.

2011-01-01

GENE-counter is a complete Perl-based computational pipeline for analyzing RNA-Sequencing (RNA-Seq) data for differential gene expression. In addition to its use in studying transcriptomes of eukaryotic model organisms, GENE-counter is applicable for prokaryotes and non-model organisms without an available genome reference sequence. For alignments, GENE-counter is configured for CASHX, Bowtie, and BWA, but an end user can use any Sequence Alignment/Map (SAM)-compliant program of preference. To analyze data for differential gene expression, GENE-counter can be run with any one of three statistics packages that are based on variations of the negative binomial distribution. The default method is a new and simple statistical test we developed based on an over-parameterized version of the negative binomial distribution. GENE-counter also includes three different methods for assessing differentially expressed features for enriched gene ontology (GO) terms. Results are transparent and data are systematically stored in a MySQL relational database to facilitate additional analyses as well as quality assessment. We used next generation sequencing to generate a small-scale RNA-Seq dataset derived from the heavily studied defense response of Arabidopsis thaliana and used GENE-counter to process the data. Collectively, the support from analysis of microarrays as well as the observed and substantial overlap in results from each of the three statistics packages demonstrates that GENE-counter is well suited for handling the unique characteristics of small sample sizes and high variability in gene counts. PMID:21998647

Narrow log-periodic modulations in non-Markovian random walks

NASA Astrophysics Data System (ADS)

Diniz, R. M. B.; Cressoni, J. C.; da Silva, M. A. A.; Mariz, A. M.; de Araújo, J. M.

2017-12-01

What are the necessary ingredients for log-periodicity to appear in the dynamics of a random walk model? Can they be subtle enough to be overlooked? Previous studies suggest that long-range damaged memory and negative feedback together are necessary conditions for the emergence of log-periodic oscillations. The role of negative feedback would then be crucial, forcing the system to change direction. In this paper we show that small-amplitude log-periodic oscillations can emerge when the system is driven by positive feedback. Due to their very small amplitude, these oscillations can easily be mistaken for numerical finite-size effects. The models we use consist of discrete-time random walks with strong memory correlations where the decision process is taken from memory profiles based either on a binomial distribution or on a delta distribution. Anomalous superdiffusive behavior and log-periodic modulations are shown to arise in the large time limit for convenient choices of the models parameters.

Selecting Tools to Model Integer and Binomial Multiplication

ERIC Educational Resources Information Center

Pratt, Sarah Smitherman; Eddy, Colleen M.

2017-01-01

Mathematics teachers frequently provide concrete manipulatives to students during instruction; however, the rationale for using certain manipulatives in conjunction with concepts may not be explored. This article focuses on area models that are currently used in classrooms to provide concrete examples of integer and binomial multiplication. The…

Prediction of different ovarian responses using anti-Müllerian hormone following a long agonist treatment protocol for IVF.

PubMed

Heidar, Z; Bakhtiyari, M; Mirzamoradi, M; Zadehmodarres, S; Sarfjoo, F S; Mansournia, M A

2015-09-01

The purpose of this study was to predict the poor and excessive ovarian response using anti-Müllerian hormone (AMH) levels following a long agonist protocol in IVF candidates. Through a prospective cohort study, the type of relationship and appropriate scale for AMH were determined using the fractional polynomial regression. To determine the effect of AMH on the outcomes of ovarian stimulation and different ovarian responses, the multi-nominal and negative binomial regression models were fitted using backward stepwise method. The ovarian response of study subject who entered a standard long-term treatment cycle with GnRH agonist was evaluated using prediction model, separately and in combined models with (ROC) curves. The use of standard long-term treatments with GnRH agonist led to positive pregnancy test results in 30% of treated patients. With each unit increase in the log of AMH, the odds ratio of having poor response compared to normal response decreases by 64% (OR 0.36, 95% CI 0.19-0.68). Also the results of negative binomial regression model indicated that for one unit increase in the log of AMH blood levels, the odds of releasing an oocyte increased 24% (OR 1.24, 95% CI 1.14-1.35). The optimal cut-off points of AMH for predicting excessive and poor ovarian responses were 3.4 and 1.2 ng/ml, respectively, with area under curves of 0.69 (0.60-0.77) and 0.76 (0.66-0.86), respectively. By considering the age of the patient undergoing infertility treatment as a variable affecting ovulation, use of AMH levels showed to be a good test to discriminate between different ovarian responses.

Clinical and MRI activity as determinants of sample size for pediatric multiple sclerosis trials

PubMed Central

Verhey, Leonard H.; Signori, Alessio; Arnold, Douglas L.; Bar-Or, Amit; Sadovnick, A. Dessa; Marrie, Ruth Ann; Banwell, Brenda

2013-01-01

Objective: To estimate sample sizes for pediatric multiple sclerosis (MS) trials using new T2 lesion count, annualized relapse rate (ARR), and time to first relapse (TTFR) endpoints. Methods: Poisson and negative binomial models were fit to new T2 lesion and relapse count data, and negative binomial time-to-event and exponential models were fit to TTFR data of 42 children with MS enrolled in a national prospective cohort study. Simulations were performed by resampling from the best-fitting model of new T2 lesion count, number of relapses, or TTFR, under various assumptions of the effect size, trial duration, and model parameters. Results: Assuming a 50% reduction in new T2 lesions over 6 months, 90 patients/arm are required, whereas 165 patients/arm are required for a 40% treatment effect. Sample sizes for 2-year trials using relapse-related endpoints are lower than that for 1-year trials. For 2-year trials and a conservative assumption of overdispersion (ϑ), sample sizes range from 70 patients/arm (using ARR) to 105 patients/arm (TTFR) for a 50% reduction in relapses, and 230 patients/arm (ARR) to 365 patients/arm (TTFR) for a 30% relapse reduction. Assuming a less conservative ϑ, 2-year trials using ARR require 45 patients/arm (60 patients/arm for TTFR) for a 50% reduction in relapses and 145 patients/arm (200 patients/arm for TTFR) for a 30% reduction. Conclusion: Six-month phase II trials using new T2 lesion count as an endpoint are feasible in the pediatric MS population; however, trials powered on ARR or TTFR will need to be 2 years in duration and will require multicentered collaboration. PMID:23966255

Indicators of Terrorism Vulnerability in Africa

DTIC Science & Technology

2015-03-26

the terror threat and vulnerabilities across Africa. Key words: Terrorism, Africa, Negative Binomial Regression, Classification Tree iv I would like...31 Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Log -likelihood...70 viii Page 5.3 Classification Tree Description

A crash-prediction model for multilane roads.

PubMed

Caliendo, Ciro; Guida, Maurizio; Parisi, Alessandra

2007-07-01

Considerable research has been carried out in recent years to establish relationships between crashes and traffic flow, geometric infrastructure characteristics and environmental factors for two-lane rural roads. Crash-prediction models focused on multilane rural roads, however, have rarely been investigated. In addition, most research has paid but little attention to the safety effects of variables such as stopping sight distance and pavement surface characteristics. Moreover, the statistical approaches have generally included Poisson and Negative Binomial regression models, whilst Negative Multinomial regression model has been used to a lesser extent. Finally, as far as the authors are aware, prediction models involving all the above-mentioned factors have still not been developed in Italy for multilane roads, such as motorways. Thus, in this paper crash-prediction models for a four-lane median-divided Italian motorway were set up on the basis of accident data observed during a 5-year monitoring period extending between 1999 and 2003. The Poisson, Negative Binomial and Negative Multinomial regression models, applied separately to tangents and curves, were used to model the frequency of accident occurrence. Model parameters were estimated by the Maximum Likelihood Method, and the Generalized Likelihood Ratio Test was applied to detect the significant variables to be included in the model equation. Goodness-of-fit was measured by means of both the explained fraction of total variation and the explained fraction of systematic variation. The Cumulative Residuals Method was also used to test the adequacy of a regression model throughout the range of each variable. The candidate set of explanatory variables was: length (L), curvature (1/R), annual average daily traffic (AADT), sight distance (SD), side friction coefficient (SFC), longitudinal slope (LS) and the presence of a junction (J). Separate prediction models for total crashes and for fatal and injury crashes only were considered. For curves it is shown that significant variables are L, 1/R and AADT, whereas for tangents they are L, AADT and junctions. The effect of rain precipitation was analysed on the basis of hourly rainfall data and assumptions about drying time. It is shown that a wet pavement significantly increases the number of crashes. The models developed in this paper for Italian motorways appear to be useful for many applications such as the detection of critical factors, the estimation of accident reduction due to infrastructure and pavement improvement, and the predictions of accidents counts when comparing different design options. Thus this research may represent a point of reference for engineers in adjusting or designing multilane roads.

Random parameter models of interstate crash frequencies by severity, number of vehicles involved, collision and location type.

PubMed

Venkataraman, Narayan; Ulfarsson, Gudmundur F; Shankar, Venky N

2013-10-01

A nine-year (1999-2007) continuous panel of crash histories on interstates in Washington State, USA, was used to estimate random parameter negative binomial (RPNB) models for various aggregations of crashes. A total of 21 different models were assessed in terms of four ways to aggregate crashes, by: (a) severity, (b) number of vehicles involved, (c) crash type, and by (d) location characteristics. The models within these aggregations include specifications for all severities (property damage only, possible injury, evident injury, disabling injury, and fatality), number of vehicles involved (one-vehicle to five-or-more-vehicle), crash type (sideswipe, same direction, overturn, head-on, fixed object, rear-end, and other), and location types (urban interchange, rural interchange, urban non-interchange, rural non-interchange). A total of 1153 directional road segments comprising of the seven Washington State interstates were analyzed, yielding statistical models of crash frequency based on 10,377 observations. These results suggest that in general there was a significant improvement in log-likelihood when using RPNB compared to a fixed parameter negative binomial baseline model. Heterogeneity effects are most noticeable for lighting type, road curvature, and traffic volume (ADT). Median lighting or right-side lighting are linked to increased crash frequencies in many models for more than half of the road segments compared to both-sides lighting. Both-sides lighting thereby appears to generally lead to a safety improvement. Traffic volume has a random parameter but the effect is always toward increasing crash frequencies as expected. However that the effect is random shows that the effect of traffic volume on crash frequency is complex and varies by road segment. The number of lanes has a random parameter effect only in the interchange type models. The results show that road segment-specific insights into crash frequency occurrence can lead to improved design policy and project prioritization. Copyright © 2013 Elsevier Ltd. All rights reserved.

Factors affecting road mortality of white-tailed deer in eastern South Dakota

USGS Publications Warehouse

Grovenburg, Troy W.; Jenks, Jonathan A.; Klaver, Robert W.; Monteith, Kevin L.; Galster, Dwight H.; Schauer, Ron J.; Morlock, Wilbert W.; Delger, Joshua A.

2008-01-01

White-tailed deer (Odocoileus virginianus) mortalities (n = 4,433) caused by collisions with automobiles during 2003 were modeled in 35 counties in eastern South Dakota. Seventeen independent variables and 5 independent variable interactions were evaluated to explain deer mortalities. A negative binomial regression model (Ln Y = 1.25 – 0.12 [percentage tree coverage] + 0.0002 [county area] + 5.39 [county hunter success rate] + 0.0023 [vehicle proxy 96–104 km/hr roads], model deviance = 33.43, χ2 = 27.53, df = 27) was chosen using a combination of a priori model selection and AICc. Management options include use of the model to predict road mortalities and to increase the number of hunting licenses, which could result in fewer DVCs.

Quantifying the safety effects of horizontal curves on two-way, two-lane rural roads.

PubMed

Gooch, Jeffrey P; Gayah, Vikash V; Donnell, Eric T

2016-07-01

The objective of this study is to quantify the safety performance of horizontal curves on two-way, two-lane rural roads relative to tangent segments. Past research is limited by small samples sizes, outdated statistical evaluation methods, and unreported standard errors. This study overcomes these drawbacks by using the propensity scores-potential outcomes framework. The impact of adjacent curves on horizontal curve safety is also explored using a cross-sectional regression model of only horizontal curves. The models estimated in the present study used eight years of crash data (2005-2012) obtained from over 10,000 miles of state-owned two-lane rural roads in Pennsylvania. These data included information on roadway geometry (e.g., horizontal curvature, lane width, and shoulder width), traffic volume, roadside hazard rating, and the presence of various low-cost safety countermeasures (e.g., centerline and shoulder rumble strips, curve and intersection warning pavement markings, and aggressive driving pavement dots). Crash prediction is performed by means of mixed effects negative binomial regression using the explanatory variables noted previously, as well as attributes of adjacent horizontal curves. The results indicate that both the presence of a horizontal curve and its degree of curvature must be considered when predicting the frequency of total crashes on horizontal curves. Both are associated with an increase in crash frequency, which is consistent with previous findings in the literature. Mixed effects negative binomial regression models for total crash frequency on horizontal curves indicate that the distance to adjacent curves is not statistically significant. However, the degree of curvature of adjacent curves in close proximity (within 0.75 miles) was found to be statistically significant and negatively correlated with crash frequency on the subject curve. This is logical, as drivers exiting a sharp curve are likely to be driving slower and with more awareness as they approach the next horizontal curve. Copyright © 2016 Elsevier Ltd. All rights reserved.

Population Characteristics and the Nature of Egg Shells of two Phthirapteran Species Parasitizing Indian Cattle Egrets

PubMed Central

Ahmad, Aftab; Khan, Vikram; Badola, Smita; Arya, Gaurav; Bansal, Nayanci; Saxena, A. K.

2010-01-01

The prevalence, intensities of infestation, range of infestation and population composition of two phthirapteran species, Ardeicola expallidus Blagoveshtchensky (Phthiraptera: Philopteridae) and Ciconiphilus decimfasciatus Boisduval and Lacordaire (Menoponidae) on seventy cattle egrets were recorded during August 2004 to March 2005, in India. The frequency distribution patterns of both the species were skewed but did not correspond to the negative binomial model. The oviposition sites, egg laying patterns and the nature of the eggs of the two species were markedly different. PMID:21067416

[The reentrant binomial model of nuclear anomalies growth in rhabdomyosarcoma RA-23 cell populations under increasing doze of rare ionizing radiation].

PubMed

Alekseeva, N P; Alekseev, A O; Vakhtin, Iu B; Kravtsov, V Iu; Kuzovatov, S N; Skorikova, T I

2008-01-01

Distributions of nuclear morphology anomalies in transplantable rabdomiosarcoma RA-23 cell populations were investigated under effect of ionizing radiation from 0 to 45 Gy. Internuclear bridges, nuclear protrusions and dumbbell-shaped nuclei were accepted for morphological anomalies. Empirical distributions of the number of anomalies per 100 nuclei were used. The adequate model of reentrant binomial distribution has been found. The sum of binomial random variables with binomial number of summands has such distribution. Averages of these random variables were named, accordingly, internal and external average reentrant components. Their maximum likelihood estimations were received. Statistical properties of these estimations were investigated by means of statistical modeling. It has been received that at equally significant correlation between the radiation dose and the average of nuclear anomalies in cell populations after two-three cellular cycles from the moment of irradiation in vivo the irradiation doze significantly correlates with internal average reentrant component, and in remote descendants of cell transplants irradiated in vitro - with external one.

Confidence Intervals for Weighted Composite Scores under the Compound Binomial Error Model

ERIC Educational Resources Information Center

Kim, Kyung Yong; Lee, Won-Chan

2018-01-01

Reporting confidence intervals with test scores helps test users make important decisions about examinees by providing information about the precision of test scores. Although a variety of estimation procedures based on the binomial error model are available for computing intervals for test scores, these procedures assume that items are randomly…

Patterns of medicinal plant use: an examination of the Ecuadorian Shuar medicinal flora using contingency table and binomial analyses.

PubMed

Bennett, Bradley C; Husby, Chad E

2008-03-28

Botanical pharmacopoeias are non-random subsets of floras, with some taxonomic groups over- or under-represented. Moerman [Moerman, D.E., 1979. Symbols and selectivity: a statistical analysis of Native American medical ethnobotany, Journal of Ethnopharmacology 1, 111-119] introduced linear regression/residual analysis to examine these patterns. However, regression, the commonly-employed analysis, suffers from several statistical flaws. We use contingency table and binomial analyses to examine patterns of Shuar medicinal plant use (from Amazonian Ecuador). We first analyzed the Shuar data using Moerman's approach, modified to better meet requirements of linear regression analysis. Second, we assessed the exact randomization contingency table test for goodness of fit. Third, we developed a binomial model to test for non-random selection of plants in individual families. Modified regression models (which accommodated assumptions of linear regression) reduced R(2) to from 0.59 to 0.38, but did not eliminate all problems associated with regression analyses. Contingency table analyses revealed that the entire flora departs from the null model of equal proportions of medicinal plants in all families. In the binomial analysis, only 10 angiosperm families (of 115) differed significantly from the null model. These 10 families are largely responsible for patterns seen at higher taxonomic levels. Contingency table and binomial analyses offer an easy and statistically valid alternative to the regression approach.

Modeling factors influencing the demand for emergency department services in Ontario: a comparison of methods.

PubMed

Moineddin, Rahim; Meaney, Christopher; Agha, Mohammad; Zagorski, Brandon; Glazier, Richard Henry

2011-08-19

Emergency departments are medical treatment facilities, designed to provide episodic care to patients suffering from acute injuries and illnesses as well as patients who are experiencing sporadic flare-ups of underlying chronic medical conditions which require immediate attention. Supply and demand for emergency department services varies across geographic regions and time. Some persons do not rely on the service at all whereas; others use the service on repeated occasions. Issues regarding increased wait times for services and crowding illustrate the need to investigate which factors are associated with increased frequency of emergency department utilization. The evidence from this study can help inform policy makers on the appropriate mix of supply and demand targeted health care policies necessary to ensure that patients receive appropriate health care delivery in an efficient and cost-effective manner. The purpose of this report is to assess those factors resulting in increased demand for emergency department services in Ontario. We assess how utilization rates vary according to the severity of patient presentation in the emergency department. We are specifically interested in the impact that access to primary care physicians has on the demand for emergency department services. Additionally, we wish to investigate these trends using a series of novel regression models for count outcomes which have yet to be employed in the domain of emergency medical research. Data regarding the frequency of emergency department visits for the respondents of Canadian Community Health Survey (CCHS) during our study interval (2003-2005) are obtained from the National Ambulatory Care Reporting System (NACRS). Patients' emergency department utilizations were linked with information from the Canadian Community Health Survey (CCHS) which provides individual level medical, socio-demographic, psychological and behavioral information for investigating predictors of increased emergency department utilization. Six different multiple regression models for count data were fitted to assess the influence of predictors on demand for emergency department services, including: Poisson, Negative Binomial, Zero-Inflated Poisson, Zero-Inflated Negative Binomial, Hurdle Poisson, and Hurdle Negative Binomial. Comparison of competing models was assessed by the Vuong test statistic. The CCHS cycle 2.1 respondents were a roughly equal mix of males (50.4%) and females (49.6%). The majority (86.2%) were young-middle aged adults between the ages of 20-64, living in predominantly urban environments (85.9%), with mid-high household incomes (92.2%) and well-educated, receiving at least a high-school diploma (84.1%). Many participants reported no chronic disease (51.9%), fell into a small number (0-5) of ambulatory diagnostic groups (62.3%), and perceived their health status as good/excellent (88.1%); however, were projected to have high Resource Utilization Band levels of health resource utilization (68.2%). These factors were largely stable for CCHS cycle 3.1 respondents. Factors influencing demand for emergency department services varied according to the severity of triage scores at initial presentation. For example, although a non-significant predictor of the odds of emergency department utilization in high severity cases, access to a primary care physician was a statistically significant predictor of the likelihood of emergency department utilization (OR: 0.69; 95% CI OR: 0.63-0.75) and the rate of emergency department utilization (RR: 0.57; 95% CI RR: 0.50-0.66) in low severity cases. Using a theoretically appropriate hurdle negative binomial regression model this unique study illustrates that access to a primary care physician is an important predictor of both the odds and rate of emergency department utilization in Ontario. Restructuring primary care services, with aims of increasing access to undersupplied populations may result in decreased emergency department utilization rates by approximately 43% for low severity triage level cases.

Accuracy of autotitrating CPAP to estimate the residual Apnea-Hypopnea Index in patients with obstructive sleep apnea on treatment with autotitrating CPAP.

PubMed

Desai, Himanshu; Patel, Anil; Patel, Pinal; Grant, Brydon J B; Mador, M Jeffery

2009-11-01

Autotitrating continuous positive airway pressure (auto-CPAP) devices now have a smart card (a pocket-sized card with embedded integrated circuits which records data from the CPAP machine such as CPAP usage, CPAP pressure, large leak, etc.) which can estimate the Apnea-Hypopnea Index (AHI) on therapy. The aim of this study was to determine the accuracy of auto-CPAP in estimating the residual AHI in patients with obstructive sleep apnea (OSA) who were treated with auto-CPAP without a CPAP titration study. We studied 99 patients with OSA from April 2005 to May 2007 who underwent a repeat sleep study using auto-CPAP. The estimated AHI from auto-CPAP was compared with the AHI from an overnight polysomnogram (PSG) on auto-CPAP using Bland-Altman plot and likelihood ratio analyses. A PSG AHI cutoff of five events per hour was used to differentiate patients optimally treated with auto-CPAP from those with residual OSA on therapy. Bland and Altman analysis showed good agreement between auto-CPAP AHI and PSG AHI. There was no significant bias when smart card estimates of AHI at home were compared to smart card estimates obtained in the sleep laboratory. An auto-CPAP cutoff for the AHI of six events per hour was shown to be optimal for differentiating patients with and without residual OSA with a sensitivity of 0.92 (95% confidence interval (CI) 0.76 to 0.98) and specificity of 0.90 (95% CI 0.82 to 0.95) with a positive likelihood ratio (LR) of 9.6 (95% CI 5.1 to 21.5) and a negative likelihood ratio of 0.085 (95% CI 0.02 to 0.25). Auto-CPAP AHI of eight events per hour yielded the optimal sensitivity (0.94, 95% CI 0.73 to 0.99) and specificity (0.90, 95% CI 0.82 to 0.95) with a positive LR of 9.6 (95% CI 5.23 to 20.31) and a negative LR of 0.065 (95% CI 0.004 to 0.279) to identify patients with a PSG AHI of > or = 10 events per hour. Auto-CPAP estimate of AHI may be used to estimate residual AHI in patients with OSA of varying severity treated with auto-CPAP.

Void probability as a function of the void's shape and scale-invariant models. [in studies of spacial galactic distribution

NASA Technical Reports Server (NTRS)

Elizalde, E.; Gaztanaga, E.

1992-01-01

The dependence of counts in cells on the shape of the cell for the large scale galaxy distribution is studied. A very concrete prediction can be done concerning the void distribution for scale invariant models. The prediction is tested on a sample of the CfA catalog, and good agreement is found. It is observed that the probability of a cell to be occupied is bigger for some elongated cells. A phenomenological scale invariant model for the observed distribution of the counts in cells, an extension of the negative binomial distribution, is presented in order to illustrate how this dependence can be quantitatively determined. An original, intuitive derivation of this model is presented.

Trends in incidence of occupational asthma, contact dermatitis, noise-induced hearing loss, carpal tunnel syndrome and upper limb musculoskeletal disorders in European countries from 2000 to 2012.

PubMed

Stocks, S Jill; McNamee, Roseanne; van der Molen, Henk F; Paris, Christophe; Urban, Pavel; Campo, Giuseppe; Sauni, Riitta; Martínez Jarreta, Begoña; Valenty, Madeleine; Godderis, Lode; Miedinger, David; Jacquetin, Pascal; Gravseth, Hans M; Bonneterre, Vincent; Telle-Lamberton, Maylis; Bensefa-Colas, Lynda; Faye, Serge; Mylle, Godewina; Wannag, Axel; Samant, Yogindra; Pal, Teake; Scholz-Odermatt, Stefan; Papale, Adriano; Schouteden, Martijn; Colosio, Claudio; Mattioli, Stefano; Agius, Raymond

2015-04-01

The European Union (EU) strategy for health and safety at work underlines the need to reduce the incidence of occupational diseases (OD), but European statistics to evaluate this common goal are scarce. We aim to estimate and compare changes in incidence over time for occupational asthma, contact dermatitis, noise-induced hearing loss (NIHL), carpal tunnel syndrome (CTS) and upper limb musculoskeletal disorders across 10 European countries. OD surveillance systems that potentially reflected nationally representative trends in incidence within Belgium, the Czech Republic, Finland, France, Italy, the Netherlands, Norway, Spain, Switzerland and the UK provided data. Case counts were analysed using a negative binomial regression model with year as the main covariate. Many systems collected data from networks of 'centres', requiring the use of a multilevel negative binomial model. Some models made allowance for changes in compensation or reporting rules. Reports of contact dermatitis and asthma, conditions with shorter time between exposure to causal substances and OD, were consistently declining with only a few exceptions. For OD with physical causal exposures there was more variation between countries. Reported NIHL was increasing in Belgium, Spain, Switzerland and the Netherlands and decreasing elsewhere. Trends in CTS and upper limb musculoskeletal disorders varied widely within and between countries. This is the first direct comparison of trends in OD within Europe and is consistent with a positive impact of European initiatives addressing exposures relevant to asthma and contact dermatitis. Taking a more flexible approach allowed comparisons of surveillance data between and within countries without harmonisation of data collection methods. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Quality of maternity care and its determinants along the continuum in Kenya: A structural equation modeling analysis

PubMed Central

Mendez, Bomar Rojas

2017-01-01

Background Improving access to delivery services does not guarantee access to quality obstetric care and better survival, and therefore, concerns for quality of maternal and newborn care in low- and middle-income countries have been raised. Our study explored characteristics associated with the quality of initial assessment, intrapartum, and immediate postpartum and newborn care, and further assessed the relationships along the continuum of care. Methods The 2010 Service Provision Assessment data of Kenya for 627 routine deliveries of women aged 15–49 were used. Quality of care measures were assessed using recently validated quality of care measures during initial assessment, intrapartum, and postpartum periods. Data were analyzed with negative binomial regression and structural equation modeling technique. Results The negative binomial regression results identified a number of determinants of quality, such as the level of health facilities, managing authority, presence of delivery fee, central electricity supply and clinical guideline for maternal and neonatal care. Our structural equation modeling (SEM) further demonstrated that facility characteristics were important determinants of quality for initial assessment and postpartum care, while characteristics at the provider level became more important in shaping the quality of intrapartum care. Furthermore we also noted that quality of initial assessment had a positive association with quality of intrapartum care (β = 0.71, p < 0.001), which in turn was positively associated with the quality of newborn and immediate postpartum care (β = 1.29, p = 0.004). Conclusions A continued focus on quality of care along the continuum of maternity care is important not only to mothers but also their newborns. Policymakers should therefore ensure that required resources, as well as adequate supervision and emphasis on the quality of obstetric care, are available. PMID:28520771

Resources predicting positive and negative affect during the experience of stress: a study of older Asian Indian immigrants in the United States.

PubMed

Diwan, Sadhna; Jonnalagadda, Satya S; Balaswamy, Shantha

2004-10-01

Using the life stress model of psychological well-being, in this study we examined risks and resources predicting the occurrence of both positive and negative affect among older Asian Indian immigrants who experienced stressful life events. We collected data through a telephone survey of 226 respondents (aged 50 years and older) in the Southeastern United States. We used hierarchical, negative binomial regression analyses to examine correlates of positive and negative affect. Different coping resources influenced positive and negative affect when stressful life events were controlled for. Being female was a common risk factor for poorer positive and increased negative affect. Satisfaction with friendships and a cultural or ethnic identity that is either bicultural or more American were predictive of greater positive affect. Greater religiosity and increased mastery were resources predicting less negative affect. Cognitive and structural interventions that increase opportunities for social integration, increasing mastery, and addressing spiritual concerns are discussed as ways of coping with stress to improve the well-being of individuals in this immigrant community.

Using beta binomials to estimate classification uncertainty for ensemble models.

PubMed

Clark, Robert D; Liang, Wenkel; Lee, Adam C; Lawless, Michael S; Fraczkiewicz, Robert; Waldman, Marvin

2014-01-01

Quantitative structure-activity (QSAR) models have enormous potential for reducing drug discovery and development costs as well as the need for animal testing. Great strides have been made in estimating their overall reliability, but to fully realize that potential, researchers and regulators need to know how confident they can be in individual predictions. Submodels in an ensemble model which have been trained on different subsets of a shared training pool represent multiple samples of the model space, and the degree of agreement among them contains information on the reliability of ensemble predictions. For artificial neural network ensembles (ANNEs) using two different methods for determining ensemble classification - one using vote tallies and the other averaging individual network outputs - we have found that the distribution of predictions across positive vote tallies can be reasonably well-modeled as a beta binomial distribution, as can the distribution of errors. Together, these two distributions can be used to estimate the probability that a given predictive classification will be in error. Large data sets comprised of logP, Ames mutagenicity, and CYP2D6 inhibition data are used to illustrate and validate the method. The distributions of predictions and errors for the training pool accurately predicted the distribution of predictions and errors for large external validation sets, even when the number of positive and negative examples in the training pool were not balanced. Moreover, the likelihood of a given compound being prospectively misclassified as a function of the degree of consensus between networks in the ensemble could in most cases be estimated accurately from the fitted beta binomial distributions for the training pool. Confidence in an individual predictive classification by an ensemble model can be accurately assessed by examining the distributions of predictions and errors as a function of the degree of agreement among the constituent submodels. Further, ensemble uncertainty estimation can often be improved by adjusting the voting or classification threshold based on the parameters of the error distribution. Finally, the profiles for models whose predictive uncertainty estimates are not reliable provide clues to that effect without the need for comparison to an external test set.

Statistical inference for time course RNA-Seq data using a negative binomial mixed-effect model.

PubMed

Sun, Xiaoxiao; Dalpiaz, David; Wu, Di; S Liu, Jun; Zhong, Wenxuan; Ma, Ping

2016-08-26

Accurate identification of differentially expressed (DE) genes in time course RNA-Seq data is crucial for understanding the dynamics of transcriptional regulatory network. However, most of the available methods treat gene expressions at different time points as replicates and test the significance of the mean expression difference between treatments or conditions irrespective of time. They thus fail to identify many DE genes with different profiles across time. In this article, we propose a negative binomial mixed-effect model (NBMM) to identify DE genes in time course RNA-Seq data. In the NBMM, mean gene expression is characterized by a fixed effect, and time dependency is described by random effects. The NBMM is very flexible and can be fitted to both unreplicated and replicated time course RNA-Seq data via a penalized likelihood method. By comparing gene expression profiles over time, we further classify the DE genes into two subtypes to enhance the understanding of expression dynamics. A significance test for detecting DE genes is derived using a Kullback-Leibler distance ratio. Additionally, a significance test for gene sets is developed using a gene set score. Simulation analysis shows that the NBMM outperforms currently available methods for detecting DE genes and gene sets. Moreover, our real data analysis of fruit fly developmental time course RNA-Seq data demonstrates the NBMM identifies biologically relevant genes which are well justified by gene ontology analysis. The proposed method is powerful and efficient to detect biologically relevant DE genes and gene sets in time course RNA-Seq data.

Association between month of birth and melanoma risk: fact or fiction?

PubMed

Fiessler, Cornelia; Pfahlberg, Annette B; Keller, Andrea K; Radespiel-Tröger, Martin; Uter, Wolfgang; Gefeller, Olaf

2017-04-01

Evidence on the effect of ultraviolet radiation (UVR) exposure in infancy on melanoma risk in later life is scarce. Three recent studies suggest that people born in spring carry a higher melanoma risk. Our study aimed at verifying whether such a seasonal pattern of melanoma risk actually exists. Data from the population-based Cancer Registry Bavaria (CRB) on the birth months of 28 374 incident melanoma cases between 2002 and 2012 were analysed and compared with data from the Bavarian State Office for Statistics and Data Processing on the birth month distribution in the Bavarian population. Crude and adjusted analyses using negative binomial regression models were performed in the total study group and supplemented by several subgroup analyses. In the crude analysis, the birth months March-May were over-represented among melanoma cases. Negative binomial regression models adjusted only for sex and birth year revealed a seasonal association between melanoma risk and birth month with 13-21% higher relative incidence rates for March, April and May compared with the reference December. However, after additionally adjusting for the birth month distribution of the Bavarian population, these risk estimates decreased markedly and no association with the birth month was observed any more. Similar results emerged in all subgroup analyses. Our large registry-based study provides no evidence that people born in spring carry a higher risk for developing melanoma in later life and thus lends no support to the hypothesis of higher UVR susceptibility during the first months of life. © The Author 2016; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association

Hopelessness as a Predictor of Suicide Ideation in Depressed Male and Female Adolescent Youth.

PubMed

Wolfe, Kristin L; Nakonezny, Paul A; Owen, Victoria J; Rial, Katherine V; Moorehead, Alexandra P; Kennard, Beth D; Emslie, Graham J

2017-12-21

We examined hopelessness as a predictor of suicide ideation in depressed youth after acute medication treatment. A total of 158 depressed adolescents were administered the Children's Depression Rating Scale-Revised (CDRS-R) and Columbia Suicide Severity Rating Scale (C-SSRS) as part of a larger battery at baseline and at weekly visits across 6 weeks of acute fluoxetine treatment. The Beck Hopelessness Scale (BHS) was administered at baseline and week 6. A negative binomial regression model via a generalized estimating equation analysis of repeated measures was used to estimate suicide ideation over the 6 weeks of acute treatment from baseline measure of hopelessness. Depression severity and gender were included as covariates in the model. The negative binomial analysis was also conducted separately for the sample of males and females (in a gender-stratified analysis). Mean CDRS-R total scores were 60.30 ± 8.93 at baseline and 34.65 ± 10.41 at week 6. Mean baseline and week 6 BHS scores were 9.57 ± 5.51 and 5.59 ± 5.38, respectively. Per the C-SSRS, 43.04% and 83.54% reported having no suicide ideation at baseline and at week 6, respectively. The analyses revealed that baseline hopelessness was positively related to suicide ideation over treatment (p = .0027), independent of changes in depression severity. This significant finding persisted only for females (p = .0024). These results indicate the importance of early identification of hopelessness. © 2017 The American Association of Suicidology.

Prediction of vehicle crashes by drivers' characteristics and past traffic violations in Korea using a zero-inflated negative binomial model.

PubMed

Kim, Dae-Hwan; Ramjan, Lucie M; Mak, Kwok-Kei

2016-01-01

Traffic safety is a significant public health challenge, and vehicle crashes account for the majority of injuries. This study aims to identify whether drivers' characteristics and past traffic violations may predict vehicle crashes in Korea. A total of 500,000 drivers were randomly selected from the 11.6 million driver records of the Ministry of Land, Transport and Maritime Affairs in Korea. Records of traffic crashes were obtained from the archives of the Korea Insurance Development Institute. After matching the past violation history for the period 2004-2005 with the number of crashes in year 2006, a total of 488,139 observations were used for the analysis. Zero-inflated negative binomial model was used to determine the incident risk ratio (IRR) of vehicle crashes by past violations of individual drivers. The included covariates were driver's age, gender, district of residence, vehicle choice, and driving experience. Drivers violating (1) a hit-and-run or drunk driving regulation at least once and (2) a signal, central line, or speed regulation more than once had a higher risk of a vehicle crash with respective IRRs of 1.06 and 1.15. Furthermore, female gender, a younger age, fewer years of driving experience, and middle-sized vehicles were all significantly associated with a higher likelihood of vehicle crashes. Drivers' demographic characteristics and past traffic violations could predict vehicle crashes in Korea. Greater resources should be assigned to the provision of traffic safety education programs for the high-risk driver groups.

Time-frequency approach to underdetermined blind source separation.

PubMed

Xie, Shengli; Yang, Liu; Yang, Jun-Mei; Zhou, Guoxu; Xiang, Yong

2012-02-01

This paper presents a new time-frequency (TF) underdetermined blind source separation approach based on Wigner-Ville distribution (WVD) and Khatri-Rao product to separate N non-stationary sources from M(M <; N) mixtures. First, an improved method is proposed for estimating the mixing matrix, where the negative value of the auto WVD of the sources is fully considered. Then after extracting all the auto-term TF points, the auto WVD value of the sources at every auto-term TF point can be found out exactly with the proposed approach no matter how many active sources there are as long as N ≤ 2M-1. Further discussion about the extraction of auto-term TF points is made and finally the numerical simulation results are presented to show the superiority of the proposed algorithm by comparing it with the existing ones.

[Multiple myeloma: Maintenance therapy after autologous hematopoietic stem cell transplantation, depending on minimal residual disease].

PubMed

Solovyev, M V; Mendeleeva, L P; Pokrovskaya, O S; Nareyko, M V; Firsova, M V; Galtseva, I V; Davydova, Yu O; Kapranov, N M; Kuzmina, L A; Gemdzhian, E G; Savchenko, V G

To determine the efficiency of maintenance therapy with bortezomib in patients with multiple myeloma (MM) who have achieved complete remission (CR) after autologous hematopoietic stem cell (auto-HSCT), depending on the presence of minimal residual disease (MRD). In January 2014 to February 2016, fifty-two MM patients (19 men and 33 women) aged 24 to 66 years (median 54 years), who had achieved CR after auto-HSCT, were randomized to perform maintenance therapy with bortezomib during a year. On day 100 after auto-HSCT, all the patients underwent immunophenotyping of bone marrow plasma cells by 6-color flow cytometry to detect MRD. Relapse-free survival (RFS) was chosen as a criterion for evaluating the efficiency of maintenance therapy. After auto-HSCT, MRD-negative patients had a statistically significantly higher 2-year RFS rate than MRD-positive patients: 52.9% (95% confidence interval (CI), 35.5 to 70.5%) versus 37.2% (95% CI, 25.4 to 49.3%) (p=0.05). The presence of MRD statistically significantly increased the risk of relapse (odds ratio 1.7; 95% CI, 1.2 to 3.4; p=0.05). Two-year cumulative risk of relapse (using the Kaplan-Meier) after auto-HSCT did not statistically significantly differ in MRD-negative patients receiving (n=15) and not receiving (n=10) maintenance therapy with bortezomib (p=0.58). After completion of maintenance treatment, 42% of the MRD-positive patients achieved a negative status. In the MRD-positive patients who had received maintenance therapy, the average time to recurrence was 5 months longer than that in the naïve patients: 17.3 versus 12.3 months. The MRD status determined in MM patients who have achieved CR after auto-HSCT is an important factor for deciding on the use of maintenance therapy.

What factors affect the carriage of epinephrine auto-injectors by teenagers?

PubMed

Macadam, Clare; Barnett, Julie; Roberts, Graham; Stiefel, Gary; King, Rosemary; Erlewyn-Lajeunesse, Michel; Holloway, Judith A; Lucas, Jane S

2012-02-02

Teenagers with allergies are at particular risk of severe and fatal reactions, but epinephrine auto-injectors are not always carried as prescribed. We investigated barriers to carriage. Patients aged 12-18 years old under a specialist allergy clinic, who had previously been prescribed an auto-injector were invited to participate. Semi-structured interviews explored the factors that positively or negatively impacted on carriage. Twenty teenagers with food or venom allergies were interviewed. Only two patients had used their auto-injector in the community, although several had been treated for severe reactions in hospital. Most teenagers made complex risk assessments to determine whether to carry the auto-injector. Most but not all decisions were rational and were at least partially informed by knowledge. Factors affecting carriage included location, who else would be present, the attitudes of others and physical features of the auto-injector. Teenagers made frequent risk assessments when deciding whether to carry their auto-injectors, and generally wanted to remain safe. Their decisions were complex, multi-faceted and highly individualised. Rather than aiming for 100% carriage of auto-injectors, which remains an ambitious ideal, personalised education packages should aim to empower teenagers to make and act upon informed risk assessments.

Cyclical absenteeism among private sector, public sector and self-employed workers.

PubMed

Pfeifer, Christian

2013-03-01

This research note analyzes differences in the number of absent working days and doctor visits and in their cyclicality between private sector, public sector and self-employed workers. For this purpose, I used large-scale German survey data for the years 1995 to 2007 to estimate random effects negative binomial (count data) models. The main findings are as follows. (i) Public sector workers have on average more absent working days than private sector and self-employed workers. Self-employed workers have fewer absent working days and doctor visits than dependent employed workers. (ii) The regional unemployment rate is on average negatively correlated with the number of absent working days among private and public sector workers as well as among self-employed men. The correlations between regional unemployment rate and doctor visits are only significantly negative among private sector workers. Copyright © 2012 John Wiley & Sons, Ltd.

A Monte Carlo Risk Analysis of Life Cycle Cost Prediction.

DTIC Science & Technology

1975-09-01

process which occurs with each FLU failure. With this in mind there is no alternative other than the binomial distribution. 24 GOR/SM/75D-6 With all of...Weibull distribution of failures as selected by user. For each failure of the ith FLU, the model then samples from the binomial distribution to deter- mine...which is sampled from the binomial . Neither of the two conditions for normality are met, i.e., that RTS Ie close to .5 and the number of samples close

Enumerative and binomial sampling plans for citrus mealybug (Homoptera: pseudococcidae) in citrus groves.

PubMed

Martínez-Ferrer, María Teresa; Ripollés, José Luís; Garcia-Marí, Ferran

2006-06-01

The spatial distribution of the citrus mealybug, Planococcus citri (Risso) (Homoptera: Pseudococcidae), was studied in citrus groves in northeastern Spain. Constant precision sampling plans were designed for all developmental stages of citrus mealybug under the fruit calyx, for late stages on fruit, and for females on trunks and main branches; more than 66, 286, and 101 data sets, respectively, were collected from nine commercial fields during 1992-1998. Dispersion parameters were determined using Taylor's power law, giving aggregated spatial patterns for citrus mealybug populations in three locations of the tree sampled. A significant relationship between the number of insects per organ and the percentage of occupied organs was established using either Wilson and Room's binomial model or Kono and Sugino's empirical formula. Constant precision (E = 0.25) sampling plans (i.e., enumerative plans) for estimating mean densities were developed using Green's equation and the two binomial models. For making management decisions, enumerative counts may be less labor-intensive than binomial sampling. Therefore, we recommend enumerative sampling plans for the use in an integrated pest management program in citrus. Required sample sizes for the range of population densities near current management thresholds, in the three plant locations calyx, fruit, and trunk were 50, 110-330, and 30, respectively. Binomial sampling, especially the empirical model, required a higher sample size to achieve equivalent levels of precision.

Density of wild prey modulates lynx kill rates on free-ranging domestic sheep.

PubMed

Odden, John; Nilsen, Erlend B; Linnell, John D C

2013-01-01

Understanding the factors shaping the dynamics of carnivore-livestock conflicts is vital to facilitate large carnivore conservation in multi-use landscapes. We investigated how the density of their main wild prey, roe deer Capreolus capreolus, modulates individual Eurasian lynx Lynx lynx kill rates on free-ranging domestic sheep Ovis aries across a range of sheep and roe deer densities. Lynx kill rates on free-ranging domestic sheep were collected in south-eastern Norway from 1995 to 2011 along a gradient of different livestock and wild prey densities using VHF and GPS telemetry. We used zero-inflated negative binomial (ZINB) models including lynx sex, sheep density and an index of roe deer density as explanatory variables to model observed kill rates on sheep, and ranked the models based on their AICc values. The model including the effects of lynx sex and sheep density in the zero-inflation model and the effect of lynx sex and roe deer density in the negative binomial part received most support. Irrespective of sheep density and sex, we found the lowest sheep kill rates in areas with high densities of roe deer. As roe deer density decreased, males killed sheep at higher rates, and this pattern held for both high and low sheep densities. Similarly, females killed sheep at higher rates in areas with high densities of sheep and low densities of roe deer. However, when sheep densities were low females rarely killed sheep irrespective of roe deer density. Our quantification of depredation rates can be the first step towards establishing fairer compensation systems based on more accurate and area specific estimation of losses. This study demonstrates how we can use ecological theory to predict where losses of sheep will be greatest, and can be used to identify areas where mitigation measures are most likely to be needed.

Density of Wild Prey Modulates Lynx Kill Rates on Free-Ranging Domestic Sheep

PubMed Central

Odden, John; Nilsen, Erlend B.; Linnell, John D. C.

2013-01-01

Understanding the factors shaping the dynamics of carnivore–livestock conflicts is vital to facilitate large carnivore conservation in multi-use landscapes. We investigated how the density of their main wild prey, roe deer Capreolus capreolus, modulates individual Eurasian lynx Lynx lynx kill rates on free-ranging domestic sheep Ovis aries across a range of sheep and roe deer densities. Lynx kill rates on free-ranging domestic sheep were collected in south-eastern Norway from 1995 to 2011 along a gradient of different livestock and wild prey densities using VHF and GPS telemetry. We used zero-inflated negative binomial (ZINB) models including lynx sex, sheep density and an index of roe deer density as explanatory variables to model observed kill rates on sheep, and ranked the models based on their AICc values. The model including the effects of lynx sex and sheep density in the zero-inflation model and the effect of lynx sex and roe deer density in the negative binomial part received most support. Irrespective of sheep density and sex, we found the lowest sheep kill rates in areas with high densities of roe deer. As roe deer density decreased, males killed sheep at higher rates, and this pattern held for both high and low sheep densities. Similarly, females killed sheep at higher rates in areas with high densities of sheep and low densities of roe deer. However, when sheep densities were low females rarely killed sheep irrespective of roe deer density. Our quantification of depredation rates can be the first step towards establishing fairer compensation systems based on more accurate and area specific estimation of losses. This study demonstrates how we can use ecological theory to predict where losses of sheep will be greatest, and can be used to identify areas where mitigation measures are most likely to be needed. PMID:24278123

Categorical Data Analysis Using a Skewed Weibull Regression Model

NASA Astrophysics Data System (ADS)

Caron, Renault; Sinha, Debajyoti; Dey, Dipak; Polpo, Adriano

2018-03-01

In this paper, we present a Weibull link (skewed) model for categorical response data arising from binomial as well as multinomial model. We show that, for such types of categorical data, the most commonly used models (logit, probit and complementary log-log) can be obtained as limiting cases. We further compare the proposed model with some other asymmetrical models. The Bayesian as well as frequentist estimation procedures for binomial and multinomial data responses are presented in details. The analysis of two data sets to show the efficiency of the proposed model is performed.

Modeling the frequency of opposing left-turn conflicts at signalized intersections using generalized linear regression models.

PubMed

Zhang, Xin; Liu, Pan; Chen, Yuguang; Bai, Lu; Wang, Wei

2014-01-01

The primary objective of this study was to identify whether the frequency of traffic conflicts at signalized intersections can be modeled. The opposing left-turn conflicts were selected for the development of conflict predictive models. Using data collected at 30 approaches at 20 signalized intersections, the underlying distributions of the conflicts under different traffic conditions were examined. Different conflict-predictive models were developed to relate the frequency of opposing left-turn conflicts to various explanatory variables. The models considered include a linear regression model, a negative binomial model, and separate models developed for four traffic scenarios. The prediction performance of different models was compared. The frequency of traffic conflicts follows a negative binominal distribution. The linear regression model is not appropriate for the conflict frequency data. In addition, drivers behaved differently under different traffic conditions. Accordingly, the effects of conflicting traffic volumes on conflict frequency vary across different traffic conditions. The occurrences of traffic conflicts at signalized intersections can be modeled using generalized linear regression models. The use of conflict predictive models has potential to expand the uses of surrogate safety measures in safety estimation and evaluation.

Lotka's Law and Institutional Productivity.

ERIC Educational Resources Information Center

Kumar, Suresh; Sharma, Praveen; Garg, K. C.

1998-01-01

Examines the applicability of Lotka's Law, negative binomial distribution, and lognormal distribution for institutional productivity in the same way as it is to authors and their productivity. Results indicate that none of the distributions are applicable for institutional productivity in engineering sciences. (Author/LRW)

Charged particle multiplicities in deep inelastic scattering at HERA

NASA Astrophysics Data System (ADS)

Aid, S.; Anderson, M.; Andreev, V.; Andrieu, B.; Appuhn, R.-D.; Babaev, A.; Bähr, J.; Bán, J.; Ban, Y.; Baranov, P.; Barrelet, E.; Barschke, R.; Bartel, W.; Barth, M.; Bassler, U.; Beck, H. P.; Behrend, H.-J.; Belousov, A.; Berger, Ch.; Bernardi, G.; Bertrand-Coremans, G.; Besançon, M.; Beyer, R.; Biddulph, P.; Bispham, P.; Bizot, J. C.; Blobel, V.; Borras, K.; Botterweck, F.; Boudry, V.; Braemer, A.; Braunschweig, W.; Brisson, V.; Bruel, P.; Bruncko, D.; Brune, C.; Buchholz, R.; Büngener, L.; Bürger, J.; Büsser, F. W.; Buniatian, A.; Burke, S.; Burton, M. J.; Calvet, D.; Campbell, A. J.; Carli, T.; Charlet, M.; Clarke, D.; Clegg, A. B.; Clerbaux, B.; Cocks, S.; Contreras, J. G.; Cormack, C.; Coughlan, J. A.; Courau, A.; Cousinou, M.-C.; Cozzika, G.; Criegee, L.; Cussans, D. G.; Cvach, J.; Dagoret, S.; Dainton, J. B.; Dau, W. D.; Daum, K.; David, M.; Davis, C. L.; Delcourt, B.; de Roeck, A.; de Wolf, E. A.; Dirkmann, M.; Dixon, P.; di Nezza, P.; Dlugosz, W.; Dollfus, C.; Dowell, J. D.; Dreis, H. B.; Droutskoi, A.; Dünger, O.; Duhm, H.; Ebert, J.; Ebert, T. R.; Eckerlin, G.; Efremenko, V.; Egli, S.; Eichler, R.; Eisele, F.; Eisenhandler, E.; Elsen, E.; Erdmann, M.; Erdmann, W.; Evrard, E.; Fahr, A. B.; Favart, L.; Fedotov, A.; Feeken, D.; Felst, R.; Feltesse, J.; Ferencei, J.; Ferrarotto, F.; Flamm, K.; Fleischer, M.; Flieser, M.; Flügge, G.; Fomenko, A.; Fominykh, B.; Formánek, J.; Foster, J. M.; Franke, G.; Fretwurst, E.; Gabathuler, E.; Gabathuler, K.; Gaede, F.; Garvey, J.; Gayler, J.; Gebauer, M.; Genzel, H.; Gerhards, R.; Glazov, A.; Goerlach, U.; Goerlich, L.; Gogitidze, N.; Goldberg, M.; Goldner, D.; Golec-Biernat, K.; Gonzalez-Pineiro, B.; Gorelov, I.; Grab, C.; Grässler, H.; Greenshaw, T.; Griffiths, R. K.; Grindhammer, G.; Gruber, A.; Gruber, C.; Haack, J.; Hadig, T.; Haidt, D.; Hajduk, L.; Hampel, M.; Haynes, W. J.; Heinzelmann, G.; Henderson, R. C. W.; Henschel, H.; Herynek, I.; Hess, M. F.; Hewitt, K.; Hildesheim, W.; Hiller, K. H.; Hilton, C. D.; Hladký, J.; Hoeger, K. C.; Höppner, M.; Hoffmann, D.; Holtom, T.; Horisberger, R.; Hudgson, V. L.; Hütte, M.; Ibbotson, M.; Itterbeck, H.; Jacholkowska, A.; Jacobsson, C.; Jaffre, M.; Janoth, J.; Jansen, T.; Jönsson, L.; Johnson, D. P.; Jung, H.; Kalmus, P. I. P.; Kander, M.; Kant, D.; Kaschowitz, R.; Kathage, U.; Katzy, J.; Kaufmann, H. H.; Kaufmann, O.; Kazarian, S.; Kenyon, I. R.; Kermiche, S.; Keuker, C.; Kiesling, C.; Klein, M.; Kleinwort, C.; Knies, G.; Köhler, T.; Köhne, J. H.; Kolanoski, H.; Kole, F.; Kolya, S. D.; Korbel, V.; Korn, M.; Kostka, P.; Kotelnikov, S. K.; Krämerkämper, T.; Krasny, M. W.; Krehbiel, H.; Krücker, D.; Küster, H.; Kuhlen, M.; Kurča, T.; Kurzhöfer, J.; Lacour, D.; Laforge, B.; Lander, R.; Landon, M. P. J.; Lange, W.; Langenegger, U.; Laporte, J.-F.; Lebedev, A.; Lehner, F.; Levonian, S.; Lindström, G.; Lindstroem, M.; Link, J.; Linsel, F.; Lipinski, J.; List, B.; Lobo, G.; Lomas, J. W.; Lopez, G. C.; Lubimov, V.; Lüke, D.; Magnussen, N.; Malinovski, E.; Mani, S.; Maraček, R.; Marage, P.; Marks, J.; Marshall, R.; Martens, J.; Martin, G.; Martin, R.; Martyn, H.-U.; Martyniak, J.; Mavroidis, T.; Maxfield, S. J.; McMahon, S. J.; Mehta, A.; Meier, K.; Meyer, A.; Meyer, A.; Meyer, H.; Meyer, J.; Meyer, P.-O.; Migliori, A.; Mikocki, S.; Milstead, D.; Moeck, J.; Moreau, F.; Morris, J. V.; Mroczko, E.; Müller, D.; Müller, G.; Müller, K.; Müller, M.; Murín, P.; Nagovizin, V.; Nahnhauer, R.; Naroska, B.; Naumann, Th.; Négri, I.; Newman, P. R.; Newton, D.; Nguyen, H. K.; Nicholls, T. C.; Niebergall, F.; Niebuhr, C.; Niedzballa, Ch.; Niggli, H.; Nisius, R.; Nowak, G.; Noyes, G. W.; Nyberg-Werther, M.; Oakden, M.; Oberlack, H.; Olsson, J. E.; Ozerov, D.; Palmen, P.; Panaro, E.; Panitch, A.; Pascaud, C.; Patel, G. D.; Pawletta, H.; Peppel, E.; Perez, E.; Phillips, J. P.; Pieuchot, A.; Pitzl, D.; Pope, G.; Prell, S.; Rabbertz, K.; Rädel, G.; Reimer, P.; Reinshagen, S.; Rick, H.; Riech, V.; Riedlberger, J.; Riepenhausen, F.; Riess, S.; Rizvi, E.; Robertson, S. M.; Robmann, P.; Roloff, H. E.; Roosen, R.; Rosenbauer, K.; Rostovtsev, A.; Rouse, F.; Royon, C.; Rüter, K.; Rusakov, S.; Rybicki, K.; Sankey, D. P. C.; Schacht, P.; Schiek, S.; Schleif, S.; Schleper, P.; von Schlippe, W.; Schmidt, D.; Schmidt, G.; Schöning, A.; Schröder, V.; Schuhmann, E.; Schwab, B.; Sefkow, F.; Seidel, M.; Sell, R.; Semenov, A.; Shekelyan, V.; Sheviakov, I.; Shtarkov, L. N.; Siegmon, G.; Siewert, U.; Sirois, Y.; Skillicorn, I. O.; Smirnov, P.; Smith, J. R.; Solochenko, V.; Soloviev, Y.; Specka, A.; Spiekermann, J.; Spielman, S.; Spitzer, H.; Squinabol, F.; Steenbock, M.; Steffen, P.; Steinberg, R.; Steiner, H.; Steinhart, J.; Stella, B.; Stellberger, A.; Stier, J.; Stiewe, J.; Stößlein, U.; Stolze, K.; Straumann, U.; Struczinski, W.; Sutton, J. P.; Tapprogge, S.; Taševský, M.; Tchernyshov, V.; Tchetchelnitski, S.; Theissen, J.; Thiebaux, C.; Thompson, G.; Truöl, P.; Tsipolitis, G.; Turnau, J.; Tutas, J.; Uelkes, P.; Usik, A.; Valkár, S.; Valkárová, A.; Vallée, C.; Vandenplas, D.; van Esch, P.; van Mechelen, P.; Vazdik, Y.; Verrecchia, P.; Villet, G.; Wacker, K.; Wagener, A.; Wagener, M.; Walther, A.; Waugh, B.; Weber, G.; Weber, M.; Wegener, D.; Wegner, A.; Wengler, T.; Werner, M.; West, L. R.; Wilksen, T.; Willard, S.; Winde, M.; Winter, G.-G.; Wittek, C.; Wobisch, M.; Wünsch, E.; Žáček, J.; Zarbock, D.; Zhang, Z.; Zhokin, A.; Zini, P.; Zomer, F.; Zsembery, J.; Zuber, K.; Zurnedden, M.

1996-12-01

Using the H1 detector at HERA, charged particle multiplicity distributions in deep inelastic e + p scattering have been measured over a large kinematical region. The evolution with W and Q 2 of the multiplicity distribution and of the multiplicity moments in pseudorapidity domains of varying size is studied in the current fragmentation region of the hadronic centre-of-mass frame. The results are compared with data from fixed target lepton-nucleon interactions, e + e - annihilations and hadron-hadron collisions as well as with expectations from QCD based parton models. Fits to the Negative Binomial and Lognormal distributions are presented.

A modified chain binomial model to analyse the ongoing measles epidemic in Greece, July 2017 to February 2018

PubMed Central

Lytras, Theodore; Georgakopoulou, Theano; Tsiodras, Sotirios

2018-01-01

Greece is currently experiencing a large measles outbreak, in the context of multiple similar outbreaks across Europe. We devised and applied a modified chain-binomial epidemic model, requiring very simple data, to estimate the transmission parameters of this outbreak. Model results indicate sustained measles transmission among the Greek Roma population, necessitating a targeted mass vaccination campaign to halt further spread of the epidemic. Our model may be useful for other countries facing similar measles outbreaks. PMID:29717695

A modified chain binomial model to analyse the ongoing measles epidemic in Greece, July 2017 to February 2018.

PubMed

Lytras, Theodore; Georgakopoulou, Theano; Tsiodras, Sotirios

2018-04-01

Greece is currently experiencing a large measles outbreak, in the context of multiple similar outbreaks across Europe. We devised and applied a modified chain-binomial epidemic model, requiring very simple data, to estimate the transmission parameters of this outbreak. Model results indicate sustained measles transmission among the Greek Roma population, necessitating a targeted mass vaccination campaign to halt further spread of the epidemic. Our model may be useful for other countries facing similar measles outbreaks.

The arcsine is asinine: the analysis of proportions in ecology.

PubMed

Warton, David I; Hui, Francis K C

2011-01-01

The arcsine square root transformation has long been standard procedure when analyzing proportional data in ecology, with applications in data sets containing binomial and non-binomial response variables. Here, we argue that the arcsine transform should not be used in either circumstance. For binomial data, logistic regression has greater interpretability and higher power than analyses of transformed data. However, it is important to check the data for additional unexplained variation, i.e., overdispersion, and to account for it via the inclusion of random effects in the model if found. For non-binomial data, the arcsine transform is undesirable on the grounds of interpretability, and because it can produce nonsensical predictions. The logit transformation is proposed as an alternative approach to address these issues. Examples are presented in both cases to illustrate these advantages, comparing various methods of analyzing proportions including untransformed, arcsine- and logit-transformed linear models and logistic regression (with or without random effects). Simulations demonstrate that logistic regression usually provides a gain in power over other methods.

Use of the binomial distribution to predict impairment: application in a nonclinical sample.

PubMed

Axelrod, Bradley N; Wall, Jacqueline R; Estes, Bradley W

2008-01-01

A mathematical model based on the binomial theory was developed to illustrate when abnormal score variations occur by chance in a multitest battery (Ingraham & Aiken, 1996). It has been successfully used as a comparison for obtained test scores in clinical samples, but not in nonclinical samples. In the current study, this model has been applied to demographically corrected scores on the Halstead-Reitan Neuropsychological Test Battery, obtained from a sample of 94 nonclinical college students. Results found that 15% of the sample had impairments suggested by the Halstead Impairment Index, using criteria established by Reitan and Wolfson (1993). In addition, one-half of the sample obtained impaired scores on one or two tests. These results were compared to that predicted by the binomial model and found to be consistent. The model therefore serves as a useful resource for clinicians considering the probability of impaired test performance.

Comparing statistical methods for analyzing skewed longitudinal count data with many zeros: an example of smoking cessation.

PubMed

Xie, Haiyi; Tao, Jill; McHugo, Gregory J; Drake, Robert E

2013-07-01

Count data with skewness and many zeros are common in substance abuse and addiction research. Zero-adjusting models, especially zero-inflated models, have become increasingly popular in analyzing this type of data. This paper reviews and compares five mixed-effects Poisson family models commonly used to analyze count data with a high proportion of zeros by analyzing a longitudinal outcome: number of smoking quit attempts from the New Hampshire Dual Disorders Study. The findings of our study indicated that count data with many zeros do not necessarily require zero-inflated or other zero-adjusting models. For rare event counts or count data with small means, a simpler model such as the negative binomial model may provide a better fit. Copyright © 2013 Elsevier Inc. All rights reserved.

Improving removal-based estimates of abundance by sampling a population of spatially distinct subpopulations

USGS Publications Warehouse

Dorazio, R.M.; Jelks, H.L.; Jordan, F.

2005-01-01

 A statistical modeling framework is described for estimating the abundances of spatially distinct subpopulations of animals surveyed using removal sampling. To illustrate this framework, hierarchical models are developed using the Poisson and negative-binomial distributions to model variation in abundance among subpopulations and using the beta distribution to model variation in capture probabilities. These models are fitted to the removal counts observed in a survey of a federally endangered fish species. The resulting estimates of abundance have similar or better precision than those computed using the conventional approach of analyzing the removal counts of each subpopulation separately. Extension of the hierarchical models to include spatial covariates of abundance is straightforward and may be used to identify important features of an animal's habitat or to predict the abundance of animals at unsampled locations.

Modeling Polio Data Using the First Order Non-Negative Integer-Valued Autoregressive, INAR(1), Model

NASA Astrophysics Data System (ADS)

Vazifedan, Turaj; Shitan, Mahendran

Time series data may consists of counts, such as the number of road accidents, the number of patients in a certain hospital, the number of customers waiting for service at a certain time and etc. When the value of the observations are large it is usual to use Gaussian Autoregressive Moving Average (ARMA) process to model the time series. However if the observed counts are small, it is not appropriate to use ARMA process to model the observed phenomenon. In such cases we need to model the time series data by using Non-Negative Integer valued Autoregressive (INAR) process. The modeling of counts data is based on the binomial thinning operator. In this paper we illustrate the modeling of counts data using the monthly number of Poliomyelitis data in United States between January 1970 until December 1983. We applied the AR(1), Poisson regression model and INAR(1) model and the suitability of these models were assessed by using the Index of Agreement(I.A.). We found that INAR(1) model is more appropriate in the sense it had a better I.A. and it is natural since the data are counts.

Iterative model building, structure refinement and density modification with the PHENIX AutoBuild wizard.

PubMed

Terwilliger, Thomas C; Grosse-Kunstleve, Ralf W; Afonine, Pavel V; Moriarty, Nigel W; Zwart, Peter H; Hung, Li Wei; Read, Randy J; Adams, Paul D

2008-01-01

The PHENIX AutoBuild wizard is a highly automated tool for iterative model building, structure refinement and density modification using RESOLVE model building, RESOLVE statistical density modification and phenix.refine structure refinement. Recent advances in the AutoBuild wizard and phenix.refine include automated detection and application of NCS from models as they are built, extensive model-completion algorithms and automated solvent-molecule picking. Model-completion algorithms in the AutoBuild wizard include loop building, crossovers between chains in different models of a structure and side-chain optimization. The AutoBuild wizard has been applied to a set of 48 structures at resolutions ranging from 1.1 to 3.2 A, resulting in a mean R factor of 0.24 and a mean free R factor of 0.29. The R factor of the final model is dependent on the quality of the starting electron density and is relatively independent of resolution.

On the Relationship between Molecular Hit Rates in High-Throughput Screening and Molecular Descriptors.

PubMed

Hansson, Mari; Pemberton, John; Engkvist, Ola; Feierberg, Isabella; Brive, Lars; Jarvis, Philip; Zander-Balderud, Linda; Chen, Hongming

2014-06-01

High-throughput screening (HTS) is widely used in the pharmaceutical industry to identify novel chemical starting points for drug discovery projects. The current study focuses on the relationship between molecular hit rate in recent in-house HTS and four common molecular descriptors: lipophilicity (ClogP), size (heavy atom count, HEV), fraction of sp(3)-hybridized carbons (Fsp3), and fraction of molecular framework (f(MF)). The molecular hit rate is defined as the fraction of times the molecule has been assigned as active in the HTS campaigns where it has been screened. Beta-binomial statistical models were built to model the molecular hit rate as a function of these descriptors. The advantage of the beta-binomial statistical models is that the correlation between the descriptors is taken into account. Higher degree polynomial terms of the descriptors were also added into the beta-binomial statistic model to improve the model quality. The relative influence of different molecular descriptors on molecular hit rate has been estimated, taking into account that the descriptors are correlated to each other through applying beta-binomial statistical modeling. The results show that ClogP has the largest influence on the molecular hit rate, followed by Fsp3 and HEV. f(MF) has only a minor influence besides its correlation with the other molecular descriptors. © 2013 Society for Laboratory Automation and Screening.

Modeling species-abundance relationships in multi-species collections

USGS Publications Warehouse

Peng, S.; Yin, Z.; Ren, H.; Guo, Q.

2003-01-01

Species-abundance relationship is one of the most fundamental aspects of community ecology. Since Motomura first developed the geometric series model to describe the feature of community structure, ecologists have developed many other models to fit the species-abundance data in communities. These models can be classified into empirical and theoretical ones, including (1) statistical models, i.e., negative binomial distribution (and its extension), log-series distribution (and its extension), geometric distribution, lognormal distribution, Poisson-lognormal distribution, (2) niche models, i.e., geometric series, broken stick, overlapping niche, particulate niche, random assortment, dominance pre-emption, dominance decay, random fraction, weighted random fraction, composite niche, Zipf or Zipf-Mandelbrot model, and (3) dynamic models describing community dynamics and restrictive function of environment on community. These models have different characteristics and fit species-abundance data in various communities or collections. Among them, log-series distribution, lognormal distribution, geometric series, and broken stick model have been most widely used.

Performance and structure of single-mode bosonic codes

NASA Astrophysics Data System (ADS)

Albert, Victor V.; Noh, Kyungjoo; Duivenvoorden, Kasper; Young, Dylan J.; Brierley, R. T.; Reinhold, Philip; Vuillot, Christophe; Li, Linshu; Shen, Chao; Girvin, S. M.; Terhal, Barbara M.; Jiang, Liang

2018-03-01

The early Gottesman, Kitaev, and Preskill (GKP) proposal for encoding a qubit in an oscillator has recently been followed by cat- and binomial-code proposals. Numerically optimized codes have also been proposed, and we introduce codes of this type here. These codes have yet to be compared using the same error model; we provide such a comparison by determining the entanglement fidelity of all codes with respect to the bosonic pure-loss channel (i.e., photon loss) after the optimal recovery operation. We then compare achievable communication rates of the combined encoding-error-recovery channel by calculating the channel's hashing bound for each code. Cat and binomial codes perform similarly, with binomial codes outperforming cat codes at small loss rates. Despite not being designed to protect against the pure-loss channel, GKP codes significantly outperform all other codes for most values of the loss rate. We show that the performance of GKP and some binomial codes increases monotonically with increasing average photon number of the codes. In order to corroborate our numerical evidence of the cat-binomial-GKP order of performance occurring at small loss rates, we analytically evaluate the quantum error-correction conditions of those codes. For GKP codes, we find an essential singularity in the entanglement fidelity in the limit of vanishing loss rate. In addition to comparing the codes, we draw parallels between binomial codes and discrete-variable systems. First, we characterize one- and two-mode binomial as well as multiqubit permutation-invariant codes in terms of spin-coherent states. Such a characterization allows us to introduce check operators and error-correction procedures for binomial codes. Second, we introduce a generalization of spin-coherent states, extending our characterization to qudit binomial codes and yielding a multiqudit code.

An Instructional Method for the AutoCAD Modeling Environment.

ERIC Educational Resources Information Center

Mohler, James L.

1997-01-01

Presents a command organizer for AutoCAD to aid new uses in operating within the 3-D modeling environment. Addresses analyzing the problem, visualization skills, nonlinear tools, a static view of a dynamic model, the AutoCAD organizer, environment attributes, and control of the environment. Contains 11 references. (JRH)

What factors affect the carriage of epinephrine auto-injectors by teenagers?

PubMed Central

2012-01-01

Background Teenagers with allergies are at particular risk of severe and fatal reactions, but epinephrine auto-injectors are not always carried as prescribed. We investigated barriers to carriage. Methods Patients aged 12-18 years old under a specialist allergy clinic, who had previously been prescribed an auto-injector were invited to participate. Semi-structured interviews explored the factors that positively or negatively impacted on carriage. Results Twenty teenagers with food or venom allergies were interviewed. Only two patients had used their auto-injector in the community, although several had been treated for severe reactions in hospital. Most teenagers made complex risk assessments to determine whether to carry the auto-injector. Most but not all decisions were rational and were at least partially informed by knowledge. Factors affecting carriage included location, who else would be present, the attitudes of others and physical features of the auto-injector. Teenagers made frequent risk assessments when deciding whether to carry their auto-injectors, and generally wanted to remain safe. Their decisions were complex, multi-faceted and highly individualised. Conclusions Rather than aiming for 100% carriage of auto-injectors, which remains an ambitious ideal, personalised education packages should aim to empower teenagers to make and act upon informed risk assessments. PMID:22409884

Children at risk: A comparison of child pedestrian traffic collisions in Santiago, Chile, and Seoul, South Korea.

PubMed

Blazquez, Carola; Lee, Jae Seung; Zegras, Christopher

2016-01-01

We examine and compare pedestrian-vehicle collisions and injury outcomes involving school-age children between 5 and 18 years of age in the capital cities of Santiago, Chile, and Seoul, South Korea. We conduct descriptive analysis of the child pedestrian-vehicle collision (P-VC) data (904 collisions for Santiago and 3,505 for Seoul) reported by the police between 2010 and 2011. We also statistically analyze factors associated with child P-VCs, by both incident severity and age group, using 3 regression models: negative binomial, probit, and spatial lag models. Descriptive statistics suggest that child pedestrians in Seoul have a higher risk of being involved in traffic crashes than their counterparts in Santiago. However, in Seoul a greater proportion of children are unharmed as a result of these incidents, whereas more child pedestrians are killed in Santiago. Younger children in Seoul suffer more injuries from P-VCs than in Santiago. The majority of P-VCs in both cities tend to occur in the afternoon and evening, at intersections in Santiago and at midblock locations in Seoul. Our model results suggest that the resident population of children is positively associated with P-VCs in both cities, and school concentrations apparently increase P-VC risk among older children in Santiago. Bus stops are associated with higher P-VCs in Seoul, and subway stations relate to higher P-VCs among older children in Santiago. Zone-level land use mix was negatively related to child P-VCs in Seoul but not in Santiago. Arterial roads are associated with fewer P-VCs, especially for younger children in both cities. A share of collector roads is associated with increased P-VCs in Seoul but fewer P-VCs in Santiago. Hilliness is related to fewer P-VCs in both cities. Differences in these model results for Santiago and Seoul warrant additional analysis, as do the differences in results across model type (negative binomial versus spatial lag models). To reduce child P-VCs, this study suggests the need to assess subway station and bus stop area conditions in Santiago and Seoul, respectively; areas with high density of schools in Santiago; areas with greater concentrations of children in both cities; and collector roads in Seoul.

Factors Associated with Hospital Length of Stay among Cancer Patients with Febrile Neutropenia

PubMed Central

Rosa, Regis G.; Goldani, Luciano Z.

2014-01-01

Purpose This study sought to evaluate factors associated with hospital length of stay in cancer patients with febrile neutropenia. Methods A prospective cohort study was performed at a single tertiary referral hospital in southern Brazil from October 2009 to August 2011. All adult cancer patients with febrile neutropenia admitted to the hematology ward were evaluated. Stepwise random-effects negative binomial regression was performed to identify risk factors for prolonged length of hospital stay. Results In total, 307 cases of febrile neutropenia were evaluated. The overall median length of hospital stay was 16 days (interquartile range 18 days). According to multiple negative binomial regression analysis, hematologic neoplasms (P = 0.003), high-dose chemotherapy regimens (P<0.001), duration of neutropenia (P<0.001), and bloodstream infection involving Gram-negative multi-drug-resistant bacteria (P = 0.003) were positively associated with prolonged hospital length of stay in patients with febrile neutropenia. The condition index showed no evidence of multi-collinearity effect among the independent variables. Conclusions Hematologic neoplasms, high-dose chemotherapy regimens, prolonged periods of neutropenia, and bloodstream infection with Gram-negative multi-drug-resistant bacteria are predictors of prolonged length hospital of stay among adult cancer patients with febrile neutropenia. PMID:25285790

Risk factors related to Toxoplasma gondii seroprevalence in indoor-housed Dutch dairy goats.

PubMed

Deng, Huifang; Dam-Deisz, Cecile; Luttikholt, Saskia; Maas, Miriam; Nielen, Mirjam; Swart, Arno; Vellema, Piet; van der Giessen, Joke; Opsteegh, Marieke

2016-02-01

Toxoplasma gondii can cause disease in goats, but also has impact on human health through food-borne transmission. Our aims were to determine the seroprevalence of T. gondii infection in indoor-housed Dutch dairy goats and to identify the risk factors related to T. gondii seroprevalence. Fifty-two out of ninety approached farmers with indoor-kept goats (58%) participated by answering a standardized questionnaire and contributing 32 goat blood samples each. Serum samples were tested for T. gondii SAG1 antibodies by ELISA and results showed that the frequency distribution of the log10-transformed OD-values fitted well with a binary mixture of a shifted gamma and a shifted reflected gamma distribution. The overall animal seroprevalence was 13.3% (95% CI: 11.7–14.9%), and at least one seropositive animal was found on 61.5% (95% CI: 48.3–74.7%) of the farms. To evaluate potential risk factors on herd level, three modeling strategies (Poisson, negative binomial and zero-inflated) were compared. The negative binomial model fitted the data best with the number of cats (1–4 cats: IR: 2.6, 95% CI: 1.1–6.5; > = 5 cats:IR: 14.2, 95% CI: 3.9–51.1) and mean animal age (IR: 1.5, 95% CI: 1.1–2.1) related to herd positivity. In conclusion, the ELISA test was 100% sensitive and specific based on binary mixture analysis. T. gondii infection is prevalent in indoor housed Dutch dairy goats but at a lower overall animal level seroprevalence than outdoor farmed goats in other European countries, and cat exposure is an important risk factor.

Dental plaque, preventive care, and tooth brushing associated with dental caries in primary teeth in schoolchildren ages 6–9 years of Leon, Nicaragua

PubMed Central

del Socorro Herrera, Miriam; Medina-Solis, Carlo Eduardo; Minaya-Sánchez, Mirna; Pontigo-Loyola, América Patricia; Villalobos-Rodelo, Juan José; Islas-Granillo, Horacio; de la Rosa-Santillana, Rubén; Maupomé, Gerardo

2013-01-01

Background Our study aimed to evaluate the effect of various risk indicators for dental caries on primary teeth of Nicaraguan children (from Leon, Nicaragua) ages 6 to 9, using the negative binomial regression model. Material/Methods A cross-sectional study was carried out to collect clinical, demographic, socioeconomic, and behavioral data from 794 schoolchildren ages 6 to 9 years, randomly selected from 25 schools in the city of León, Nicaragua. Clinical examinations for dental caries (dmft index) were performed by 2 trained and standardized examiners. Socio-demographic, socioeconomic, and behavioral data were self-reported using questionnaires. Multivariate negative binomial regression (NBR) analysis was used. Results Mean age was 7.49±1.12 years. Boys accounted for 50.1% of the sample. Mean dmft was 3.54±3.13 and caries prevalence (dmft >0) was 77.6%. In the NBR multivariate model (p<0.05), for each year of age, the expected mean dmft decreased by 7.5%. Brushing teeth at least once a day and having received preventive dental care in the last year before data collection were associated with declines in the expected mean dmft by 19.5% and 69.6%, respectively. Presence of dental plaque increased the expected mean dmft by 395.5%. Conclusions The proportion of students with caries in this sample was high. We found associations between dental caries in the primary dentition and dental plaque, brushing teeth at least once a day, and having received preventive dental care. To improve oral health, school programs and/or age-appropriate interventions need to be developed based on the specific profile of caries experience and the associated risk indicators. PMID:24247119

Frequency distribution of Echinococcus multilocularis and other helminths of foxes in Kyrgyzstan

PubMed Central

I., Ziadinov; P., Deplazes; A., Mathis; B., Mutunova; K., Abdykerimov; R., Nurgaziev; P.R, Torgerson

2010-01-01

Echinococcosis is a major emerging zoonosis in central Asia. A study of the helminth fauna of foxes from Naryn Oblast in central Kyrgyzstan was undertaken to investigate the abundance of Echinococcus multilocularis in a district where a high prevalence of this parasite had previously been detected in dogs. A total of 151 foxes (Vulpes vulpes) were investigated in a necropsy study. Of these 96 (64%) were infected with E. multilocularis with a mean abundance of 8669 parasites per fox. This indicates that red foxes are a major definitive host of E. multilocularis in this country. This also demonstrates that the abundance and prevalence of E. multilocularis in the natural definitive host are likely to be high in geographical regions where there is a concomitant high prevalence in alternative definitive hosts such as dogs. In addition Mesocestoides spp., Dipylidium caninum, Taenia spp., Toxocara canis, Toxascaris leonina, Capillaria and Acanthocephala spp. were found in 99 (66%), 50 (33%), 48 (32%), 46 (30%), 9 (6%), 34 (23%) and 2 (1%) of foxes, respectively. The prevalence but not the abundance of E. multilocularis decreased with age. The abundance of Dipylidium caninum also decreased with age. The frequency distribution of E. multilocularis and Mesocestoides spp. followed a zero inflated negative binomial distribution, whilst all other helminths had a negative binomial distribution. This demonstrates that the frequency distribution of positive counts and not just the frequency of zeros in the data set can determine if a zero inflated or non-zero inflated model is more appropriate. This is because the prevalences of E. multolocularis and Mesocestoides spp. were the highest (and hence had fewest zero counts) yet the parasite distribution nevertheless gave a better fit to the zero inflated models. PMID:20434845

Mycophenolic Acid Pharmacokinetics and Relapse in Children with Steroid–Dependent Idiopathic Nephrotic Syndrome

PubMed Central

Tellier, Stéphanie; Dallocchio, Aymeric; Guigonis, Vincent; Saint-Marcoux, Frank; Llanas, Brigitte; Ichay, Lydia; Bandin, Flavio; Godron, Astrid; Morin, Denis; Brochard, Karine; Gandia, Peggy; Bouchet, Stéphane; Marquet, Pierre; Decramer, Stéphane

2016-01-01

Background and objectives Therapeutic drug monitoring of mycophenolic acid can improve clinical outcome in organ transplantation and lupus, but data are scarce in idiopathic nephrotic syndrome. The aim of our study was to investigate whether mycophenolic acid pharmacokinetics are associated with disease control in children receiving mycophenolate mofetil for the treatment of steroid–dependent nephrotic syndrome. Design, setting, participants, & measurements This was a retrospective multicenter study including 95 children with steroid–dependent nephrotic syndrome treated with mycophenolate mofetil with or without steroids. Area under the concentration-time curve of mycophenolic acid was determined in all children on the basis of sampling times at 20, 60, and 180 minutes postdose, using Bayesian estimation. The association between a threshold value of the area under the concentration-time curve of mycophenolic acid and the relapse rate was assessed using a negative binomial model. Results In total, 140 areas under the concentration-time curve of mycophenolic acid were analyzed. The findings indicate individual dose adaptation in 53 patients (38%) to achieve an area under the concentration-time curve target of 30–60 mg·h/L. In a multivariable negative binomial model including sex, age at disease onset, time to start of mycophenolate mofetil, previous immunomodulatory treatment, and concomitant prednisone dose, a level of area under the concentration-time curve of mycophenolic acid >45 mg·h/L was significantly associated with a lower relapse rate (rate ratio, 0.65; 95% confidence interval, 0.46 to 0.89; P=0.01). Conclusions Therapeutic drug monitoring leading to individualized dosing may improve the efficacy of mycophenolate mofetil in steroid–dependent nephrotic syndrome. Additional prospective studies are warranted to determine the optimal target for area under the concentration-time curve of mycophenolic acid in this population. PMID:27445161

Mycophenolic Acid Pharmacokinetics and Relapse in Children with Steroid-Dependent Idiopathic Nephrotic Syndrome.

PubMed

Tellier, Stéphanie; Dallocchio, Aymeric; Guigonis, Vincent; Saint-Marcoux, Frank; Llanas, Brigitte; Ichay, Lydia; Bandin, Flavio; Godron, Astrid; Morin, Denis; Brochard, Karine; Gandia, Peggy; Bouchet, Stéphane; Marquet, Pierre; Decramer, Stéphane; Harambat, Jérôme

2016-10-07

Therapeutic drug monitoring of mycophenolic acid can improve clinical outcome in organ transplantation and lupus, but data are scarce in idiopathic nephrotic syndrome. The aim of our study was to investigate whether mycophenolic acid pharmacokinetics are associated with disease control in children receiving mycophenolate mofetil for the treatment of steroid-dependent nephrotic syndrome. This was a retrospective multicenter study including 95 children with steroid-dependent nephrotic syndrome treated with mycophenolate mofetil with or without steroids. Area under the concentration-time curve of mycophenolic acid was determined in all children on the basis of sampling times at 20, 60, and 180 minutes postdose, using Bayesian estimation. The association between a threshold value of the area under the concentration-time curve of mycophenolic acid and the relapse rate was assessed using a negative binomial model. In total, 140 areas under the concentration-time curve of mycophenolic acid were analyzed. The findings indicate individual dose adaptation in 53 patients (38%) to achieve an area under the concentration-time curve target of 30-60 mg·h/L. In a multivariable negative binomial model including sex, age at disease onset, time to start of mycophenolate mofetil, previous immunomodulatory treatment, and concomitant prednisone dose, a level of area under the concentration-time curve of mycophenolic acid >45 mg·h/L was significantly associated with a lower relapse rate (rate ratio, 0.65; 95% confidence interval, 0.46 to 0.89; P =0.01). Therapeutic drug monitoring leading to individualized dosing may improve the efficacy of mycophenolate mofetil in steroid-dependent nephrotic syndrome. Additional prospective studies are warranted to determine the optimal target for area under the concentration-time curve of mycophenolic acid in this population. Copyright © 2016 by the American Society of Nephrology.

Gingival recession and associated factors in a homogeneous Mexican adult male population: A cross-sectional study

PubMed Central

Minaya-Sánchez, Mirna; Medina-Solís, Carlo E.; Vallejos-Sánchez, Ana A.; Marquez-Corona, Maria L.; Pontigo-Loyola, América P.; Islas-Granillo, Horacio; Maupomé, Gerardo

2012-01-01

Background: Diverse variables are implicated in the pathogenesis of gingival recession; more detailed knowledge about the relationship between the clinical presentation of gingival recession and assorted risk indicators may lead to improved patient monitoring, early intervention, and subsequent prevention. The objective was to evaluate clinically gingival recession in a homogeneous Mexican adult male population and to determine the strength of association with related factors. Method: A cross-sectional study was carried out in a largely homogeneous group in terms of ethnic background, socioeconomic status, gender, occupation, and medical/dental insurance, in Campeche, Mexico. Periodontal examinations were undertaken to determine diverse clinical dental variables. All periodontal clinical examinations were assessed using the Florida Probe System, a dental chair and one examiner. Questionnaires were used to collect diverse risk indicators. Statistical analyses were undertaken with negative binomial regression models. Results: The mean number of sites with gingival recession per subject was 6.73±5.81; the prevalence was 87.6%. In the negative binomial regression model we observed that for (i) each year of age, and (ii) each percentage unit of increase in sites with plaque, and (iii) with suppuration, mean sites with gingival recession increased 2.9%, 1.0% and 13.0%, respectively. Having a spouse was associated with gingival recession. Conclusions: We observed association between gingival recession, and sociodemographic and clinical parameters. Patients need to be educated about risk indicators for gingival recession as well as the preventive maneuvers that may be implemented to minimize its occurrence. The potential of improved oral self-care to prevent a largely benign condition such as gingival recession is important, given the associated disorders that may ensue root exposure, such as root caries and root hypersensitivity. Key words:Oral health, periodontal health, gingival recession, adults, Mexico. PMID:22549678

Evaluation of logistic regression models and effect of covariates for case-control study in RNA-Seq analysis.

PubMed

Choi, Seung Hoan; Labadorf, Adam T; Myers, Richard H; Lunetta, Kathryn L; Dupuis, Josée; DeStefano, Anita L

2017-02-06

Next generation sequencing provides a count of RNA molecules in the form of short reads, yielding discrete, often highly non-normally distributed gene expression measurements. Although Negative Binomial (NB) regression has been generally accepted in the analysis of RNA sequencing (RNA-Seq) data, its appropriateness has not been exhaustively evaluated. We explore logistic regression as an alternative method for RNA-Seq studies designed to compare cases and controls, where disease status is modeled as a function of RNA-Seq reads using simulated and Huntington disease data. We evaluate the effect of adjusting for covariates that have an unknown relationship with gene expression. Finally, we incorporate the data adaptive method in order to compare false positive rates. When the sample size is small or the expression levels of a gene are highly dispersed, the NB regression shows inflated Type-I error rates but the Classical logistic and Bayes logistic (BL) regressions are conservative. Firth's logistic (FL) regression performs well or is slightly conservative. Large sample size and low dispersion generally make Type-I error rates of all methods close to nominal alpha levels of 0.05 and 0.01. However, Type-I error rates are controlled after applying the data adaptive method. The NB, BL, and FL regressions gain increased power with large sample size, large log2 fold-change, and low dispersion. The FL regression has comparable power to NB regression. We conclude that implementing the data adaptive method appropriately controls Type-I error rates in RNA-Seq analysis. Firth's logistic regression provides a concise statistical inference process and reduces spurious associations from inaccurately estimated dispersion parameters in the negative binomial framework.

Dental plaque, preventive care, and tooth brushing associated with dental caries in primary teeth in schoolchildren ages 6-9 years of Leon, Nicaragua.

PubMed

Herrera, Miriam del Socorro; Medina-Solís, Carlo Eduardo; Minaya-Sánchez, Mirna; Pontigo-Loyola, América Patricia; Villalobos-Rodelo, Juan José; Islas-Granillo, Horacio; de la Rosa-Santillana, Rubén; Maupomé, Gerardo

2013-11-19

Our study aimed to evaluate the effect of various risk indicators for dental caries on primary teeth of Nicaraguan children (from Leon, Nicaragua) ages 6 to 9, using the negative binomial regression model. A cross-sectional study was carried out to collect clinical, demographic, socioeconomic, and behavioral data from 794 schoolchildren ages 6 to 9 years, randomly selected from 25 schools in the city of León, Nicaragua. Clinical examinations for dental caries (dmft index) were performed by 2 trained and standardized examiners. Socio-demographic, socioeconomic, and behavioral data were self-reported using questionnaires. Multivariate negative binomial regression (NBR) analysis was used. Mean age was 7.49 ± 1.12 years. Boys accounted for 50.1% of the sample. Mean dmft was 3.54 ± 3.13 and caries prevalence (dmft >0) was 77.6%. In the NBR multivariate model (p<0.05), for each year of age, the expected mean dmft decreased by 7.5%. Brushing teeth at least once a day and having received preventive dental care in the last year before data collection were associated with declines in the expected mean dmft by 19.5% and 69.6%, respectively. Presence of dental plaque increased the expected mean dmft by 395.5%. The proportion of students with caries in this sample was high. We found associations between dental caries in the primary dentition and dental plaque, brushing teeth at least once a day, and having received preventive dental care. To improve oral health, school programs and/or age-appropriate interventions need to be developed based on the specific profile of caries experience and the associated risk indicators.

A visual LISP program for voxelizing AutoCAD solid models

NASA Astrophysics Data System (ADS)

Marschallinger, Robert; Jandrisevits, Carmen; Zobl, Fritz

2015-01-01

AutoCAD solid models are increasingly recognized in geological and geotechnical 3D modeling. In order to bridge the currently existing gap between AutoCAD solid models and the grid modeling realm, a Visual LISP program is presented that converts AutoCAD solid models into voxel arrays. Acad2Vox voxelizer works on a 3D-model that is made up of arbitrary non-overlapping 3D-solids. After definition of the target voxel array geometry, 3D-solids are scanned at grid positions and properties are streamed to an ASCII output file. Acad2Vox has a novel voxelization strategy that combines a hierarchical reduction of sampling dimensionality with an innovative use of AutoCAD-specific methods for a fast and memory-saving operation. Acad2Vox provides georeferenced, voxelized analogs of 3D design data that can act as regions-of-interest in later geostatistical modeling and simulation. The Supplement includes sample geological solid models with instructions for practical work with Acad2Vox.

A preliminary investigation of the relationships between historical crash and naturalistic driving.

PubMed

Pande, Anurag; Chand, Sai; Saxena, Neeraj; Dixit, Vinayak; Loy, James; Wolshon, Brian; Kent, Joshua D

2017-04-01

This paper describes a project that was undertaken using naturalistic driving data collected via Global Positioning System (GPS) devices to demonstrate a proof-of-concept for proactive safety assessments of crash-prone locations. The main hypothesis for the study is that the segments where drivers have to apply hard braking (higher jerks) more frequently might be the "unsafe" segments with more crashes over a long-term. The linear referencing methodology in ArcMap was used to link the GPS data with roadway characteristic data of US Highway 101 northbound (NB) and southbound (SB) in San Luis Obispo, California. The process used to merge GPS data with quarter-mile freeway segments for traditional crash frequency analysis is also discussed in the paper. A negative binomial regression analyses showed that proportion of high magnitude jerks while decelerating on freeway segments (from the driving data) was significantly related with the long-term crash frequency of those segments. A random parameter negative binomial model with uniformly distributed parameter for ADT and a fixed parameter for jerk provided a statistically significant estimate for quarter-mile segments. The results also indicated that roadway curvature and the presence of auxiliary lane are not significantly related with crash frequency for the highway segments under consideration. The results from this exploration are promising since the data used to derive the explanatory variable(s) can be collected using most off-the-shelf GPS devices, including many smartphones. Copyright © 2017 Elsevier Ltd. All rights reserved.

Evaluation of statistical models for forecast errors from the HBV model

NASA Astrophysics Data System (ADS)

Engeland, Kolbjørn; Renard, Benjamin; Steinsland, Ingelin; Kolberg, Sjur

2010-04-01

SummaryThree statistical models for the forecast errors for inflow into the Langvatn reservoir in Northern Norway have been constructed and tested according to the agreement between (i) the forecast distribution and the observations and (ii) median values of the forecast distribution and the observations. For the first model observed and forecasted inflows were transformed by the Box-Cox transformation before a first order auto-regressive model was constructed for the forecast errors. The parameters were conditioned on weather classes. In the second model the Normal Quantile Transformation (NQT) was applied on observed and forecasted inflows before a similar first order auto-regressive model was constructed for the forecast errors. For the third model positive and negative errors were modeled separately. The errors were first NQT-transformed before conditioning the mean error values on climate, forecasted inflow and yesterday's error. To test the three models we applied three criterions: we wanted (a) the forecast distribution to be reliable; (b) the forecast intervals to be narrow; (c) the median values of the forecast distribution to be close to the observed values. Models 1 and 2 gave almost identical results. The median values improved the forecast with Nash-Sutcliffe R eff increasing from 0.77 for the original forecast to 0.87 for the corrected forecasts. Models 1 and 2 over-estimated the forecast intervals but gave the narrowest intervals. Their main drawback was that the distributions are less reliable than Model 3. For Model 3 the median values did not fit well since the auto-correlation was not accounted for. Since Model 3 did not benefit from the potential variance reduction that lies in bias estimation and removal it gave on average wider forecasts intervals than the two other models. At the same time Model 3 on average slightly under-estimated the forecast intervals, probably explained by the use of average measures to evaluate the fit.

Iterative model building, structure refinement and density modification with the PHENIX AutoBuild wizard

PubMed Central

Terwilliger, Thomas C.; Grosse-Kunstleve, Ralf W.; Afonine, Pavel V.; Moriarty, Nigel W.; Zwart, Peter H.; Hung, Li-Wei; Read, Randy J.; Adams, Paul D.

2008-01-01

The PHENIX AutoBuild wizard is a highly automated tool for iterative model building, structure refinement and density modification using RESOLVE model building, RESOLVE statistical density modification and phenix.refine structure refinement. Recent advances in the AutoBuild wizard and phenix.refine include automated detection and application of NCS from models as they are built, extensive model-completion algorithms and automated solvent-molecule picking. Model-completion algorithms in the AutoBuild wizard include loop building, crossovers between chains in different models of a structure and side-chain optimization. The AutoBuild wizard has been applied to a set of 48 structures at resolutions ranging from 1.1 to 3.2 Å, resulting in a mean R factor of 0.24 and a mean free R factor of 0.29. The R factor of the final model is dependent on the quality of the starting electron density and is relatively independent of resolution. PMID:18094468

[Correlation of Plasma Co-stimulatory Molecules B7-H2 and B7-H3 with Platelet Auto-antibodies in Patients with Immune Thrombocytopenic Purpura].

PubMed

Zuo, Bin; Zhao, Yun-Xiao; Yang, Jian-Feng; He, Yang

2015-08-01

To investigate whether the plasma level of platelet auto- antibodies in ITP patients is related to that of co-stimulatory molecules sB7-H2 and sB7-H3. A total of 61 ITP patients and 25 healthy controls from the First Affiliated Hospital of Soochow University from June 2012 to August 2013 were enrolled in this study. The expression levels of platelet auto-antibodies against 5 glycoproteins (GPIX, GP Ib, GP IIIa, GPIIb and P-selectin) in plasma were detected by flow cytometric immuno-beads array, and the expression of soluable co-stimulatory molecules sB7-H2 and sB7-H3 was measured by ELISA. The plasma levels of 5 auto-antibodies against platelet membrance glycoproteins significantly increased in ITP patiens (P < 0.01). Compared with healthy controls, sB7-H2 levels increased (P < 0.05), while the sB7-H3 level did not significantly change (r = 0.13, P > 0.05). However, the correlation analysis showed that sB7-H3 negatively correlated with platelet P-selectin auto-antibody (r = -0.46, P < 0.05), and sB7-H2 and sB7-H3 significantly reduced in ITP patients with positive P-selectin auto-antibody (P < 0.01). In ITP patients, platelet counts negatively correlated with sB7-H2 (r = -0.3907, P < 0.01), but did not correlate with sB7-H3. Soluble costimulatory molecule sB7-H2 elevates in ITP patients, and the level of sB7-H3 is associated with auto-antibodies against P-selectin, suggesting that costimulatory molecules B7-H2 and B7-H3 may be involved in the pathogenesis of immune regulation abnormality in ITP.

DAT positivity in blood donors: a perplexing scenario.

PubMed

Bedi, Ravneet Kaur; Mittal, Kshitija; Sood, Tanvi; Kumar, Rakesh; Praveen, Ajay S

2014-04-01

A blood request was received for 70 year male patient suffering from Chronic Obstructive Pulmonary Disease with anemia. One unit was found incompatible in AHG phase. Patient's antibody screen, indirect antiglobulin test (IAT), direct antiglobulin test (DAT) and auto control was negative. DAT of donor unit was positive with anti IgG gel card and negative with C3d reagent along with positive auto control. Donor was 30 year male with no history of blood transfusion and medication and had no evidence of hemolysis. Donors with positive DAT should be deferred, notified and referred to physician but further studies are required. Copyright © 2014 Elsevier Ltd. All rights reserved.

Effects of media violence on health-related outcomes among young men.

PubMed

Brady, Sonya S; Matthews, Karen A

2006-04-01

To test the effects of media violence exposure on blood pressure, negative affect, hostile social information processing, uncooperative behavior, and attitudes toward health risk behaviors among young men varying in lifetime violence exposure within the home and community. Experimental laboratory study. University campus situated within an urban environment. One hundred male undergraduates aged 18 to 21 years. Men who had previously reported differing amounts of lifetime home and community violence were randomly assigned to play The Simpsons: Hit and Run (low-violence condition) or Grand Theft Auto III (high-violence condition). Systolic and diastolic blood pressure; negative affect; hostile social information processing; uncooperative behavior; and permissive attitudes toward violence, alcohol use, marijuana use, and sexual activity without condom use. Men randomly assigned to play Grand Theft Auto III exhibited greater increases in diastolic blood pressure from a baseline rest period to game play, greater negative affect, more permissive attitudes toward using alcohol and marijuana, and more uncooperative behavior in comparison with men randomly assigned to play The Simpsons. Only among participants with greater exposure to home and community violence, play of Grand Theft Auto III led to elevated systolic blood pressure in comparison with play of The Simpsons (mean, 13 vs 5 mm Hg). Media violence exposure may play a role in the development of negative attitudes and behaviors related to health. Although youth growing up in violent homes and communities may become more physiologically aroused by media violence exposure, all youth appear to be at risk for potentially negative outcomes.

Using the β-binomial distribution to characterize forest health

Treesearch

S.J. Zarnoch; R.L. Anderson; R.M. Sheffield

1995-01-01

The β-binomial distribution is suggested as a model for describing and analyzing the dichotomous data obtained from programs monitoring the health of forests in the United States. Maximum likelihood estimation of the parameters is given as well as asymptotic likelihood ratio tests. The procedure is illustrated with data on dogwood anthracnose infection (caused...

Relation between social information processing and intimate partner violence in dating couples.

PubMed

Setchell, Sarah; Fritz, Patti Timmons; Glasgow, Jillian

2017-07-01

We used couple-level data to predict physical acts of intimate partner violence (IPV) from self-reported negative emotions and social information-processing (SIP) abilities among 100 dating couples (n = 200; mean age = 21.45 years). Participants read a series of hypothetical conflict situation vignettes and responded to questionnaires to assess negative emotions and various facets of SIP including attributions for partner behavior, generation of response alternatives, and response selection. We conducted a series of negative binomial mixed-model regressions based on the actor-partner interdependence model (APIM; Kenny, Kashy, & Cook, 2006, Dyadic data analysis. New York, NY: Guilford Press). There were significant results for the response generation and negative emotion models. Participants who generated fewer coping response alternatives were at greater risk of victimization (actor effect). Women were at greater risk of victimization if they had partners who generated fewer coping response alternatives (sex by partner interaction effect). Generation of less competent coping response alternatives predicted greater risk of perpetration among men, whereas generation of more competent coping response alternatives predicted greater risk of victimization among women (sex by actor interaction effects). Two significant actor by partner interaction effects were found for the negative emotion models. Participants who reported discrepant levels of negative emotions from their partners were at greatest risk of perpetration. Participants who reported high levels of negative emotions were at greatest risk of victimization if they had partners who reported low levels of negative emotions. This research has implications for researchers and clinicians interested in addressing the problem of IPV. Aggr. Behav. 43:329-341, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

The role of ENSO in understanding changes in Colombia's annual malaria burden by region, 1960–2006

PubMed Central

Mantilla, Gilma; Oliveros, Hugo; Barnston, Anthony G

2009-01-01

Background Malaria remains a serious problem in Colombia. The number of malaria cases is governed by multiple climatic and non-climatic factors. Malaria control policies, and climate controls such as rainfall and temperature variations associated with the El Niño/Southern Oscillation (ENSO), have been associated with malaria case numbers. Using historical climate data and annual malaria case number data from 1960 to 2006, statistical models are developed to isolate the effects of climate in each of Colombia's five contrasting geographical regions. Methods Because year to year climate variability associated with ENSO causes interannual variability in malaria case numbers, while changes in population and institutional control policy result in more gradual trends, the chosen predictors in the models are annual indices of the ENSO state (sea surface temperature [SST] in the tropical Pacific Ocean) and time reference indices keyed to two major malaria trends during the study period. Two models were used: a Poisson and a Negative Binomial regression model. Two ENSO indices, two time reference indices, and one dummy variable are chosen as candidate predictors. The analysis was conducted using the five geographical regions to match the similar aggregation used by the National Institute of Health for its official reports. Results The Negative Binomial regression model is found better suited to the malaria cases in Colombia. Both the trend variables and the ENSO measures are significant predictors of malaria case numbers in Colombia as a whole, and in two of the five regions. A one degree Celsius change in SST (indicating a weak to moderate ENSO event) is seen to translate to an approximate 20% increase in malaria cases, holding other variables constant. Conclusion Regional differentiation in the role of ENSO in understanding changes in Colombia's annual malaria burden during 1960–2006 was found, constituting a new approach to use ENSO as a significant predictor of the malaria cases in Colombia. These results naturally point to additional needed work: (1) refining the regional and seasonal dependence of climate on the ENSO state, and of malaria on the climate variables; (2) incorporating ENSO-related climate variability into dynamic malaria models. PMID:19133152

School Violence: The Role of Parental and Community Involvement

ERIC Educational Resources Information Center

Lesneskie, Eric; Block, Steven

2017-01-01

This study utilizes the School Survey on Crime and Safety to identify variables that predict lower levels of violence from four domains: school security, school climate, parental involvement, and community involvement. Negative binomial regression was performed and the findings indicate that statistically significant results come from all four…

An Alternate Approach to Alternating Sums: A Method to DIE for

ERIC Educational Resources Information Center

Benjamin, Arthur T.; Quinn, Jennifer J.

2008-01-01

Positive sums count. Alternating sums match. Alternating sums of binomial coefficients, Fibonacci numbers, and other combinatorial quantities are analyzed using sign-reversing involutions. In particular, we describe the quantity being considered, match positive and negative terms through an Involution, and count the Exceptions to the matching rule…

Partitioning Detectability Components in Populations Subject to Within-Season Temporary Emigration Using Binomial Mixture Models

PubMed Central

O’Donnell, Katherine M.; Thompson, Frank R.; Semlitsch, Raymond D.

2015-01-01

Detectability of individual animals is highly variable and nearly always < 1; imperfect detection must be accounted for to reliably estimate population sizes and trends. Hierarchical models can simultaneously estimate abundance and effective detection probability, but there are several different mechanisms that cause variation in detectability. Neglecting temporary emigration can lead to biased population estimates because availability and conditional detection probability are confounded. In this study, we extend previous hierarchical binomial mixture models to account for multiple sources of variation in detectability. The state process of the hierarchical model describes ecological mechanisms that generate spatial and temporal patterns in abundance, while the observation model accounts for the imperfect nature of counting individuals due to temporary emigration and false absences. We illustrate our model’s potential advantages, including the allowance of temporary emigration between sampling periods, with a case study of southern red-backed salamanders Plethodon serratus. We fit our model and a standard binomial mixture model to counts of terrestrial salamanders surveyed at 40 sites during 3–5 surveys each spring and fall 2010–2012. Our models generated similar parameter estimates to standard binomial mixture models. Aspect was the best predictor of salamander abundance in our case study; abundance increased as aspect became more northeasterly. Increased time-since-rainfall strongly decreased salamander surface activity (i.e. availability for sampling), while higher amounts of woody cover objects and rocks increased conditional detection probability (i.e. probability of capture, given an animal is exposed to sampling). By explicitly accounting for both components of detectability, we increased congruence between our statistical modeling and our ecological understanding of the system. We stress the importance of choosing survey locations and protocols that maximize species availability and conditional detection probability to increase population parameter estimate reliability. PMID:25775182

Smart-card-based automatic meal record system intervention tool for analysis using data mining approach.

PubMed

Zenitani, Satoko; Nishiuchi, Hiromu; Kiuchi, Takahiro

2010-04-01

The Smart-card-based Automatic Meal Record system for company cafeterias (AutoMealRecord system) was recently developed and used to monitor employee eating habits. The system could be a unique nutrition assessment tool for automatically monitoring the meal purchases of all employees, although it only focuses on company cafeterias and has never been validated. Before starting an interventional study, we tested the reliability of the data collected by the system using the data mining approach. The AutoMealRecord data were examined to determine if it could predict current obesity. All data used in this study (n = 899) were collected by a major electric company based in Tokyo, which has been operating the AutoMealRecord system for several years. We analyzed dietary patterns by principal component analysis using data from the system and extracted 5 major dietary patterns: healthy, traditional Japanese, Chinese, Japanese noodles, and pasta. The ability to predict current body mass index (BMI) with dietary preference was assessed with multiple linear regression analyses, and in the current study, BMI was positively correlated with male gender, preference for "Japanese noodles," mean energy intake, protein content, and frequency of body measurement at a body measurement booth in the cafeteria. There was a negative correlation with age, dietary fiber, and lunchtime cafeteria use (R(2) = 0.22). This regression model predicted "would-be obese" participants (BMI >or= 23) with 68.8% accuracy by leave-one-out cross validation. This shows that there was sufficient predictability of BMI based on data from the AutoMealRecord System. We conclude that the AutoMealRecord system is valuable for further consideration as a health care intervention tool. Copyright 2010 Elsevier Inc. All rights reserved.

Estimating the prevalence and intensity of Schistosoma mansoni infection among rural communities in Western Tanzania: The influence of sampling strategy and statistical approach

PubMed Central

Bakuza, Jared S.; Denwood, Matthew J.; Nkwengulila, Gamba

2017-01-01

Background Schistosoma mansoni is a parasite of major public health importance in developing countries, where it causes a neglected tropical disease known as intestinal schistosomiasis. However, the distribution of the parasite within many endemic regions is currently unknown, which hinders effective control. The purpose of this study was to characterize the prevalence and intensity of infection of S. mansoni in a remote area of western Tanzania. Methodology/Principal findings Stool samples were collected from 192 children and 147 adults residing in Gombe National Park and four nearby villages. Children were actively sampled in local schools, and adults were sampled passively by voluntary presentation at the local health clinics. The two datasets were therefore analysed separately. Faecal worm egg count (FWEC) data were analysed using negative binomial and zero-inflated negative binomial (ZINB) models with explanatory variables of site, sex, and age. The ZINB models indicated that a substantial proportion of the observed zero FWEC reflected a failure to detect eggs in truly infected individuals, meaning that the estimated true prevalence was much higher than the apparent prevalence as calculated based on the simple proportion of non-zero FWEC. For the passively sampled data from adults, the data were consistent with close to 100% true prevalence of infection. Both the prevalence and intensity of infection differed significantly between sites, but there were no significant associations with sex or age. Conclusions/Significance Overall, our data suggest a more widespread distribution of S. mansoni in this part of Tanzania than was previously thought. The apparent prevalence estimates substantially under-estimated the true prevalence as determined by the ZINB models, and the two types of sampling strategies also resulted in differing conclusions regarding prevalence of infection. We therefore recommend that future surveillance programmes designed to assess risk factors should use active sampling whenever possible, in order to avoid the self-selection bias associated with passive sampling. PMID:28934206

Detection of influenza-like illness aberrations by directly monitoring Pearson residuals of fitted negative binomial regression models.

PubMed

Chan, Ta-Chien; Teng, Yung-Chu; Hwang, Jing-Shiang

2015-02-21

Emerging novel influenza outbreaks have increasingly been a threat to the public and a major concern of public health departments. Real-time data in seamless surveillance systems such as health insurance claims data for influenza-like illnesses (ILI) are ready for analysis, making it highly desirable to develop practical techniques to analyze such readymade data for outbreak detection so that the public can receive timely influenza epidemic warnings. This study proposes a simple and effective approach to analyze area-based health insurance claims data including outpatient and emergency department (ED) visits for early detection of any aberrations of ILI. The health insurance claims data during 2004-2009 from a national health insurance research database were used for developing early detection methods. The proposed approach fitted the daily new ILI visits and monitored the Pearson residuals directly for aberration detection. First, negative binomial regression was used for both outpatient and ED visits to adjust for potentially influential factors such as holidays, weekends, seasons, temporal dependence and temperature. Second, if the Pearson residuals exceeded 1.96, aberration signals were issued. The empirical validation of the model was done in 2008 and 2009. In addition, we designed a simulation study to compare the time of outbreak detection, non-detection probability and false alarm rate between the proposed method and modified CUSUM. The model successfully detected the aberrations of 2009 pandemic (H1N1) influenza virus in northern, central and southern Taiwan. The proposed approach was more sensitive in identifying aberrations in ED visits than those in outpatient visits. Simulation studies demonstrated that the proposed approach could detect the aberrations earlier, and with lower non-detection probability and mean false alarm rate in detecting aberrations compared to modified CUSUM methods. The proposed simple approach was able to filter out temporal trends, adjust for temperature, and issue warning signals for the first wave of the influenza epidemic in a timely and accurate manner.

I Remember You: Independence and the Binomial Model

ERIC Educational Resources Information Center

Levine, Douglas W.; Rockhill, Beverly

2006-01-01

We focus on the problem of ignoring statistical independence. A binomial experiment is used to determine whether judges could match, based on looks alone, dogs to their owners. The experimental design introduces dependencies such that the probability of a given judge correctly matching a dog and an owner changes from trial to trial. We show how…

Analysis of multiple tank car releases in train accidents.

PubMed

Liu, Xiang; Liu, Chang; Hong, Yili

2017-10-01

There are annually over two million carloads of hazardous materials transported by rail in the United States. The American railroads use large blocks of tank cars to transport petroleum crude oil and other flammable liquids from production to consumption sites. Being different from roadway transport of hazardous materials, a train accident can potentially result in the derailment and release of multiple tank cars, which may result in significant consequences. The prior literature predominantly assumes that the occurrence of multiple tank car releases in a train accident is a series of independent Bernoulli processes, and thus uses the binomial distribution to estimate the total number of tank car releases given the number of tank cars derailing or damaged. This paper shows that the traditional binomial model can incorrectly estimate multiple tank car release probability by magnitudes in certain circumstances, thereby significantly affecting railroad safety and risk analysis. To bridge this knowledge gap, this paper proposes a novel, alternative Correlated Binomial (CB) model that accounts for the possible correlations of multiple tank car releases in the same train. We test three distinct correlation structures in the CB model, and find that they all outperform the conventional binomial model based on empirical tank car accident data. The analysis shows that considering tank car release correlations would result in a significantly improved fit of the empirical data than otherwise. Consequently, it is prudent to consider alternative modeling techniques when analyzing the probability of multiple tank car releases in railroad accidents. Copyright © 2017 Elsevier Ltd. All rights reserved.

AutoCAD-To-NASTRAN Translator Program

NASA Technical Reports Server (NTRS)

Jones, A.

1989-01-01

Program facilitates creation of finite-element mathematical models from geometric entities. AutoCAD to NASTRAN translator (ACTON) computer program developed to facilitate quick generation of small finite-element mathematical models for use with NASTRAN finite-element modeling program. Reads geometric data of drawing from Data Exchange File (DXF) used in AutoCAD and other PC-based drafting programs. Written in Microsoft Quick-Basic (Version 2.0).

Rapid automated method for screening of enteric pathogens from stool specimens.

PubMed Central

Villasante, P A; Agulla, A; Merino, F J; Pérez, T; Ladrón de Guevara, C; Velasco, A C

1987-01-01

A total of 800 colonies suggestive of Salmonella, Shigella, or Yersinia species isolated on stool differential agar media were inoculated onto both conventional biochemical test media (triple sugar iron agar, urea agar, and phenylalanine agar) and Entero Pathogen Screen cards of the AutoMicrobic system (Vitek Systems, Inc., Hazelwood, Mo.). Based on the conventional tests, the AutoMicrobic system method yielded the following results: 587 true-negatives, 185 true-positives, 2 false-negatives, and 26 false-positives (sensitivity, 99%; specificity, 96%). Both true-positive and true-negative results were achieved considerably earlier than false results (P less than 0.001). The Entero Pathogen Screen card method is a fast, easy, and sensitive method for screening for Salmonella, Shigella, or Yersinia species. The impossibility of screening for oxidase-positive pathogens is a minor disadvantage of this method. PMID:3553230

Neighborhood characteristics contribute to urban alcohol availability: Accounting for race/ethnicity and social disorganization.

PubMed

Snowden, Aleksandra J

2016-01-01

This study examined the role that race/ethnicity and social disorganization play in alcohol availability in Milwaukee, Wisconsin, census block groups. This study estimated negative binomial regression models to examine separately the relationship between neighborhood racial/ethnic composition and social disorganization levels for (1) total, (2) on-premise, and (3) off-premise alcohol outlets. Results of this study suggest that proportion Hispanic was positively associated with total and with off-premise alcohol outlets. Second, proportion African American was negatively associated with on-premise alcohol outlets and positively associated with off-premise alcohol outlets. Proportion Asian was not associated with total, on-premise, or off-premise alcohol outlets. However, the effects of race/ethnicity on alcohol availability were either unrelated or negatively related to alcohol outlet availability once neighborhood social disorganization levels were taken into account, and social disorganization was positively and significantly associated with all alcohol outlet types. Neighborhood characteristics contribute to alcohol availability and must be considered in any efforts aimed toward prevention of alcohol-related negative health and social outcomes.

Fast auto-focus scheme based on optical defocus fitting model

NASA Astrophysics Data System (ADS)

Wang, Yeru; Feng, Huajun; Xu, Zhihai; Li, Qi; Chen, Yueting; Cen, Min

2018-04-01

An optical defocus fitting model-based (ODFM) auto-focus scheme is proposed. Considering the basic optical defocus principle, the optical defocus fitting model is derived to approximate the potential-focus position. By this accurate modelling, the proposed auto-focus scheme can make the stepping motor approach the focal plane more accurately and rapidly. Two fitting positions are first determined for an arbitrary initial stepping motor position. Three images (initial image and two fitting images) at these positions are then collected to estimate the potential-focus position based on the proposed ODFM method. Around the estimated potential-focus position, two reference images are recorded. The auto-focus procedure is then completed by processing these two reference images and the potential-focus image to confirm the in-focus position using a contrast based method. Experimental results prove that the proposed scheme can complete auto-focus within only 5 to 7 steps with good performance even under low-light condition.

Anti-pentraxin 3 auto-antibodies might be protective in lupus nephritis: a large cohort study.

PubMed

Yuan, Mo; Tan, Ying; Pang, Yun; Li, Yong-Zhe; Song, Yan; Yu, Feng; Zhao, Ming-Hui

2017-11-01

Anti-pentraxin 3 (PTX3) auto-antibodies were found to be associated with the absence of renal involvement in systemic lupus erythematosus (SLE). This study is to investigate the prevalence of anti-PTX3 auto-antibodies and their clinical significance based on a large Chinese lupus nephritis cohort. One hundred and ninety-six active lupus nephritis patients, 150 SLE patients without clinical renal involvement, and 100 healthy controls were enrolled. Serum anti-PTX3 auto-antibodies and PTX3 levels were screened by enzyme-linked immunosorbent assay (ELISA). The associations between anti-PTX3 auto-antibodies and clinicopathological parameters in lupus nephritis were further analyzed. Anti-PTX3 auto-antibodies were less prevalent in active lupus nephritis patients compared with SLE without renal involvement (19.4% (38/196) versus 40.7% (61/150), p < .001). The serum levels of anti-PTX3 auto-antibodies were negatively correlated with proteinuria in lupus nephritis (r = -.143, p = .047). The levels of proteinuria, serum creatinine, and the prevalence of thrombotic microangiopathy were significantly higher in patients with higher PTX3 levels (≥3.207 ng/ml) and without anti-PTX3 auto-antibodies compared with patients with lower PTX3 levels (<3.207 ng/ml) and with anti-PTX3 auto-antibodies (4.79 (3.39-8.28) versus 3.95 (1.78-7.0), p = .03; 168.84 ± 153.63 versus 101.44 ± 47.36, p = .01; 34.1% (14/41) versus 0% (0/9), p = .04; respectively). Anti-PTX3 auto-antibodies were less prevalent in active lupus nephritis patients compared with SLE without renal involvement and associated with less severe renal damage, especially with the combined evaluation of serum PTX3 levels.

Abstract knowledge versus direct experience in processing of binomial expressions

PubMed Central

Morgan, Emily; Levy, Roger

2016-01-01

We ask whether word order preferences for binomial expressions of the form A and B (e.g. bread and butter) are driven by abstract linguistic knowledge of ordering constraints referencing the semantic, phonological, and lexical properties of the constituent words, or by prior direct experience with the specific items in questions. Using forced-choice and self-paced reading tasks, we demonstrate that online processing of never-before-seen binomials is influenced by abstract knowledge of ordering constraints, which we estimate with a probabilistic model. In contrast, online processing of highly frequent binomials is primarily driven by direct experience, which we estimate from corpus frequency counts. We propose a trade-off wherein processing of novel expressions relies upon abstract knowledge, while reliance upon direct experience increases with increased exposure to an expression. Our findings support theories of language processing in which both compositional generation and direct, holistic reuse of multi-word expressions play crucial roles. PMID:27776281

AutoQSAR: an automated machine learning tool for best-practice quantitative structure-activity relationship modeling.

PubMed

Dixon, Steven L; Duan, Jianxin; Smith, Ethan; Von Bargen, Christopher D; Sherman, Woody; Repasky, Matthew P

2016-10-01

We introduce AutoQSAR, an automated machine-learning application to build, validate and deploy quantitative structure-activity relationship (QSAR) models. The process of descriptor generation, feature selection and the creation of a large number of QSAR models has been automated into a single workflow within AutoQSAR. The models are built using a variety of machine-learning methods, and each model is scored using a novel approach. Effectiveness of the method is demonstrated through comparison with literature QSAR models using identical datasets for six end points: protein-ligand binding affinity, solubility, blood-brain barrier permeability, carcinogenicity, mutagenicity and bioaccumulation in fish. AutoQSAR demonstrates similar or better predictive performance as compared with published results for four of the six endpoints while requiring minimal human time and expertise.

A review of statistical estimators for risk-adjusted length of stay: analysis of the Australian and new Zealand Intensive Care Adult Patient Data-Base, 2008-2009.

PubMed

Moran, John L; Solomon, Patricia J

2012-05-16

For the analysis of length-of-stay (LOS) data, which is characteristically right-skewed, a number of statistical estimators have been proposed as alternatives to the traditional ordinary least squares (OLS) regression with log dependent variable. Using a cohort of patients identified in the Australian and New Zealand Intensive Care Society Adult Patient Database, 2008-2009, 12 different methods were used for estimation of intensive care (ICU) length of stay. These encompassed risk-adjusted regression analysis of firstly: log LOS using OLS, linear mixed model [LMM], treatment effects, skew-normal and skew-t models; and secondly: unmodified (raw) LOS via OLS, generalised linear models [GLMs] with log-link and 4 different distributions [Poisson, gamma, negative binomial and inverse-Gaussian], extended estimating equations [EEE] and a finite mixture model including a gamma distribution. A fixed covariate list and ICU-site clustering with robust variance were utilised for model fitting with split-sample determination (80%) and validation (20%) data sets, and model simulation was undertaken to establish over-fitting (Copas test). Indices of model specification using Bayesian information criterion [BIC: lower values preferred] and residual analysis as well as predictive performance (R2, concordance correlation coefficient (CCC), mean absolute error [MAE]) were established for each estimator. The data-set consisted of 111663 patients from 131 ICUs; with mean(SD) age 60.6(18.8) years, 43.0% were female, 40.7% were mechanically ventilated and ICU mortality was 7.8%. ICU length-of-stay was 3.4(5.1) (median 1.8, range (0.17-60)) days and demonstrated marked kurtosis and right skew (29.4 and 4.4 respectively). BIC showed considerable spread, from a maximum of 509801 (OLS-raw scale) to a minimum of 210286 (LMM). R2 ranged from 0.22 (LMM) to 0.17 and the CCC from 0.334 (LMM) to 0.149, with MAE 2.2-2.4. Superior residual behaviour was established for the log-scale estimators. There was a general tendency for over-prediction (negative residuals) and for over-fitting, the exception being the GLM negative binomial estimator. The mean-variance function was best approximated by a quadratic function, consistent with log-scale estimation; the link function was estimated (EEE) as 0.152(0.019, 0.285), consistent with a fractional-root function. For ICU length of stay, log-scale estimation, in particular the LMM, appeared to be the most consistently performing estimator(s). Neither the GLM variants nor the skew-regression estimators dominated.

Conditional analysis of mixed Poisson processes with baseline counts: implications for trial design and analysis.

PubMed

Cook, Richard J; Wei, Wei

2003-07-01

The design of clinical trials is typically based on marginal comparisons of a primary response under two or more treatments. The considerable gains in efficiency afforded by models conditional on one or more baseline responses has been extensively studied for Gaussian models. The purpose of this article is to present methods for the design and analysis of clinical trials in which the response is a count or a point process, and a corresponding baseline count is available prior to randomization. The methods are based on a conditional negative binomial model for the response given the baseline count and can be used to examine the effect of introducing selection criteria on power and sample size requirements. We show that designs based on this approach are more efficient than those proposed by McMahon et al. (1994).

Fermi-Pasta-Ulam auto recurrence in the description of the electrical activity of the heart.

PubMed

Novopashin, M A; Shmid, A V; Berezin, A A

2017-04-01

The authors proposed and mathematically described model of a new type of the Fermi-Pasta-Ulam recurrence (the FPU auto recurrence) and hypothesized an adequate description of the heart's electrical dynamics within the observed phenomenon. The dynamics of the FPU auto recurrence making appropriate electrical dynamics of the normal functioning of the heart in the form of an electrocardiogram (ECG) was obtained by a computer model study. The model solutions in the form of the FPU auto recurrence - ECG Fourier spectrum were evaluated for resistance to external disturbances in the form of random effects, as well as periodic perturbation at a frequency close to the heart beating rate of about 1Hz. In addition, in order to simulate the dynamics of myocardial infarction model, studied the effect of the surface area of the myocardium on the stability and shape of the auto recurrence - ECG spectrum. It has been found that the intense external disturbing periodic impacts at a frequency of about 1Hz lead to a sharp disturbance spectrum shape FPU auto recurrence - ECG structure. In addition, the decrease in the surface of the myocardium by 50% in the model led to the destruction of structures of the auto recurrence - ECG, which corresponds to the state of atrial myocardium. Research models have revealed a hypothetical basis of coronary heart disease in the form of increasing the energy of high-frequency harmonics spectrum of the auto recurrence by reducing the energy of low-frequency harmonic spectrum of the auto recurrence, which ultimately leads to a sharp decrease in myocardial contractility. In order to test the hypothesis has been studied more than 20,000 ECGs both healthy people and patients with cardiovascular disease. As a result of these studies, it was found that the dynamics of the electrical activity of normal functioning of the heart can be interpreted by the display of the detected by authors the FPU auto recurrence, and coronary heart disease is a violation of the energy ratio between the low and high frequency harmonics of the FPU auto recurrence Fourier spectrum equal to the ECG spectrum. Thus, the hypothesis has been confirmed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Finite mixture modeling for vehicle crash data with application to hotspot identification.

PubMed

Park, Byung-Jung; Lord, Dominique; Lee, Chungwon

2014-10-01

The application of finite mixture regression models has recently gained an interest from highway safety researchers because of its considerable potential for addressing unobserved heterogeneity. Finite mixture models assume that the observations of a sample arise from two or more unobserved components with unknown proportions. Both fixed and varying weight parameter models have been shown to be useful for explaining the heterogeneity and the nature of the dispersion in crash data. Given the superior performance of the finite mixture model, this study, using observed and simulated data, investigated the relative performance of the finite mixture model and the traditional negative binomial (NB) model in terms of hotspot identification. For the observed data, rural multilane segment crash data for divided highways in California and Texas were used. The results showed that the difference measured by the percentage deviation in ranking orders was relatively small for this dataset. Nevertheless, the ranking results from the finite mixture model were considered more reliable than the NB model because of the better model specification. This finding was also supported by the simulation study which produced a high number of false positives and negatives when a mis-specified model was used for hotspot identification. Regarding an optimal threshold value for identifying hotspots, another simulation analysis indicated that there is a discrepancy between false discovery (increasing) and false negative rates (decreasing). Since the costs associated with false positives and false negatives are different, it is suggested that the selected optimal threshold value should be decided by considering the trade-offs between these two costs so that unnecessary expenses are minimized. Copyright © 2014 Elsevier Ltd. All rights reserved.

Using linked household-level data sets to explain consumer response to bovine spongiform encepalopathy (BSE) in Canada.

PubMed

Wang, Xin; Maynard, Leigh J; Butler, J S; Goddard, Ellen W

2011-01-01

Household-level Canadian meat purchases from 2002 to 2008 and a Food Opinions Survey conducted in 2008 were used to explore consumer responses to bovine spongiform encephalopathy (BSE) at the national level in Canada. Consumption in terms of the number of unit purchases was analyzed with a random-effects negative binomial model. In this study, household heterogeneity in meat purchases was partially explained using data from a self-reported food opinions survey. Of special interest was the hypothesis that consumers responded consistently to BSE in a one-time survey and in actual meat purchase behavior spanning years. Regional differences appeared, with consumers in eastern Canada reacting most negatively to BSE. Consumers responded more to the perception that food decision makers are honest about food safety than to the perception that they are knowledgeable, in maintaining beef purchases during BSE events.

General Strain Theory as a Basis for the Design of School Interventions

ERIC Educational Resources Information Center

Moon, Byongook; Morash, Merry

2013-01-01

The research described in this article applies general strain theory to identify possible points of intervention for reducing delinquency of students in two middle schools. Data were collected from 296 youths, and separate negative binomial regression analyses were used to identify predictors of violent, property, and status delinquency. Emotional…

A New Zero-Inflated Negative Binomial Methodology for Latent Category Identification

ERIC Educational Resources Information Center

Blanchard, Simon J.; DeSarbo, Wayne S.

2013-01-01

We introduce a new statistical procedure for the identification of unobserved categories that vary between individuals and in which objects may span multiple categories. This procedure can be used to analyze data from a proposed sorting task in which individuals may simultaneously assign objects to multiple piles. The results of a synthetic…

The Effectiveness of an Electronic Security Management System in a Privately Owned Apartment Complex

ERIC Educational Resources Information Center

Greenberg, David F.; Roush, Jeffrey B.

2009-01-01

Poisson and negative binomial regression methods are used to analyze the monthly time series data to determine the effects of introducing an integrated security management system including closed-circuit television (CCTV), door alarm monitoring, proximity card access, and emergency call boxes to a large privately-owned complex of apartment…

Should we systematically test patients with clinically isolated syndrome for auto-antibodies?

PubMed

Negrotto, Laura; Tur, Carmen; Tintoré, Mar; Arrambide, Georgina; Sastre-Garriga, Jaume; Río, Jordi; Comabella, Manuel; Nos, Carlos; Galán, Ingrid; Vidal-Jordana, Angela; Simon, Eva; Castilló, Joaquín; Palavra, Filipe; Mitjana, Raquel; Auger, Cristina; Rovira, Àlex; Montalban, Xavier

2015-12-01

Several autoimmune diseases (ADs) can mimic multiple sclerosis (MS). For this reason, testing for auto-antibodies (auto-Abs) is often included in the diagnostic work-up of patients with a clinically isolated syndrome (CIS). The purpose was to study how useful it was to systematically determine antinuclear-antibodies, anti-SSA and anti-SSB in a non-selected cohort of CIS patients, regarding the identification of other ADs that could represent an alternative diagnosis. From a prospective CIS cohort, we selected 772 patients in which auto-Ab levels were tested within the first year from CIS. Baseline characteristics of auto-Ab positive and negative patients were compared. A retrospective revision of clinical records was then performed in the auto-Ab positive patients to identify those who developed ADs during follow-up. One or more auto-Ab were present in 29.4% of patients. Only 1.8% of patients developed other ADs during a mean follow-up of 6.6 years. In none of these cases the concurrent AD was considered the cause of the CIS. In all cases the diagnosis of the AD resulted from the development of signs and/or symptoms suggestive of each disease. Antinuclear-antibodies, anti-SSA and anti-SSB should not be routinely determined in CIS patients but only in those presenting symptoms suggestive of other ADs. © The Author(s), 2015.

Simplified pupal surveys of Aedes aegypti (L.) for entomologic surveillance and dengue control.

PubMed

Barrera, Roberto

2009-07-01

Pupal surveys of Aedes aegypti (L.) are useful indicators of risk for dengue transmission, although sample sizes for reliable estimations can be large. This study explores two methods for making pupal surveys more practical yet reliable and used data from 10 pupal surveys conducted in Puerto Rico during 2004-2008. The number of pupae per person for each sampling followed a negative binomial distribution, thus showing aggregation. One method found a common aggregation parameter (k) for the negative binomial distribution, a finding that enabled the application of a sequential sampling method requiring few samples to determine whether the number of pupae/person was above a vector density threshold for dengue transmission. A second approach used the finding that the mean number of pupae/person is correlated with the proportion of pupa-infested households and calculated equivalent threshold proportions of pupa-positive households. A sequential sampling program was also developed for this method to determine whether observed proportions of infested households were above threshold levels. These methods can be used to validate entomological thresholds for dengue transmission.

Forecast of severe fever with thrombocytopenia syndrome incidence with meteorological factors.

PubMed

Sun, Ji-Min; Lu, Liang; Liu, Ke-Ke; Yang, Jun; Wu, Hai-Xia; Liu, Qi-Yong

2018-06-01

Severe fever with thrombocytopenia syndrome (SFTS) is emerging and some studies reported that SFTS incidence was associated with meteorological factors, while no report on SFTS forecast models was reported up to date. In this study, we constructed and compared three forecast models using autoregressive integrated moving average (ARIMA) model, negative binomial regression model (NBM), and quasi-Poisson generalized additive model (GAM). The dataset from 2011 to 2015 were used for model construction and the dataset in 2016 were used for external validity assessment. All the three models fitted the SFTS cases reasonably well during the training process and forecast process, while the NBM model forecasted better than other two models. Moreover, we demonstrated that temperature and relative humidity played key roles in explaining the temporal dynamics of SFTS occurrence. Our study contributes to better understanding of SFTS dynamics and provides predictive tools for the control and prevention of SFTS. Copyright © 2018 Elsevier B.V. All rights reserved.

Analysis of railroad tank car releases using a generalized binomial model.

PubMed

Liu, Xiang; Hong, Yili

2015-11-01

The United States is experiencing an unprecedented boom in shale oil production, leading to a dramatic growth in petroleum crude oil traffic by rail. In 2014, U.S. railroads carried over 500,000 tank carloads of petroleum crude oil, up from 9500 in 2008 (a 5300% increase). In light of continual growth in crude oil by rail, there is an urgent national need to manage this emerging risk. This need has been underscored in the wake of several recent crude oil release incidents. In contrast to highway transport, which usually involves a tank trailer, a crude oil train can carry a large number of tank cars, having the potential for a large, multiple-tank-car release incident. Previous studies exclusively assumed that railroad tank car releases in the same train accident are mutually independent, thereby estimating the number of tank cars releasing given the total number of tank cars derailed based on a binomial model. This paper specifically accounts for dependent tank car releases within a train accident. We estimate the number of tank cars releasing given the number of tank cars derailed based on a generalized binomial model. The generalized binomial model provides a significantly better description for the empirical tank car accident data through our numerical case study. This research aims to provide a new methodology and new insights regarding the further development of risk management strategies for improving railroad crude oil transportation safety. Copyright © 2015 Elsevier Ltd. All rights reserved.

Accuracy of a novel auto-CPAP device to evaluate the residual apnea-hypopnea index in patients with obstructive sleep apnea.

PubMed

Nigro, Carlos Alberto; González, Sergio; Arce, Anabella; Aragone, María Rosario; Nigro, Luciana

2015-05-01

Patients under treatment with continuous positive airway pressure (CPAP) may have residual sleep apnea (RSA). The main objective of our study was to evaluate a novel auto-CPAP for the diagnosis of RSA. All patients referred to the sleep laboratory to undergo CPAP polysomnography were evaluated. Patients treated with oxygen or noninvasive ventilation and split-night polysomnography (PSG), PSG with artifacts, or total sleep time less than 180 min were excluded. The PSG was manually analyzed before generating the automatic report from auto-CPAP. PSG variables (respiratory disturbance index (RDI), obstructive apnea index, hypopnea index, and central apnea index) were compared with their counterparts from auto-CPAP through Bland-Altman plots and intraclass correlation coefficient. The diagnostic accuracy of autoscoring from auto-CPAP using different cutoff points of RDI (≥5 and 10) was evaluated by the receiver operating characteristics (ROCs) curve. The study included 114 patients (24 women; mean age and BMI, 59 years old and 33 kg/m(2); RDI and apnea/hypopnea index (AHI)-auto median, 5 and 2, respectively). The average difference between the AHI-auto and the RDI was -3.5 ± 3.9. The intraclass correlation coefficient (ICC) between the total number of central apneas, obstructive, and hypopneas between the PSG and the auto-CPAP were 0.69, 0.16, and 0.15, respectively. An AHI-auto >2 (RDI ≥ 5) or >4 (RDI ≥ 10) had an area under the ROC curve, sensitivity, specificity, positive likelihood ratio, and negative for diagnosis of residual sleep apnea of 0.84/0.89, 84/81%, 82/91%, 4.5/9.5, and 0.22/0.2, respectively. The automatic analysis from auto-CPAP (S9 Autoset) showed a good diagnostic accuracy to identify residual sleep apnea. The absolute agreement between PSG and auto-CPAP to classify the respiratory events correctly varied from very low (obstructive apneas, hypopneas) to moderate (central apneas).

Relationship between suicide rate and economic growth and stock market in the People's Republic of China: 2004-2013.

PubMed

Yin, Honglei; Xu, Lin; Shao, Yechang; Li, Liping; Wan, Chengsong

2016-01-01

The objective of this study was to estimate the features of suicide rate and its association with economic development and stock market during the past decade in the People's Republic of China. Official data were gathered and analyzed in the People's Republic of China during the period 2004-2013. Nationwide suicide rate was stratified by four year age-groups, sex, urban/rural areas, and regions (East, Central, and West). Annual economic indexes including gross domestic product (GDP) per capita and rural and urban income per capita were all adjusted for inflation. Variation coefficient of market index (VCMI) was also included as an economic index to measure the fluctuation of the stock market. Negative binomial regression was performed to examine the time trend of region-level suicide rates and effects of sex, age, urban/rural area, region, and economic index on the suicide rates. Suicide rates of each age-group, sex, urban/rural area, and region were generally decreased from 2004 to 2013, while annual GDP per capita and rural and urban income per capita were generally increased by year. VCMI fluctuated largely, which peaked around 2009 and decreased after that time. Negative binomial regression showed that the decreased suicide rate in East and Central rural areas was the main cause of the decrease in suicide rate in the People's Republic of China. Suicide rate in the People's Republic of China for the study period increased with age and was higher in rural than in urban area, higher in males than in females, and the highest in the Central region. When GDP per capita increased by 2,787 RMB, the suicide rate decreased by 0.498 times. VCMI showed no significant relationship with suicide rate in the negative binomial regression. Suicide rate decreased in 2004-2013; varied among different age-groups, sex, urban/rural areas, and regions; and was negatively associated with the economic growth in the People's Republic of China. Stock market showed no relationship with suicide rate, but this finding needs to be verified in a future study.

Relationship between suicide rate and economic growth and stock market in the People’s Republic of China: 2004–2013

PubMed Central

Yin, Honglei; Xu, Lin; Shao, Yechang; Li, Liping; Wan, Chengsong

2016-01-01

Objectives The objective of this study was to estimate the features of suicide rate and its association with economic development and stock market during the past decade in the People’s Republic of China. Methods Official data were gathered and analyzed in the People’s Republic of China during the period 2004–2013. Nationwide suicide rate was stratified by four year age-groups, sex, urban/rural areas, and regions (East, Central, and West). Annual economic indexes including gross domestic product (GDP) per capita and rural and urban income per capita were all adjusted for inflation. Variation coefficient of market index (VCMI) was also included as an economic index to measure the fluctuation of the stock market. Negative binomial regression was performed to examine the time trend of region-level suicide rates and effects of sex, age, urban/rural area, region, and economic index on the suicide rates. Results Suicide rates of each age-group, sex, urban/rural area, and region were generally decreased from 2004 to 2013, while annual GDP per capita and rural and urban income per capita were generally increased by year. VCMI fluctuated largely, which peaked around 2009 and decreased after that time. Negative binomial regression showed that the decreased suicide rate in East and Central rural areas was the main cause of the decrease in suicide rate in the People’s Republic of China. Suicide rate in the People’s Republic of China for the study period increased with age and was higher in rural than in urban area, higher in males than in females, and the highest in the Central region. When GDP per capita increased by 2,787 RMB, the suicide rate decreased by 0.498 times. VCMI showed no significant relationship with suicide rate in the negative binomial regression. Conclusion Suicide rate decreased in 2004–2013; varied among different age-groups, sex, urban/rural areas, and regions; and was negatively associated with the economic growth in the People’s Republic of China. Stock market showed no relationship with suicide rate, but this finding needs to be verified in a future study. PMID:27994468

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

Los Alamos National Laboratory, Mailstop M888, Los Alamos, NM 87545, USA; Lawrence Berkeley National Laboratory, One Cyclotron Road, Building 64R0121, Berkeley, CA 94720, USA; Department of Haematology, University of Cambridge, Cambridge CB2 0XY, England

The PHENIX AutoBuild Wizard is a highly automated tool for iterative model-building, structure refinement and density modification using RESOLVE or TEXTAL model-building, RESOLVE statistical density modification, and phenix.refine structure refinement. Recent advances in the AutoBuild Wizard and phenix.refine include automated detection and application of NCS from models as they are built, extensive model completion algorithms, and automated solvent molecule picking. Model completion algorithms in the AutoBuild Wizard include loop-building, crossovers between chains in different models of a structure, and side-chain optimization. The AutoBuild Wizard has been applied to a set of 48 structures at resolutions ranging from 1.1 {angstrom} tomore » 3.2 {angstrom}, resulting in a mean R-factor of 0.24 and a mean free R factor of 0.29. The R-factor of the final model is dependent on the quality of the starting electron density, and relatively independent of resolution.« less

76 FR 4219 - Airworthiness Directives; Airbus Model A330-200 Series Airplanes; Model A330-300 Series Airplanes...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-01-25

... A340 aeroplane will revert to alternate law, the autopilot (AP) and the auto-thrust (A/THR... guidance computers will: --Display FD bars again, and --Enable autopilot and auto-thrust re-engagement... A330 or A340 aeroplane will revert to alternate law, the autopilot (AP) and the auto-thrust (A/THR...

Spiritual and ceremonial plants in North America: an assessment of Moerman's ethnobotanical database comparing Residual, Binomial, Bayesian and Imprecise Dirichlet Model (IDM) analysis.

PubMed

Turi, Christina E; Murch, Susan J

2013-07-09

Ethnobotanical research and the study of plants used for rituals, ceremonies and to connect with the spirit world have led to the discovery of many novel psychoactive compounds such as nicotine, caffeine, and cocaine. In North America, spiritual and ceremonial uses of plants are well documented and can be accessed online via the University of Michigan's Native American Ethnobotany Database. The objective of the study was to compare Residual, Bayesian, Binomial and Imprecise Dirichlet Model (IDM) analyses of ritual, ceremonial and spiritual plants in Moerman's ethnobotanical database and to identify genera that may be good candidates for the discovery of novel psychoactive compounds. The database was queried with the following format "Family Name AND Ceremonial OR Spiritual" for 263 North American botanical families. Spiritual and ceremonial flora consisted of 86 families with 517 species belonging to 292 genera. Spiritual taxa were then grouped further into ceremonial medicines and items categories. Residual, Bayesian, Binomial and IDM analysis were performed to identify over and under-utilized families. The 4 statistical approaches were in good agreement when identifying under-utilized families but large families (>393 species) were underemphasized by Binomial, Bayesian and IDM approaches for over-utilization. Residual, Binomial, and IDM analysis identified similar families as over-utilized in the medium (92-392 species) and small (<92 species) classes. The families Apiaceae, Asteraceae, Ericacea, Pinaceae and Salicaceae were identified as significantly over-utilized as ceremonial medicines in medium and large sized families. Analysis of genera within the Apiaceae and Asteraceae suggest that the genus Ligusticum and Artemisia are good candidates for facilitating the discovery of novel psychoactive compounds. The 4 statistical approaches were not consistent in the selection of over-utilization of flora. Residual analysis revealed overall trends that were supported by Binomial analysis when separated into small, medium and large families. The Bayesian, Binomial and IDM approaches identified different genera as potentially important. Species belonging to the genus Artemisia and Ligusticum were most consistently identified and may be valuable in future studies of the ethnopharmacology. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

A comparison of methods for the analysis of binomial clustered outcomes in behavioral research.

PubMed

Ferrari, Alberto; Comelli, Mario

2016-12-01

In behavioral research, data consisting of a per-subject proportion of "successes" and "failures" over a finite number of trials often arise. This clustered binary data are usually non-normally distributed, which can distort inference if the usual general linear model is applied and sample size is small. A number of more advanced methods is available, but they are often technically challenging and a comparative assessment of their performances in behavioral setups has not been performed. We studied the performances of some methods applicable to the analysis of proportions; namely linear regression, Poisson regression, beta-binomial regression and Generalized Linear Mixed Models (GLMMs). We report on a simulation study evaluating power and Type I error rate of these models in hypothetical scenarios met by behavioral researchers; plus, we describe results from the application of these methods on data from real experiments. Our results show that, while GLMMs are powerful instruments for the analysis of clustered binary outcomes, beta-binomial regression can outperform them in a range of scenarios. Linear regression gave results consistent with the nominal level of significance, but was overall less powerful. Poisson regression, instead, mostly led to anticonservative inference. GLMMs and beta-binomial regression are generally more powerful than linear regression; yet linear regression is robust to model misspecification in some conditions, whereas Poisson regression suffers heavily from violations of the assumptions when used to model proportion data. We conclude providing directions to behavioral scientists dealing with clustered binary data and small sample sizes. Copyright © 2016 Elsevier B.V. All rights reserved.

Relaxed Poisson cure rate models.

PubMed

Rodrigues, Josemar; Cordeiro, Gauss M; Cancho, Vicente G; Balakrishnan, N

2016-03-01

The purpose of this article is to make the standard promotion cure rate model (Yakovlev and Tsodikov, ) more flexible by assuming that the number of lesions or altered cells after a treatment follows a fractional Poisson distribution (Laskin, ). It is proved that the well-known Mittag-Leffler relaxation function (Berberan-Santos, ) is a simple way to obtain a new cure rate model that is a compromise between the promotion and geometric cure rate models allowing for superdispersion. So, the relaxed cure rate model developed here can be considered as a natural and less restrictive extension of the popular Poisson cure rate model at the cost of an additional parameter, but a competitor to negative-binomial cure rate models (Rodrigues et al., ). Some mathematical properties of a proper relaxed Poisson density are explored. A simulation study and an illustration of the proposed cure rate model from the Bayesian point of view are finally presented. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Detection of Clostridium difficile infection clusters, using the temporal scan statistic, in a community hospital in southern Ontario, Canada, 2006-2011.

PubMed

Faires, Meredith C; Pearl, David L; Ciccotelli, William A; Berke, Olaf; Reid-Smith, Richard J; Weese, J Scott

2014-05-12

In hospitals, Clostridium difficile infection (CDI) surveillance relies on unvalidated guidelines or threshold criteria to identify outbreaks. This can result in false-positive and -negative cluster alarms. The application of statistical methods to identify and understand CDI clusters may be a useful alternative or complement to standard surveillance techniques. The objectives of this study were to investigate the utility of the temporal scan statistic for detecting CDI clusters and determine if there are significant differences in the rate of CDI cases by month, season, and year in a community hospital. Bacteriology reports of patients identified with a CDI from August 2006 to February 2011 were collected. For patients detected with CDI from March 2010 to February 2011, stool specimens were obtained. Clostridium difficile isolates were characterized by ribotyping and investigated for the presence of toxin genes by PCR. CDI clusters were investigated using a retrospective temporal scan test statistic. Statistically significant clusters were compared to known CDI outbreaks within the hospital. A negative binomial regression model was used to identify associations between year, season, month and the rate of CDI cases. Overall, 86 CDI cases were identified. Eighteen specimens were analyzed and nine ribotypes were classified with ribotype 027 (n = 6) the most prevalent. The temporal scan statistic identified significant CDI clusters at the hospital (n = 5), service (n = 6), and ward (n = 4) levels (P ≤ 0.05). Three clusters were concordant with the one C. difficile outbreak identified by hospital personnel. Two clusters were identified as potential outbreaks. The negative binomial model indicated years 2007-2010 (P ≤ 0.05) had decreased CDI rates compared to 2006 and spring had an increased CDI rate compared to the fall (P = 0.023). Application of the temporal scan statistic identified several clusters, including potential outbreaks not detected by hospital personnel. The identification of time periods with decreased or increased CDI rates may have been a result of specific hospital events. Understanding the clustering of CDIs can aid in the interpretation of surveillance data and lead to the development of better early detection systems.

Analysis of overdispersed count data by mixtures of Poisson variables and Poisson processes.

PubMed

Hougaard, P; Lee, M L; Whitmore, G A

1997-12-01

Count data often show overdispersion compared to the Poisson distribution. Overdispersion is typically modeled by a random effect for the mean, based on the gamma distribution, leading to the negative binomial distribution for the count. This paper considers a larger family of mixture distributions, including the inverse Gaussian mixture distribution. It is demonstrated that it gives a significantly better fit for a data set on the frequency of epileptic seizures. The same approach can be used to generate counting processes from Poisson processes, where the rate or the time is random. A random rate corresponds to variation between patients, whereas a random time corresponds to variation within patients.

The coverage of a random sample from a biological community.

PubMed

Engen, S

1975-03-01

A taxonomic group will frequently have a large number of species with small abundances. When a sample is drawn at random from this group, one is therefore faced with the problem that a large proportion of the species will not be discovered. A general definition of quantitative measures of "sample coverage" is proposed, and the problem of statistical inference is considered for two special cases, (1) the actual total relative abundance of those species that are represented in the sample, and (2) their relative contribution to the information index of diversity. The analysis is based on a extended version of the negative binomial species frequency model. The results are tabulated.

Utilization of accident databases and fuzzy sets to estimate frequency of HazMat transport accidents.

PubMed

Qiao, Yuanhua; Keren, Nir; Mannan, M Sam

2009-08-15

Risk assessment and management of transportation of hazardous materials (HazMat) require the estimation of accident frequency. This paper presents a methodology to estimate hazardous materials transportation accident frequency by utilizing publicly available databases and expert knowledge. The estimation process addresses route-dependent and route-independent variables. Negative binomial regression is applied to an analysis of the Department of Public Safety (DPS) accident database to derive basic accident frequency as a function of route-dependent variables, while the effects of route-independent variables are modeled by fuzzy logic. The integrated methodology provides the basis for an overall transportation risk analysis, which can be used later to develop a decision support system.

Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications.

PubMed

Van den Berge, Koen; Perraudeau, Fanny; Soneson, Charlotte; Love, Michael I; Risso, Davide; Vert, Jean-Philippe; Robinson, Mark D; Dudoit, Sandrine; Clement, Lieven

2018-02-26

Dropout events in single-cell RNA sequencing (scRNA-seq) cause many transcripts to go undetected and induce an excess of zero read counts, leading to power issues in differential expression (DE) analysis. This has triggered the development of bespoke scRNA-seq DE methods to cope with zero inflation. Recent evaluations, however, have shown that dedicated scRNA-seq tools provide no advantage compared to traditional bulk RNA-seq tools. We introduce a weighting strategy, based on a zero-inflated negative binomial model, that identifies excess zero counts and generates gene- and cell-specific weights to unlock bulk RNA-seq DE pipelines for zero-inflated data, boosting performance for scRNA-seq.

Statistical procedures for analyzing mental health services data.

PubMed

Elhai, Jon D; Calhoun, Patrick S; Ford, Julian D

2008-08-15

In mental health services research, analyzing service utilization data often poses serious problems, given the presence of substantially skewed data distributions. This article presents a non-technical introduction to statistical methods specifically designed to handle the complexly distributed datasets that represent mental health service use, including Poisson, negative binomial, zero-inflated, and zero-truncated regression models. A flowchart is provided to assist the investigator in selecting the most appropriate method. Finally, a dataset of mental health service use reported by medical patients is described, and a comparison of results across several different statistical methods is presented. Implications of matching data analytic techniques appropriately with the often complexly distributed datasets of mental health services utilization variables are discussed.

Differential Associations of UPPS-P Impulsivity Traits With Alcohol Problems.

PubMed

McCarty, Kayleigh N; Morris, David H; Hatz, Laura E; McCarthy, Denis M

2017-07-01

The UPPS-P model posits that impulsivity comprises five factors: positive urgency, negative urgency, lack of planning, lack of perseverance, and sensation seeking. Negative and positive urgency are the traits most consistently associated with alcohol problems. However, previous work has examined alcohol problems either individually or in the aggregate, rather than examining multiple problem domains simultaneously. Recent work has also questioned the utility of distinguishing between positive and negative urgency, as this distinction did not meaningfully differ in predicting domains of psychopathology. The aims of this study were to address these issues by (a) testing unique associations of UPPS-P with specific domains of alcohol problems and (b) determining the utility of distinguishing between positive and negative urgency as risk factors for specific alcohol problems. Associations between UPPS-P traits and alcohol problem domains were examined in two cross-sectional data sets using negative binomial regression models. In both samples, negative urgency was associated with social/interpersonal, self-perception, risky behaviors, and blackout drinking problems. Positive urgency was associated with academic/occupational and physiological dependence problems. Both urgency traits were associated with impaired control and self-care problems. Associations for other UPPS-P traits did not replicate across samples. Results indicate that negative and positive urgency have differential associations with alcohol problem domains. Results also suggest a distinction between the type of alcohol problems associated with these traits-negative urgency was associated with problems experienced during a drinking episode, whereas positive urgency was associated with alcohol problems that result from longer-term drinking trends.

Is extra-glandular organ damage in primary Sjögren's syndrome related to the presence of systemic auto-antibodies and/or hypergammaglobulinemia? A long-term cohort study with 110 patients from the Netherlands.

PubMed

Ter Borg, Evert-Jan; Kelder, Johannes Cornelis

2017-07-01

To test the hypothesis that systemic auto-antibodies or hypergammaglobulinemia are related to the prevalence of extra-glandular tissue organ damage (EGOD) in primary Sjögren's syndrome (SS). A real practice-based investigation of a relatively large (n = 110) Dutch cohort of primary SS patients systematically followed up in a large non-academic hospital. After a follow up of mean 8.2 years a significant correlation was found between disease duration and the prevalence of EGOD. We did not observe a relationship between the total number or type of systemic auto-antibodies or hypergammaglobulinemia and the total number of EGOD. However, there was a correlation between the prevalence of polyneuropathy (PNP) and antinuclear antibodies (ANA) as well as anti-Ro/SS-A positivity and there was an inverse relationship between the presence of anti-Ro/SS-A antibodies and primary biliary cirrhosis (PBC). All PBC cases were anti-Ro/SS-A and anti-La/SS-B negative but ANA positive. There was a trend for a higher occurrence of pleuro-pulmonary disease in the ANA negative cases. Although we did not find a relationship between the total number or type of systemic auto-antibodies and the total number of EGOD, there were correlations between specific systemic auto-antibodies and specific types of EGOD. The presence of ANA and anti-Ro/SS-A was associated with the occurrence of PNP, as well as was the absence of anti-Ro/SS-A with PBC. © 2017 Asia Pacific League of Associations for Rheumatology and John Wiley & Sons Australia, Ltd.

Economic evaluation of epinephrine auto-injectors for peanut allergy.

PubMed

Shaker, Marcus; Bean, Katherine; Verdi, Marylee

2017-08-01

Three commercial epinephrine auto-injectors were available in the United States in the summer of 2016: EpiPen, Adrenaclick, and epinephrine injection, USP auto-injector. To describe the variation in pharmacy costs among epinephrine auto-injector devices in New England and evaluate the additional expense associated with incremental auto-injector costs. Decision analysis software was used to evaluate costs of the most and least expensive epinephrine auto-injector devices for children with peanut allergy. To evaluate regional variation in epinephrine auto-injector costs, a random sample of New England national and corporate pharmacies was compared with a convenience sample of pharmacies from 10 Canadian provinces. Assuming prescriptions written for 2 double epinephrine packs each year (home and school), the mean costs of food allergy over the 20-year model horizon totaled $58,667 (95% confidence interval [CI] $57,745-$59,588) when EpiPen was prescribed and $45,588 (95% CI $44,873-$46,304) when epinephrine injection, USP auto-injector was prescribed. No effectiveness differences were evident between groups, with 17.19 (95% CI 17.11-17.27) quality-adjusted life years accruing for each subject. The incremental cost per episode of anaphylaxis treated with epinephrine over the model horizon was $12,576 for EpiPen vs epinephrine injection, USP auto-injector. EpiPen costs were lowest at Canadian pharmacies ($96, 95% CI $85-$107). There was price consistency between corporate and independent pharmacies throughout New England by device brand, with the epinephrine injection, USP auto-injector being the most affordable device. Cost differences among epinephrine auto-injectors were significant. More expensive auto-injector brands did not appear to provide incremental benefit. Copyright © 2017 American College of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

Football fever: goal distributions and non-Gaussian statistics

NASA Astrophysics Data System (ADS)

Bittner, E.; Nußbaumer, A.; Janke, W.; Weigel, M.

2009-02-01

Analyzing football score data with statistical techniques, we investigate how the not purely random, but highly co-operative nature of the game is reflected in averaged properties such as the probability distributions of scored goals for the home and away teams. As it turns out, especially the tails of the distributions are not well described by the Poissonian or binomial model resulting from the assumption of uncorrelated random events. Instead, a good effective description of the data is provided by less basic distributions such as the negative binomial one or the probability densities of extreme value statistics. To understand this behavior from a microscopical point of view, however, no waiting time problem or extremal process need be invoked. Instead, modifying the Bernoulli random process underlying the Poissonian model to include a simple component of self-affirmation seems to describe the data surprisingly well and allows to understand the observed deviation from Gaussian statistics. The phenomenological distributions used before can be understood as special cases within this framework. We analyzed historical football score data from many leagues in Europe as well as from international tournaments, including data from all past tournaments of the “FIFA World Cup” series, and found the proposed models to be applicable rather universally. In particular, here we analyze the results of the German women’s premier football league and consider the two separate German men’s premier leagues in the East and West during the cold war times as well as the unified league after 1990 to see how scoring in football and the component of self-affirmation depend on cultural and political circumstances.

Perceived Prevalence of Teasing and Bullying Predicts High School Dropout Rates

ERIC Educational Resources Information Center

Cornell, Dewey; Gregory, Anne; Huang, Francis; Fan, Xitao

2013-01-01

This prospective study of 276 Virginia public high schools found that the prevalence of teasing and bullying (PTB) as perceived by both 9th-grade students and teachers was predictive of dropout rates for this cohort 4 years later. Negative binomial regression indicated that one standard deviation increases in student- and teacher-reported PTB were…

The Influence of Television Advertisements on Promoting Calls to Telephone Quitlines

ERIC Educational Resources Information Center

Farrelly, Matthew; Mann, Nathan; Watson, Kimberly; Pechacek, Terry

2013-01-01

The aim of the study was to assess the relative effectiveness of cessation, secondhand smoke and other tobacco control television advertisements in promoting quitlines in nine states from 2002 through 2005. Quarterly, the number of individuals who used quitlines per 10 000 adult smokers in a media market are measured. Negative binomial regression…

On Statistical Modeling of Sequencing Noise in High Depth Data to Assess Tumor Evolution

NASA Astrophysics Data System (ADS)

Rabadan, Raul; Bhanot, Gyan; Marsilio, Sonia; Chiorazzi, Nicholas; Pasqualucci, Laura; Khiabanian, Hossein

2018-07-01

One cause of cancer mortality is tumor evolution to therapy-resistant disease. First line therapy often targets the dominant clone, and drug resistance can emerge from preexisting clones that gain fitness through therapy-induced natural selection. Such mutations may be identified using targeted sequencing assays by analysis of noise in high-depth data. Here, we develop a comprehensive, unbiased model for sequencing error background. We find that noise in sufficiently deep DNA sequencing data can be approximated by aggregating negative binomial distributions. Mutations with frequencies above noise may have prognostic value. We evaluate our model with simulated exponentially expanded populations as well as data from cell line and patient sample dilution experiments, demonstrating its utility in prognosticating tumor progression. Our results may have the potential to identify significant mutations that can cause recurrence. These results are relevant in the pretreatment clinical setting to determine appropriate therapy and prepare for potential recurrence pretreatment.

On Statistical Modeling of Sequencing Noise in High Depth Data to Assess Tumor Evolution

NASA Astrophysics Data System (ADS)

Rabadan, Raul; Bhanot, Gyan; Marsilio, Sonia; Chiorazzi, Nicholas; Pasqualucci, Laura; Khiabanian, Hossein

2017-12-01

One cause of cancer mortality is tumor evolution to therapy-resistant disease. First line therapy often targets the dominant clone, and drug resistance can emerge from preexisting clones that gain fitness through therapy-induced natural selection. Such mutations may be identified using targeted sequencing assays by analysis of noise in high-depth data. Here, we develop a comprehensive, unbiased model for sequencing error background. We find that noise in sufficiently deep DNA sequencing data can be approximated by aggregating negative binomial distributions. Mutations with frequencies above noise may have prognostic value. We evaluate our model with simulated exponentially expanded populations as well as data from cell line and patient sample dilution experiments, demonstrating its utility in prognosticating tumor progression. Our results may have the potential to identify significant mutations that can cause recurrence. These results are relevant in the pretreatment clinical setting to determine appropriate therapy and prepare for potential recurrence pretreatment.

Modeling regional variation in riverine fish biodiversity in the Arkansas-White-Red River basin

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

Schweizer, Peter E; Jager, Yetta

The patterns of biodiversity in freshwater systems are shaped by biogeography, environmental gradients, and human-induced factors. In this study, we developed empirical models to explain fish species richness in subbasins of the Arkansas White Red River basin as a function of discharge, elevation, climate, land cover, water quality, dams, and longitudinal position. We used information-theoretic criteria to compare generalized linear mixed models and identified well-supported models. Subbasin attributes that were retained as predictors included discharge, elevation, number of downstream dams, percent forest, percent shrubland, nitrate, total phosphorus, and sediment. The random component of our models, which assumed a negative binomialmore » distribution, included spatial correlation within larger river basins and overdispersed residual variance. This study differs from previous biodiversity modeling efforts in several ways. First, obtaining likelihoods for negative binomial mixed models, and thereby avoiding reliance on quasi-likelihoods, has only recently become practical. We found the ranking of models based on these likelihood estimates to be more believable than that produced using quasi-likelihoods. Second, because we had access to a regional-scale watershed model for this river basin, we were able to include model-estimated water quality attributes as predictors. Thus, the resulting models have potential value as tools with which to evaluate the benefits of water quality improvements to fish.« less

A deep auto-encoder model for gene expression prediction.

PubMed

Xie, Rui; Wen, Jia; Quitadamo, Andrew; Cheng, Jianlin; Shi, Xinghua

2017-11-17

Gene expression is a key intermediate level that genotypes lead to a particular trait. Gene expression is affected by various factors including genotypes of genetic variants. With an aim of delineating the genetic impact on gene expression, we build a deep auto-encoder model to assess how good genetic variants will contribute to gene expression changes. This new deep learning model is a regression-based predictive model based on the MultiLayer Perceptron and Stacked Denoising Auto-encoder (MLP-SAE). The model is trained using a stacked denoising auto-encoder for feature selection and a multilayer perceptron framework for backpropagation. We further improve the model by introducing dropout to prevent overfitting and improve performance. To demonstrate the usage of this model, we apply MLP-SAE to a real genomic datasets with genotypes and gene expression profiles measured in yeast. Our results show that the MLP-SAE model with dropout outperforms other models including Lasso, Random Forests and the MLP-SAE model without dropout. Using the MLP-SAE model with dropout, we show that gene expression quantifications predicted by the model solely based on genotypes, align well with true gene expression patterns. We provide a deep auto-encoder model for predicting gene expression from SNP genotypes. This study demonstrates that deep learning is appropriate for tackling another genomic problem, i.e., building predictive models to understand genotypes' contribution to gene expression. With the emerging availability of richer genomic data, we anticipate that deep learning models play a bigger role in modeling and interpreting genomics.

Gun control and suicide: the impact of state firearm regulations in the United States, 1995-2004.

PubMed

Rodríguez Andrés, Antonio; Hempstead, Katherine

2011-06-01

To empirically assess the impact of firearm regulation on male suicides. A negative binomial regression model was applied by using a panel of state level data for the years 1995-2004. The model was used to identify the association between several firearm regulations and male suicide rates. Our empirical analysis suggest that firearms regulations which function to reduce overall gun availability have a significant deterrent effect on male suicide, while regulations that seek to prohibit high risk individuals from owning firearms have a lesser effect. Restricting access to lethal means has been identified as an effective approach to suicide prevention, and firearms regulations are one way to reduce gun availability. The analysis suggests that gun control measures such as permit and licensing requirements have a negative effect on suicide rates among males. Since there is considerable heterogeneity among states with regard to gun control, these results suggest that there are opportunities for many states to reduce suicide by expanding their firearms regulations. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Univariate and bivariate likelihood-based meta-analysis methods performed comparably when marginal sensitivity and specificity were the targets of inference.

PubMed

Dahabreh, Issa J; Trikalinos, Thomas A; Lau, Joseph; Schmid, Christopher H

2017-03-01

To compare statistical methods for meta-analysis of sensitivity and specificity of medical tests (e.g., diagnostic or screening tests). We constructed a database of PubMed-indexed meta-analyses of test performance from which 2 × 2 tables for each included study could be extracted. We reanalyzed the data using univariate and bivariate random effects models fit with inverse variance and maximum likelihood methods. Analyses were performed using both normal and binomial likelihoods to describe within-study variability. The bivariate model using the binomial likelihood was also fit using a fully Bayesian approach. We use two worked examples-thoracic computerized tomography to detect aortic injury and rapid prescreening of Papanicolaou smears to detect cytological abnormalities-to highlight that different meta-analysis approaches can produce different results. We also present results from reanalysis of 308 meta-analyses of sensitivity and specificity. Models using the normal approximation produced sensitivity and specificity estimates closer to 50% and smaller standard errors compared to models using the binomial likelihood; absolute differences of 5% or greater were observed in 12% and 5% of meta-analyses for sensitivity and specificity, respectively. Results from univariate and bivariate random effects models were similar, regardless of estimation method. Maximum likelihood and Bayesian methods produced almost identical summary estimates under the bivariate model; however, Bayesian analyses indicated greater uncertainty around those estimates. Bivariate models produced imprecise estimates of the between-study correlation of sensitivity and specificity. Differences between methods were larger with increasing proportion of studies that were small or required a continuity correction. The binomial likelihood should be used to model within-study variability. Univariate and bivariate models give similar estimates of the marginal distributions for sensitivity and specificity. Bayesian methods fully quantify uncertainty and their ability to incorporate external evidence may be useful for imprecisely estimated parameters. Copyright © 2017 Elsevier Inc. All rights reserved.

Directly auto-transplanted mesenchymal stem cells induce bone formation in a ceramic bone substitute in an ectopic sheep model

PubMed Central

Boos, Anja M; Loew, Johanna S; Deschler, Gloria; Arkudas, Andreas; Bleiziffer, Oliver; Gulle, Heinz; Dragu, Adrian; Kneser, Ulrich; Horch, Raymund E; Beier, Justus P

2011-01-01

Abstract Bone tissue engineering approaches increasingly focus on the use of mesenchymal stem cells (MSC). In most animal transplantation models MSC are isolated and expanded before auto cell transplantation which might be critical for clinical application in the future. Hence this study compares the potential of directly auto-transplanted versus in vitro expanded MSC with or without bone morphogenetic protein-2 (BMP-2) to induce bone formation in a large volume ceramic bone substitute in the sheep model. MSC were isolated from bone marrow aspirates and directly auto-transplanted or expanded in vitro and characterized using fluorescence activated cell sorting (FACS) and RT-PCR analysis before subcutaneous implantation in combination with BMP-2 and β-tricalcium phosphate/hydroxyapatite (β-TCP/HA) granules. Constructs were explanted after 1 to 12 weeks followed by histological and RT-PCR evaluation. Sheep MSC were CD29+, CD44+ and CD166+ after selection by Ficoll gradient centrifugation, while directly auto-transplanted MSC-populations expressed CD29 and CD166 at lower levels. Both, directly auto-transplanted and expanded MSC, were constantly proliferating and had a decreasing apoptosis over time in vivo. Directly auto-transplanted MSC led to de novo bone formation in a heterotopic sheep model using a β-TCP/HA matrix comparable to the application of 60 μg/ml BMP-2 only or implantation of expanded MSC. Bone matrix proteins were up-regulated in constructs following direct auto-transplantation and in expanded MSC as well as in BMP-2 constructs. Up-regulation was detected using immunohistology methods and RT-PCR. Dense vascularization was demonstrated by CD31 immunohistology staining in all three groups. Ectopic bone could be generated using directly auto-transplanted or expanded MSC with β-TCP/HA granules alone. Hence BMP-2 stimulation might become dispensable in the future, thus providing an attractive, clinically feasible approach to bone tissue engineering. PMID:20636333

Effects of Cognition, Function, and Behavioral and Psychological Symptoms on Medicare Expenditures and Health Care Utilization for Persons With Dementia.

PubMed

Jutkowitz, Eric; Kane, Robert L; Dowd, Bryan; Gaugler, Joseph E; MacLehose, Richard F; Kuntz, Karen M

2017-06-01

Clinical features of dementia (cognition, function, and behavioral/psychological symptoms [BPSD]) may differentially affect Medicare expenditures/health care utilization. We linked cross-sectional data from the Aging, Demographics, and Memory Study to Medicare data to evaluate the association between dementia clinical features among those with dementia and Medicare expenditures/health care utilization (n = 234). Cognition was evaluated using the Mini-Mental State Examination (MMSE). Function was evaluated as the number of functional limitations (0-10). BPSD was evaluated as the number of symptoms (0-12). Expenditures were estimated with a generalized linear model (log-link and gamma distribution). Number of hospitalizations, institutional outpatient visits, and physician visits were estimated with a negative binomial regression. Medicare covered skilled nursing days were estimated with a zero-inflated negative binomial model. Cognition and BPSD were not associated with expenditures. Among individuals with less than seven functional limitations, one additional limitation was associated with $123 (95% confidence interval: $19-$227) additional monthly Medicare spending. Better cognition and poorer function were associated with more hospitalizations among those with an MMSE less than three and less than six functional limitations, respectively. BPSD had no effect on hospitalizations. Poorer function and fewer BPSD were associated with more skilled nursing among individuals with one to seven functional limitations and more than four symptoms, respectively. Cognition had no effect on skilled nursing care. No clinical feature was associated with institutional outpatient care. Of individuals with an MMSE less than 15, poorer cognition was associated with fewer physician visits. Among those with more than six functional limitations, poorer function was associated with fewer physician visits. Poorer function, not cognition or BPSD, was associated with higher Medicare expenditures. © The Author 2017. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Factors associated with the frequency of monitoring of liver enzymes, renal function and lipid laboratory markers among individuals initiating combination antiretroviral therapy: a cohort study.

PubMed

Gillis, Jennifer; Bayoumi, Ahmed M; Burchell, Ann N; Cooper, Curtis; Klein, Marina B; Loutfy, Mona; Machouf, Nima; Montaner, Julio Sg; Tsoukas, Chris; Hogg, Robert S; Raboud, Janet

2015-10-26

As the average age of the HIV-positive population increases, there is increasing need to monitor patients for the development of comorbidities as well as for drug toxicities. We examined factors associated with the frequency of measurement of liver enzymes, renal function tests, and lipid levels among participants of the Canadian Observational Cohort (CANOC) collaboration which follows people who initiated HIV antiretroviral therapy in 2000 or later. We used zero-inflated negative binomial regression models to examine the associations of demographic and clinical characteristics with the rates of measurement during follow-up. Generalized estimating equations with a logit link were used to examine factors associated with gaps of 12 months or more between measurements. Electronic laboratory data were available for 3940 of 7718 CANOC participants. The median duration of electronic follow-up was 3.5 years. The median (interquartile) rates of tests per year were 2.76 (1.60, 3.73), 2.55 (1.44, 3.38) and 1.42 (0.50, 2.52) for liver, renal and lipid parameters, respectively. In multivariable zero-inflated negative binomial regression models, individuals infected through injection drug use (IDU) were significantly less likely to have any measurements. Among participants with at least one measurement, rates of measurement of liver, renal and lipid tests were significantly lower for younger individuals and Aboriginal Peoples. Hepatitis C co-infected individuals with a history of IDU had lower rates of measurement and were at greater risk of having 12 month gaps between measurements. Hepatitis C co-infected participants infected through IDU were at increased risk of gaps in testing, despite publicly funded health care and increased risk of comorbid conditions. This should be taken into consideration in analyses examining factors associated with outcomes based on laboratory parameters.

Fault tolerant control of multivariable processes using auto-tuning PID controller.

PubMed

Yu, Ding-Li; Chang, T K; Yu, Ding-Wen

2005-02-01

Fault tolerant control of dynamic processes is investigated in this paper using an auto-tuning PID controller. A fault tolerant control scheme is proposed composing an auto-tuning PID controller based on an adaptive neural network model. The model is trained online using the extended Kalman filter (EKF) algorithm to learn system post-fault dynamics. Based on this model, the PID controller adjusts its parameters to compensate the effects of the faults, so that the control performance is recovered from degradation. The auto-tuning algorithm for the PID controller is derived with the Lyapunov method and therefore, the model predicted tracking error is guaranteed to converge asymptotically. The method is applied to a simulated two-input two-output continuous stirred tank reactor (CSTR) with various faults, which demonstrate the applicability of the developed scheme to industrial processes.

Estimating relative risks for common outcome using PROC NLP.

PubMed

Yu, Binbing; Wang, Zhuoqiao

2008-05-01

In cross-sectional or cohort studies with binary outcomes, it is biologically interpretable and of interest to estimate the relative risk or prevalence ratio, especially when the response rates are not rare. Several methods have been used to estimate the relative risk, among which the log-binomial models yield the maximum likelihood estimate (MLE) of the parameters. Because of restrictions on the parameter space, the log-binomial models often run into convergence problems. Some remedies, e.g., the Poisson and Cox regressions, have been proposed. However, these methods may give out-of-bound predicted response probabilities. In this paper, a new computation method using the SAS Nonlinear Programming (NLP) procedure is proposed to find the MLEs. The proposed NLP method was compared to the COPY method, a modified method to fit the log-binomial model. Issues in the implementation are discussed. For illustration, both methods were applied to data on the prevalence of microalbuminuria (micro-protein leakage into urine) for kidney disease patients from the Diabetes Control and Complications Trial. The sample SAS macro for calculating relative risk is provided in the appendix.

Child Schooling in Ethiopia: The Role of Maternal Autonomy.

PubMed

Gebremedhin, Tesfaye Alemayehu; Mohanty, Itismita

2016-01-01

This paper examines the effects of maternal autonomy on child schooling outcomes in Ethiopia using a nationally representative Ethiopian Demographic and Health survey for 2011. The empirical strategy uses a Hurdle Negative Binomial Regression model to estimate years of schooling. An ordered probit model is also estimated to examine age grade distortion using a trichotomous dependent variable that captures three states of child schooling. The large sample size and the range of questions available in this dataset allow us to explore the influence of individual and household level social, economic and cultural factors on child schooling. The analysis finds statistically significant effects of maternal autonomy variables on child schooling in Ethiopia. The roles of maternal autonomy and other household-level factors on child schooling are important issues in Ethiopia, where health and education outcomes are poor for large segments of the population.

A Real-Time Non-invasive Auto-bioluminescent Urinary Bladder Cancer Xenograft Model.

PubMed

John, Bincy Anu; Xu, Tingting; Ripp, Steven; Wang, Hwa-Chain Robert

2017-02-01

The study was to develop an auto-bioluminescent urinary bladder cancer (UBC) xenograft animal model for pre-clinical research. The study used a humanized, bacteria-originated lux reporter system consisting of six (luxCDABEfrp) genes to express components required for producing bioluminescent signals in human UBC J82, J82-Ras, and SW780 cells without exogenous substrates. Immune-deficient nude mice were inoculated with Lux-expressing UBC cells to develop auto-bioluminescent xenograft tumors that were monitored by imaging and physical examination. Lux-expressing auto-bioluminescent J82-Lux, J82-Ras-Lux, and SW780-Lux cell lines were established. Xenograft tumors derived from tumorigenic Lux-expressing auto-bioluminescent J82-Ras-Lux cells allowed a serial, non-invasive, real-time monitoring by imaging of tumor development prior to the presence of palpable tumors in animals. Using Lux-expressing auto-bioluminescent tumorigenic cells enabled us to monitor the entire course of xenograft tumor development through tumor cell implantation, adaptation, and growth to visible/palpable tumors in animals.

Automatic Assembly of Combined Checkingfixture for Auto-Body Components Based Onfixture Elements Libraries

NASA Astrophysics Data System (ADS)

Jiang, Jingtao; Sui, Rendong; Shi, Yan; Li, Furong; Hu, Caiqi

In this paper 3-D models of combined fixture elements are designed, classified by their functions, and saved in computer as supporting elements library, jointing elements library, basic elements library, localization elements library, clamping elements library, and adjusting elements library etc. Then automatic assembly of 3-D combined checking fixture for auto-body part is presented based on modularization theory. And in virtual auto-body assembly space, Locating constraint mapping technique and assembly rule-based reasoning technique are used to calculate the position of modular elements according to localization points and clamp points of auto-body part. Auto-body part model is transformed from itself coordinate system space to virtual assembly space by homogeneous transformation matrix. Automatic assembly of different functional fixture elements and auto-body part is implemented with API function based on the second development of UG. It is proven in practice that the method in this paper is feasible and high efficiency.

A case of jaundice of obscure origin.

PubMed

Khan, Fahad M; Alcorn, Joseph; Hanson, Joshua

2014-05-01

Idiopathic painless jaundice with significant elevations in serum transaminases, occurring in a previously healthy patient, invokes a circumscribed set of possibilities including viral hepatitis, auto-immune hepatitis (AIH) and drug-induced liver injury (DILI). In this described case, common causes of cholestatic jaundice were considered including drug-induced liver injury, viral causes of hepatitis, and auto-immune antibodies. Biliary obstruction was excluded by appropriate imaging studies. Liver biopsy was obtained, though not definitive. After detailed investigation failed to reveal a cause of the jaundice, an empiric trial of steroids was initiated on the possibility that our patient had antibody-negative AIH and not DILI, with an associated grave prognosis. Empiric treatment with prednisone led to rapid resolution of jaundice and to the conclusion that the correct diagnosis was antibody-negative AIH.

Spatial Distribution of Adult Anthonomus grandis Boheman (Coleoptera: Curculionidae) and Damage to Cotton Flower Buds Due to Feeding and Oviposition.

PubMed

Grigolli, J F J; Souza, L A; Fernandes, M G; Busoli, A C

2017-08-01

The cotton boll weevil Anthonomus grandis Boheman (Coleoptera: Curculionidae) is the main pest in cotton crop around the world, directly affecting cotton production. In order to establish a sequential sampling plan, it is crucial to understand the spatial distribution of the pest population and the damage it causes to the crop through the different developmental stages of cotton plants. Therefore, this study aimed to investigate the spatial distribution of adults in the cultivation area and their oviposition and feeding behavior throughout the development of the cotton plants. The experiment was conducted in Maracaju, Mato Grosso do Sul, Brazil, in the 2012/2013 and 2013/2014 growing seasons, in an area of 10,000 m 2 , planted with the cotton cultivar FM 993. The experimental area was divided into 100 plots of 100 m 2 (10 × 10 m) each, and five plants per plot were sampled weekly throughout the crop cycle. The number of flower buds with feeding and oviposition punctures and of adult A. grandis was recorded throughout the crop cycle in five plants per plot. After determining the aggregation indices (variance/mean ratio, Morisita's index, exponent k of the negative binomial distribution, and Green's coefficient) and adjusting the frequencies observed in the field to the distribution of frequencies (Poisson, negative binomial, and positive binomial) using the chi-squared test, it was observed that flower buds with punctures derived from feeding, oviposition, and feeding + oviposition showed an aggregated distribution in the cultivation area until 85 days after emergence and a random distribution after this stage. The adults of A. grandis presented a random distribution in the cultivation area.

Estimating the Economic Value of National Parks with Count Data Models Using On-Site, Secondary Data: The Case of the Great Sand Dunes National Park and Preserve

NASA Astrophysics Data System (ADS)

Heberling, Matthew T.; Templeton, Joshua J.

2009-04-01

We estimate an individual travel cost model for Great Sand Dunes National Park and Preserve (GSD) in Colorado using on-site, secondary data. The purpose of the on-site survey was to help the National Park Service better understand the visitors of GSD; it was not intended for a travel cost model. Variables such as travel cost and income were estimated based on respondents’ Zip Codes. Following approaches found in the literature, a negative binomial model corrected for truncation and endogenous stratification fit the data the best. We estimate a recreational benefit of U.S. 89/visitor/year or U.S. 54/visitor/24-h recreational day (in 2002 U.S. ). Based on the approach presented here, there are other data sets for national parks, preserves, and battlefields where travel cost models could be estimated and used to support National Park Service management decisions.

Recurrent suicide attempts in patients with depressive and anxiety disorders: the role of borderline personality traits.

PubMed

Stringer, Barbara; van Meijel, Berno; Eikelenboom, Merijn; Koekkoek, Bauke; Licht, Carmilla M M; Kerkhof, Ad J F M; Penninx, Brenda W J H; Beekman, Aartjan T F

2013-10-01

The presence of a comorbid borderline personality disorder (BPD) may be associated with an increase of suicidal behaviors in patients with depressive and anxiety disorders. The aim of this study is to examine the role of borderline personality traits on recurrent suicide attempts. The Netherlands Study on Depression and Anxiety included 1838 respondents with lifetime depressive and/or anxiety disorders, of whom 309 reported at least one previous suicide attempt. A univariable negative binomial regression analysis was performed to examine the association between comorbid borderline personality traits and suicide attempts. Univariable and multivariable negative binomial regression analyses were performed to identify risk factors for the number of recurrent suicide attempts in four clusters (type and severity of axis-I disorders, BPD traits, determinants of suicide attempts and socio-demographics). In the total sample the suicide attempt rate ratio increased with 33% for every unit increase in BPD traits. A lifetime diagnosis of dysthymia and comorbid BPD traits, especially the symptoms anger and fights, were independently and significantly associated with recurrent suicide attempts in the final model (n=309). The screening of personality disorders was added to the NESDA assessments at the 4-year follow-up for the first time. Therefore we were not able to examine the influence of comorbid BPD traits on suicide attempts over time. Persons with a lifetime diagnosis of dysthymia combined with borderline personality traits especially difficulties in coping with anger seemed to be at high risk for recurrent suicide attempts. For clinical practice, it is recommended to screen for comorbid borderline personality traits and to strengthen the patient's coping skills with regard to anger. © 2013 Elsevier B.V. All rights reserved.

Analysing recurrent hospitalizations in heart failure: a review of statistical methodology, with application to CHARM-Preserved.

PubMed

Rogers, Jennifer K; Pocock, Stuart J; McMurray, John J V; Granger, Christopher B; Michelson, Eric L; Östergren, Jan; Pfeffer, Marc A; Solomon, Scott D; Swedberg, Karl; Yusuf, Salim

2014-01-01

Heart failure is characterized by recurrent hospitalizations, but often only the first event is considered in clinical trial reports. In chronic diseases, such as heart failure, analysing all events gives a more complete picture of treatment benefit. We describe methods of analysing repeat hospitalizations, and illustrate their value in one major trial. The Candesartan in Heart failure Assessment of Reduction in Mortality and morbidity (CHARM)-Preserved study compared candesartan with placebo in 3023 patients with heart failure and preserved systolic function. The heart failure hospitalization rates were 12.5 and 8.9 per 100 patient-years in the placebo and candesartan groups, respectively. The repeat hospitalizations were analysed using the Andersen-Gill, Poisson, and negative binomial methods. Death was incorporated into analyses by treating it as an additional event. The win ratio method and a method that jointly models hospitalizations and mortality were also considered. Using repeat events gave larger treatment benefits than time to first event analysis. The negative binomial method for the composite of recurrent heart failure hospitalizations and cardiovascular death gave a rate ratio of 0.75 [95% confidence interval (CI) 0.62-0.91, P = 0.003], whereas the hazard ratio for time to first heart failure hospitalization or cardiovascular death was 0.86 (95% CI 0.74-1.00, P = 0.050). In patients with preserved EF, candesartan reduces the rate of admissions for worsening heart failure, to a greater extent than apparent from analysing only first hospitalizations. Recurrent events should be routinely incorporated into the analysis of future clinical trials in heart failure. © 2013 The Authors. European Journal of Heart Failure © 2013 European Society of Cardiology.

Assessing historical rate changes in global tsunami occurrence

USGS Publications Warehouse

Geist, E.L.; Parsons, T.

2011-01-01

The global catalogue of tsunami events is examined to determine if transient variations in tsunami rates are consistent with a Poisson process commonly assumed for tsunami hazard assessments. The primary data analyzed are tsunamis with maximum sizes >1m. The record of these tsunamis appears to be complete since approximately 1890. A secondary data set of tsunamis >0.1m is also analyzed that appears to be complete since approximately 1960. Various kernel density estimates used to determine the rate distribution with time indicate a prominent rate change in global tsunamis during the mid-1990s. Less prominent rate changes occur in the early- and mid-20th century. To determine whether these rate fluctuations are anomalous, the distribution of annual event numbers for the tsunami catalogue is compared to Poisson and negative binomial distributions, the latter of which includes the effects of temporal clustering. Compared to a Poisson distribution, the negative binomial distribution model provides a consistent fit to tsunami event numbers for the >1m data set, but the Poisson null hypothesis cannot be falsified for the shorter duration >0.1m data set. Temporal clustering of tsunami sources is also indicated by the distribution of interevent times for both data sets. Tsunami event clusters consist only of two to four events, in contrast to protracted sequences of earthquakes that make up foreshock-main shock-aftershock sequences. From past studies of seismicity, it is likely that there is a physical triggering mechanism responsible for events within the tsunami source 'mini-clusters'. In conclusion, prominent transient rate increases in the occurrence of global tsunamis appear to be caused by temporal grouping of geographically distinct mini-clusters, in addition to the random preferential location of global M >7 earthquakes along offshore fault zones.

Factors Associated with Dental Caries in a Group of American Indian Children at age 36 Months

PubMed Central

Warren, John J.; Blanchette, Derek; Dawson, Deborah V.; Marshall, Teresa A.; Phipps, Kathy R.; Starr, Delores; Drake, David R.

2015-01-01

Objectives Early childhood caries (ECC) is rampant among American Indian children, but there has been relatively little study of this problem. This paper reports on risk factors for caries for a group of American Indian children at age 36 months as part of a longitudinal study. Methods Pregnant women from a Northern Plains Tribal community were recruited to participate in a longitudinal study of caries and caries risk factors. Standardized dental examinations were completed on children and questionnaires were completed by mothers at baseline and when children were 4, 8, 12, 16, 22, 28 and 36 months of age. Examinations were surface-specific for dental caries, and the questionnaires collected data on demographic, dietary and behavioral factors. Non-parametric bivariate tests and logistic regression models were used to identify risk factors for caries at 36 months, and negative binomial regression was used to identify factors related to caries severity (dmf counts). Results Among the 232 children, and caries prevalence for cavitated lesions was 80%, with an additional 15% having only non-cavitated lesions. The mean dmfs was 9.6, and of the total dmfs, nearly 62% of affected surfaces were decayed, 31% were missing, and 7% were filled. Logistic regression identified higher added sugar beverage consumption, younger maternal age at baseline, higher maternal DMFS at baseline, and greater number of people in the household as significant (p<0.05) risk factors. Negative binomial regression found that only maternal DMFS was associated with child dmf counts. Conclusions By the age of 36 months, dental caries is nearly universal in this population of American Indian children. Caries risk factors included sugared beverage consumption, greater household size and maternal factors, but further analyses are needed to better understand caries in this population. PMID:26544674

Homicide mortality rates in Canada, 2000-2009: Youth at increased risk.

PubMed

Basham, C Andrew; Snider, Carolyn

2016-10-20

To estimate and compare Canadian homicide mortality rates (HMRs) and trends in HMRs across age groups, with a focus on trends for youth. Data for the period of 2000 to 2009 were collected from Statistics Canada's CANSIM (Canadian Statistical Information Management) Table 102-0540 with the following ICD-10-CA coded external causes of death: X85 to Y09 (assault) and Y87.1 (sequelae of assault). Annual population counts from 2000 to 2009 were obtained from Statistics Canada's CANSIM Table 051-0001. Both death and population counts were organized into five-year age groups. A random effects negative binomial regression analysis was conducted to estimate age group-specific rates, rate ratios, and trends in homicide mortality. There were 9,878 homicide deaths in Canada during the study period. The increase in the overall homicide mortality rate (HMR) of 0.3% per year was not statistically significant (95% CI: -1.1% to +1.8%). Canadians aged 15-19 years and 20-24 years had the highest HMRs during the study period, and experienced statistically significant annual increases in their HMRs of 3% and 4% respectively (p < 0.05). A general, though not statistically significant, decrease in the HMR was observed for all age groups 50+ years. A fixed effects negative binomial regression model showed that the HMR for males was higher than for females over the study period [RRfemale/male = 0.473 (95% CI: 0.361, 0.621)], but no significant difference in sex-specific trends in the HMR was found. An increasing risk of homicide mortality was identified among Canadian youth, ages 15-24, over the 10-year study period. Research that seeks to understand the reasons for the increased homicide risk facing Canada's youth, and public policy responses to reduce this risk, are warranted.

Binomial tree method for pricing a regime-switching volatility stock loans

NASA Astrophysics Data System (ADS)

Putri, Endah R. M.; Zamani, Muhammad S.; Utomo, Daryono B.

2018-03-01

Binomial model with regime switching may represents the price of stock loan which follows the stochastic process. Stock loan is one of alternative that appeal investors to get the liquidity without selling the stock. The stock loan mechanism resembles that of American call option when someone can exercise any time during the contract period. From the resembles both of mechanism, determination price of stock loan can be interpreted from the model of American call option. The simulation result shows the behavior of the price of stock loan under a regime-switching with respect to various interest rate and maturity.

Which Types of Televised Anti-Tobacco Campaigns Prompt More Quitline Calls from Disadvantaged Groups?

ERIC Educational Resources Information Center

Durkin, Sarah J.; Wakefield, Melanie A.; Spittal, Matthew J.

2011-01-01

To examine the efficacy of different types of mass media ads in driving lower socio-economic smokers (SES) to utilize quitlines. This study collected all 33 719 calls to the Victorian quitline in Australia over a 2-year period. Negative binomial regressions examined the relationship between weekly levels of exposure to different types of…

Co-Infestation and Spatial Distribution of Bactrocera carambolae and Anastrepha spp. (Diptera: Tephritidae) in Common Guava in the Eastern Amazon

PubMed Central

Deus, E. G.; Godoy, W. A. C.; Sousa, M. S. M.; Lopes, G. N.; Jesus-Barros, C. R.; Silva, J. G.; Adaime, R.

2016-01-01

Field infestation and spatial distribution of introduced Bactrocera carambolae Drew and Hancock and native species of Anastrepha in common guavas [Psidium guajava (L.)] were investigated in the eastern Amazon. Fruit sampling was carried out in the municipalities of Calçoene and Oiapoque in the state of Amapá, Brazil. The frequency distribution of larvae in fruit was fitted to the negative binomial distribution. Anastrepha striata was more abundant in both sampled areas in comparison to Anastrepha fraterculus (Wiedemann) and B. carambolae. The frequency distribution analysis of adults revealed an aggregated pattern for B. carambolae as well as for A. fraterculus and Anastrepha striata Schiner, described by the negative binomial distribution. Although the populations of Anastrepha spp. may have suffered some impact due to the presence of B. carambolae, the results are still not robust enough to indicate effective reduction in the abundance of Anastrepha spp. caused by B. carambolae in a general sense. The high degree of aggregation observed for both species suggests interspecific co-occurrence with the simultaneous presence of both species in the analysed fruit. Moreover, a significant fraction of uninfested guavas also indicated absence of competitive displacement. PMID:27638949

AutoLens: Automated Modeling of a Strong Lens's Light, Mass and Source

NASA Astrophysics Data System (ADS)

Nightingale, J. W.; Dye, S.; Massey, Richard J.

2018-05-01

This work presents AutoLens, the first entirely automated modeling suite for the analysis of galaxy-scale strong gravitational lenses. AutoLens simultaneously models the lens galaxy's light and mass whilst reconstructing the extended source galaxy on an adaptive pixel-grid. The method's approach to source-plane discretization is amorphous, adapting its clustering and regularization to the intrinsic properties of the lensed source. The lens's light is fitted using a superposition of Sersic functions, allowing AutoLens to cleanly deblend its light from the source. Single component mass models representing the lens's total mass density profile are demonstrated, which in conjunction with light modeling can detect central images using a centrally cored profile. Decomposed mass modeling is also shown, which can fully decouple a lens's light and dark matter and determine whether the two component are geometrically aligned. The complexity of the light and mass models are automatically chosen via Bayesian model comparison. These steps form AutoLens's automated analysis pipeline, such that all results in this work are generated without any user-intervention. This is rigorously tested on a large suite of simulated images, assessing its performance on a broad range of lens profiles, source morphologies and lensing geometries. The method's performance is excellent, with accurate light, mass and source profiles inferred for data sets representative of both existing Hubble imaging and future Euclid wide-field observations.

DEsingle for detecting three types of differential expression in single-cell RNA-seq data.

PubMed

Miao, Zhun; Deng, Ke; Wang, Xiaowo; Zhang, Xuegong

2018-04-24

The excessive amount of zeros in single-cell RNA-seq data include "real" zeros due to the on-off nature of gene transcription in single cells and "dropout" zeros due to technical reasons. Existing differential expression (DE) analysis methods cannot distinguish these two types of zeros. We developed an R package DEsingle which employed Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect 3 types of DE genes in single-cell RNA-seq data with higher accuracy. The R package DEsingle is freely available at https://github.com/miaozhun/DEsingle and is under Bioconductor's consideration now. zhangxg@tsinghua.edu.cn. Supplementary data are available at Bioinformatics online.

AMP: Assembly Matching Pursuit.

PubMed

Biswas, S; Jojic, V

2013-01-01

Metagenomics, the study of the total genetic material isolated from a biological host, promises to reveal host-microbe or microbe-microbe interactions that may help to personalize medicine or improve agronomic practice. We introduce a method that discovers metagenomic units (MGUs) relevant for phenotype prediction through sequence-based dictionary learning. The method aggregates patient-specific dictionaries and estimates MGU abundances in order to summarize a whole population and yield universally predictive biomarkers. We analyze the impact of Gaussian, Poisson, and Negative Binomial read count models in guiding dictionary construction by examining classification efficiency on a number of synthetic datasets and a real dataset from Ref. 1. Each outperforms standard methods of dictionary composition, such as random projection and orthogonal matching pursuit. Additionally, the predictive MGUs they recover are biologically relevant.

Demonstration of fundamental statistics by studying timing of electronics signals in a physics-based laboratory

NASA Astrophysics Data System (ADS)

Beach, Shaun E.; Semkow, Thomas M.; Remling, David J.; Bradt, Clayton J.

2017-07-01

We have developed accessible methods to demonstrate fundamental statistics in several phenomena, in the context of teaching electronic signal processing in a physics-based college-level curriculum. A relationship between the exponential time-interval distribution and Poisson counting distribution for a Markov process with constant rate is derived in a novel way and demonstrated using nuclear counting. Negative binomial statistics is demonstrated as a model for overdispersion and justified by the effect of electronic noise in nuclear counting. The statistics of digital packets on a computer network are shown to be compatible with the fractal-point stochastic process leading to a power-law as well as generalized inverse Gaussian density distributions of time intervals between packets.

Effects of health intervention programs and arsenic exposure on child mortality from acute lower respiratory infections in rural Bangladesh.

PubMed

Jochem, Warren C; Razzaque, Abdur; Root, Elisabeth Dowling

2016-09-01

Respiratory infections continue to be a public health threat, particularly to young children in developing countries. Understanding the geographic patterns of diseases and the role of potential risk factors can help improve future mitigation efforts. Toward this goal, this paper applies a spatial scan statistic combined with a zero-inflated negative-binomial regression to re-examine the impacts of a community-based treatment program on the geographic patterns of acute lower respiratory infection (ALRI) mortality in an area of rural Bangladesh. Exposure to arsenic-contaminated drinking water is also a serious threat to the health of children in this area, and the variation in exposure to arsenic must be considered when evaluating the health interventions. ALRI mortality data were obtained for children under 2 years old from 1989 to 1996 in the Matlab Health and Demographic Surveillance System. This study period covers the years immediately following the implementation of an ALRI control program. A zero-inflated negative binomial (ZINB) regression model was first used to simultaneously estimate mortality rates and the likelihood of no deaths in groups of related households while controlling for socioeconomic status, potential arsenic exposure, and access to care. Next a spatial scan statistic was used to assess the location and magnitude of clusters of ALRI mortality. The ZINB model was used to adjust the scan statistic for multiple social and environmental risk factors. The results of the ZINB models and spatial scan statistic suggest that the ALRI control program was successful in reducing child mortality in the study area. Exposure to arsenic-contaminated drinking water was not associated with increased mortality. Higher socioeconomic status also significantly reduced mortality rates, even among households who were in the treatment program area. Community-based ALRI interventions can be effective at reducing child mortality, though socioeconomic factors may continue to influence mortality patterns. The combination of spatial and non-spatial methods used in this paper has not been applied previously in the literature, and this study demonstrates the importance of such approaches for evaluating and improving public health intervention programs.

Comparing ELISA test-positive prevalence, risk factors and management recommendations for Johne's disease prevention between organic and conventional dairy farms in Ontario, Canada.

PubMed

Pieper, Laura; Sorge, Ulrike S; DeVries, Trevor; Godkin, Ann; Lissemore, Kerry; Kelton, David

2015-11-01

Johne's disease (JD) is a chronic, infectious disease in cattle. Between 2010 and 2013, a voluntary JD control program was successfully launched in Ontario, Canada, including a Risk Assessment and Management Plan (RAMP) and JD ELISA testing of the entire milking herd. Over the last decade, the organic dairy sector has been growing. However, organic farming regulations and philosophies may influence the risk for JD transmission on Ontario organic dairy farms. The aim of this cross-sectional study was to investigate differences in JD ELISA test positive prevalence, risk factors for JD and recommendations for JD prevention between organic and conventional dairy herds in Ontario. RAMP results (i.e. RAMP scores and recommendations) and ELISA results were available for 2103 dairy herds, including 42 organic herds. If available, additional data on milk production, milk quality, and herd characteristics were gathered. Organic and conventional herds had a similar herd-level JD ELISA test-positive prevalence (26.2% and 27.2%, respectively). Organic herds (4.2%) had a higher within-herd JD ELISA test-positive prevalence compared to conventional herds (2.3%) if they had at least one JD test-positive animal on the farm. Organic farms had lower risk scores for biosecurity (9 points lower), and higher scores in the calving (7 points higher) and the calf-rearing management areas (4 points higher). After accounting for RAMP score, organic farms received fewer recommendations for the calving management area (Odds Ratio=0.41) and more recommendations in the adult cow management area (Odds Ratio=2.70). A zero-inflated negative binomial model was built with purchase of animals and the herd size included in the logistic portion of the model. Herd type (organic or conventional), colostrum and milk feeding practices, average bulk tank somatic cell count, and presence of non-Holstein breeds were included in the negative binomial portion of the model. Organic farms had a higher number of test positive animals (Count Ratio=2.02). Further research is necessary to investigate the apparent disconnect between risk factors and recommendations on organic dairy farms. Copyright © 2015 Elsevier B.V. All rights reserved.

Logit and probit model in toll sensitivity analysis of Solo-Ngawi, Kartasura-Palang Joglo segment based on Willingness to Pay (WTP)

NASA Astrophysics Data System (ADS)

Handayani, Dewi; Cahyaning Putri, Hera; Mahmudah, AMH

2017-12-01

Solo-Ngawi toll road project is part of the mega project of the Trans Java toll road development initiated by the government and is still under construction until now. PT Solo Ngawi Jaya (SNJ) as the Solo-Ngawi toll management company needs to determine the toll fare that is in accordance with the business plan. The determination of appropriate toll rates will affect progress in regional economic sustainability and decrease the traffic congestion. These policy instruments is crucial for achieving environmentally sustainable transport. Therefore, the objective of this research is to find out how the toll fare sensitivity of Solo-Ngawi toll road based on Willingness To Pay (WTP). Primary data was obtained by distributing stated preference questionnaires to four wheeled vehicle users in Kartasura-Palang Joglo artery road segment. Further data obtained will be analysed with logit and probit model. Based on the analysis, it is found that the effect of fare change on the amount of WTP on the binomial logit model is more sensitive than the probit model on the same travel conditions. The range of tariff change against values of WTP on the binomial logit model is 20% greater than the range of values in the probit model . On the other hand, the probability results of the binomial logit model and the binary probit have no significant difference (less than 1%).

Development of a generic auto-calibration package for regional ecological modeling and application in the Central Plains of the United States

USGS Publications Warehouse

Wu, Yiping; Liu, Shuguang; Li, Zhengpeng; Dahal, Devendra; Young, Claudia J.; Schmidt, Gail L.; Liu, Jinxun; Davis, Brian; Sohl, Terry L.; Werner, Jeremy M.; Oeding, Jennifer

2014-01-01

Process-oriented ecological models are frequently used for predicting potential impacts of global changes such as climate and land-cover changes, which can be useful for policy making. It is critical but challenging to automatically derive optimal parameter values at different scales, especially at regional scale, and validate the model performance. In this study, we developed an automatic calibration (auto-calibration) function for a well-established biogeochemical model—the General Ensemble Biogeochemical Modeling System (GEMS)-Erosion Deposition Carbon Model (EDCM)—using data assimilation technique: the Shuffled Complex Evolution algorithm and a model-inversion R package—Flexible Modeling Environment (FME). The new functionality can support multi-parameter and multi-objective auto-calibration of EDCM at the both pixel and regional levels. We also developed a post-processing procedure for GEMS to provide options to save the pixel-based or aggregated county-land cover specific parameter values for subsequent simulations. In our case study, we successfully applied the updated model (EDCM-Auto) for a single crop pixel with a corn–wheat rotation and a large ecological region (Level II)—Central USA Plains. The evaluation results indicate that EDCM-Auto is applicable at multiple scales and is capable to handle land cover changes (e.g., crop rotations). The model also performs well in capturing the spatial pattern of grain yield production for crops and net primary production (NPP) for other ecosystems across the region, which is a good example for implementing calibration and validation of ecological models with readily available survey data (grain yield) and remote sensing data (NPP) at regional and national levels. The developed platform for auto-calibration can be readily expanded to incorporate other model inversion algorithms and potential R packages, and also be applied to other ecological models.

The Volpe Center's inter-regional auto trip model : a review of methodology, assumptions, and application

DOT National Transportation Integrated Search

1994-10-31

The Volpe Center first estimated an inter-regional auto trip model as part of its effort to assess the market feasibility of maglev for the National Maglev Initiative (NMI). The original intent was to develop a direct demand model for estimating inte...

[Correlation coefficient-based classification method of hydrological dependence variability: With auto-regression model as example].

PubMed

Zhao, Yu Xi; Xie, Ping; Sang, Yan Fang; Wu, Zi Yi

2018-04-01

Hydrological process evaluation is temporal dependent. Hydrological time series including dependence components do not meet the data consistency assumption for hydrological computation. Both of those factors cause great difficulty for water researches. Given the existence of hydrological dependence variability, we proposed a correlationcoefficient-based method for significance evaluation of hydrological dependence based on auto-regression model. By calculating the correlation coefficient between the original series and its dependence component and selecting reasonable thresholds of correlation coefficient, this method divided significance degree of dependence into no variability, weak variability, mid variability, strong variability, and drastic variability. By deducing the relationship between correlation coefficient and auto-correlation coefficient in each order of series, we found that the correlation coefficient was mainly determined by the magnitude of auto-correlation coefficient from the 1 order to p order, which clarified the theoretical basis of this method. With the first-order and second-order auto-regression models as examples, the reasonability of the deduced formula was verified through Monte-Carlo experiments to classify the relationship between correlation coefficient and auto-correlation coefficient. This method was used to analyze three observed hydrological time series. The results indicated the coexistence of stochastic and dependence characteristics in hydrological process.

Post-Streptococcal Auto-Antibodies Inhibit Protein Disulfide Isomerase and Are Associated with Insulin Resistance

PubMed Central

Aran, Adi; Weiner, Karin; Lin, Ling; Finn, Laurel Ann; Greco, Mary Ann; Peppard, Paul; Young, Terry; Ofran, Yanay; Mignot, Emmanuel

2010-01-01

Post-streptococcal autoimmunity affects millions worldwide, targeting multiple organs including the heart, brain, and kidneys. To explore the post-streptococcal autoimmunity spectrum, we used western blot analyses, to screen 310 sera from healthy subjects with (33%) and without (67%) markers of recent streptococcal infections [anti-Streptolysin O (ASLO) or anti-DNAse B (ADB)]. A 58 KDa protein, reacting strongly with post-streptococcal sera, was identified as Protein Disulfide Isomerase (PDI), an abundant protein with pleiotropic metabolic, immunologic, and thrombotic effects. Anti-PDI autoantibodies, purified from human sera, targeted similar epitopes in Streptolysin O (SLO, P51-61) and PDI (P328-338). The correlation between post-streptococcal status and anti-human PDI auto-immunity was further confirmed in a total of 2987 samples (13.6% in 530 ASLO positive versus 5.6% in 2457 ASLO negative samples, p<0.0001). Finally, anti-PDI auto-antibodies inhibited PDI-mediated insulin degradation in vitro (n = 90, p<0.001), and correlated with higher serum insulin (14.1 iu/ml vs. 12.2 iu/ml, n = 1215, p = 0.039) and insulin resistance (Homeostatic Model Assessment (HOMA) 4.1 vs. 3.1, n = 1215, p = 0.004), in a population-based cohort. These results identify PDI as a major target of post-streptococcal autoimmunity, and establish a new link between infection, autoimmunity, and metabolic disturbances. PMID:20886095

Directly auto-transplanted mesenchymal stem cells induce bone formation in a ceramic bone substitute in an ectopic sheep model.

PubMed

Boos, Anja M; Loew, Johanna S; Deschler, Gloria; Arkudas, Andreas; Bleiziffer, Oliver; Gulle, Heinz; Dragu, Adrian; Kneser, Ulrich; Horch, Raymund E; Beier, Justus P

2011-06-01

Bone tissue engineering approaches increasingly focus on the use of mesenchymal stem cells (MSC). In most animal transplantation models MSC are isolated and expanded before auto cell transplantation which might be critical for clinical application in the future. Hence this study compares the potential of directly auto-transplanted versus in vitro expanded MSC with or without bone morphogenetic protein-2 (BMP-2) to induce bone formation in a large volume ceramic bone substitute in the sheep model. MSC were isolated from bone marrow aspirates and directly auto-transplanted or expanded in vitro and characterized using fluorescence activated cell sorting (FACS) and RT-PCR analysis before subcutaneous implantation in combination with BMP-2 and β-tricalcium phosphate/hydroxyapatite (β-TCP/HA) granules. Constructs were explanted after 1 to 12 weeks followed by histological and RT-PCR evaluation. Sheep MSC were CD29(+), CD44(+) and CD166(+) after selection by Ficoll gradient centrifugation, while directly auto-transplanted MSC-populations expressed CD29 and CD166 at lower levels. Both, directly auto-transplanted and expanded MSC, were constantly proliferating and had a decreasing apoptosis over time in vivo. Directly auto-transplanted MSC led to de novo bone formation in a heterotopic sheep model using a β-TCP/HA matrix comparable to the application of 60 μg/ml BMP-2 only or implantation of expanded MSC. Bone matrix proteins were up-regulated in constructs following direct auto-transplantation and in expanded MSC as well as in BMP-2 constructs. Up-regulation was detected using immunohistology methods and RT-PCR. Dense vascularization was demonstrated by CD31 immunohistology staining in all three groups. Ectopic bone could be generated using directly auto-transplanted or expanded MSC with β-TCP/HA granules alone. Hence BMP-2 stimulation might become dispensable in the future, thus providing an attractive, clinically feasible approach to bone tissue engineering. © 2011 The Authors Journal of Cellular and Molecular Medicine © 2011 Foundation for Cellular and Molecular Medicine/Blackwell Publishing Ltd.

A quantile count model of water depth constraints on Cape Sable seaside sparrows

USGS Publications Warehouse

Cade, B.S.; Dong, Q.

2008-01-01

1. A quantile regression model for counts of breeding Cape Sable seaside sparrows Ammodramus maritimus mirabilis (L.) as a function of water depth and previous year abundance was developed based on extensive surveys, 1992-2005, in the Florida Everglades. The quantile count model extends linear quantile regression methods to discrete response variables, providing a flexible alternative to discrete parametric distributional models, e.g. Poisson, negative binomial and their zero-inflated counterparts. 2. Estimates from our multiplicative model demonstrated that negative effects of increasing water depth in breeding habitat on sparrow numbers were dependent on recent occupation history. Upper 10th percentiles of counts (one to three sparrows) decreased with increasing water depth from 0 to 30 cm when sites were not occupied in previous years. However, upper 40th percentiles of counts (one to six sparrows) decreased with increasing water depth for sites occupied in previous years. 3. Greatest decreases (-50% to -83%) in upper quantiles of sparrow counts occurred as water depths increased from 0 to 15 cm when previous year counts were 1, but a small proportion of sites (5-10%) held at least one sparrow even as water depths increased to 20 or 30 cm. 4. A zero-inflated Poisson regression model provided estimates of conditional means that also decreased with increasing water depth but rates of change were lower and decreased with increasing previous year counts compared to the quantile count model. Quantiles computed for the zero-inflated Poisson model enhanced interpretation of this model but had greater lack-of-fit for water depths > 0 cm and previous year counts 1, conditions where the negative effect of water depths were readily apparent and fitted better with the quantile count model.

A Circadian Clock-Regulated Toggle Switch Explains AtGRP7 and AtGRP8 Oscillations in Arabidopsis thaliana

PubMed Central

Schmal, Christoph; Reimann, Peter; Staiger, Dorothee

2013-01-01

The circadian clock controls many physiological processes in higher plants and causes a large fraction of the genome to be expressed with a 24h rhythm. The transcripts encoding the RNA-binding proteins AtGRP7 (Arabidopsis thaliana Glycine Rich Protein 7) and AtGRP8 oscillate with evening peaks. The circadian clock components CCA1 and LHY negatively affect AtGRP7 expression at the level of transcription. AtGRP7 and AtGRP8, in turn, negatively auto-regulate and reciprocally cross-regulate post-transcriptionally: high protein levels promote the generation of an alternative splice form that is rapidly degraded. This clock-regulated feedback loop has been proposed to act as a molecular slave oscillator in clock output. While mathematical models describing the circadian core oscillator in Arabidopsis thaliana were introduced recently, we propose here the first model of a circadian slave oscillator. We define the slave oscillator in terms of ordinary differential equations and identify the model's parameters by an optimization procedure based on experimental results. The model successfully reproduces the pertinent experimental findings such as waveforms, phases, and half-lives of the time-dependent concentrations. Furthermore, we obtain insights into possible mechanisms underlying the observed experimental dynamics: the negative auto-regulation and reciprocal cross-regulation via alternative splicing could be responsible for the sharply peaking waveforms of the AtGRP7 and AtGRP8 mRNA. Moreover, our results suggest that the AtGRP8 transcript oscillations are subordinated to those of AtGRP7 due to a higher impact of AtGRP7 protein on alternative splicing of its own and of the AtGRP8 pre-mRNA compared to the impact of AtGRP8 protein. Importantly, a bifurcation analysis provides theoretical evidence that the slave oscillator could be a toggle switch, arising from the reciprocal cross-regulation at the post-transcriptional level. In view of this, transcriptional repression of AtGRP7 and AtGRP8 by LHY and CCA1 induces oscillations of the toggle switch, leading to the observed high-amplitude oscillations of AtGRP7 mRNA. PMID:23555221

Logistic regression for dichotomized counts.

PubMed

Preisser, John S; Das, Kalyan; Benecha, Habtamu; Stamm, John W

2016-12-01

Sometimes there is interest in a dichotomized outcome indicating whether a count variable is positive or zero. Under this scenario, the application of ordinary logistic regression may result in efficiency loss, which is quantifiable under an assumed model for the counts. In such situations, a shared-parameter hurdle model is investigated for more efficient estimation of regression parameters relating to overall effects of covariates on the dichotomous outcome, while handling count data with many zeroes. One model part provides a logistic regression containing marginal log odds ratio effects of primary interest, while an ancillary model part describes the mean count of a Poisson or negative binomial process in terms of nuisance regression parameters. Asymptotic efficiency of the logistic model parameter estimators of the two-part models is evaluated with respect to ordinary logistic regression. Simulations are used to assess the properties of the models with respect to power and Type I error, the latter investigated under both misspecified and correctly specified models. The methods are applied to data from a randomized clinical trial of three toothpaste formulations to prevent incident dental caries in a large population of Scottish schoolchildren. © The Author(s) 2014.

AutoGen Version 5.0

NASA Technical Reports Server (NTRS)

Gladden, Roy E.; Khanampornpan, Teerapat; Fisher, Forest W.

2010-01-01

Version 5.0 of the AutoGen software has been released. Previous versions, variously denoted Autogen and autogen, were reported in two articles: Automated Sequence Generation Process and Software (NPO-30746), Software Tech Briefs (Special Supplement to NASA Tech Briefs), September 2007, page 30, and Autogen Version 2.0 (NPO- 41501), NASA Tech Briefs, Vol. 31, No. 10 (October 2007), page 58. To recapitulate: AutoGen (now signifying automatic sequence generation ) automates the generation of sequences of commands in a standard format for uplink to spacecraft. AutoGen requires fewer workers than are needed for older manual sequence-generation processes, and greatly reduces sequence-generation times. The sequences are embodied in spacecraft activity sequence files (SASFs). AutoGen automates generation of SASFs by use of another previously reported program called APGEN. AutoGen encodes knowledge of different mission phases and of how the resultant commands must differ among the phases. AutoGen also provides means for customizing sequences through use of configuration files. The approach followed in developing AutoGen has involved encoding the behaviors of a system into a model and encoding algorithms for context-sensitive customizations of the modeled behaviors. This version of AutoGen addressed the MRO (Mars Reconnaissance Orbiter) primary science phase (PSP) mission phase. On previous Mars missions this phase has more commonly been referred to as mapping phase. This version addressed the unique aspects of sequencing orbital operations and specifically the mission specific adaptation of orbital operations for MRO. This version also includes capabilities for MRO s role in Mars relay support for UHF relay communications with the MER rovers and the Phoenix lander.

CUMBIN - CUMULATIVE BINOMIAL PROGRAMS

NASA Technical Reports Server (NTRS)

Bowerman, P. N.

1994-01-01

The cumulative binomial program, CUMBIN, is one of a set of three programs which calculate cumulative binomial probability distributions for arbitrary inputs. The three programs, CUMBIN, NEWTONP (NPO-17556), and CROSSER (NPO-17557), can be used independently of one another. CUMBIN can be used by statisticians and users of statistical procedures, test planners, designers, and numerical analysts. The program has been used for reliability/availability calculations. CUMBIN calculates the probability that a system of n components has at least k operating if the probability that any one operating is p and the components are independent. Equivalently, this is the reliability of a k-out-of-n system having independent components with common reliability p. CUMBIN can evaluate the incomplete beta distribution for two positive integer arguments. CUMBIN can also evaluate the cumulative F distribution and the negative binomial distribution, and can determine the sample size in a test design. CUMBIN is designed to work well with all integer values 0 < k <= n. To run the program, the user simply runs the executable version and inputs the information requested by the program. The program is not designed to weed out incorrect inputs, so the user must take care to make sure the inputs are correct. Once all input has been entered, the program calculates and lists the result. The CUMBIN program is written in C. It was developed on an IBM AT with a numeric co-processor using Microsoft C 5.0. Because the source code is written using standard C structures and functions, it should compile correctly with most C compilers. The program format is interactive. It has been implemented under DOS 3.2 and has a memory requirement of 26K. CUMBIN was developed in 1988.

Aggregate Auto Travel Forecasting : State of the Art and Suggestions for Future Research

DOT National Transportation Integrated Search

1976-12-01

The report reviews existing forecasting models of auto vehicle miles of travel (VMT), and presents evidence that such models incorrectly omit time cost and spatial form variables. The omission of these variables biases parameter estimates in existing...

Computer-Based Resource Accounting Model for Generating Aggregate Resource Impacts of Alternative Automobile Technologies : Volume 1. Fleet Attributes Model

DOT National Transportation Integrated Search

1977-01-01

Auto production and operation consume energy, material, capital and labor resources. Numerous substitution possibilities exist within and between resource sectors, corresponding to the broad spectrum of potential design technologies. Alternative auto...

Analysis of the Automobile Market : Modeling the Long-Run Determinants of the Demand for Automobiles : Volume 1. The Wharton EFA Automobile Demand Model

DOT National Transportation Integrated Search

1979-12-01

An econometric model is developed which provides long-run policy analysis and forecasting of annual trends, for U.S. auto stock, new sales, and their composition by auto size-class. The concept of "desired" (equilibrium) stock is introduced. "Desired...

Child Schooling in Ethiopia: The Role of Maternal Autonomy

PubMed Central

Mohanty, Itismita

2016-01-01

This paper examines the effects of maternal autonomy on child schooling outcomes in Ethiopia using a nationally representative Ethiopian Demographic and Health survey for 2011. The empirical strategy uses a Hurdle Negative Binomial Regression model to estimate years of schooling. An ordered probit model is also estimated to examine age grade distortion using a trichotomous dependent variable that captures three states of child schooling. The large sample size and the range of questions available in this dataset allow us to explore the influence of individual and household level social, economic and cultural factors on child schooling. The analysis finds statistically significant effects of maternal autonomy variables on child schooling in Ethiopia. The roles of maternal autonomy and other household-level factors on child schooling are important issues in Ethiopia, where health and education outcomes are poor for large segments of the population. PMID:27942039

A Stab in the Dark?: A Research Note on Temporal Patterns of Street Robbery.

PubMed

Tompson, Lisa; Bowers, Kate

2013-11-01

Test the influence of darkness in the street robbery crime event alongside temperature. Negative binomial regression models tested darkness and temperature as predictors of street robbery. Units of analysis were four 6-hr time intervals in two U.K. study areas that have different levels of darkness and variations of temperature throughout the year. Darkness is a key factor related to robbery events in both study areas. Traversing from full daylight to full darkness increased the predicted volume of robbery by a multiple of 2.6 in London and 1.2 in Glasgow. Temperature was significant only in the London study area. Interaction terms did not enhance the predictive power of the models. Darkness is an important driving factor in seasonal variation of street robbery. A further implication of the research is that time of the day patterns are crucial to understanding seasonal trends in crime data.

Understanding poisson regression.

PubMed

Hayat, Matthew J; Higgins, Melinda

2014-04-01

Nurse investigators often collect study data in the form of counts. Traditional methods of data analysis have historically approached analysis of count data either as if the count data were continuous and normally distributed or with dichotomization of the counts into the categories of occurred or did not occur. These outdated methods for analyzing count data have been replaced with more appropriate statistical methods that make use of the Poisson probability distribution, which is useful for analyzing count data. The purpose of this article is to provide an overview of the Poisson distribution and its use in Poisson regression. Assumption violations for the standard Poisson regression model are addressed with alternative approaches, including addition of an overdispersion parameter or negative binomial regression. An illustrative example is presented with an application from the ENSPIRE study, and regression modeling of comorbidity data is included for illustrative purposes. Copyright 2014, SLACK Incorporated.

Older driver fitness-to-drive evaluation using naturalistic driving data.

PubMed

Guo, Feng; Fang, Youjia; Antin, Jonathan F

2015-09-01

As our driving population continues to age, it is becoming increasingly important to find a small set of easily administered fitness metrics that can meaningfully and reliably identify at-risk seniors requiring more in-depth evaluation of their driving skills and weaknesses. Sixty driver assessment metrics related to fitness-to-drive were examined for 20 seniors who were followed for a year using the naturalistic driving paradigm. Principal component analysis and negative binomial regression modeling approaches were used to develop parsimonious models relating the most highly predictive of the driver assessment metrics to the safety-related outcomes observed in the naturalistic driving data. This study provides important confirmation using naturalistic driving methods of the relationship between contrast sensitivity and crash-related events. The results of this study provide crucial information on the continuing journey to identify metrics and protocols that could be applied to determine seniors' fitness to drive. Published by Elsevier Ltd.

Small area estimation for estimating the number of infant mortality in West Java, Indonesia

NASA Astrophysics Data System (ADS)

Anggreyani, Arie; Indahwati, Kurnia, Anang

2016-02-01

Demographic and Health Survey Indonesia (DHSI) is a national designed survey to provide information regarding birth rate, mortality rate, family planning and health. DHSI was conducted by BPS in cooperation with National Population and Family Planning Institution (BKKBN), Indonesia Ministry of Health (KEMENKES) and USAID. Based on the publication of DHSI 2012, the infant mortality rate for a period of five years before survey conducted is 32 for 1000 birth lives. In this paper, Small Area Estimation (SAE) is used to estimate the number of infant mortality in districts of West Java. SAE is a special model of Generalized Linear Mixed Models (GLMM). In this case, the incidence of infant mortality is a Poisson distribution which has equdispersion assumption. The methods to handle overdispersion are binomial negative and quasi-likelihood model. Based on the results of analysis, quasi-likelihood model is the best model to overcome overdispersion problem. The basic model of the small area estimation used basic area level model. Mean square error (MSE) which based on resampling method is used to measure the accuracy of small area estimates.

Review and Recommendations for Zero-inflated Count Regression Modeling of Dental Caries Indices in Epidemiological Studies

PubMed Central

Stamm, John W.; Long, D. Leann; Kincade, Megan E.

2012-01-01

Over the past five to ten years, zero-inflated count regression models have been increasingly applied to the analysis of dental caries indices (e.g., DMFT, dfms, etc). The main reason for that is linked to the broad decline in children’s caries experience, such that dmf and DMF indices more frequently generate low or even zero counts. This article specifically reviews the application of zero-inflated Poisson and zero-inflated negative binomial regression models to dental caries, with emphasis on the description of the models and the interpretation of fitted model results given the study goals. The review finds that interpretations provided in the published caries research are often imprecise or inadvertently misleading, particularly with respect to failing to discriminate between inference for the class of susceptible persons defined by such models and inference for the sampled population in terms of overall exposure effects. Recommendations are provided to enhance the use as well as the interpretation and reporting of results of count regression models when applied to epidemiological studies of dental caries. PMID:22710271

A method for modeling co-occurrence propensity of clinical codes with application to ICD-10-PCS auto-coding.

PubMed

Subotin, Michael; Davis, Anthony R

2016-09-01

Natural language processing methods for medical auto-coding, or automatic generation of medical billing codes from electronic health records, generally assign each code independently of the others. They may thus assign codes for closely related procedures or diagnoses to the same document, even when they do not tend to occur together in practice, simply because the right choice can be difficult to infer from the clinical narrative. We propose a method that injects awareness of the propensities for code co-occurrence into this process. First, a model is trained to estimate the conditional probability that one code is assigned by a human coder, given than another code is known to have been assigned to the same document. Then, at runtime, an iterative algorithm is used to apply this model to the output of an existing statistical auto-coder to modify the confidence scores of the codes. We tested this method in combination with a primary auto-coder for International Statistical Classification of Diseases-10 procedure codes, achieving a 12% relative improvement in F-score over the primary auto-coder baseline. The proposed method can be used, with appropriate features, in combination with any auto-coder that generates codes with different levels of confidence. The promising results obtained for International Statistical Classification of Diseases-10 procedure codes suggest that the proposed method may have wider applications in auto-coding. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Evaluation Of Statistical Models For Forecast Errors From The HBV-Model

NASA Astrophysics Data System (ADS)

Engeland, K.; Kolberg, S.; Renard, B.; Stensland, I.

2009-04-01

Three statistical models for the forecast errors for inflow to the Langvatn reservoir in Northern Norway have been constructed and tested according to how well the distribution and median values of the forecasts errors fit to the observations. For the first model observed and forecasted inflows were transformed by the Box-Cox transformation before a first order autoregressive model was constructed for the forecast errors. The parameters were conditioned on climatic conditions. In the second model the Normal Quantile Transformation (NQT) was applied on observed and forecasted inflows before a similar first order autoregressive model was constructed for the forecast errors. For the last model positive and negative errors were modeled separately. The errors were first NQT-transformed before a model where the mean values were conditioned on climate, forecasted inflow and yesterday's error. To test the three models we applied three criterions: We wanted a) the median values to be close to the observed values; b) the forecast intervals to be narrow; c) the distribution to be correct. The results showed that it is difficult to obtain a correct model for the forecast errors, and that the main challenge is to account for the auto-correlation in the errors. Model 1 and 2 gave similar results, and the main drawback is that the distributions are not correct. The 95% forecast intervals were well identified, but smaller forecast intervals were over-estimated, and larger intervals were under-estimated. Model 3 gave a distribution that fits better, but the median values do not fit well since the auto-correlation is not properly accounted for. If the 95% forecast interval is of interest, Model 2 is recommended. If the whole distribution is of interest, Model 3 is recommended.

AutoCAD-To-GIFTS Translator Program

NASA Technical Reports Server (NTRS)

Jones, Andrew

1989-01-01

AutoCAD-to-GIFTS translator program, ACTOG, developed to facilitate quick generation of small finite-element models using CASA/GIFTS finite-element modeling program. Reads geometric data of drawing from Data Exchange File (DXF) used in AutoCAD and other PC-based drafting programs. Geometric entities recognized by ACTOG include points, lines, arcs, solids, three-dimensional lines, and three-dimensional faces. From this information, ACTOG creates GIFTS SRC file, which then reads into GIFTS preprocessor BULKM or modified and reads into EDITM to create finite-element model. SRC file used as is or edited for any number of uses. Written in Microsoft Quick-Basic (Version 2.0).

Analysis of the Automobile Market : Modeling the Long-Run Determinants of the Demand for Automobiles : Volume 3. Appendices to the Wharton EFA Automobile Demand Model

DOT National Transportation Integrated Search

1979-12-01

An econometric model is developed which provides long-run policy analysis and forecasting of annual trends, for U.S. auto stock, new sales, and their composition by auto size-class. The concept of "desired" (equilibrium) stock is introduced. "Desired...

Analysis of the Automobile Market : Modeling the Long-Run Determinants of the Demand for Automobiles : Volume 2. Simulation Analysis Using the Wharton EFA Automobile Demand Model

DOT National Transportation Integrated Search

1979-12-01

An econometric model is developed which provides long-run policy analysis and forecasting of annual trends, for U.S. auto stock, new sales, and their composition by auto size-class. The concept of "desired" (equilibrium) stock is introduced. "Desired...

Modeling abundance using multinomial N-mixture models

USGS Publications Warehouse

Royle, Andy

2016-01-01

Multinomial N-mixture models are a generalization of the binomial N-mixture models described in Chapter 6 to allow for more complex and informative sampling protocols beyond simple counts. Many commonly used protocols such as multiple observer sampling, removal sampling, and capture-recapture produce a multivariate count frequency that has a multinomial distribution and for which multinomial N-mixture models can be developed. Such protocols typically result in more precise estimates than binomial mixture models because they provide direct information about parameters of the observation process. We demonstrate the analysis of these models in BUGS using several distinct formulations that afford great flexibility in the types of models that can be developed, and we demonstrate likelihood analysis using the unmarked package. Spatially stratified capture-recapture models are one class of models that fall into the multinomial N-mixture framework, and we discuss analysis of stratified versions of classical models such as model Mb, Mh and other classes of models that are only possible to describe within the multinomial N-mixture framework.

Event-by-event gluon multiplicity, energy density, and eccentricities in ultrarelativistic heavy-ion collisions

NASA Astrophysics Data System (ADS)

Schenke, Björn; Tribedy, Prithwish; Venugopalan, Raju

2012-09-01

The event-by-event multiplicity distribution, the energy densities and energy density weighted eccentricity moments ɛn (up to n=6) at early times in heavy-ion collisions at both the BNL Relativistic Heavy Ion Collider (RHIC) (s=200GeV) and the CERN Large Hardron Collider (LHC) (s=2.76TeV) are computed in the IP-Glasma model. This framework combines the impact parameter dependent saturation model (IP-Sat) for nucleon parton distributions (constrained by HERA deeply inelastic scattering data) with an event-by-event classical Yang-Mills description of early-time gluon fields in heavy-ion collisions. The model produces multiplicity distributions that are convolutions of negative binomial distributions without further assumptions or parameters. In the limit of large dense systems, the n-particle gluon distribution predicted by the Glasma-flux tube model is demonstrated to be nonperturbatively robust. In the general case, the effect of additional geometrical fluctuations is quantified. The eccentricity moments are compared to the MC-KLN model; a noteworthy feature is that fluctuation dominated odd moments are consistently larger than in the MC-KLN model.

Zero-inflated count models for longitudinal measurements with heterogeneous random effects.

PubMed

Zhu, Huirong; Luo, Sheng; DeSantis, Stacia M

2017-08-01

Longitudinal zero-inflated count data arise frequently in substance use research when assessing the effects of behavioral and pharmacological interventions. Zero-inflated count models (e.g. zero-inflated Poisson or zero-inflated negative binomial) with random effects have been developed to analyze this type of data. In random effects zero-inflated count models, the random effects covariance matrix is typically assumed to be homogeneous (constant across subjects). However, in many situations this matrix may be heterogeneous (differ by measured covariates). In this paper, we extend zero-inflated count models to account for random effects heterogeneity by modeling their variance as a function of covariates. We show via simulation that ignoring intervention and covariate-specific heterogeneity can produce biased estimates of covariate and random effect estimates. Moreover, those biased estimates can be rectified by correctly modeling the random effects covariance structure. The methodological development is motivated by and applied to the Combined Pharmacotherapies and Behavioral Interventions for Alcohol Dependence (COMBINE) study, the largest clinical trial of alcohol dependence performed in United States with 1383 individuals.

Extended Poisson process modelling and analysis of grouped binary data.

PubMed

Faddy, Malcolm J; Smith, David M

2012-05-01

A simple extension of the Poisson process results in binomially distributed counts of events in a time interval. A further extension generalises this to probability distributions under- or over-dispersed relative to the binomial distribution. Substantial levels of under-dispersion are possible with this modelling, but only modest levels of over-dispersion - up to Poisson-like variation. Although simple analytical expressions for the moments of these probability distributions are not available, approximate expressions for the mean and variance are derived, and used to re-parameterise the models. The modelling is applied in the analysis of two published data sets, one showing under-dispersion and the other over-dispersion. More appropriate assessment of the precision of estimated parameters and reliable model checking diagnostics follow from this more general modelling of these data sets. © 2012 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

The effects of temperament, psychopathy, and childhood trauma among delinquent youth: A test of DeLisi and Vaughn's temperament-based theory of crime.

PubMed

DeLisi, Matt; Fox, Bryanna H; Fully, Matthew; Vaughn, Michael G

Recent interest among criminologists on the construct of temperament has been fueled by DeLisi and Vaughn's (2014) temperament-based theory of antisocial behavior. Their theory suggests that core self-regulation capacity and negative emotionality are the most salient temperament features for understanding the emergence and maintenance of antisocial and violent behavior, even among offending populations. The present study tests the relative effects of these temperamental features along with psychopathic traits and trauma in their association with violent and non-violent delinquency in a sample of 252 juvenile offenders. Results from a series of negative binomial regression models indicate that temperament was uniformly more strongly associated with violent and non-violent delinquency than psychopathic traits and childhood traumatic events. Exploratory classification models suggested that temperament and psychopathy possessed similar predictive capacity, but neither surpassed prior history of violence and delinquency as a predictor of future offending. Overall, findings are supportive of DeLisi and Vaughn's temperament-based theory and suggest temperament as conceptualized and measured in the present study may play an important role as a risk factor for violent and non-violent delinquency. Copyright © 2018 Elsevier Ltd. All rights reserved.

Diagnostic test accuracy and prevalence inferences based on joint and sequential testing with finite population sampling.

PubMed

Su, Chun-Lung; Gardner, Ian A; Johnson, Wesley O

2004-07-30

The two-test two-population model, originally formulated by Hui and Walter, for estimation of test accuracy and prevalence estimation assumes conditionally independent tests, constant accuracy across populations and binomial sampling. The binomial assumption is incorrect if all individuals in a population e.g. child-care centre, village in Africa, or a cattle herd are sampled or if the sample size is large relative to population size. In this paper, we develop statistical methods for evaluating diagnostic test accuracy and prevalence estimation based on finite sample data in the absence of a gold standard. Moreover, two tests are often applied simultaneously for the purpose of obtaining a 'joint' testing strategy that has either higher overall sensitivity or specificity than either of the two tests considered singly. Sequential versions of such strategies are often applied in order to reduce the cost of testing. We thus discuss joint (simultaneous and sequential) testing strategies and inference for them. Using the developed methods, we analyse two real and one simulated data sets, and we compare 'hypergeometric' and 'binomial-based' inferences. Our findings indicate that the posterior standard deviations for prevalence (but not sensitivity and specificity) based on finite population sampling tend to be smaller than their counterparts for infinite population sampling. Finally, we make recommendations about how small the sample size should be relative to the population size to warrant use of the binomial model for prevalence estimation. Copyright 2004 John Wiley & Sons, Ltd.

Bycatch, bait, anglers, and roads: quantifying vector activity and propagule introduction risk across lake ecosystems.

PubMed

Drake, D Andrew R; Mandrak, Nicholas E

2014-06-01

Long implicated in the invasion process, live-bait anglers are highly mobile species vectors with frequent overland transport of fishes. To test hypotheses about the role of anglers in propagule transport, we developed a social-ecological model quantifying the opportunity for species transport beyond the invaded range resulting from bycatch during commercial bait operations, incidental transport, and release to lake ecosystems by anglers. We combined a gravity model with a stochastic, agent-based simulation, representing a 1-yr iteration of live-bait angling and the dynamics of propagule transport at fine spatiotemporal scales (i.e., probability of introducing n propagules per lake per year). A baseline scenario involving round goby (Neogobius melanostomus) indicated that most angling trips were benign; irrespective of lake visitation, anglers failed to purchase and transport propagules (benign trips, median probability P = 0.99912). However, given the large number of probability trials (4.2 million live-bait angling events per year), even the rarest sequence of events (uptake, movement, and deposition of propagules) is anticipated to occur. Risky trips (modal P = 0.00088 trips per year; approximately 1 in 1136) were sufficient to introduce a substantial number of propagules (modal values, Poisson model = 3715 propagules among 1288 lakes per year; zero-inflated negative binomial model = 6722 propagules among 1292 lakes per year). Two patterns of lake-specific introduction risk emerged. Large lakes supporting substantial angling activity experienced propagule pressure likely to surpass demographic barriers to establishment (top 2.5% of lakes with modal outcomes of five to 76 propagules per year; 303 high-risk lakes with three or more propagules, per year). Small or remote lakes were less likely to receive propagules; however, most risk distributions were leptokurtic with a long right tail, indicating the rare occurrence of high propagule loads to most waterbodies. Infestation simulations indicated that the number of high-risk waterbodies could be as great as 1318 (zero-inflated negative binomial), whereas a 90% reduction in bycatch from baseline would reduce the modal number of high risk lakes to zero. Results indicate that the combination of invasive bycatch and live-bait anglers warrants management concern as a species vector, but that risk is confined to a subset of individuals and recipient sites that may be effectively managed with targeted strategies.

The relationship between social support and adolescent dating violence: a comparison across genders.

PubMed

Richards, Tara N; Branch, Kathryn A

2012-05-01

Although much research has focused on the function of social support in adult intimate partner violence, little is known about the role of social support in adolescent dating violence. This study is an exploratory analysis of the independent impact of social support from friends and family on the risk of adolescent dating violence perpetration and victimization among a large sample of youth (n = 970). Approximately, 21% of the sample reported experiencing victimization in a dating relationship whereas 23% indicated perpetrating dating violence. Male youth reported significantly more involvement in dating violence as both perpetrators and victims. Negative binomial regression modeling indicated that increased levels of support from friends was associated with significantly less dating violence perpetration and victimization; however, when gendered models were explored, the protective role of social support was only maintained for female youth. Family support was not significantly related to dating violence in any model. Implications for dating violence curriculum and future research are addressed.

Measuring demand for flat water recreation using a two-stage/disequilibrium travel cost model with adjustment for overdispersion and self-selection

NASA Astrophysics Data System (ADS)

McKean, John R.; Johnson, Donn; Taylor, R. Garth

2003-04-01

An alternate travel cost model is applied to an on-site sample to estimate the value of flat water recreation on the impounded lower Snake River. Four contiguous reservoirs would be eliminated if the dams are breached to protect endangered Pacific salmon and steelhead trout. The empirical method applies truncated negative binomial regression with adjustment for endogenous stratification. The two-stage decision model assumes that recreationists allocate their time among work and leisure prior to deciding among consumer goods. The allocation of time and money among goods in the second stage is conditional on the predetermined work time and income. The second stage is a disequilibrium labor market which also applies if employers set work hours or if recreationists are not in the labor force. When work time is either predetermined, fixed by contract, or nonexistent, recreationists must consider separate prices and budgets for time and money.

Self-affirmation model for football goal distributions

NASA Astrophysics Data System (ADS)

Bittner, E.; Nußbaumer, A.; Janke, W.; Weigel, M.

2007-06-01

Analyzing football score data with statistical techniques, we investigate how the highly co-operative nature of the game is reflected in averaged properties such as the distributions of scored goals for the home and away teams. It turns out that in particular the tails of the distributions are not well described by independent Bernoulli trials, but rather well modeled by negative binomial or generalized extreme value distributions. To understand this behavior from first principles, we suggest to modify the Bernoulli random process to include a simple component of self-affirmation which seems to describe the data surprisingly well and allows to interpret the observed deviation from Gaussian statistics. The phenomenological distributions used before can be understood as special cases within this framework. We analyzed historical football score data from many leagues in Europe as well as from international tournaments and found the proposed models to be applicable rather universally. In particular, here we compare men's and women's leagues and the separate German leagues during the cold war times and find some remarkable differences.

[Determinants of health care utilization in Costa Rica].

PubMed

Morera Salas, Melvin; Aparicio Llanos, Amada

2010-01-01

To analyze the determinants of health care utilization (visits to the doctor) in Costa Rica using an econometric approach. Data were drawn from the National Survey of Health for Costa Rica 2006. We modeled the Grossman approach to the demand for health services by using a standard negative binomial regression, and used a hurdle model for the principal-agent specification. The factors determining healthcare utilization were level of education, self-assessed health, number of declared chronic diseases and geographic region of residence. The number of outpatient visits to the doctor depends on the proxies for medical need, but we found no multivariate association between the use of outpatient visits and income or insurance status. This result suggests that there is no problem with access in the public - almost universal - Costa Rican health system. No conclusive results were obtained on the influence of the physician on the frequency of use of health care services, as postulated by the principal-agent model. Copyright © 2010 SESPAS. Published by Elsevier Espana. All rights reserved.

Performance of the modified Poisson regression approach for estimating relative risks from clustered prospective data.

PubMed

Yelland, Lisa N; Salter, Amy B; Ryan, Philip

2011-10-15

Modified Poisson regression, which combines a log Poisson regression model with robust variance estimation, is a useful alternative to log binomial regression for estimating relative risks. Previous studies have shown both analytically and by simulation that modified Poisson regression is appropriate for independent prospective data. This method is often applied to clustered prospective data, despite a lack of evidence to support its use in this setting. The purpose of this article is to evaluate the performance of the modified Poisson regression approach for estimating relative risks from clustered prospective data, by using generalized estimating equations to account for clustering. A simulation study is conducted to compare log binomial regression and modified Poisson regression for analyzing clustered data from intervention and observational studies. Both methods generally perform well in terms of bias, type I error, and coverage. Unlike log binomial regression, modified Poisson regression is not prone to convergence problems. The methods are contrasted by using example data sets from 2 large studies. The results presented in this article support the use of modified Poisson regression as an alternative to log binomial regression for analyzing clustered prospective data when clustering is taken into account by using generalized estimating equations.

Crime Seasonality: Examining the Temporal Fluctuations of Property Crime in Cities With Varying Climates.

PubMed

Linning, Shannon J; Andresen, Martin A; Brantingham, Paul J

2017-12-01

This study investigates whether crime patterns fluctuate periodically throughout the year using data containing different property crime types in two Canadian cities with differing climates. Using police report data, a series of ordinary least squares (OLS; Vancouver, British Columbia) and negative binomial (Ottawa, Ontario) regressions were employed to examine the corresponding temporal patterns of property crime in Vancouver (2003-2013) and Ottawa (2006-2008). Moreover, both aggregate and disaggregate models were run to examine whether different weather and temporal variables had a distinctive impact on particular offences. Overall, results suggest that cities that experience greater variations in weather throughout the year have more distinct increases of property offences in the summer months and that different climate variables affect certain crime types, thus advocating for disaggregate analysis in the future.

The effect of laminar air flow and door openings on operating room contamination.

PubMed

Smith, Eric B; Raphael, Ibrahim J; Maltenfort, Mitchell G; Honsawek, Sittisak; Dolan, Kyle; Younkins, Elizabeth A

2013-10-01

We evaluate the association of laminar airflow (LAF) and OR traffic with intraoperative contamination rates. Two sterile basins were placed in each room during 81 cases, one inside and one outside the LAF. One Replicate Organism Detection and Counting (RODAC) plate from each basin was sent for culture at successive 30-minute intervals from incision time until wound closure. At successive 30-minute intervals more plates were contaminated outside than inside the LAF. A negative binomial model showed that the bacteria colony forming units (CFU) depended on whether there were any door openings (P=0.02) and the presence of LAF (P=0.003). LAF decreases CFU by 36.6%. LAF independently reduces the risk of contamination and microbial counts for surgeries lasting 90 minutes or less. © 2013.

Linking crime guns: the impact of ballistics imaging technology on the productivity of the Boston Police Department's Ballistics Unit.

PubMed

Braga, Anthony A; Pierce, Glenn L

2004-07-01

Ballistics imaging technology has received national attention as a potent tool for moving the law enforcement response to violent gun criminals forward by linking multiple crime scenes to one firearm. This study examines the impact of ballistics imaging technology on the productivity of the Boston Police Department's Ballistics Unit. Using negative binomial regression models to analyze times series data on ballistics matches, we find that ballistics imaging technology was associated with a more than sixfold increase in the monthly number of ballistics matches made by the Boston Police Department's Ballistics Unit. Cost-effectiveness estimates and qualitative evidence also suggest that ballistics imaging technology allows law enforcement agencies to make hits that would not have been possible using traditional ballistics methods.

Civic communities and urban violence.

PubMed

Doucet, Jessica M; Lee, Matthew R

2015-07-01

Civic communities have a spirit of entrepreneurialism, a locally invested population and an institutional structure fostering civic engagement. Prior research, mainly confined to studying rural communities and fairly large geographic areas, has demonstrated that civic communities have lower rates of violence. The current study analyzes the associations between the components of civic communities and homicide rates for New Orleans neighborhoods (census tracts) in the years following Hurricane Katrina. Results from negative binomial regression models adjusting for spatial autocorrelation reveal that community homicide rates are lower where an entrepreneurial business climate is more pronounced and where there is more local investment. Additionally, an interaction between the availability of civic institutions and resource disadvantage reveals that the protective effects of civic institutions are only evident in disadvantaged communities. Copyright © 2015 Elsevier Inc. All rights reserved.

Bursts of Self-Conscious Emotions in the Daily Lives of Emerging Adults.

PubMed

Conroy, David E; Ram, Nilam; Pincus, Aaron L; Rebar, Amanda L

Self-conscious emotions play a role in regulating daily achievement strivings, social behavior, and health, but little is known about the processes underlying their daily manifestation. Emerging adults (n = 182) completed daily diaries for eight days and multilevel models were estimated to evaluate whether, how much, and why their emotions varied from day-to-day. Within-person variation in authentic pride was normally-distributed across people and days whereas the other emotions were burst-like and characterized by zero-inflated, negative binomial distributions. Perceiving social interactions as generally communal increased the odds of hubristic pride activation and reduced the odds of guilt activation; daily communal behavior reduced guilt intensity. Results illuminated processes through which meaning about the self-in-relation-to-others is constructed during a critical period of development.

Infant Mortality and Income in 4 World Cities: New York, London, Paris, and Tokyo

PubMed Central

Rodwin, Victor G.; Neuberg, Leland G.

2005-01-01

Objectives. We investigated the association between average income or deprivation and infant mortality rate across neighborhoods of 4 world cities. Methods. Using a maximum likelihood negative binomial regression model that controls for births, we analyzed data for 1988–1992 and 1993–1997. Results. In Manhattan, for both periods, we found an association (.05% significance level) between income and infant mortality. In Tokyo, for both periods, and in Paris and London for period 1, we found none (5% significance level). For period 2, the association just missed statistical significance for Paris, whereas for London it was significant (5% level). Conclusions. In stark contrast to Tokyo, Paris, and London, the association of income and infant mortality rate was strongly evident in Manhattan. PMID:15623865

Willingness to pay for non angler recreation at the lower Snake River reservoirs

USGS Publications Warehouse

McKean, J.R.; Johnson, D.; Taylor, R.G.; Johnson, Richard L.

2005-01-01

This study applied the travel cost method to estimate demand for non angler recreation at the impounded Snake River in eastern Washington. Net value per person per recreation trip is estimated for the full non angler sample and separately for camping, boating, water-skiing, and swimming/picnicking. Certain recreation activities would be reduced or eliminated and new activities would be added if the dams were breached to protect endangered salmon and steelhead. The effect of breaching on non angling benefits was found by subtracting our benefits estimate from the projected non angling benefits with breaching. Major issues in demand model specification and definition of the price variables are discussed. The estimation method selected was truncated negative binomial regression with adjustment for self selection bias.

Temperature dependent structural and dynamical properties of liquid Cu80Si20 binary alloy

NASA Astrophysics Data System (ADS)

Suthar, P. H.; Shah, A. K.; Gajjar, P. N.

2018-05-01

Ashcroft and Langreth binary structure factor have been used to study for pair correlation function and the study of dynamical variable: velocity auto correlation functions, power spectrum and mean square displacement calculated based on the static harmonic well approximation in liquid Cu80Si20 binary alloy at wide temperature range (1140K, 1175K, 1210K, 1250K, 1373K, 1473K.). The effective interaction for the binary alloy is computed by our well established local pseudopotential along with the exchange and correction functions Sarkar et al(S). The negative dip in velocity auto correlation decreases as the various temperature is increases. For power spectrum as temperature increases, the peak of power spectrum shifts toward lower ω. Good agreement with the experiment is observed for the pair correlation functions. Velocity auto correlation showing the transferability of the local pseudopotential used for metallic liquid environment in the case of copper based binary alloys.

Project IN/VEST: A Guaranteed Investment

ERIC Educational Resources Information Center

Geier, Charlene

1977-01-01

Describes a simulated auto insurance company at Greenfield High School (Greenfield, Wisconsin), a comprehensive model designed for business students but involving other high school classes such as distributive education, home economics, and auto mechanics. The model is noted to not only train students for an opportunity field but provide them with…

Sequential sampling of ribes populations in the control of white pine blister rust (Cronartium ribicola Fischer) in California

Treesearch

Harold R. Offord

1966-01-01

Sequential sampling based on a negative binomial distribution of ribes populations required less than half the time taken by regular systematic line transect sampling in a comparison test. It gave the same control decision as the regular method in 9 of 13 field trials. A computer program that permits sequential plans to be built readily for other white pine regions is...

Design and analysis of three-arm trials with negative binomially distributed endpoints.

PubMed

Mütze, Tobias; Munk, Axel; Friede, Tim

2016-02-20

A three-arm clinical trial design with an experimental treatment, an active control, and a placebo control, commonly referred to as the gold standard design, enables testing of non-inferiority or superiority of the experimental treatment compared with the active control. In this paper, we propose methods for designing and analyzing three-arm trials with negative binomially distributed endpoints. In particular, we develop a Wald-type test with a restricted maximum-likelihood variance estimator for testing non-inferiority or superiority. For this test, sample size and power formulas as well as optimal sample size allocations will be derived. The performance of the proposed test will be assessed in an extensive simulation study with regard to type I error rate, power, sample size, and sample size allocation. For the purpose of comparison, Wald-type statistics with a sample variance estimator and an unrestricted maximum-likelihood estimator are included in the simulation study. We found that the proposed Wald-type test with a restricted variance estimator performed well across the considered scenarios and is therefore recommended for application in clinical trials. The methods proposed are motivated and illustrated by a recent clinical trial in multiple sclerosis. The R package ThreeArmedTrials, which implements the methods discussed in this paper, is available on CRAN. Copyright © 2015 John Wiley & Sons, Ltd.

Numerical simulation and validation of SI-CAI hybrid combustion in a CAI/HCCI gasoline engine

NASA Astrophysics Data System (ADS)

Wang, Xinyan; Xie, Hui; Xie, Liyan; Zhang, Lianfang; Li, Le; Chen, Tao; Zhao, Hua

2013-02-01

SI-CAI hybrid combustion, also known as spark-assisted compression ignition (SACI), is a promising concept to extend the operating range of CAI (Controlled Auto-Ignition) and achieve the smooth transition between spark ignition (SI) and CAI in the gasoline engine. In this study, a SI-CAI hybrid combustion model (HCM) has been constructed on the basis of the 3-Zones Extended Coherent Flame Model (ECFM3Z). An ignition model is included to initiate the ECFM3Z calculation and induce the flame propagation. In order to precisely depict the subsequent auto-ignition process of the unburned fuel and air mixture independently after the initiation of flame propagation, the tabulated chemistry concept is adopted to describe the auto-ignition chemistry. The methodology for extracting tabulated parameters from the chemical kinetics calculations is developed so that both cool flame reactions and main auto-ignition combustion can be well captured under a wider range of thermodynamic conditions. The SI-CAI hybrid combustion model (HCM) is then applied in the three-dimensional computational fluid dynamics (3-D CFD) engine simulation. The simulation results are compared with the experimental data obtained from a single cylinder VVA engine. The detailed analysis of the simulations demonstrates that the SI-CAI hybrid combustion process is characterised with the early flame propagation and subsequent multi-site auto-ignition around the main flame front, which is consistent with the optical results reported by other researchers. Besides, the systematic study of the in-cylinder condition reveals the influence mechanism of the early flame propagation on the subsequent auto-ignition.

How does the emotive content of televised anti-smoking mass media campaigns influence monthly calls to the NHS Stop Smoking helpline in England?

PubMed

Richardson, Sol; Langley, Tessa; Szatkowski, Lisa; Sims, Michelle; Gilmore, Anna; McNeill, Ann; Lewis, Sarah

2014-12-01

To investigate the effects of different types of televised mass media campaign content on calls to the English NHS Stop Smoking helpline. We used UK government-funded televised tobacco control campaigns from April 2005 to April 2010, categorised as either "positive" (eliciting happiness, satisfaction or hope) or "negative" (eliciting fear, guilt or disgust). We built negative binomial generalised additive models (GAMs) with linear and smooth terms for monthly per capita exposure to each campaign type (expressed as Gross Ratings Points, or GRPs) to determine their effect on calls in the same month. We adjusted for seasonal trends, inflation-adjusted weighted average cigarette prices and other tobacco control policies. We found non-linear associations between exposure to positive and negative emotive campaigns and quitline calls. The rate of calls increased more than 50% as exposure to positive campaigns increased from 0 to 400 GRPs (rate ratio: 1.58, 95% CI: 1.25-2.01). An increase in calls in response to negative emotive campaigns was only apparent after monthly exposure exceeded 400 GRPs. While positive campaigns were most effective at increasing quitline calls, those with negative emotive content were also found to impact on call rates but only at higher levels of exposure. Copyright © 2014. Published by Elsevier Inc.

Automatic Generation of Algorithms for the Statistical Analysis of Planetary Nebulae Images

NASA Technical Reports Server (NTRS)

Fischer, Bernd

2004-01-01

Analyzing data sets collected in experiments or by observations is a Core scientific activity. Typically, experimentd and observational data are &aught with uncertainty, and the analysis is based on a statistical model of the conjectured underlying processes, The large data volumes collected by modern instruments make computer support indispensible for this. Consequently, scientists spend significant amounts of their time with the development and refinement of the data analysis programs. AutoBayes [GF+02, FS03] is a fully automatic synthesis system for generating statistical data analysis programs. Externally, it looks like a compiler: it takes an abstract problem specification and translates it into executable code. Its input is a concise description of a data analysis problem in the form of a statistical model as shown in Figure 1; its output is optimized and fully documented C/C++ code which can be linked dynamically into the Matlab and Octave environments. Internally, however, it is quite different: AutoBayes derives a customized algorithm implementing the given model using a schema-based process, and then further refines and optimizes the algorithm into code. A schema is a parameterized code template with associated semantic constraints which define and restrict the template s applicability. The schema parameters are instantiated in a problem-specific way during synthesis as AutoBayes checks the constraints against the original model or, recursively, against emerging sub-problems. AutoBayes schema library contains problem decomposition operators (which are justified by theorems in a formal logic in the domain of Bayesian networks) as well as machine learning algorithms (e.g., EM, k-Means) and nu- meric optimization methods (e.g., Nelder-Mead simplex, conjugate gradient). AutoBayes augments this schema-based approach by symbolic computation to derive closed-form solutions whenever possible. This is a major advantage over other statistical data analysis systems which use numerical approximations even in cases where closed-form solutions exist. AutoBayes is implemented in Prolog and comprises approximately 75.000 lines of code. In this paper, we take one typical scientific data analysis problem-analyzing planetary nebulae images taken by the Hubble Space Telescope-and show how AutoBayes can be used to automate the implementation of the necessary anal- ysis programs. We initially follow the analysis described by Knuth and Hajian [KHO2] and use AutoBayes to derive code for the published models. We show the details of the code derivation process, including the symbolic computations and automatic integration of library procedures, and compare the results of the automatically generated and manually implemented code. We then go beyond the original analysis and use AutoBayes to derive code for a simple image segmentation procedure based on a mixture model which can be used to automate a manual preproceesing step. Finally, we combine the original approach with the simple segmentation which yields a more detailed analysis. This also demonstrates that AutoBayes makes it easy to combine different aspects of data analysis.

Computing Mass Properties From AutoCAD

NASA Technical Reports Server (NTRS)

Jones, A.

1990-01-01

Mass properties of structures computed from data in drawings. AutoCAD to Mass Properties (ACTOMP) computer program developed to facilitate quick calculations of mass properties of structures containing many simple elements in such complex configurations as trusses or sheet-metal containers. Mathematically modeled in AutoCAD or compatible computer-aided design (CAD) system in minutes by use of three-dimensional elements. Written in Microsoft Quick-Basic (Version 2.0).

Modeling the effects of AADT on predicting multiple-vehicle crashes at urban and suburban signalized intersections.

PubMed

Chen, Chen; Xie, Yuanchang

2016-06-01

Annual Average Daily Traffic (AADT) is often considered as a main covariate for predicting crash frequencies at urban and suburban intersections. A linear functional form is typically assumed for the Safety Performance Function (SPF) to describe the relationship between the natural logarithm of expected crash frequency and covariates derived from AADTs. Such a linearity assumption has been questioned by many researchers. This study applies Generalized Additive Models (GAMs) and Piecewise Linear Negative Binomial (PLNB) regression models to fit intersection crash data. Various covariates derived from minor-and major-approach AADTs are considered. Three different dependent variables are modeled, which are total multiple-vehicle crashes, rear-end crashes, and angle crashes. The modeling results suggest that a nonlinear functional form may be more appropriate. Also, the results show that it is important to take into consideration the joint safety effects of multiple covariates. Additionally, it is found that the ratio of minor to major-approach AADT has a varying impact on intersection safety and deserves further investigations. Copyright © 2016 Elsevier Ltd. All rights reserved.

Modelling infant mortality rate in Central Java, Indonesia use generalized poisson regression method

NASA Astrophysics Data System (ADS)

Prahutama, Alan; Sudarno

2018-05-01

The infant mortality rate is the number of deaths under one year of age occurring among the live births in a given geographical area during a given year, per 1,000 live births occurring among the population of the given geographical area during the same year. This problem needs to be addressed because it is an important element of a country’s economic development. High infant mortality rate will disrupt the stability of a country as it relates to the sustainability of the population in the country. One of regression model that can be used to analyze the relationship between dependent variable Y in the form of discrete data and independent variable X is Poisson regression model. Recently The regression modeling used for data with dependent variable is discrete, among others, poisson regression, negative binomial regression and generalized poisson regression. In this research, generalized poisson regression modeling gives better AIC value than poisson regression. The most significant variable is the Number of health facilities (X1), while the variable that gives the most influence to infant mortality rate is the average breastfeeding (X9).

A Joint Model for the Kinetics of CTC Count and PSA Concentration During Treatment in Metastatic Castration-Resistant Prostate Cancer*

PubMed Central

Wilbaux, M; Tod, M; De Bono, J; Lorente, D; Mateo, J; Freyer, G; You, B; Hénin, E

2015-01-01

Assessment of treatment efficacy in metastatic castration-resistant prostate cancer (mCRPC) is limited by frequent nonmeasurable bone metastases. The count of circulating tumor cells (CTCs) is a promising surrogate marker that may replace the widely used prostate-specific antigen (PSA). The purpose of this study was to quantify the dynamic relationships between the longitudinal kinetics of these markers during treatment in patients with mCRPC. Data from 223 patients with mCRPC treated by chemotherapy and/or hormonotherapy were analyzed for up to 6 months of treatment. A semimechanistic model was built, combining the following several pharmacometric advanced features: (1) Kinetic-Pharmacodynamic (K-PD) compartments for treatments (chemotherapy and hormonotherapy); (2) a latent variable linking both marker kinetics; (3) modeling of CTC kinetics with a cell lifespan model; and (4) a negative binomial distribution for the CTC random sampling. Linked with survival, this model would potentially be useful for predicting treatment efficacy during drug development or for therapeutic adjustment in treated patients. PMID:26225253

Crash data modeling with a generalized estimator.

PubMed

Ye, Zhirui; Xu, Yueru; Lord, Dominique

2018-08-01

The investigation of relationships between traffic crashes and relevant factors is important in traffic safety management. Various methods have been developed for modeling crash data. In real world scenarios, crash data often display the characteristics of over-dispersion. However, on occasions, some crash datasets have exhibited under-dispersion, especially in cases where the data are conditioned upon the mean. The commonly used models (such as the Poisson and the NB regression models) have associated limitations to cope with various degrees of dispersion. In light of this, a generalized event count (GEC) model, which can be generally used to handle over-, equi-, and under-dispersed data, is proposed in this study. This model was first applied to case studies using data from Toronto, characterized by over-dispersion, and then to crash data from railway-highway crossings in Korea, characterized with under-dispersion. The results from the GEC model were compared with those from the Negative binomial and the hyper-Poisson models. The cases studies show that the proposed model provides good performance for crash data characterized with over- and under-dispersion. Moreover, the proposed model simplifies the modeling process and the prediction of crash data. Copyright © 2018 Elsevier Ltd. All rights reserved.

Clinical Impact of Pretransplant Multidrug-Resistant Gram-Negative Colonization in Autologous and Allogeneic Hematopoietic Stem Cell Transplantation.

PubMed

Forcina, Alessandra; Lorentino, Francesca; Marasco, Vincenzo; Oltolini, Chiara; Marcatti, Magda; Greco, Raffaella; Lupo-Stanghellini, Maria Teresa; Carrabba, Matteo; Bernardi, Massimo; Peccatori, Jacopo; Corti, Consuelo; Ciceri, Fabio

2018-03-02

Multidrug-resistant Gram-negative bacteria (MDR-GNB) are an emerging cause of morbidity and mortality after hematopoietic stem cell transplantation (HSCT). Three-hundred forty-eight consecutive patients transplanted at our hospital from July 2012 to January 2016 were screened for a pretransplant MDR-GNB colonization and evaluated for clinical outcomes. A pretransplant MDR-GNB colonization was found in 16.9% of allo-HSCT and in 9.6% of auto-HSCT recipients. Both in auto- and in allo-HSCT, carriers of a MDR-GNB showed no significant differences in overall survival (OS), transplant-related mortality (TRM), or infection-related mortality (IRM) compared with noncarriers. OS at 2 years for carriers compared with noncarriers was 85% versus 81% (P = .262) in auto-HSCT and 50% versus 43% (P = .091) in allo-HSCT. TRM at 2 years was 14% versus 5% (P = .405) in auto-HSCT and 31% versus 25% (P = .301) in allo-HSCT. IRM at 2 years was 14% versus 2% (P = .142) in auto-HSCT and 23% versus 14% (P = .304) in allo-HSCT. In multivariate analysis, only grade III to IV acute graft-versus-host disease was an independent factor for reduced OS (P < .001) and increased TRM (P < .001) and IRM (P < .001). During the first year after transplant, we collected 73 GNB bloodstream infectious (BSI) episodes in 54 patients, 42.4% of which sustained by a MDR-GNB. Rectal swabs positivity associated with the pathogen causing subsequent MDR-GNB BSI episodes in 13 of 31 (41.9%). Overall, OS at 4 months from MDR-GNB BSI episode onset was of 67.9%, with a 14-day attributed mortality of 12.9%, not being significantly different between carriers and noncarriers (P = .207). We conclude that in this extended single-center experience, a pretransplant MDR-GNB colonization did not significantly influence OS, TRM, and IRM both in auto- and allo-HSCT settings and that MDR-GNB attributed mortality can be controlled in carriers when an early pre-emptive antimicrobial therapy is started in case of neutropenic fever. Copyright © 2018 The American Society for Blood and Marrow Transplantation. Published by Elsevier Inc. All rights reserved.

A voxel visualization and analysis system based on AutoCAD

NASA Astrophysics Data System (ADS)

Marschallinger, Robert

1996-05-01

A collection of AutoLISP programs is presented which enable the visualization and analysis of voxel models by AutoCAD rel. 12/rel. 13. The programs serve as an interactive, graphical front end for manipulating the results of three-dimensional modeling software producing block estimation data. ASCII data files describing geometry and attributes per estimation block are imported and stored as a voxel array. Each voxel may contain multiple attributes, therefore different parameters may be incorporated in one voxel array. Voxel classification is implemented on a layer basis providing flexible treatment of voxel classes such as recoloring, peeling, or volumetry. A versatile clipping tool enables slicing voxel arrays according to combinations of three perpendicular clipping planes. The programs feature an up-to-date, graphical user interface for user-friendly operation by non AutoCAD specialists.

Chemotactic droplet swimmers in complex geometries

NASA Astrophysics Data System (ADS)

Jin, Chenyu; Hokmabad, Babak V.; Baldwin, Kyle A.; Maass, Corinna C.

2018-02-01

Chemotaxis1 and auto-chemotaxis are key mechanisms in the dynamics of micro-organisms, e.g. in the acquisition of nutrients and in the communication between individuals, influencing the collective behaviour. However, chemical signalling and the natural environment of biological swimmers are generally complex, making them hard to access analytically. We present a well-controlled, tunable artificial model to study chemotaxis and autochemotaxis in complex geometries, using microfluidic assays of self-propelling oil droplets in an aqueous surfactant solution (Herminghaus et al 2014 Soft Matter 10 7008-22 Krüger et al 2016 Phys. Rev. Lett. 117). Droplets propel via interfacial Marangoni stresses powered by micellar solubilisation. Moreover, filled micelles act as a chemical repellent by diffusive phoretic gradient forces. We have studied these chemotactic effects in a series of microfluidic geometries, as published in Jin et al (2017 Proc. Natl Acad. Sci. 114 5089-94): first, droplets are guided along the shortest path through a maze by surfactant diffusing into the maze from the exit. Second, we let auto-chemotactic droplet swimmers pass through bifurcating microfluidic channels and record anticorrelations between the branch choices of consecutive droplets. We present an analytical Langevin model matching the experimental data. In a previously unpublished experiment, pillar arrays of variable sizes and shapes provide a convex wall interacting with the swimmer and, in the case of attachment, bending its trajectory and forcing it to revert to its own trail. We observe different behaviours based on the interplay of wall curvature and negative autochemotaxis, i.e. no attachment for highly curved interfaces, stable trapping at large pillars, and a narrow transition region where negative autochemotaxis makes the swimmers detach after a single orbit.

An asymptotic theory for cross-correlation between auto-correlated sequences and its application on neuroimaging data.

PubMed

Zhou, Yunyi; Tao, Chenyang; Lu, Wenlian; Feng, Jianfeng

2018-04-20

Functional connectivity is among the most important tools to study brain. The correlation coefficient, between time series of different brain areas, is the most popular method to quantify functional connectivity. Correlation coefficient in practical use assumes the data to be temporally independent. However, the time series data of brain can manifest significant temporal auto-correlation. A widely applicable method is proposed for correcting temporal auto-correlation. We considered two types of time series models: (1) auto-regressive-moving-average model, (2) nonlinear dynamical system model with noisy fluctuations, and derived their respective asymptotic distributions of correlation coefficient. These two types of models are most commonly used in neuroscience studies. We show the respective asymptotic distributions share a unified expression. We have verified the validity of our method, and shown our method exhibited sufficient statistical power for detecting true correlation on numerical experiments. Employing our method on real dataset yields more robust functional network and higher classification accuracy than conventional methods. Our method robustly controls the type I error while maintaining sufficient statistical power for detecting true correlation in numerical experiments, where existing methods measuring association (linear and nonlinear) fail. In this work, we proposed a widely applicable approach for correcting the effect of temporal auto-correlation on functional connectivity. Empirical results favor the use of our method in functional network analysis. Copyright © 2018. Published by Elsevier B.V.

Maternal and Child Supplementation with Lipid-Based Nutrient Supplements, but Not Child Supplementation Alone, Decreases Self-Reported Household Food Insecurity in Some Settings

PubMed Central

Ayifah, Emmanuel; Arimond, Mary; Arnold, Charles D; Cummins, Joseph; Matias, Susana L; Ashorn, Ulla; Lartey, Anna; Maleta, Kenneth M; Vosti, Stephen A; Dewey, Kathryn G

2017-01-01

Background: It is unknown whether self-reported measures of household food insecurity change in response to food-based nutrient supplementation. Objective: We assessed the impacts of providing lipid-based nutrient supplements (LNSs) to women during pregnancy and postpartum and/or to their children on self-reported household food insecurity in Malawi [DOSE and DYAD trial in Malawi (DYAD-M)], Ghana [DYAD trial in Ghana (DYAD-G)], and Bangladesh [Rang-Din Nutrition Study (RDNS) trial]. Methods: Longitudinal household food-insecurity data were collected during 3 individually randomized trials and 1 cluster-randomized trial testing the efficacy or effectiveness of LNSs (generally 118 kcal/d). Seasonally adjusted Household Food Insecurity Access Scale (HFIAS) scores were constructed for 1127 DOSE households, 732 DYAD-M households, 1109 DYAD-G households, and 3671 RDNS households. The impact of providing LNSs to women during pregnancy and the first 6 mo postpartum and/or to their children from 6 to 18–24 mo on seasonally adjusted HFIAS scores was assessed by using negative binomial models (DOSE, DYAD-M, and DYAD-G trials) and mixed-effect negative binomial models (RDNS trial). Results: In the DOSE and DYAD-G trials, seasonally adjusted HFIAS scores were not different between the LNS and non-LNS groups. In the DYAD-M trial, the average household food-insecurity scores were 14% lower (P = 0.01) in LNS households than in non-LNS households. In the RDNS trial, compared with non-LNS households, food-insecurity scores were 17% lower (P = 0.02) during pregnancy and the first 6 mo postpartum and 15% lower (P = 0.02) at 6–24 mo postpartum in LNS households. Conclusions: The daily provision of LNSs to mothers and their children throughout much of the “first 1000 d” may improve household food security in some settings, which could be viewed as an additional benefit that may accrue in households should policy makers choose to invest in LNSs to promote child growth and development. These trials were registered at clinicaltrials.gov as NCT00945698 (DOSE) NCT01239693 (DYAD-M), NCT00970866 (DYAD-G) and NCT01715038 (RDNS). PMID:28978680

Maternal and Child Supplementation with Lipid-Based Nutrient Supplements, but Not Child Supplementation Alone, Decreases Self-Reported Household Food Insecurity in Some Settings.

PubMed

Adams, Katherine P; Ayifah, Emmanuel; Phiri, Thokozani E; Mridha, Malay K; Adu-Afarwuah, Seth; Arimond, Mary; Arnold, Charles D; Cummins, Joseph; Hussain, Sohrab; Kumwenda, Chiza; Matias, Susana L; Ashorn, Ulla; Lartey, Anna; Maleta, Kenneth M; Vosti, Stephen A; Dewey, Kathryn G

2017-12-01

Background: It is unknown whether self-reported measures of household food insecurity change in response to food-based nutrient supplementation. Objective: We assessed the impacts of providing lipid-based nutrient supplements (LNSs) to women during pregnancy and postpartum and/or to their children on self-reported household food insecurity in Malawi [DOSE and DYAD trial in Malawi (DYAD-M)], Ghana [DYAD trial in Ghana (DYAD-G)], and Bangladesh [Rang-Din Nutrition Study (RDNS) trial]. Methods: Longitudinal household food-insecurity data were collected during 3 individually randomized trials and 1 cluster-randomized trial testing the efficacy or effectiveness of LNSs (generally 118 kcal/d). Seasonally adjusted Household Food Insecurity Access Scale (HFIAS) scores were constructed for 1127 DOSE households, 732 DYAD-M households, 1109 DYAD-G households, and 3671 RDNS households. The impact of providing LNSs to women during pregnancy and the first 6 mo postpartum and/or to their children from 6 to 18-24 mo on seasonally adjusted HFIAS scores was assessed by using negative binomial models (DOSE, DYAD-M, and DYAD-G trials) and mixed-effect negative binomial models (RDNS trial). Results: In the DOSE and DYAD-G trials, seasonally adjusted HFIAS scores were not different between the LNS and non-LNS groups. In the DYAD-M trial, the average household food-insecurity scores were 14% lower ( P = 0.01) in LNS households than in non-LNS households. In the RDNS trial, compared with non-LNS households, food-insecurity scores were 17% lower ( P = 0.02) during pregnancy and the first 6 mo postpartum and 15% lower ( P = 0.02) at 6-24 mo postpartum in LNS households. Conclusions: The daily provision of LNSs to mothers and their children throughout much of the "first 1000 d" may improve household food security in some settings, which could be viewed as an additional benefit that may accrue in households should policy makers choose to invest in LNSs to promote child growth and development. These trials were registered at clinicaltrials.gov as NCT00945698 (DOSE) NCT01239693 (DYAD-M), NCT00970866 (DYAD-G) and NCT01715038 (RDNS).

Alcohol expectancies and alcohol outcomes: effects of the use of protective behavioral strategies.

PubMed

Grazioli, Véronique S; Lewis, Melissa A; Garberson, Lisa A; Fossos-Wong, Nicole; Lee, Christine M; Larimer, Mary E

2015-05-01

Alcohol expectancies (AEs) are positively associated with drinking behaviors, whereas the use of protective behavioural strategies (PBS) is negatively related to alcohol outcomes among young adults. PBS have been shown to weaken relationships between some alcohol risk factors and alcohol outcomes. This study aimed to examine longitudinally the moderating effect of PBS on the relationships between AEs and alcohol outcomes among young adults. Participants (N = 188; 61.7% female) were U.S. young adults participating in a larger longitudinal study. Measures of PBS, AEs, alcohol use, and related consequences were used from the baseline and 12-month follow-up assessments. Negative binomial hurdle models found that PBS (total score) significantly moderated the relationship between positive AEs and consequences, such that among high school seniors endorsing higher positive AEs, those using more PBS in high school reported fewer negative consequences 1 year later. PBS (Manner of Drinking) also moderated the relationship between negative AEs and alcohol use, revealing the use of PBS in high school as having a protective function against later drinking among participants with high positive AEs. Last, PBS (Serious Harm Reduction) significantly moderated the associations between positive AEs and alcohol use and between negative AEs and consequences, such that participants with higher AEs and higher PBS use in high school were at greatest risk for drinking and experiencing negative consequences later. Overall, these findings suggest that PBS use may be protective by weakening relationships between positive AEs and alcohol outcomes. Limitations and future directions are discussed.

The Auto Industry's New Model. Car Companies and Community Colleges Collaborate to Provide High-Technology Training.

ERIC Educational Resources Information Center

Cantor, Jeffrey A.

1991-01-01

Major auto manufacturers have developed cooperative apprenticeship programs with community colleges, offering alternating periods of study and work experience under the supervision of master technicians. (SK)

Modelling road accident blackspots data with the discrete generalized Pareto distribution.

PubMed

Prieto, Faustino; Gómez-Déniz, Emilio; Sarabia, José María

2014-10-01

This study shows how road traffic networks events, in particular road accidents on blackspots, can be modelled with simple probabilistic distributions. We considered the number of crashes and the number of fatalities on Spanish blackspots in the period 2003-2007, from Spanish General Directorate of Traffic (DGT). We modelled those datasets, respectively, with the discrete generalized Pareto distribution (a discrete parametric model with three parameters) and with the discrete Lomax distribution (a discrete parametric model with two parameters, and particular case of the previous model). For that, we analyzed the basic properties of both parametric models: cumulative distribution, survival, probability mass, quantile and hazard functions, genesis and rth-order moments; applied two estimation methods of their parameters: the μ and (μ+1) frequency method and the maximum likelihood method; used two goodness-of-fit tests: Chi-square test and discrete Kolmogorov-Smirnov test based on bootstrap resampling; and compared them with the classical negative binomial distribution in terms of absolute probabilities and in models including covariates. We found that those probabilistic models can be useful to describe the road accident blackspots datasets analyzed. Copyright © 2014 Elsevier Ltd. All rights reserved.

A Note on a Family of Alternating Sums of Products of Binomial Numbers

ERIC Educational Resources Information Center

Gauthier, N.

2013-01-01

We study the following family of integral-valued alternating sums, where -infinity equal to or less than m equal to or less than infinity and n equal to or greater than 0 are integers [equation omitted]. We first consider h[subscript m](n) for m and n non-negative integers and show that it is of the form 2[superscript n + 2m] - P[subscript m](n),…

Time-dependent summary receiver operating characteristics for meta-analysis of prognostic studies.

PubMed

Hattori, Satoshi; Zhou, Xiao-Hua

2016-11-20

Prognostic studies are widely conducted to examine whether biomarkers are associated with patient's prognoses and play important roles in medical decisions. Because findings from one prognostic study may be very limited, meta-analyses may be useful to obtain sound evidence. However, prognostic studies are often analyzed by relying on a study-specific cut-off value, which can lead to difficulty in applying the standard meta-analysis techniques. In this paper, we propose two methods to estimate a time-dependent version of the summary receiver operating characteristics curve for meta-analyses of prognostic studies with a right-censored time-to-event outcome. We introduce a bivariate normal model for the pair of time-dependent sensitivity and specificity and propose a method to form inferences based on summary statistics reported in published papers. This method provides a valid inference asymptotically. In addition, we consider a bivariate binomial model. To draw inferences from this bivariate binomial model, we introduce a multiple imputation method. The multiple imputation is found to be approximately proper multiple imputation, and thus the standard Rubin's variance formula is justified from a Bayesian view point. Our simulation study and application to a real dataset revealed that both methods work well with a moderate or large number of studies and the bivariate binomial model coupled with the multiple imputation outperforms the bivariate normal model with a small number of studies. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Unintentional Epinephrine Auto-injector Injuries: A National Poison Center Observational Study.

PubMed

Anshien, Marco; Rose, S Rutherfoord; Wills, Brandon K

2016-11-24

Epinephrine is the only first-line therapeutic agent used to treat life-threatening anaphylaxis. Epinephrine auto-injectors are commonly carried by patients at risk for anaphylaxis, and reported cases of unintentional auto-injector injury have increased over the last decade. Modifications of existing designs and release of a new style of auto-injector are intended to reduce epinephrine auto-injector misuse. The aim of the study was to characterize reported cases of unintentional epinephrine auto-injector exposures from 2013 to 2014 and compare demographics, auto-injector model, and anatomical site of such exposures. The American Association of Poison Control Center's National Poison Data System was searched from January 1, 2013, to December 31, 2014, for cases of unintentional epinephrine auto-injector exposures. Anatomical site data were obtained from all cases reported to the Virginia Poison Center and participating regional poison center for Auvi-Q cases. A total of 6806 cases of unintentional epinephrine auto-injector exposures were reported to US Poison Centers in 2013 and 2014. Of these cases, 3933 occurred with EpiPen, 2829 with EpiPen Jr, 44 with Auvi-Q, and no case reported of Adrenaclick. The most common site of unintentional injection for traditional epinephrine auto-injectors was the digit or thumb, with 58% of cases for EpiPen and 39% of cases with EpiPen Jr. With Auvi-Q, the most common site was the leg (78% of cases). The number of unintentional epinephrine auto-injector cases reported to American Poison Centers in 2013-2014 has increased compared with previous data. Most EpiPen exposures were in the digits, whereas Auvi-Q was most frequently in the leg. Because of the limitations of Poison Center data, more research is needed to identify incidence of unintentional exposures and the effectiveness of epinephrine auto-injector redesign.

A framework for feature extraction from hospital medical data with applications in risk prediction.

PubMed

Tran, Truyen; Luo, Wei; Phung, Dinh; Gupta, Sunil; Rana, Santu; Kennedy, Richard Lee; Larkins, Ann; Venkatesh, Svetha

2014-12-30

Feature engineering is a time consuming component of predictive modeling. We propose a versatile platform to automatically extract features for risk prediction, based on a pre-defined and extensible entity schema. The extraction is independent of disease type or risk prediction task. We contrast auto-extracted features to baselines generated from the Elixhauser comorbidities. Hospital medical records was transformed to event sequences, to which filters were applied to extract feature sets capturing diversity in temporal scales and data types. The features were evaluated on a readmission prediction task, comparing with baseline feature sets generated from the Elixhauser comorbidities. The prediction model was through logistic regression with elastic net regularization. Predictions horizons of 1, 2, 3, 6, 12 months were considered for four diverse diseases: diabetes, COPD, mental disorders and pneumonia, with derivation and validation cohorts defined on non-overlapping data-collection periods. For unplanned readmissions, auto-extracted feature set using socio-demographic information and medical records, outperformed baselines derived from the socio-demographic information and Elixhauser comorbidities, over 20 settings (5 prediction horizons over 4 diseases). In particular over 30-day prediction, the AUCs are: COPD-baseline: 0.60 (95% CI: 0.57, 0.63), auto-extracted: 0.67 (0.64, 0.70); diabetes-baseline: 0.60 (0.58, 0.63), auto-extracted: 0.67 (0.64, 0.69); mental disorders-baseline: 0.57 (0.54, 0.60), auto-extracted: 0.69 (0.64,0.70); pneumonia-baseline: 0.61 (0.59, 0.63), auto-extracted: 0.70 (0.67, 0.72). The advantages of auto-extracted standard features from complex medical records, in a disease and task agnostic manner were demonstrated. Auto-extracted features have good predictive power over multiple time horizons. Such feature sets have potential to form the foundation of complex automated analytic tasks.

Analysis on the workspace of palletizing robot based on AutoCAD

NASA Astrophysics Data System (ADS)

Li, Jin-quan; Zhang, Rui; Guan, Qi; Cui, Fang; Chen, Kuan

2017-10-01

In this paper, a four-degree-of-freedom articulated palletizing robot is used as the object of research. Based on the analysis of the overall configuration of the robot, the kinematic mathematical model is established by D-H method to figure out the workspace of the robot. In order to meet the needs of design and analysis, using AutoCAD secondary development technology and AutoLisp language to develop AutoCAD-based 2D and 3D workspace simulation interface program of palletizing robot. At last, using AutoCAD plugin, the influence of structural parameters on the shape and position of the working space is analyzed when the structure parameters of the robot are changed separately. This study laid the foundation for the design, control and planning of palletizing robots.

Auto Body. Instructional System Development Model for Vermont Area Vocational Centers.

ERIC Educational Resources Information Center

1975

The model curriculum guide was developed to teach auto body repair in secondary schools in Vermont. From a needs assessment of the occupational opportunities in automotive services in the state, a group of selected occupations were analyzed for skill content and translated into the curriculum content. The guide consists of 14 units, each with a…

Anomaly detection for medical images based on a one-class classification

NASA Astrophysics Data System (ADS)

Wei, Qi; Ren, Yinhao; Hou, Rui; Shi, Bibo; Lo, Joseph Y.; Carin, Lawrence

2018-02-01

Detecting an anomaly such as a malignant tumor or a nodule from medical images including mammogram, CT or PET images is still an ongoing research problem drawing a lot of attention with applications in medical diagnosis. A conventional way to address this is to learn a discriminative model using training datasets of negative and positive samples. The learned model can be used to classify a testing sample into a positive or negative class. However, in medical applications, the high unbalance between negative and positive samples poses a difficulty for learning algorithms, as they will be biased towards the majority group, i.e., the negative one. To address this imbalanced data issue as well as leverage the huge amount of negative samples, i.e., normal medical images, we propose to learn an unsupervised model to characterize the negative class. To make the learned model more flexible and extendable for medical images of different scales, we have designed an autoencoder based on a deep neural network to characterize the negative patches decomposed from large medical images. A testing image is decomposed into patches and then fed into the learned autoencoder to reconstruct these patches themselves. The reconstruction error of one patch is used to classify this patch into a binary class, i.e., a positive or a negative one, leading to a one-class classifier. The positive patches highlight the suspicious areas containing anomalies in a large medical image. The proposed method has been tested on InBreast dataset and achieves an AUC of 0.84. The main contribution of our work can be summarized as follows. 1) The proposed one-class learning requires only data from one class, i.e., the negative data; 2) The patch-based learning makes the proposed method scalable to images of different sizes and helps avoid the large scale problem for medical images; 3) The training of the proposed deep convolutional neural network (DCNN) based auto-encoder is fast and stable.

Bayesian analysis of volcanic eruptions

NASA Astrophysics Data System (ADS)

Ho, Chih-Hsiang

1990-10-01

The simple Poisson model generally gives a good fit to many volcanoes for volcanic eruption forecasting. Nonetheless, empirical evidence suggests that volcanic activity in successive equal time-periods tends to be more variable than a simple Poisson with constant eruptive rate. An alternative model is therefore examined in which eruptive rate(λ) for a given volcano or cluster(s) of volcanoes is described by a gamma distribution (prior) rather than treated as a constant value as in the assumptions of a simple Poisson model. Bayesian analysis is performed to link two distributions together to give the aggregate behavior of the volcanic activity. When the Poisson process is expanded to accomodate a gamma mixing distribution on λ, a consequence of this mixed (or compound) Poisson model is that the frequency distribution of eruptions in any given time-period of equal length follows the negative binomial distribution (NBD). Applications of the proposed model and comparisons between the generalized model and simple Poisson model are discussed based on the historical eruptive count data of volcanoes Mauna Loa (Hawaii) and Etna (Italy). Several relevant facts lead to the conclusion that the generalized model is preferable for practical use both in space and time.

Violent video games and delinquent behavior in adolescents: A risk factor perspective.

PubMed

Exelmans, Liese; Custers, Kathleen; Van den Bulck, Jan

2015-05-01

Over the years, criminological research has identified a number of risk factors that contribute to the development of aggressive and delinquent behavior. Although studies have identified media violence in general and violent video gaming in particular as significant predictors of aggressive behavior, exposure to violent video games has been largely omitted from the risk factor literature on delinquent behavior. This cross-sectional study therefore investigates the relationship between violent video game play and adolescents' delinquent behavior using a risk factor approach. An online survey was completed by 3,372 Flemish adolescents, aged 12-18 years old. Data were analyzed by means of negative binomial regression modelling. Results indicated a significant contribution of violent video games in delinquent behavior over and beyond multiple known risk variables (peer delinquency, sensation seeking, prior victimization, and alienation). Moreover, the final model that incorporated the gaming genres proved to be significantly better than the model without the gaming genres. Results provided support for a cumulative and multiplicative risk model for delinquent behavior. Aggr. Behav. 41:267-279, 2015. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Computational Aspects of N-Mixture Models

PubMed Central

Dennis, Emily B; Morgan, Byron JT; Ridout, Martin S

2015-01-01

The N-mixture model is widely used to estimate the abundance of a population in the presence of unknown detection probability from only a set of counts subject to spatial and temporal replication (Royle, 2004, Biometrics 60, 105–115). We explain and exploit the equivalence of N-mixture and multivariate Poisson and negative-binomial models, which provides powerful new approaches for fitting these models. We show that particularly when detection probability and the number of sampling occasions are small, infinite estimates of abundance can arise. We propose a sample covariance as a diagnostic for this event, and demonstrate its good performance in the Poisson case. Infinite estimates may be missed in practice, due to numerical optimization procedures terminating at arbitrarily large values. It is shown that the use of a bound, K, for an infinite summation in the N-mixture likelihood can result in underestimation of abundance, so that default values of K in computer packages should be avoided. Instead we propose a simple automatic way to choose K. The methods are illustrated by analysis of data on Hermann's tortoise Testudo hermanni. PMID:25314629

Comparative assessment of analytical approaches to quantify the risk for introduction of rare animal diseases: the example of avian influenza in Spain.

PubMed

Sánchez-Vizcaíno, Fernando; Perez, Andrés; Martínez-López, Beatriz; Sánchez-Vizcaíno, José Manuel

2012-08-01

Trade of animals and animal products imposes an uncertain and variable risk for exotic animal diseases introduction into importing countries. Risk analysis provides importing countries with an objective, transparent, and internationally accepted method for assessing that risk. Over the last decades, European Union countries have conducted probabilistic risk assessments quite frequently to quantify the risk for rare animal diseases introduction into their territories. Most probabilistic animal health risk assessments have been typically classified into one-level and multilevel binomial models. One-level models are more simple than multilevel models because they assume that animals or products originate from one single population. However, it is unknown whether such simplification may result in substantially different results compared to those obtained through the use of multilevel models. Here, data used on a probabilistic multilevel binomial model formulated to assess the risk for highly pathogenic avian influenza introduction into Spain were reanalyzed using a one-level binomial model and their outcomes were compared. An alternative ordinal model is also proposed here, which makes use of simpler assumptions and less information compared to those required by traditional one-level and multilevel approaches. Results suggest that, at least under certain circumstances, results of the one-level and ordinal approaches are similar to those obtained using multilevel models. Consequently, we argue that, when data are insufficient to run traditional probabilistic models, the ordinal approach presented here may be a suitable alternative to rank exporting countries in terms of the risk that they impose for the spread of rare animal diseases into disease-free countries. © 2012 Society for Risk Analysis.

Commissioning of two RF operation modes for RF negative ion source experimental setup at HUST

NASA Astrophysics Data System (ADS)

Li, D.; Chen, D.; Liu, K.; Zhao, P.; Zuo, C.; Wang, X.; Wang, H.; Zhang, L.

2017-08-01

An RF-driven negative ion source experimental setup, without a cesium oven and an extraction system, has been built at Huazhong University of Science and Technology (HUST). The working gas is hydrogen, and the typical operational gas pressure is 0.3 Pa. The RF generator is capable of delivering up to 20 kW at 0.9 - 1.1 MHz, and has two operation modes, the fixed-frequency mode and auto-tuning mode. In the fixed-frequency mode, it outputs a steady RF forward power (Pf) at a fixed frequency. In the auto-tuning mode, it adjusts the operating frequency to seek and track the minimum standing wave ratio (SWR) during plasma discharge. To achieve fast frequency tuning, the RF signal source adopts a direct digital synthesizer (DDS). To withstand high SWR during the discharge, a tetrode amplifier is chosen as the final stage amplifier. The trend of maximum power reflection coefficient |ρ|2 at plasma ignition is presented at the fixed frequency of 1.02 MHz with the Pf increasing from 5 kW to 20 kW, which shows the maximum |ρ|2 tends to be "steady" under high RF power. The experiments in auto-tuning mode fail due to over-current protection of screen grid. The possible reason is the relatively large equivalent anode impedance caused by the frequency tuning. The corresponding analysis and possible solution are presented.

The Healthy Immigrant Effect on Mental Health: Determinants and Implications for Mental Health Policy in Spain.

PubMed

Rivera, Berta; Casal, Bruno; Currais, Luis

2016-07-01

Since the mid-1990s, Spain has started to receive a great number of migrant populations. The migration process can have a significantly negative impact on mental health of immigrant population and, consequently, generate implications for the delivery of mental health services. The aim of this article is to provide empirical evidence to demonstrate that the mental health of immigrants in Spain deteriorates the longer they are resident in the country. An empirical approach to this relationship is carried out with data from the National Survey of Health of Spain 2011-2012 and poisson and negative binomial models. Results show that immigrants who reside <10 years in Spain appear to be in a better state of mental health than that observed for the national population. Studying health disparities in the foreign population and its evolution are relevant to ensure the population's access to health services and care. The need for further research is especially true in the case of the immigrant population's mental health in Spain because there is scant evidence available on their situation.

Automated Pressure Injury Risk Assessment System Incorporated Into an Electronic Health Record System.

PubMed

Jin, Yinji; Jin, Taixian; Lee, Sun-Mi

Pressure injury risk assessment is the first step toward preventing pressure injuries, but traditional assessment tools are time-consuming, resulting in work overload and fatigue for nurses. The objectives of the study were to build an automated pressure injury risk assessment system (Auto-PIRAS) that can assess pressure injury risk using data, without requiring nurses to collect or input additional data, and to evaluate the validity of this assessment tool. A retrospective case-control study and a system development study were conducted in a 1,355-bed university hospital in Seoul, South Korea. A total of 1,305 pressure injury patients and 5,220 nonpressure injury patients participated for the development of a risk scoring algorithm: 687 and 2,748 for the validation of the algorithm and 237 and 994 for validation after clinical implementation, respectively. A total of 4,211 pressure injury-related clinical variables were extracted from the electronic health record (EHR) systems to develop a risk scoring algorithm, which was validated and incorporated into the EHR. That program was further evaluated for predictive and concurrent validity. Auto-PIRAS, incorporated into the EHR system, assigned a risk assessment score of high, moderate, or low and displayed this on the Kardex nursing record screen. Risk scores were updated nightly according to 10 predetermined risk factors. The predictive validity measures of the algorithm validation stage were as follows: sensitivity = .87, specificity = .90, positive predictive value = .68, negative predictive value = .97, Youden index = .77, and the area under the receiver operating characteristic curve = .95. The predictive validity measures of the Braden Scale were as follows: sensitivity = .77, specificity = .93, positive predictive value = .72, negative predictive value = .95, Youden index = .70, and the area under the receiver operating characteristic curve = .85. The kappa of the Auto-PIRAS and Braden Scale risk classification result was .73. The predictive performance of the Auto-PIRAS was similar to Braden Scale assessments conducted by nurses. Auto-PIRAS is expected to be used as a system that assesses pressure injury risk automatically without additional data collection by nurses.

Predictors and outcomes of non-adherence in patients receiving maintenance hemodialysis.

PubMed

Tohme, Fadi; Mor, Maria K; Pena-Polanco, Julio; Green, Jamie A; Fine, Michael J; Palevsky, Paul M; Weisbord, Steven D

2017-08-01

Predictors of and outcomes associated with non-adherent behavior among patients on chronic hemodialysis (HD) have been incompletely elucidated. We conducted a post hoc analysis of data from the SMILE trial to identify patient factors associated with non-adherence to dialysis-related treatments and the associations of non-adherence with clinical outcomes. We defined non-adherence as missed HD and abbreviated HD. We used negative binomial regression to model the associations of demographic and clinical factors with measures of non-adherence, and negative binomial and Cox regression to analyze the associations of non-adherence with hospitalizations and mortality, respectively. We followed 286 patients for up to 24 months. Factors independently associated with missing HD included Tuesday/Thursday/Saturday HD schedule [incident rate ratio (IRR) 1.85, p < 0.01], current smoking (IRR 2.22, p < 0.01), higher pain score (IRR 1.04, p < 0.01), lower healthy literacy (IRR 3.01, p < 0.01), lower baseline quality of life (IRR 0.89, p = 0.01), and younger age (IRR 1.35, p < 0.01). Factors independently associated with abbreviating HD included dialysis vintage (IRR 1.07, p < 0.01), higher pain score (IRR 1.02, p < 0.01), current non-smoking (IRR 1.32, p = 0.03), and younger age (IRR 1.22, p < 0.01). Abbreviating HD was independently associated with an increased number of total (IRR 1.70, p < 0.01) and ESRD-related (IRR 1.66, p < 0.01) hospitalizations, while missing HD was independently associated with mortality (HR 2.36, p = 0.04). We identified several previously described and novel factors independently associated with non-adherence to HD-related treatments, and independent associations of non-adherence with hospitalization and mortality. These findings should inform the development and implementation of interventions to improve adherence and reduce health resource utilization.

Factors associated with dental caries in a group of American Indian children at age 36 months.

PubMed

Warren, John J; Blanchette, Derek; Dawson, Deborah V; Marshall, Teresa A; Phipps, Kathy R; Starr, Delores; Drake, David R

2016-04-01

Early childhood caries (ECC) is rampant among American Indian children, but there has been relatively little study of this problem. This article reports on risk factors for caries for a group of American Indian children at age 36 months as part of a longitudinal study. Pregnant women from a Northern Plains Tribal community were recruited to participate in a longitudinal study of caries and caries risk factors. Standardized dental examinations were completed on children, and questionnaires were completed by mothers at baseline and when children were 4, 8, 12, 16, 22, 28, and 36 months of age. Examinations were surface-specific for dental caries, and the questionnaires collected data on demographic, dietary, and behavioral factors. Nonparametric bivariate tests and logistic regression models were used to identify risk factors for caries at 36 months, and negative binomial regression was used to identify factors related to caries severity (dmf counts). Among the 232 children, and caries prevalence for cavitated lesions was 80%, with an additional 15% having only noncavitated lesions. The mean dmfs was 9.6, and of the total dmfs, nearly 62% of affected surfaces were decayed, 31% were missing, and 7% were filled. Logistic regression identified higher added-sugar beverage consumption, younger maternal age at baseline, higher maternal DMFS at baseline, and greater number of people in the household as significant (P < 0.05) risk factors. Negative binomial regression found that only maternal DMFS was associated with child dmf counts. By the age of 36 months, dental caries is nearly universal in this population of American Indian children. Caries risk factors included sugared beverage consumption, greater household size, and maternal factors, but further analyses are needed to better understand caries in this population. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Cocoa Farmers’ Compliance with Safety Precautions in Spraying Agrochemicals and Use of Personal Protective Equipment (PPE) in Cameroon

PubMed Central

2018-01-01

The inability of farmers to comply with essential precautions in the course of spraying agrochemicals remains a policy dilemma, especially in developing countries. The objectives of this paper were to assess compliance of cocoa farmers with agrochemical safety measures, analyse the factors explaining involvement of cocoa farmers in the practice of reusing agrochemical containers and wearing of personal protective equipment (PPE). Data were collected with structured questionnaires from 667 cocoa farmers from the Centre and South West regions in Cameroon. Data analyses were carried out with Probit regression and Negative Binomial regression models. The results showed that average cocoa farm sizes were 3.55 ha and 2.82 ha in South West and Centre regions, respectively, and 89.80% and 42.64% complied with manufacturers’ instructions in the use of insecticides. Eating or drinking while spraying insecticides and fungicides was reported by 4.20% and 5.10% of all farmers in the two regions, respectively. However, 37.78% and 57.57% of all farmers wore hand gloves and safety boots while spraying insecticides in the South West and Centre regions of Cameroon, respectively. In addition, 7.80% of all the farmers would wash agrochemical containers and use them at home, while 42.43% would wash and use them on their farms. Probit regression results showed that probability of reusing agrochemical containers was significantly influenced (p < 0.05) by region of residence of cocoa farmers, gender, possession of formal education and farming as primary occupation. The Negative Binomial regression results showed that the log of number PPE worn was significantly influenced (p < 0.10) by region, marital status, attainment of formal education, good health, awareness of manufacturers’ instructions, land area and contact index. It was among others concluded that efforts to train farmers on the need to be familiar with manufacturers’ instructions and use PPE would enhance their safety in the course of spraying agrochemicals. PMID:29438333

Cocoa Farmers' Compliance with Safety Precautions in Spraying Agrochemicals and Use of Personal Protective Equipment (PPE) in Cameroon.

PubMed

Oyekale, Abayomi Samuel

2018-02-13

The inability of farmers to comply with essential precautions in the course of spraying agrochemicals remains a policy dilemma, especially in developing countries. The objectives of this paper were to assess compliance of cocoa farmers with agrochemical safety measures, analyse the factors explaining involvement of cocoa farmers in the practice of reusing agrochemical containers and wearing of personal protective equipment (PPE). Data were collected with structured questionnaires from 667 cocoa farmers from the Centre and South West regions in Cameroon. Data analyses were carried out with Probit regression and Negative Binomial regression models. The results showed that average cocoa farm sizes were 3.55 ha and 2.82 ha in South West and Centre regions, respectively, and 89.80% and 42.64% complied with manufacturers' instructions in the use of insecticides. Eating or drinking while spraying insecticides and fungicides was reported by 4.20% and 5.10% of all farmers in the two regions, respectively. However, 37.78% and 57.57% of all farmers wore hand gloves and safety boots while spraying insecticides in the South West and Centre regions of Cameroon, respectively. In addition, 7.80% of all the farmers would wash agrochemical containers and use them at home, while 42.43% would wash and use them on their farms. Probit regression results showed that probability of reusing agrochemical containers was significantly influenced ( p < 0.05) by region of residence of cocoa farmers, gender, possession of formal education and farming as primary occupation. The Negative Binomial regression results showed that the log of number PPE worn was significantly influenced ( p < 0.10) by region, marital status, attainment of formal education, good health, awareness of manufacturers' instructions, land area and contact index. It was among others concluded that efforts to train farmers on the need to be familiar with manufacturers' instructions and use PPE would enhance their safety in the course of spraying agrochemicals.

Auto-antibodies and Autoimmune Disease during Treatment of Children with Chronic Hepatitis C

PubMed Central

Molleston, Jean P.; Mellman, William; Narkewicz, Michael R.; Balistreri, William F.; Gonzalez-Peralta, Regino P.; Jonas, Maureen M.; Lobritto, Steven J.; Mohan, Parvathi; Murray, Karen F.; Njoku, Dolores; Rosenthal, Philip; Barton, Bruce A.; Talor, Monica V.; Cheng, Irene; Schwarz, Kathleen B.; Haber, Barbara A.

2012-01-01

Objectives Auto-antibodies were studied in a well-characterized cohort of children with chronic hepatitis C (CHC) during treatment with PEG-IFN and ribavirin to assess the relationship to treatment and development of autoimmune disease. Methods 114 children (5–17 years), previously screened for the presence of high titer autoantibodies, were randomized to Peg-IFN with or without ribavirin. Anti-nuclear (ANA), anti-liver-kidney-microsomal (LKM), anti-thyroglobulin (TG), anti-thyroid peroxidase (TPO), insulin (IA2), anti-glutamic acid decarboxylase (GAD) antibodies were measured after trial completion using frozen sera. Results At baseline,19% had auto-antibodies: ANA (8%), LKM (4%), and GAD (4%). At 24 and 72 weeks (24 weeks after treatment completion), 23% and 26% had auto-antibodies (p=0.50, 0.48 compared to baseline). One child developed diabetes and two hypothyroidism during treatment; none developed autoimmune hepatitis. At 24 weeks, the incidence of flu-like symptoms, gastrointestinal symptoms, and headaches were 42%, 8% and 19% in those with auto-antibodies vs. 52%, 17%, and 26% in those without (p=0.18, 0.36, and 0.20, respectively). In children with negative HCV PCR at 24 weeks, there was no difference in the rate of early virologic response /sustained virologic response respectively in those with auto-antibodies 76%/69%, vs 58%/65% in those without (p=0.48). Conclusions Despite screening, we found autoantibodies commonly at baseline, during treatment for CHC and after. The presence of antibodies did not correlate with viral response, side effects, or autoimmune hepatitis. Neither screening nor archived samples assayed for thyroid and diabetes-related antibodies identified the 3 subjects who developed overt autoimmune disease, diabetes (1) and hypothyroidism (2). PMID:23439301

Visualization of water usage and photosynthetic activity of street trees exposed to 2 ppm of SO 2—A combined evaluation by cold neutron and chlorophyll fluorescence imaging

NASA Astrophysics Data System (ADS)

Matsushima, U.; Kardjilov, N.; Hilger, A.; Manke, I.; Shono, H.; Herppich, W. B.

2009-06-01

Photosynthetic efficacy and auto-exhaust-fume resistance of street trees were evaluated by cold neutron radiography (CNR) with D 2O tracer and chlorophyll fluorescence (CF) imaging. With these techniques, information on the responses of water usage and photosynthetic activity of plants exposed to simulate toxic auto-exhaust fumes (2 ppm SO 2 in air) were obtained. Branches of hibiscus trees were detached, placed into a tub with aerated water and used for the experiments after rooting. A CF image was taken before SO 2 was applied for 1 h. During the experiment, CNR and CF imaging were conduced. H 2O and D 2O in the plant container were exchanged every 30 min to observe water uptake. D 2O tracer clearly showed water uptake into the hibiscus stem during each treatment. When the atmosphere was changed from simulated auto-exhaust fumes to normal air again, the amount of D 2O and, hence, water uptake increased. CF imaging was well suited to evaluate the effects of SO 2 as simulated toxic auto-exhaust fumes on plants. The maximum photochemical efficiency ( Fv/ Fm), a sensitive indicator of the efficacy and the integrity of plants' photosynthesis, immediately dropped by 30% after supplying the simulated auto-exhaust fumes. This indicates that toxic auto-exhaust fumes negatively affected the photosynthetic activity of hibiscus leaves. Simultaneous CNR and CF imaging successfully visualized variations of photosynthetic activity and water uptake in the sample. Thus, this combination method was effective to non-destructive analyze the physiological status of plants.

The feasibility of employing a home healthcare model for education and treatment of opioid overdose using a naloxone auto-injector in a private practice pain medicine clinic.

PubMed

Dragovich, Anthony; Brason, Fred; Beltran, Thomas; McCoart, Amy; Plunkett, Anthony R

2018-04-18

The purpose of this study was to determine if employing a home healthcare model for education and treatment of opioid overdose using Evzio® (Naloxone)* auto-injector in a private practice pain clinic. A prospective survey was used to determine the feasibility of integrating a naloxone auto-injector within the patient's home with a home care training model. Twenty moderate or high-risk patients were enrolled from our chronic pain clinic. Patients who were moderate or high risk completed an evaluation survey. The naloxone auto-injector was dispensed to all patients meeting criteria. The treating provider after prescribing the naloxone auto-injector then consulted home health per standard clinical practice. All patients had home health consulted to perform overdose identification and rescue training. A Cochran's Q test was conducted to examine differences in patient knowledge pre and post training. The post training test was done 2-4 weeks later. Forty subjects enrolled after meeting inclusion/exclusion criteria. Twenty withdrew because their insurance declined coverage for the naloxone auto-injector. Those completing home health showed a statistically significant difference in their ability to correctly identify the steps needed to effectively respond to an overdose p = 0.03 Discussion: Preliminary evidence would suggest training on overdose symptom recognition and proper use of prescription naloxone for treatment in the home setting by home health staff would prove more beneficial than the clinic setting, but feasibility was hindered by unaffordable costs related to insurance coverage limitations.

Are Regional Habitat Models Useful at a Local-Scale? A Case Study of Threatened and Common Insectivorous Bats in South-Eastern Australia

PubMed Central

McConville, Anna; Law, Bradley S.; Mahony, Michael J.

2013-01-01

Habitat modelling and predictive mapping are important tools for conservation planning, particularly for lesser known species such as many insectivorous bats. However, the scale at which modelling is undertaken can affect the predictive accuracy and restrict the use of the model at different scales. We assessed the validity of existing regional-scale habitat models at a local-scale and contrasted the habitat use of two morphologically similar species with differing conservation status (Mormopterus norfolkensis and Mormopterus species 2). We used negative binomial generalised linear models created from indices of activity and environmental variables collected from systematic acoustic surveys. We found that habitat type (based on vegetation community) best explained activity of both species, which were more active in floodplain areas, with most foraging activity recorded in the freshwater wetland habitat type. The threatened M. norfolkensis avoided urban areas, which contrasts with M. species 2 which occurred frequently in urban bushland. We found that the broad habitat types predicted from local-scale models were generally consistent with those from regional-scale models. However, threshold-dependent accuracy measures indicated a poor fit and we advise caution be applied when using the regional models at a fine scale, particularly when the consequences of false negatives or positives are severe. Additionally, our study illustrates that habitat type classifications can be important predictors and we suggest they are more practical for conservation than complex combinations of raw variables, as they are easily communicated to land managers. PMID:23977296

Bursts of Self-Conscious Emotions in the Daily Lives of Emerging Adults

PubMed Central

Conroy, David E.; Ram, Nilam; Pincus, Aaron L.; Rebar, Amanda L.

2015-01-01

Self-conscious emotions play a role in regulating daily achievement strivings, social behavior, and health, but little is known about the processes underlying their daily manifestation. Emerging adults (n = 182) completed daily diaries for eight days and multilevel models were estimated to evaluate whether, how much, and why their emotions varied from day-to-day. Within-person variation in authentic pride was normally-distributed across people and days whereas the other emotions were burst-like and characterized by zero-inflated, negative binomial distributions. Perceiving social interactions as generally communal increased the odds of hubristic pride activation and reduced the odds of guilt activation; daily communal behavior reduced guilt intensity. Results illuminated processes through which meaning about the self-in-relation-to-others is constructed during a critical period of development. PMID:25859164

Comparing the Caputo, Caputo-Fabrizio and Atangana-Baleanu derivative with fractional order: Fractional cubic isothermal auto-catalytic chemical system

NASA Astrophysics Data System (ADS)

Saad, K. M.

2018-03-01

In this work we extend the standard model for a cubic isothermal auto-catalytic chemical system (CIACS) to a new model of a fractional cubic isothermal auto-catalytic chemical system (FCIACS) based on Caputo (C), Caputo-Fabrizio (CF) and Atangana-Baleanu in the Liouville-Caputo sense (ABC) fractional time derivatives, respectively. We present approximate solutions for these extended models using the q -homotopy analysis transform method ( q -HATM). We solve the FCIACS with the C derivative and compare our results with those obtained using the CF and ABC derivatives. The ranges of convergence of the solutions are found and the optimal values of h , the auxiliary parameter, are derived. Finally, these solutions are compared with numerical solutions of the various models obtained using finite differences and excellent agreement is found.

Marginalized zero-altered models for longitudinal count data.

PubMed

Tabb, Loni Philip; Tchetgen, Eric J Tchetgen; Wellenius, Greg A; Coull, Brent A

2016-10-01

Count data often exhibit more zeros than predicted by common count distributions like the Poisson or negative binomial. In recent years, there has been considerable interest in methods for analyzing zero-inflated count data in longitudinal or other correlated data settings. A common approach has been to extend zero-inflated Poisson models to include random effects that account for correlation among observations. However, these models have been shown to have a few drawbacks, including interpretability of regression coefficients and numerical instability of fitting algorithms even when the data arise from the assumed model. To address these issues, we propose a model that parameterizes the marginal associations between the count outcome and the covariates as easily interpretable log relative rates, while including random effects to account for correlation among observations. One of the main advantages of this marginal model is that it allows a basis upon which we can directly compare the performance of standard methods that ignore zero inflation with that of a method that explicitly takes zero inflation into account. We present simulations of these various model formulations in terms of bias and variance estimation. Finally, we apply the proposed approach to analyze toxicological data of the effect of emissions on cardiac arrhythmias.

Marginalized zero-altered models for longitudinal count data

PubMed Central

Tabb, Loni Philip; Tchetgen, Eric J. Tchetgen; Wellenius, Greg A.; Coull, Brent A.

2015-01-01

Count data often exhibit more zeros than predicted by common count distributions like the Poisson or negative binomial. In recent years, there has been considerable interest in methods for analyzing zero-inflated count data in longitudinal or other correlated data settings. A common approach has been to extend zero-inflated Poisson models to include random effects that account for correlation among observations. However, these models have been shown to have a few drawbacks, including interpretability of regression coefficients and numerical instability of fitting algorithms even when the data arise from the assumed model. To address these issues, we propose a model that parameterizes the marginal associations between the count outcome and the covariates as easily interpretable log relative rates, while including random effects to account for correlation among observations. One of the main advantages of this marginal model is that it allows a basis upon which we can directly compare the performance of standard methods that ignore zero inflation with that of a method that explicitly takes zero inflation into account. We present simulations of these various model formulations in terms of bias and variance estimation. Finally, we apply the proposed approach to analyze toxicological data of the effect of emissions on cardiac arrhythmias. PMID:27867423

Use of Internet viral marketing to promote smoke-free lifestyles among Chinese adolescents.

PubMed

Ip, Patrick; Lam, Tai-Hing; Chan, Sophia Siu-Chee; Ho, Frederick Ka-Wing; Lo, Lewis A; Chiu, Ivy Wing-Sze; Wong, Wilfred Hing-Sang; Chow, Chun-Bong

2014-01-01

Youth smoking is a global public health concern. Health educators are increasingly using Internet-based technologies, but the effectiveness of Internet viral marketing in promoting health remains uncertain. This prospective pilot study assessed the efficacy of an online game-based viral marketing campaign in promoting a smoke-free attitude among Chinese adolescents. One hundred and twenty-one Hong Kong Chinese adolescents aged 10 to 24 were invited to participate in an online multiple-choice quiz game competition designed to deliver tobacco-related health information. Participants were encouraged to refer others to join. A zero-inflated negative binomial model was used to explore the factors contributing to the referral process. Latent transition analysis utilising a pre- and post-game survey was used to detect attitudinal changes toward smoking. The number of participants increased almost eightfold from 121 to 928 (34.6% current or ex-smokers) during the 22-day campaign. Participants exhibited significant attitudinal change, with 73% holding negative attitudes toward smoking after the campaign compared to 57% before it. The transition probabilities from positive to negative and neutral to negative attitudes were 0.52 and 0.48, respectively. It was also found that attempting every 20 quiz questions was associated with lower perceived smoking decision in future (OR = 0.95, p-value <0.01). Our online game-based viral marketing programme was effective in reaching a large number of smoking and non-smoking participants and changing their attitudes toward smoking. It constitutes a promising practical and cost-effective model for engaging young smokers and promulgating smoking-related health information among Chinese adolescents.

Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods

PubMed Central

Stone, Bryan L; Johnson, Michael D; Tarczy-Hornoch, Peter; Wilcox, Adam B; Mooney, Sean D; Sheng, Xiaoming; Haug, Peter J; Nkoy, Flory L

2017-01-01

Background To improve health outcomes and cut health care costs, we often need to conduct prediction/classification using large clinical datasets (aka, clinical big data), for example, to identify high-risk patients for preventive interventions. Machine learning has been proposed as a key technology for doing this. Machine learning has won most data science competitions and could support many clinical activities, yet only 15% of hospitals use it for even limited purposes. Despite familiarity with data, health care researchers often lack machine learning expertise to directly use clinical big data, creating a hurdle in realizing value from their data. Health care researchers can work with data scientists with deep machine learning knowledge, but it takes time and effort for both parties to communicate effectively. Facing a shortage in the United States of data scientists and hiring competition from companies with deep pockets, health care systems have difficulty recruiting data scientists. Building and generalizing a machine learning model often requires hundreds to thousands of manual iterations by data scientists to select the following: (1) hyper-parameter values and complex algorithms that greatly affect model accuracy and (2) operators and periods for temporally aggregating clinical attributes (eg, whether a patient’s weight kept rising in the past year). This process becomes infeasible with limited budgets. Objective This study’s goal is to enable health care researchers to directly use clinical big data, make machine learning feasible with limited budgets and data scientist resources, and realize value from data. Methods This study will allow us to achieve the following: (1) finish developing the new software, Automated Machine Learning (Auto-ML), to automate model selection for machine learning with clinical big data and validate Auto-ML on seven benchmark modeling problems of clinical importance; (2) apply Auto-ML and novel methodology to two new modeling problems crucial for care management allocation and pilot one model with care managers; and (3) perform simulations to estimate the impact of adopting Auto-ML on US patient outcomes. Results We are currently writing Auto-ML’s design document. We intend to finish our study by around the year 2022. Conclusions Auto-ML will generalize to various clinical prediction/classification problems. With minimal help from data scientists, health care researchers can use Auto-ML to quickly build high-quality models. This will boost wider use of machine learning in health care and improve patient outcomes. PMID:28851678

Face detection assisted auto exposure: supporting evidence from a psychophysical study

NASA Astrophysics Data System (ADS)

Jin, Elaine W.; Lin, Sheng; Dharumalingam, Dhandapani

2010-01-01

Face detection has been implemented in many digital still cameras and camera phones with the promise of enhancing existing camera functions (e.g. auto exposure) and adding new features to cameras (e.g. blink detection). In this study we examined the use of face detection algorithms in assisting auto exposure (AE). The set of 706 images, used in this study, was captured using Canon Digital Single Lens Reflex cameras and subsequently processed with an image processing pipeline. A psychophysical study was performed to obtain optimal exposure along with the upper and lower bounds of exposure for all 706 images. Three methods of marking faces were utilized: manual marking, face detection algorithm A (FD-A), and face detection algorithm B (FD-B). The manual marking method found 751 faces in 426 images, which served as the ground-truth for face regions of interest. The remaining images do not have any faces or the faces are too small to be considered detectable. The two face detection algorithms are different in resource requirements and in performance. FD-A uses less memory and gate counts compared to FD-B, but FD-B detects more faces and has less false positives. A face detection assisted auto exposure algorithm was developed and tested against the evaluation results from the psychophysical study. The AE test results showed noticeable improvement when faces were detected and used in auto exposure. However, the presence of false positives would negatively impact the added benefit.

The influence of impulsiveness on binge eating and problem gambling: A prospective study of gender differences in Canadian adults.

PubMed

Farstad, Sarah M; von Ranson, Kristin M; Hodgins, David C; El-Guebaly, Nady; Casey, David M; Schopflocher, Don P

2015-09-01

This study investigated the degree to which facets of impulsiveness predicted future binge eating and problem gambling, 2 theorized forms of behavioral addiction. Participants were 596 women and 406 men from 4 age cohorts randomly recruited from a Canadian province. Participants completed self-report measures of 3 facets of impulsiveness (negative urgency, sensation seeking, lack of persistence), binge-eating frequency, and problem-gambling symptoms. Impulsiveness was assessed at baseline, and assessments of binge eating and problem gambling were followed up after 3 years. Weighted data were analyzed using zero-inflated negative binomial and Poisson regression models. We found evidence of transdiagnostic and disorder-specific predictors of binge eating and problem gambling. Negative urgency emerged as a common predictor of binge eating and problem gambling among women and men. There were disorder-specific personality traits identified among men only: High lack-of-persistence scores predicted binge eating and high sensation-seeking scores predicted problem gambling. Among women, younger age predicted binge eating and older age predicted problem gambling. Thus, there are gender differences in facets of impulsiveness that longitudinally predict binge eating and problem gambling, suggesting that treatments for these behaviors should consider gender-specific personality and demographic traits in addition to the common personality trait of negative urgency. (c) 2015 APA, all rights reserved).

How does the emotive content of televised anti-smoking mass media campaigns influence monthly calls to the NHS Stop Smoking helpline in England?

PubMed Central

Richardson, Sol; Langley, Tessa; Szatkowski, Lisa; Sims, Michelle; Gilmore, Anna; McNeill, Ann; Lewis, Sarah

2014-01-01

Objective To investigate the effects of different types of televised mass media campaign content on calls to the English NHS Stop Smoking helpline. Method We used UK government-funded televised tobacco control campaigns from April 2005 to April 2010, categorised as either “positive” (eliciting happiness, satisfaction or hope) or “negative” (eliciting fear, guilt or disgust). We built negative binomial generalised additive models (GAMs) with linear and smooth terms for monthly per capita exposure to each campaign type (expressed as Gross Ratings Points, or GRPs) to determine their effect on calls in the same month. We adjusted for seasonal trends, inflation-adjusted weighted average cigarette prices and other tobacco control policies. Results We found non-linear associations between exposure to positive and negative emotive campaigns and quitline calls. The rate of calls increased more than 50% as exposure to positive campaigns increased from 0 to 400 GRPs (rate ratio: 1.58, 95% CI: 1.25–2.01). An increase in calls in response to negative emotive campaigns was only apparent after monthly exposure exceeded 400 GRPs. Conclusion While positive campaigns were most effective at increasing quitline calls, those with negative emotive content were also found to impact on call rates but only at higher levels of exposure. PMID:25197004

The Relationship Between Gun Ownership and Stranger and Nonstranger Firearm Homicide Rates in the United States, 1981–2010

PubMed Central

Negussie, Yamrot; Vanture, Sarah; Pleskunas, Jane; Ross, Craig S.; King, Charles

2014-01-01

Objectives. We examined the relationship between gun ownership and stranger versus nonstranger homicide rates. Methods. Using data from the Supplemental Homicide Reports of the Federal Bureau of Investigation’s Uniform Crime Reports for all 50 states for 1981 to 2010, we modeled stranger and nonstranger homicide rates as a function of state-level gun ownership, measured by a proxy, controlling for potential confounders. We used a negative binomial regression model with fixed effects for year, accounting for clustering of observations among states by using generalized estimating equations. Results. We found no robust, statistically significant correlation between gun ownership and stranger firearm homicide rates. However, we found a positive and significant association between gun ownership and nonstranger firearm homicide rates. The incidence rate ratio for nonstranger firearm homicide rate associated with gun ownership was 1.014 (95% confidence interval = 1.009, 1.019). Conclusions. Our findings challenge the argument that gun ownership deters violent crime, in particular, homicides. PMID:25121817

The relationship between gun ownership and stranger and nonstranger firearm homicide rates in the United States, 1981-2010.

PubMed

Siegel, Michael; Negussie, Yamrot; Vanture, Sarah; Pleskunas, Jane; Ross, Craig S; King, Charles

2014-10-01

We examined the relationship between gun ownership and stranger versus nonstranger homicide rates. Using data from the Supplemental Homicide Reports of the Federal Bureau of Investigation's Uniform Crime Reports for all 50 states for 1981 to 2010, we modeled stranger and nonstranger homicide rates as a function of state-level gun ownership, measured by a proxy, controlling for potential confounders. We used a negative binomial regression model with fixed effects for year, accounting for clustering of observations among states by using generalized estimating equations. We found no robust, statistically significant correlation between gun ownership and stranger firearm homicide rates. However, we found a positive and significant association between gun ownership and nonstranger firearm homicide rates. The incidence rate ratio for nonstranger firearm homicide rate associated with gun ownership was 1.014 (95% confidence interval=1.009, 1.019). Our findings challenge the argument that gun ownership deters violent crime, in particular, homicides.

Remote sensing of earth terrain

NASA Technical Reports Server (NTRS)

Kong, J. A.

1988-01-01

Two monographs and 85 journal and conference papers on remote sensing of earth terrain have been published, sponsored by NASA Contract NAG5-270. A multivariate K-distribution is proposed to model the statistics of fully polarimetric data from earth terrain with polarizations HH, HV, VH, and VV. In this approach, correlated polarizations of radar signals, as characterized by a covariance matrix, are treated as the sum of N n-dimensional random vectors; N obeys the negative binomial distribution with a parameter alpha and mean bar N. Subsequently, and n-dimensional K-distribution, with either zero or non-zero mean, is developed in the limit of infinite bar N or illuminated area. The probability density function (PDF) of the K-distributed vector normalized by its Euclidean norm is independent of the parameter alpha and is the same as that derived from a zero-mean Gaussian-distributed random vector. The above model is well supported by experimental data provided by MIT Lincoln Laboratory and the Jet Propulsion Laboratory in the form of polarimetric measurements.

Searching for the Kinkeepers: Historian Gender, Age, and Type 2 Diabetes Family History.

PubMed

Giordimaina, Alicia M; Sheldon, Jane P; Kiedrowski, Lesli A; Jayaratne, Toby Epstein

2015-12-01

Kinkeepers facilitate family communication and may be key to family medical history collection and dissemination. Middle-aged women are frequently kinkeepers. Using type 2 diabetes (T2DM) as a model, we explored whether the predicted gender and age effects of kinkeeping can be extended to family medical historians. Through a U.S. telephone survey, nondiabetic Mexican Americans (n = 385), Blacks (n = 387), and Whites (n = 396) reported family histories of T2DM. Negative binomial regressions used age and gender to predict the number of affected relatives reported. Models were examined for the gender gap, parabolic age effect, and gender-by-age interaction predicted by kinkeeping. Results demonstrated support for gender and parabolic age effects but only among Whites. Kinkeeping may have application to the study of White family medical historians, but not Black or Mexican American historians, perhaps because of differences in family structure, salience of T2DM, and/or gender roles. © 2015 Society for Public Health Education.

Spatio-temporal modelling of dengue fever incidence in Malaysia

NASA Astrophysics Data System (ADS)

Che-Him, Norziha; Ghazali Kamardan, M.; Saifullah Rusiman, Mohd; Sufahani, Suliadi; Mohamad, Mahathir; @ Kamariah Kamaruddin, Nafisah

2018-04-01

Previous studies reported significant relationship between dengue incidence rate (DIR) and both climatic and non-climatic factors. Therefore, this study proposes a generalised additive model (GAM) framework for dengue risk in Malaysia by using both climatic and non-climatic factors. The data used is monthly DIR for 12 states of Malaysia from 2001 to 2009. In this study, we considered an annual trend, seasonal effects, population, population density and lagged DIR, rainfall, temperature, number of rainy days and El Niño-Southern Oscillation (ENSO). The population density is found to be positively related to monthly DIR. There are generally weak relationships between monthly DIR and climate variables. A negative binomial GAM shows that there are statistically significant relationships between DIR with climatic and non-climatic factors. These include mean rainfall and temperature, the number of rainy days, sea surface temperature and the interaction between mean temperature (lag 1 month) and sea surface temperature (lag 6 months). These also apply to DIR (lag 3 months) and population density.

Hope and Hopelessness: The Role of Hope in Buffering the Impact of Hopelessness on Suicidal Ideation.

PubMed

Huen, Jenny M Y; Ip, Brian Y T; Ho, Samuel M Y; Yip, Paul S F

2015-01-01

The present study investigated whether hope and hopelessness are better conceptualized as a single construct of bipolar spectrum or two distinct constructs and whether hope can moderate the relationship between hopelessness and suicidal ideation. Hope, hopelessness, and suicidal ideation were measured in a community sample of 2106 participants through a population-based household survey. Confirmatory factor analyses showed that a measurement model with separate, correlated second-order factors of hope and hopelessness provided a good fit to the data and was significantly better than that of the model collapsing hope and hopelessness into a single second-order factor. Negative binomial regression showed that hope and hopelessness interacted such that the effect of hopelessness on suicidal ideation was lower in individuals with higher hope than individuals with lower hope. Hope and hopelessness are two distinct but correlated constructs. Hope can act as a resilience factor that buffers the impact of hopelessness on suicidal ideation. Inducing hope in people may be a promising avenue for suicide prevention.

Effect of Brazil's conditional cash transfer programme on tuberculosis incidence.

PubMed

Nery, J S; Rodrigues, L C; Rasella, D; Aquino, R; Barreira, D; Torrens, A W; Boccia, D; Penna, G O; Penna, M L F; Barreto, M L; Pereira, S M

2017-07-01

To evaluate the impact of the Brazilian cash transfer programme (Bolsa Família Programme, BFP) on tuberculosis (TB) incidence in Brazil from 2004 to 2012. We studied tuberculosis surveillance data using a combination of an ecological multiple-group and time-trend design covering 2458 Brazilian municipalities. The main independent variable was BFP coverage and the outcome was the TB incidence rate. All study variables were obtained from national databases. We used fixed-effects negative binomial models for panel data adjusted for selected covariates and a variable representing time. After controlling for covariates, TB incidence rates were significantly reduced in municipalities with high BFP coverage compared with those with low and intermediate coverage (in a model with a time variable incidence rate ratio = 0.96, 95%CI 0.93-0.99). This was the first evidence of a statistically significant association between the increase in cash transfer programme coverage and a reduction in TB incidence rate. Our findings provide support for social protection interventions for tackling TB worldwide.

Estimation of population trajectories from count data

USGS Publications Warehouse

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

1997-01-01

Monitoring of changes in animal population size is rarely possible through complete censuses; frequently, the only feasible means of monitoring changes in population size is to use counts of animals obtained by skilled observers as indices to abundance. Analysis of changes in population size can be severely biased if factors related to the acquisition of data are not adequately controlled for. In particular we identify two types of observer effects: these correspond to baseline differences in observer competence, and to changes through time in the ability of individual observers. We present a family of models for count data in which the first of these observer effects is treated as a nuisance parameter. Conditioning on totals of negative binomial counts yields a Dirichlet compound multinomial vector for each observer. Quasi-likelihood is used to estimate parameters related to population trajectory and other parameters of interest; model selection is carried out on the basis of Akaike's information criterion. An example is presented using data on Wood thrush from the North American Breeding Bird Survey.

Trans-dimensional joint inversion of seabed scattering and reflection data.

PubMed

Steininger, Gavin; Dettmer, Jan; Dosso, Stan E; Holland, Charles W

2013-03-01

This paper examines joint inversion of acoustic scattering and reflection data to resolve seabed interface roughness parameters (spectral strength, exponent, and cutoff) and geoacoustic profiles. Trans-dimensional (trans-D) Bayesian sampling is applied with both the number of sediment layers and the order (zeroth or first) of auto-regressive parameters in the error model treated as unknowns. A prior distribution that allows fluid sediment layers over an elastic basement in a trans-D inversion is derived and implemented. Three cases are considered: Scattering-only inversion, joint scattering and reflection inversion, and joint inversion with the trans-D auto-regressive error model. Including reflection data improves the resolution of scattering and geoacoustic parameters. The trans-D auto-regressive model further improves scattering resolution and correctly differentiates between strongly and weakly correlated residual errors.

AutoClickChem: click chemistry in silico.

PubMed

Durrant, Jacob D; McCammon, J Andrew

2012-01-01

Academic researchers and many in industry often lack the financial resources available to scientists working in "big pharma." High costs include those associated with high-throughput screening and chemical synthesis. In order to address these challenges, many researchers have in part turned to alternate methodologies. Virtual screening, for example, often substitutes for high-throughput screening, and click chemistry ensures that chemical synthesis is fast, cheap, and comparatively easy. Though both in silico screening and click chemistry seek to make drug discovery more feasible, it is not yet routine to couple these two methodologies. We here present a novel computer algorithm, called AutoClickChem, capable of performing many click-chemistry reactions in silico. AutoClickChem can be used to produce large combinatorial libraries of compound models for use in virtual screens. As the compounds of these libraries are constructed according to the reactions of click chemistry, they can be easily synthesized for subsequent testing in biochemical assays. Additionally, in silico modeling of click-chemistry products may prove useful in rational drug design and drug optimization. AutoClickChem is based on the pymolecule toolbox, a framework that may facilitate the development of future python-based programs that require the manipulation of molecular models. Both the pymolecule toolbox and AutoClickChem are released under the GNU General Public License version 3 and are available for download from http://autoclickchem.ucsd.edu.

AutoClickChem: Click Chemistry in Silico

PubMed Central

Durrant, Jacob D.; McCammon, J. Andrew

2012-01-01

Academic researchers and many in industry often lack the financial resources available to scientists working in “big pharma.” High costs include those associated with high-throughput screening and chemical synthesis. In order to address these challenges, many researchers have in part turned to alternate methodologies. Virtual screening, for example, often substitutes for high-throughput screening, and click chemistry ensures that chemical synthesis is fast, cheap, and comparatively easy. Though both in silico screening and click chemistry seek to make drug discovery more feasible, it is not yet routine to couple these two methodologies. We here present a novel computer algorithm, called AutoClickChem, capable of performing many click-chemistry reactions in silico. AutoClickChem can be used to produce large combinatorial libraries of compound models for use in virtual screens. As the compounds of these libraries are constructed according to the reactions of click chemistry, they can be easily synthesized for subsequent testing in biochemical assays. Additionally, in silico modeling of click-chemistry products may prove useful in rational drug design and drug optimization. AutoClickChem is based on the pymolecule toolbox, a framework that may facilitate the development of future python-based programs that require the manipulation of molecular models. Both the pymolecule toolbox and AutoClickChem are released under the GNU General Public License version 3 and are available for download from http://autoclickchem.ucsd.edu. PMID:22438795

M-Bonomial Coefficients and Their Identities

ERIC Educational Resources Information Center

Asiru, Muniru A.

2010-01-01

In this note, we introduce M-bonomial coefficients or (M-bonacci binomial coefficients). These are similar to the binomial and the Fibonomial (or Fibonacci-binomial) coefficients and can be displayed in a triangle similar to Pascal's triangle from which some identities become obvious.

The effect of objectively measured crime on walking in minority adults.

PubMed

McDonald, Noreen C

2008-01-01

Evaluate the relationship between neighborhood crime and the amount of daily walking by minority adults. This was a cross-sectional study of minority adult walking behavior and crime. Setting. Oakland, California was chosen as the study area because of the substantial spatial variation in levels of criminal activity combined with detailed information on walking trips. The study was restricted to minority adults who responded to the 2000 Bay Area Travel Survey and lived in Oakland, California (n = 359). Data on leisure and utilitarian walking were collected through the 2000 Bay Area Travel Survey and combined with crime data from the Oakland Police Department. A negative binomial model was used to test if violent, property, or quality of life crimes had significant associations with daily minutes walked, controlling for individual and neighborhood covariates. The model showed a significant negative association between violent crime and minutes walked per day (b = -.07; p = .016). Neither property nor quality of life crimes were correlated with amount of walking. Reductions in violent crime may increase opportunities for minority residents in urban areas to participate in physical activity such as walking, thereby providing another reason to pursue anticrime measures. Urban designers' efforts to increase physical activity by improving neighborhood walkability may consider violent crime prevention in their designs.

Area characteristics and admission rates of people with schizophrenia and affective disorders in a German rural catchment area.

PubMed

Losert, C; Schmauß, M; Becker, T; Kilian, R

2012-12-01

Studies in urban areas identified environmental risk factors for mental illness, but little research on this topic has been performed in rural areas. Hospital admission rates were computed for 174 rural municipalities in the catchment area of the state psychiatric hospital in Günzburg in years 2006 to 2009 and combined with structural and socio-economic data. Relationships of overall and diagnosis-specific admission rates with municipality characteristics were analysed by means of negative binomial regression models. Admission rates of patients with a diagnosis of schizophrenia and affective disorder combined decrease with increasing population growth, population density, average income and green areas, while admission rates are positively correlated with commuter balance, income inequality, unemployment rates and traffic areas. Admission rates for schizophrenia are negatively related to population growth, average income and agricultural areas, but positively related to mobility index, income inequality and unemployment rate. Admission rates for affective disorders are negatively related to population growth, population density, average income and green areas, while higher admission rates are correlated with commuter balance, high income inequality, unemployment rate and traffic-related areas. Effects of wealth, economic inequality, population density and structural area characteristics influence psychiatric admission rates also in rural areas.

Some characteristics of repeated sickness absence

PubMed Central

Ferguson, David

1972-01-01

Ferguson, D. (1972).Brit. J. industr. Med.,29, 420-431. Some characteristics of repeated sickness absence. Several studies have shown that frequency of absence attributed to sickness is not distributed randomly but tends to follow the negative binomial distribution, and this has been taken to support the concept of `proneness' to such absence. Thus, the distribution of sickness absence resembles that of minor injury at work demonstrated over 50 years ago. Because the investigation of proneness to absence does not appear to have been reported by others in Australia, the opportunity was taken, during a wider study of health among telegraphists in a large communications undertaking, to analyse some characteristics of repeated sickness absence. The records of medically certified and uncertified sickness absence of all 769 telegraphists continuously employed in all State capitals over a two-and-a-half-year period were compared with those of 411 clerks and 415 mechanics and, in Sydney, 380 mail sorters and 80 of their supervisors. All telegraphists in Sydney, Melbourne, and Brisbane, and all mail sorters in Sydney, who were available and willing were later medically examined. From their absence pattern repeaters (employees who had had eight or more certified absences in two and a half years) were separated into three types based on a presumptive origin in chance, recurrent disease and symptomatic non-specific disorder. The observed distribution of individual frequency of certified absence over the full two-and-a-half-year period of study followed that expected from the univariate negative binomial, using maximum likelihood estimators, rather than the poisson distribution, in three of the four occupational groups in Sydney. Limited correlational and bivariate analysis supported the interpretation of proneness ascribed to the univariate fit. In the two groups studied, frequency of uncertified absence could not be fitted by the negative binomial, although the numbers of such absences in individuals in successive years were relatively highly correlated. All types of repeater were commoner in Sydney than in the other capital city offices, which differed little from each other. Repeaters were more common among those whose absence was attributed to neurosis, alimentary and upper respiratory tract disorder, and injury. Out of more than 90 health, personal, social, and industrial attributes determined at examination, only two (ethanol habit and adverse attitude to pay) showed any statistically significant association when telegraphist repeaters in Sydney were compared with employees who were rarely absent. Though repeating tended to be associated with chronic or recurrent ill health revealed at examination, one quarter of repeaters had little such ill health and one quarter of rarely absent employees had much. It was concluded that, in the population studied, the fitting of the negative binomial to frequency of certified sickness absence could, in the circumstances of the study, reasonably be given an interpretation of proneness. In that population also repeating varies geographically and occupationally, and is poorly associated with disease and other attributes uncovered at examination, with the exception of the ethanol habit. Repeaters are more often neurotic than employees who are rarely absent but also are more often stable double jobbers. The repeater should be identified for what help may be given him, if needed, otherwise it would seem more profitable to attack those features in work design and organization which influence motivation to come to work. Social factors which predispose to repeated absence are less amenable to modification. PMID:4636662

Spatial distribution of psychotic disorders in an urban area of France: an ecological study.

PubMed

Pignon, Baptiste; Schürhoff, Franck; Baudin, Grégoire; Ferchiou, Aziz; Richard, Jean-Romain; Saba, Ghassen; Leboyer, Marion; Kirkbride, James B; Szöke, Andrei

2016-05-18

Previous analyses of neighbourhood variations of non-affective psychotic disorders (NAPD) have focused mainly on incidence. However, prevalence studies provide important insights on factors associated with disease evolution as well as for healthcare resource allocation. This study aimed to investigate the distribution of prevalent NAPD cases in an urban area in France. The number of cases in each neighbourhood was modelled as a function of potential confounders and ecological variables, namely: migrant density, economic deprivation and social fragmentation. This was modelled using statistical models of increasing complexity: frequentist models (using Poisson and negative binomial regressions), and several Bayesian models. For each model, assumptions validity were checked and compared as to how this fitted to the data, in order to test for possible spatial variation in prevalence. Data showed significant overdispersion (invalidating the Poisson regression model) and residual autocorrelation (suggesting the need to use Bayesian models). The best Bayesian model was Leroux's model (i.e. a model with both strong correlation between neighbouring areas and weaker correlation between areas further apart), with economic deprivation as an explanatory variable (OR = 1.13, 95% CI [1.02-1.25]). In comparison with frequentist methods, the Bayesian model showed a better fit. The number of cases showed non-random spatial distribution and was linked to economic deprivation.

Modeling forest fire occurrences using count-data mixed models in Qiannan autonomous prefecture of Guizhou province in China.

PubMed

Xiao, Yundan; Zhang, Xiongqing; Ji, Ping

2015-01-01

Forest fires can cause catastrophic damage on natural resources. In the meantime, it can also bring serious economic and social impacts. Meteorological factors play a critical role in establishing conditions favorable for a forest fire. Effective prediction of forest fire occurrences could prevent or minimize losses. This paper uses count data models to analyze fire occurrence data which is likely to be dispersed and frequently contain an excess of zero counts (no fire occurrence). Such data have commonly been analyzed using count data models such as a Poisson model, negative binomial model (NB), zero-inflated models, and hurdle models. Data we used in this paper is collected from Qiannan autonomous prefecture of Guizhou province in China. Using the fire occurrence data from January to April (spring fire season) for the years 1996 through 2007, we introduced random effects to the count data models. In this study, the results indicated that the prediction achieved through NB model provided a more compelling and credible inferential basis for fitting actual forest fire occurrence, and mixed-effects model performed better than corresponding fixed-effects model in forest fire forecasting. Besides, among all meteorological factors, we found that relative humidity and wind speed is highly correlated with fire occurrence.

Modeling Forest Fire Occurrences Using Count-Data Mixed Models in Qiannan Autonomous Prefecture of Guizhou Province in China

PubMed Central

Ji, Ping

2015-01-01

Forest fires can cause catastrophic damage on natural resources. In the meantime, it can also bring serious economic and social impacts. Meteorological factors play a critical role in establishing conditions favorable for a forest fire. Effective prediction of forest fire occurrences could prevent or minimize losses. This paper uses count data models to analyze fire occurrence data which is likely to be dispersed and frequently contain an excess of zero counts (no fire occurrence). Such data have commonly been analyzed using count data models such as a Poisson model, negative binomial model (NB), zero-inflated models, and hurdle models. Data we used in this paper is collected from Qiannan autonomous prefecture of Guizhou province in China. Using the fire occurrence data from January to April (spring fire season) for the years 1996 through 2007, we introduced random effects to the count data models. In this study, the results indicated that the prediction achieved through NB model provided a more compelling and credible inferential basis for fitting actual forest fire occurrence, and mixed-effects model performed better than corresponding fixed-effects model in forest fire forecasting. Besides, among all meteorological factors, we found that relative humidity and wind speed is highly correlated with fire occurrence. PMID:25790309

Auto Code Generation for Simulink-Based Attitude Determination Control System

NASA Technical Reports Server (NTRS)

MolinaFraticelli, Jose Carlos

2012-01-01

This paper details the work done to auto generate C code from a Simulink-Based Attitude Determination Control System (ADCS) to be used in target platforms. NASA Marshall Engineers have developed an ADCS Simulink simulation to be used as a component for the flight software of a satellite. This generated code can be used for carrying out Hardware in the loop testing of components for a satellite in a convenient manner with easily tunable parameters. Due to the nature of the embedded hardware components such as microcontrollers, this simulation code cannot be used directly, as it is, on the target platform and must first be converted into C code; this process is known as auto code generation. In order to generate C code from this simulation; it must be modified to follow specific standards set in place by the auto code generation process. Some of these modifications include changing certain simulation models into their atomic representations which can bring new complications into the simulation. The execution order of these models can change based on these modifications. Great care must be taken in order to maintain a working simulation that can also be used for auto code generation. After modifying the ADCS simulation for the auto code generation process, it is shown that the difference between the output data of the former and that of the latter is between acceptable bounds. Thus, it can be said that the process is a success since all the output requirements are met. Based on these results, it can be argued that this generated C code can be effectively used by any desired platform as long as it follows the specific memory requirements established in the Simulink Model.

Gene-Auto: Automatic Software Code Generation for Real-Time Embedded Systems

NASA Astrophysics Data System (ADS)

Rugina, A.-E.; Thomas, D.; Olive, X.; Veran, G.

2008-08-01

This paper gives an overview of the Gene-Auto ITEA European project, which aims at building a qualified C code generator from mathematical models under Matlab-Simulink and Scilab-Scicos. The project is driven by major European industry partners, active in the real-time embedded systems domains. The Gene- Auto code generator will significantly improve the current development processes in such domains by shortening the time to market and by guaranteeing the quality of the generated code through the use of formal methods. The first version of the Gene-Auto code generator has already been released and has gone thought a validation phase on real-life case studies defined by each project partner. The validation results are taken into account in the implementation of the second version of the code generator. The partners aim at introducing the Gene-Auto results into industrial development by 2010.

Auto-recognition of surfaces and auto-generation of material removal volume for finishing process

NASA Astrophysics Data System (ADS)

Kataraki, Pramod S.; Salman Abu Mansor, Mohd

2018-03-01

Auto-recognition of a surface and auto-generation of material removal volumes for the so recognised surfaces has become a need to achieve successful downstream manufacturing activities like automated process planning and scheduling. Few researchers have contributed to generation of material removal volume for a product but resulted in material removal volume discontinuity between two adjacent material removal volumes generated from two adjacent faces that form convex geometry. The need for limitation free material removal volume generation was attempted and an algorithm that automatically recognises computer aided design (CAD) model’s surface and also auto-generate material removal volume for finishing process of the recognised surfaces was developed. The surfaces of CAD model are successfully recognised by the developed algorithm and required material removal volume is obtained. The material removal volume discontinuity limitation that occurred in fewer studies is eliminated.

Environmental, Spatial, and Sociodemographic Factors Associated with Nonfatal Injuries in Indonesia.

PubMed

Irianti, Sri; Prasetyoputra, Puguh

2017-01-01

Background . The determinants of injuries and their reoccurrence in Indonesia are not well understood, despite their importance in the prevention of injuries. Therefore, this study seeks to investigate the environmental, spatial, and sociodemographic factors associated with the reoccurrence of injuries among Indonesian people. Methods . Data from the 2013 round of the Indonesia Baseline Health Research (IBHR 2013) were analysed using a two-part hurdle regression model. A logit regression model was chosen for the zero-hurdle part , while a zero-truncated negative binomial regression model was selected for the counts part . Odds ratio (OR) and incidence rate ratio (IRR) were the measures of association, respectively. Results . The results suggest that living in a household with distant drinking water source, residing in slum areas, residing in Eastern Indonesia, having low educational attainment, being men, and being poorer are positively related to the likelihood of experiencing injury. Moreover, being a farmer or fishermen, having low educational attainment, and being men are positively associated with the frequency of injuries. Conclusion . This study would be useful to prioritise injury prevention programs in Indonesia based on the environmental, spatial, and sociodemographic characteristics.

Long Memory in STOCK Market Volatility: the International Evidence

NASA Astrophysics Data System (ADS)

Yang, Chunxia; Hu, Sen; Xia, Bingying; Wang, Rui

2012-08-01

It is still a hot topic to catch the auto-dependence behavior of volatility. Here, based on the measurement of average volatility, under different observation window size, we investigated the dependence of successive volatility of several main stock indices and their simulated GARCH(1, 1) model, there were obvious linear auto-dependence in the logarithm of volatility under a small observation window size and nonlinear auto-dependence under a big observation. After calculating the correlation and mutual information of the logarithm of volatility for Dow Jones Industrial Average during different periods, we find that some influential events can change the correlation structure and the volatilities of different periods have distinct influence on that of the remote future. Besides, GARCH model could produce similar behavior of dependence as real data and long memory property. But our analyses show that the auto-dependence of volatility in GARCH is different from that in real data, and the long memory is undervalued by GARCH.

AutoBayes Program Synthesis System Users Manual

NASA Technical Reports Server (NTRS)

Schumann, Johann; Jafari, Hamed; Pressburger, Tom; Denney, Ewen; Buntine, Wray; Fischer, Bernd

2008-01-01

Program synthesis is the systematic, automatic construction of efficient executable code from high-level declarative specifications. AutoBayes is a fully automatic program synthesis system for the statistical data analysis domain; in particular, it solves parameter estimation problems. It has seen many successful applications at NASA and is currently being used, for example, to analyze simulation results for Orion. The input to AutoBayes is a concise description of a data analysis problem composed of a parameterized statistical model and a goal that is a probability term involving parameters and input data. The output is optimized and fully documented C/C++ code computing the values for those parameters that maximize the probability term. AutoBayes can solve many subproblems symbolically rather than having to rely on numeric approximation algorithms, thus yielding effective, efficient, and compact code. Statistical analysis is faster and more reliable, because effort can be focused on model development and validation rather than manual development of solution algorithms and code.

Smisc - A collection of miscellaneous functions

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

Landon Sego, PNNL

2015-08-31

A collection of functions for statistical computing and data manipulation. These include routines for rapidly aggregating heterogeneous matrices, manipulating file names, loading R objects, sourcing multiple R files, formatting datetimes, multi-core parallel computing, stream editing, specialized plotting, etc. Smisc-package A collection of miscellaneous functions allMissing Identifies missing rows or columns in a data frame or matrix as.numericSilent Silent wrapper for coercing a vector to numeric comboList Produces all possible combinations of a set of linear model predictors cumMax Computes the maximum of the vector up to the current index cumsumNA Computes the cummulative sum of a vector without propogating NAsmore » d2binom Probability functions for the sum of two independent binomials dataIn A flexible way to import data into R. dbb The Beta-Binomial Distribution df2list Row-wise conversion of a data frame to a list dfplapply Parallelized single row processing of a data frame dframeEquiv Examines the equivalence of two dataframes or matrices dkbinom Probability functions for the sum of k independent binomials factor2character Converts all factor variables in a dataframe to character variables findDepMat Identify linearly dependent rows or columns in a matrix formatDT Converts date or datetime strings into alternate formats getExtension Filename manipulations: remove the extension or path, extract the extension or path getPath Filename manipulations: remove the extension or path, extract the extension or path grabLast Filename manipulations: remove the extension or path, extract the extension or path ifelse1 Non-vectorized version of ifelse integ Simple numerical integration routine interactionPlot Two-way Interaction Plot with Error Bar linearMap Linear mapping of a numerical vector or scalar list2df Convert a list to a data frame loadObject Loads and returns the object(s) in an ".Rdata" file more Display the contents of a file to the R terminal movAvg2 Calculate the moving average using a 2-sided window openDevice Opens a graphics device based on the filename extension p2binom Probability functions for the sum of two independent binomials padZero Pad a vector of numbers with zeros parseJob Parses a collection of elements into (almost) equal sized groups pbb The Beta-Binomial Distribution pcbinom A continuous version of the binomial cdf pkbinom Probability functions for the sum of k independent binomials plapply Simple parallelization of lapply plotFun Plot one or more functions on a single plot PowerData An example of power data pvar Prints the name and value of one or more objects qbb The Beta-Binomial Distribution rbb And numerous others (space limits reporting).« less

Variable selection for distribution-free models for longitudinal zero-inflated count responses.

PubMed

Chen, Tian; Wu, Pan; Tang, Wan; Zhang, Hui; Feng, Changyong; Kowalski, Jeanne; Tu, Xin M

2016-07-20

Zero-inflated count outcomes arise quite often in research and practice. Parametric models such as the zero-inflated Poisson and zero-inflated negative binomial are widely used to model such responses. Like most parametric models, they are quite sensitive to departures from assumed distributions. Recently, new approaches have been proposed to provide distribution-free, or semi-parametric, alternatives. These methods extend the generalized estimating equations to provide robust inference for population mixtures defined by zero-inflated count outcomes. In this paper, we propose methods to extend smoothly clipped absolute deviation (SCAD)-based variable selection methods to these new models. Variable selection has been gaining popularity in modern clinical research studies, as determining differential treatment effects of interventions for different subgroups has become the norm, rather the exception, in the era of patent-centered outcome research. Such moderation analysis in general creates many explanatory variables in regression analysis, and the advantages of SCAD-based methods over their traditional counterparts render them a great choice for addressing this important and timely issues in clinical research. We illustrate the proposed approach with both simulated and real study data. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Coronary artery calcium distributions in older persons in the AGES-Reykjavik study

PubMed Central

Gudmundsson, Elias Freyr; Gudnason, Vilmundur; Sigurdsson, Sigurdur; Launer, Lenore J.; Harris, Tamara B.; Aspelund, Thor

2013-01-01

Coronary Artery Calcium (CAC) is a sign of advanced atherosclerosis and an independent risk factor for cardiac events. Here, we describe CAC-distributions in an unselected aged population and compare modelling methods to characterize CAC-distribution. CAC is difficult to model because it has a skewed and zero inflated distribution with over-dispersion. Data are from the AGES-Reykjavik sample, a large population based study [2002-2006] in Iceland of 5,764 persons aged 66-96 years. Linear regressions using logarithmic- and Box-Cox transformations on CAC+1, quantile regression and a Zero-Inflated Negative Binomial model (ZINB) were applied. Methods were compared visually and with the PRESS-statistic, R2 and number of detected associations with concurrently measured variables. There were pronounced differences in CAC according to sex, age, history of coronary events and presence of plaque in the carotid artery. Associations with conventional coronary artery disease (CAD) risk factors varied between the sexes. The ZINB model provided the best results with respect to the PRESS-statistic, R2, and predicted proportion of zero scores. The ZINB model detected similar numbers of associations as the linear regression on ln(CAC+1) and usually with the same risk factors. PMID:22990371

Bus accident analysis of routes with/without bus priority.

PubMed

Goh, Kelvin Chun Keong; Currie, Graham; Sarvi, Majid; Logan, David

2014-04-01

This paper summarises findings on road safety performance and bus-involved accidents in Melbourne along roads where bus priority measures had been applied. Results from an empirical analysis of the accident types revealed significant reduction in the proportion of accidents involving buses hitting stationary objects and vehicles, which suggests the effect of bus priority in addressing manoeuvrability issues for buses. A mixed-effects negative binomial (MENB) regression and back-propagation neural network (BPNN) modelling of bus accidents considering wider influences on accident rates at a route section level also revealed significant safety benefits when bus priority is provided. Sensitivity analyses done on the BPNN model showed general agreement in the predicted accident frequency between both models. The slightly better performance recorded by the MENB model results suggests merits in adopting a mixed effects modelling approach for accident count prediction in practice given its capability to account for unobserved location and time-specific factors. A major implication of this research is that bus priority in Melbourne's context acts to improve road safety and should be a major consideration for road management agencies when implementing bus priority and road schemes. Copyright © 2013 Elsevier Ltd. All rights reserved.

Comparison between sensitivity of autologous skin serum test and autologous plasma skin test in patients with Chronic Idiopathic Urticaria for detection of antibody against IgE or IgE receptor (FcεRIα).

PubMed

Sajedi, Vahid; Movahedi, Masoud; Aghamohammadi, Asghar; Aghamohamadi, Asghar; Gharagozlou, Mohammad; Ghareguzlou, Mohammad; Shafiei, Alireza; Soheili, Habib; Sanajian, Nahal

2011-06-01

Intradermal injection of autologous serum and plasma elicit a cutaneous reactivity in almost 45-60% of patients with Chronic Idiopathic Urticaria (CIU). This reactivity is associated with the presence of auto antibodies against IgE or IgE receptors. This study was carried out to compare the cutaneous reactivity of autologous serum and plasma skin tests in a series of patients with CIU for diagnosis of auto antibodies against IgE or IgE receptor. Fifty eight patients with CIU were injected intradermally with autologous serum and plasma (anticoagulated by citrate). Histamine was used as positive control and normal saline as negative control. The study group was checked by routine laboratory tests (CBC, U/A etc), allergens with skin prick tests, and serum IgE level, and auto antibodies against thyroid as well. Duration of urticaria was another factor which was assessed.There was no significant difference between positive ASST and positive APST patients for the above mentioned tests. 77.6% of the patients were Positive for APST and 65.5% were ASST positive. Duration of urticaria was longer in patients with positive ASST and APST than ASST and APST negative patients, although the difference was not statistically significant.Autologus serum skin test (ASST) and autologous plasma skin test (APST) could be used for estimation of duration and severity of urticaria and planning for the treatment.

Evaluation of new automated syphilis test reagents 'IMMUNOTICLES AUTO3' series: performance, biochemical reactivity, and clinical significance.

PubMed

Yukimasa, Nobuyasu; Miura, Keisuke; Miyagawa, Yukiko; Fukuchi, Kunihiko

2015-01-01

Automated nontreponemal and treponemal test reagents based on the latex agglutination method (immunoticles auto3 RPR: ITA3RPR and immunoticles auto3 TP: ITA3TP) have been developed to improve the issues of conventional manual methods such as their subjectivity, a massive amount of assays, and so on. We evaluated these reagents in regards to their performance, reactivity to antibody isotype, and their clinical significance. ITA3RPR and ITA3TP were measured using a clinical chemistry analyzer. Reactivity to antibody isotype was examined by gel filtration analysis. ITA3RPR and ITA3TP showed reactivity to both IgM- and IgG-class antibodies and detected early infections. ITA3RPR was verified to show a higher reactivity to IgM-class antibodies than the conventional methods. ITA3RPR correlated with VDRL in the high titer range, and measurement values decreased with treatment. ITA3RPR showed a negative result earlier after treatment than conventional methods. ITA3TP showed high specificity and did not give any false-negative reaction. Significant differences in the measurement values of ITA3RPR between the infective and previous group were verified. The double test of ITA3RPR and ITA3TP enables efficient and objective judgment for syphilis diagnosis and treatments, achieving clinical availability. Copyright © 2014 Japanese Society of Chemotherapy and The Japanese Association for Infectious Diseases. Published by Elsevier Ltd. All rights reserved.

Crash Frequency Analysis Using Hurdle Models with Random Effects Considering Short-Term Panel Data

PubMed Central

Chen, Feng; Ma, Xiaoxiang; Chen, Suren; Yang, Lin

2016-01-01

Random effect panel data hurdle models are established to research the daily crash frequency on a mountainous section of highway I-70 in Colorado. Road Weather Information System (RWIS) real-time traffic and weather and road surface conditions are merged into the models incorporating road characteristics. The random effect hurdle negative binomial (REHNB) model is developed to study the daily crash frequency along with three other competing models. The proposed model considers the serial correlation of observations, the unbalanced panel-data structure, and dominating zeroes. Based on several statistical tests, the REHNB model is identified as the most appropriate one among four candidate models for a typical mountainous highway. The results show that: (1) the presence of over-dispersion in the short-term crash frequency data is due to both excess zeros and unobserved heterogeneity in the crash data; and (2) the REHNB model is suitable for this type of data. Moreover, time-varying variables including weather conditions, road surface conditions and traffic conditions are found to play importation roles in crash frequency. Besides the methodological advancements, the proposed technology bears great potential for engineering applications to develop short-term crash frequency models by utilizing detailed data from field monitoring data such as RWIS, which is becoming more accessible around the world. PMID:27792209

75 FR 24753 - The Walker Auto Group, Inc., Miamisburg, OH; Notice of Negative Determination Regarding...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-05-05

..., supplies a service (sales and service of Pontiac automobiles)'' and ``A required minimum of the workforce... foreign-made automobiles has increased continually for years, contributing importantly to an actual... was based on the continued production of Pontiac automobiles, therefore the increases of imported cars...

AutoRoute Rapid Flood Inundation Model

DTIC Science & Technology

2013-03-01

Res. 33(2): 309-319. U.S. Army Engineer Hydrologic Engineering Center. 2010. “ HEC - RAS : River Analysis System, User’s Manual, Version 4.1.” Davis...cross-section data does not exist. As such, the AutoRoute model is not meant to be as accurate as models such as HEC - RAS (U.S. Army Engineer...such as HEC - RAS assume that the defined low point of cross sections must be connected. However, in this approach the channel is assumed to be defined

Modeling and Simulation of Ceramic Arrays to Improve Ballistic Performance

DTIC Science & Technology

2014-03-01

30cal AP M2 Projectile, 762x39 PS Projectile, SPH , Aluminum 5083, SiC, DoP Expeminets, AutoDyn Sin 16. SECURITY CLASSIFICATION OF: UU a. REPORT b...projectile and are modeled using SPH elements in AutoDyn □ Center strike model validation runs with SiC tiles are conducted based on the DOP...Smoothed-particle hydrodynamics ( SPH ) used for all parts, SPH Size = 0.2 3 SiC and SiC 2 are identical in properties and dimensions

Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods.

PubMed

Luo, Gang; Stone, Bryan L; Johnson, Michael D; Tarczy-Hornoch, Peter; Wilcox, Adam B; Mooney, Sean D; Sheng, Xiaoming; Haug, Peter J; Nkoy, Flory L

2017-08-29

To improve health outcomes and cut health care costs, we often need to conduct prediction/classification using large clinical datasets (aka, clinical big data), for example, to identify high-risk patients for preventive interventions. Machine learning has been proposed as a key technology for doing this. Machine learning has won most data science competitions and could support many clinical activities, yet only 15% of hospitals use it for even limited purposes. Despite familiarity with data, health care researchers often lack machine learning expertise to directly use clinical big data, creating a hurdle in realizing value from their data. Health care researchers can work with data scientists with deep machine learning knowledge, but it takes time and effort for both parties to communicate effectively. Facing a shortage in the United States of data scientists and hiring competition from companies with deep pockets, health care systems have difficulty recruiting data scientists. Building and generalizing a machine learning model often requires hundreds to thousands of manual iterations by data scientists to select the following: (1) hyper-parameter values and complex algorithms that greatly affect model accuracy and (2) operators and periods for temporally aggregating clinical attributes (eg, whether a patient's weight kept rising in the past year). This process becomes infeasible with limited budgets. This study's goal is to enable health care researchers to directly use clinical big data, make machine learning feasible with limited budgets and data scientist resources, and realize value from data. This study will allow us to achieve the following: (1) finish developing the new software, Automated Machine Learning (Auto-ML), to automate model selection for machine learning with clinical big data and validate Auto-ML on seven benchmark modeling problems of clinical importance; (2) apply Auto-ML and novel methodology to two new modeling problems crucial for care management allocation and pilot one model with care managers; and (3) perform simulations to estimate the impact of adopting Auto-ML on US patient outcomes. We are currently writing Auto-ML's design document. We intend to finish our study by around the year 2022. Auto-ML will generalize to various clinical prediction/classification problems. With minimal help from data scientists, health care researchers can use Auto-ML to quickly build high-quality models. This will boost wider use of machine learning in health care and improve patient outcomes. ©Gang Luo, Bryan L Stone, Michael D Johnson, Peter Tarczy-Hornoch, Adam B Wilcox, Sean D Mooney, Xiaoming Sheng, Peter J Haug, Flory L Nkoy. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 29.08.2017.

Shortening of the Lactobacillus paracasei subsp. paracasei BGNJ1-64 AggLb Protein Switches Its Activity from Auto-aggregation to Biofilm Formation.

PubMed

Miljkovic, Marija; Bertani, Iris; Fira, Djordje; Jovcic, Branko; Novovic, Katarina; Venturi, Vittorio; Kojic, Milan

2016-01-01

AggLb is the largest (318.6 kDa) aggregation-promoting protein of Lactobacillus paracasei subsp. paracasei BGNJ1-64 responsible for forming large cell aggregates, which causes auto-aggregation, collagen binding and pathogen exclusion in vitro. It contains an N-terminus leader peptide, followed by six successive collagen binding domains, 20 successive repeats (CnaB-like domains) and an LPXTG sorting signal at the C-terminus for cell wall anchoring. Experimental information about the roles of the domains of AggLb is currently unknown. To define the domain that confers cell aggregation and the key domains for interactions of specific affinity between AggLb and components of the extracellular matrix, we constructed a series of variants of the aggLb gene and expressed them in Lactococcus lactis subsp. lactis BGKP1-20 using a lactococcal promoter. All of the variants contained a leader peptide, an inter collagen binding-CnaB domain region (used to raise an anti-AggLb antibody), an anchor domain and a different number of collagen binding and CnaB-like domains. The role of the collagen binding repeats of the N-terminus in auto-aggregation and binding to collagen and fibronectin was confirmed. Deletion of the collagen binding repeats II, III, and IV resulted in a loss of the strong auto-aggregation, collagen and fibronectin binding abilities whereas the biofilm formation capability was increased. The strong auto-aggregation, collagen and fibronectin binding abilities of AggLb were negatively correlated to biofilm formation.

Problems on Divisibility of Binomial Coefficients

ERIC Educational Resources Information Center

Osler, Thomas J.; Smoak, James

2004-01-01

Twelve unusual problems involving divisibility of the binomial coefficients are represented in this article. The problems are listed in "The Problems" section. All twelve problems have short solutions which are listed in "The Solutions" section. These problems could be assigned to students in any course in which the binomial theorem and Pascal's…

Application of binomial-edited CPMG to shale characterization

USGS Publications Warehouse

Washburn, Kathryn E.; Birdwell, Justin E.

2014-01-01

Unconventional shale resources may contain a significant amount of hydrogen in organic solids such as kerogen, but it is not possible to directly detect these solids with many NMR systems. Binomial-edited pulse sequences capitalize on magnetization transfer between solids, semi-solids, and liquids to provide an indirect method of detecting solid organic materials in shales. When the organic solids can be directly measured, binomial-editing helps distinguish between different phases. We applied a binomial-edited CPMG pulse sequence to a range of natural and experimentally-altered shale samples. The most substantial signal loss is seen in shales rich in organic solids while fluids associated with inorganic pores seem essentially unaffected. This suggests that binomial-editing is a potential method for determining fluid locations, solid organic content, and kerogen–bitumen discrimination.

[Comparison of predictive effect between the single auto regressive integrated moving average (ARIMA) model and the ARIMA-generalized regression neural network (GRNN) combination model on the incidence of scarlet fever].

PubMed

Zhu, Yu; Xia, Jie-lai; Wang, Jing

2009-09-01

Application of the 'single auto regressive integrated moving average (ARIMA) model' and the 'ARIMA-generalized regression neural network (GRNN) combination model' in the research of the incidence of scarlet fever. Establish the auto regressive integrated moving average model based on the data of the monthly incidence on scarlet fever of one city, from 2000 to 2006. The fitting values of the ARIMA model was used as input of the GRNN, and the actual values were used as output of the GRNN. After training the GRNN, the effect of the single ARIMA model and the ARIMA-GRNN combination model was then compared. The mean error rate (MER) of the single ARIMA model and the ARIMA-GRNN combination model were 31.6%, 28.7% respectively and the determination coefficient (R(2)) of the two models were 0.801, 0.872 respectively. The fitting efficacy of the ARIMA-GRNN combination model was better than the single ARIMA, which had practical value in the research on time series data such as the incidence of scarlet fever.

The influence of economic incentives linked to road safety indicators on accidents: the case of toll concessions in Spain.

PubMed

Rangel, Thais; Vassallo, José Manuel; Herraiz, Israel

2013-10-01

The goal of this paper is to evaluate whether the incentives incorporated in toll highway concession contracts in order to encourage private operators to adopt measures to reduce accidents are actually effective at improving safety. To this end, we implemented negative binomial regression models using information about highway characteristics and accident data from toll highway concessions in Spain from 2007 to 2009. Our results show that even though road safety is highly influenced by variables that are not managed by the contractor, such as the annual average daily traffic (AADT), the percentage of heavy vehicles on the highway, number of lanes, number of intersections and average speed; the implementation of these incentives has a positive influence on the reduction of accidents and injuries. Consequently, this measure seems to be an effective way of improving safety performance in road networks. Copyright © 2013 Elsevier Ltd. All rights reserved.

Work-related injuries involving a hand or fingers among union carpenters in Washington State, 1989 to 2008.

PubMed

Lipscomb, Hester J; Schoenfisch, Ashley; Cameron, Wilfrid

2013-07-01

We evaluated work-related injuries involving a hand or fingers and associated costs among a cohort of 24,830 carpenters between 1989 and 2008. Injury rates and rate ratios were calculated by using Poisson regression to explore higher risk on the basis of age, sex, time in the union, predominant work, and calendar time. Negative binomial regression was used to model dollars paid per claim after adjustment for inflation and discounting. Hand injuries accounted for 21.1% of reported injuries and 9.5% of paid lost time injuries. Older carpenters had proportionately more amputations, fractures, and multiple injuries, but their rates of these more severe injuries were not higher. Costs exceeded $21 million, a cost burden of $0.11 per hour worked. Older carpenters' higher proportion of serious injuries in the absence of higher rates likely reflects age-related reporting differences.

Environmental Risk Factors influencing Bicycle Theft: A Spatial Analysis in London, UK.

PubMed

Mburu, Lucy Waruguru; Helbich, Marco

2016-01-01

Urban authorities are continuously drawing up policies to promote cycling among commuters. However, these initiatives are counterproductive for the targeted objectives because they increase opportunities for bicycle theft. This paper explores Inner London as a case study to address place-specific risk factors for bicycle theft at the street-segment level while controlling for seasonal variation. The presence of certain public amenities (e.g., bicycle stands, railway stations, pawnshops) was evaluated against locations of bicycle theft between 2013 and 2016 and risk effects were estimated using negative binomial regression models. Results showed that a greater level of risk stemmed from land-use facilities than from area-based socioeconomic status. The presence of facilities such as train stations, vacant houses, pawnbrokers and payday lenders increased bicycle theft, but no evidence was found that linked police stations with crime levels. The findings have significant implications for urban crime prevention with respect to non-residential land use.

Assessment of DSM-5 Section II Personality Disorders With the MMPI-2-RF in a Nonclinical Sample.

PubMed

Sellbom, Martin; Smith, Alexander

2017-01-01

The Minnesota Multiphasic Personality Inventory-2-Restructured Form (MMPI-2-RF; Ben-Porath & Tellegen, 2008 / 2011 ) is frequently used in clinical practice. However, there has been a dearth of literature on how well this instrument can assess symptoms associated with personality disorders (PDs). This investigation examined a range of hypothesized MMPI-2-RF scales in predicting PD symptoms. We evaluated these associations in a sample of 397 university students who had been administered the MMPI-2-RF and the Structured Clinical Interview for DSM-IV Axis II Disorders-Personality Questionnaire (First, Gibbon, Spitzer, Williams, & Benjamin, 1997 ). Zero-order correlation analyses and negative binomial regression models indicated that a wide range of MMPI-2-RF scale hypotheses were supported; however, the least support was available for predicting schizoid and obsessive-compulsive PDs. Implications for MMPI-2-RF interpretation and PD diagnosis are discussed.

Assessing the Macro-Level Correlates of Malware Infections Using a Routine Activities Framework.

PubMed

Holt, Thomas J; Burruss, George W; Bossler, Adam M

2018-05-01

The ability to gain unauthorized access to computer systems to engage in espionage and data theft poses a massive threat to individuals worldwide. There has been minimal focus, however, on the role of malicious software, or malware, which can automate this process. This study examined the macro-correlates of malware infection at the national level by using an open repository of known malware infections and utilizing a routine activities framework. Negative inflated binomial models for counts indicated that nations with greater technological infrastructure, more political freedoms, and with less organized crime financial impact were more likely to report malware infections. The number of Computer Emergency Response Teams (CERTs) in a nation was not significantly related with reported malware infection. The implications of the study for the understanding of malware infection, routine activity theory, and target-hardening strategies are discussed.

The Effect of Exposure to Ultraviolet Radiation in Infancy on Melanoma Risk.

PubMed

Gefeller, Olaf; Fiessler, Cornelia; Radespiel-Tröger, Martin; Uter, Wolfgang; Pfahlberg, Annette B

2016-01-01

Evidence on the effect of ultraviolet radiation (UVR) exposure in infancy on melanoma risk in later life is scarce. Three recent studies suffering from methodological shortcomings suggested that people born in spring carry a higher melanoma risk. Data from the Bavarian population-based cancer registry on 28374 incident melanoma cases between 2002 and 2012 were analyzed to reexamine this finding. Crude and adjusted analyses - using negative binomial regression models - were performed addressing the relationship. In the crude analysis, the birth months March - May were significantly overrepresented among melanoma cases. However, after additionally adjusting for the birth month distribution of the Bavarian population, the ostensible seasonal effect disappeared. Similar results emerged in all subgroup analyses. Our large registry-based study provides no evidence that people born in spring carry a higher risk for developing melanoma in later life and thus lends no support to the hypothesis of higher UVR-susceptibility during the first months of life.

The role of self-efficacy and motivation to explain the effect of motivational interviewing time on changes in risky sexual behavior among people living with HIV: a mediation analysis.

PubMed

Chariyeva, Zulfiya; Golin, Carol E; Earp, Jo Anne; Maman, Suzanne; Suchindran, Chirayath; Zimmer, Catherine

2013-02-01

Little is known about the amount of Motivational Interviewing (MI) needed to reduce risky sexual behavior among People Living with HIV/AIDS (PLWHA) or the roles self-efficacy and motivation to practice safer sex play. Among 183 PLWHA who received safer sex MI and were surveyed every 4 months over a 12 month period, we used hierarchical negative binomial regression models to examine the association between amount of counseling time and sexual risk behavior. We performed mediation analysis to evaluate whether changes in self-efficacy and motivation explained this association. This study found that as MI time and number of provided sessions increased, participants' sexual risk behavior decreased. The effect of MI time and number of sessions on sexual behavior was mediated by self-efficacy but not by motivation to practice safer sex.

The Role of Self-Efficacy and Motivation to Explain the Effect of Motivational Interviewing Time on Changes in Risky Sexual Behavior among People Living with HIV: A Mediation Analysis

PubMed Central

Golin, Carol E.; Earp, Jo Anne; Maman, Suzanne; Suchindran, Chirayath; Zimmer, Catherine

2014-01-01

Little is known about the amount of Motivational Interviewing (MI) needed to reduce risky sexual behavior among People Living with HIV/AIDS (PLWHA) or the roles self-efficacy and motivation to practice safer sex play. Among 183 PLWHA who received safer sex MI and were surveyed every 4 months over a 12 month period, we used hierarchical negative binomial regression models to examine the association between amount of counseling time and sexual risk behavior. We performed mediation analysis to evaluate whether changes in self-efficacy and motivation explained this association. This study found that as MI time and number of provided sessions increased, participants’ sexual risk behavior decreased. The effect of MI time and number of sessions on sexual behavior was mediated by self-efficacy but not by motivation to practice safer sex. PMID:22228069

Adult Children's Education and Parents' Functional Limitations in Mexico.

PubMed

Yahirun, Jenjira J; Sheehan, Connor M; Hayward, Mark D

2016-04-01

This article asks how adult children's education influences older parents' physical health in Mexico, a context where older adults often lack access to institutional resources and rely on kin, primarily children, as a main source of support. Using logistic and negative binomial regression models and data from the first wave of the Mexican Health and Aging Study (N = 9,661), we find that parents whose children all completed high school are less likely to report any functional limitations as well as fewer limitations compared to parents with no children who completed high school. This association remains significant even after accounting for parent and offspring-level characteristics, including parents' income that accounts for children's financial transfers to parents. Future research should aim to understand the mechanisms that explain the association between adult children's education and changes to parents' health over time. © The Author(s) 2015.

Gastrointestinal parasite egg excretion in young calves in periurban livestock production in Mali.

PubMed

Wymann, Monica Natalie; Traore, Koniba; Bonfoh, Bassirou; Tembely, Saïdou; Tembely, Sékouba; Zinsstag, Jakob

2008-04-01

To acquire the information needed to improve parasite control in periurban cattle production in Mali, repeated sampling of faeces of 694 calves kept around Bamako was done in 2003/2004. The effects of season, age, breed, management type, parasite control and presence of sheep on egg and oocyst counts were determined. A Bayesian model was used with a negative binomial distribution and herd and individual effects, to account for the clustering of calves in herds and the repeated sampling. Interviews were conducted to report the current control strategies. We found eggs of Strongyloides papillosus (Age class 0-1 month: prevalence 39%, 2-3 months: 59%, 5-6 months: 42%), strongyles (14%, 24%, 36%), coccidian oocysts (37%, 68%, 64%) and at low prevalence eggs of Toxocara vitulorum, Moniezia sp., Trichuris sp. and Paramphistomum sp. Season and age effects occurred. Reported utilisation of parasite control was high (92%) but monthly recorded use was significantly lower (61%).

Mental Health Symptoms Among Student Service Members/Veterans and Civilian College Students.

PubMed

Cleveland, Sandi D; Branscum, Adam J; Bovbjerg, Viktor E; Thorburn, Sheryl

2015-01-01

The aim of this study was to investigate if and to what extent student service members/veterans differ from civilian college students in the prevalence of self-reported symptoms of poor mental health. The Fall 2011 implementation of the American College Health Association-National College Health Assessment included 27,774 respondents from 44 colleges and universities. Participants were matched using propensity scores, and the prevalence of symptoms was compared using logistic regression and zero-inflated negative binomial regression models. The odds of feeling overwhelmed in the last 12 months were significantly lower among student service members/veterans with a history of hazardous duty (odd ratio [OR] = 0.46, adjusted p value <.05) compared with civilian students. Military service, with and without hazardous duty deployment, was not a significant predictor of the total number of symptoms of poor mental health. Current student service members/veterans may not be disproportionately affected by poor psychological functioning.

Work performance decrements are associated with Australian working conditions, particularly the demand to work longer hours.

PubMed

Holden, Libby; Scuffham, Paul A; Hilton, Michael F; Vecchio, Nerina N; Whiteford, Harvey A

2010-03-01

To demonstrate the importance of including a range of working conditions in models exploring the association between health- and work-related performance. The Australian Work Outcomes Research Cost-benefit study cross-sectional screening data set was used to explore health-related absenteeism and work performance losses on a sample of approximately 78,000 working Australians, including available demographic and working condition factors. Data collected using the World Health Organization Health and Productivity Questionnaire were analyzed with negative binomial logistic regression and multinomial logistic regressions for absenteeism and work performance, respectively. Hours expected to work, annual wage, and job insecurity play a vital role in the association between health- and work-related performance for both work attendance and self-reported work performance. Australian working conditions are contributing to both absenteeism and low work performance, regardless of health status.

How to retrieve additional information from the multiplicity distributions

NASA Astrophysics Data System (ADS)

Wilk, Grzegorz; Włodarczyk, Zbigniew

2017-01-01

Multiplicity distributions (MDs) P(N) measured in multiparticle production processes are most frequently described by the negative binomial distribution (NBD). However, with increasing collision energy some systematic discrepancies have become more and more apparent. They are usually attributed to the possible multi-source structure of the production process and described using a multi-NBD form of the MD. We investigate the possibility of keeping a single NBD but with its parameters depending on the multiplicity N. This is done by modifying the widely known clan model of particle production leading to the NBD form of P(N). This is then confronted with the approach based on the so-called cascade-stochastic formalism which is based on different types of recurrence relations defining P(N). We demonstrate that a combination of both approaches allows the retrieval of additional valuable information from the MDs, namely the oscillatory behavior of the counting statistics apparently visible in the high energy data.

Potential Economic Benefits of Paid Sick Leave in Reducing Absenteeism Related to the Spread of Influenza-Like Illness.

PubMed

Asfaw, Abay; Rosa, Roger; Pana-Cryan, Regina

2017-09-01

Most U.S. employers are not required to provide paid sick leave (PSL), and there is limited information on the economic return of providing PSL. We estimated potential benefits to employers of PSL in reducing absenteeism related to the spread of influenza-like illness (ILI). We used nationally representative data and a negative binomial random effects model to estimate the impact of PSL in reducing overall absence due to illness or injury. We used published data to compute the share of ILI from the total days of absence, ILI transmission rates at workplaces, wages, and other parameters. Providing PSL could have saved employers $0.63 to $1.88 billion in reduced ILI-related absenteeism costs per year during 2007 to 2014 in 2016 dollars. These findings might help employers consider PSL as an investment rather than as a cost without any return.

Perceived health status and daily activity participation of older Malaysians.

PubMed

Ng, Sor Tho; Tengku-Aizan, Hamid; Tey, Nai Peng

2011-07-01

This article investigates the influence of perceived health status on the daily activity participation of older Malaysians. Data from the Survey on Perceptions of Needs and Problems of the Elderly, which was conducted in 1999, were used. The negative binomial regression results show that older persons with good perceived health status reported more varieties of daily activity participation, especially among the uneducated and those with below-average self-esteem. The multinomial logistic regression model suggests that older persons with good perceived health status tended to engage daily in paid work only or with leisure activities, whereas those perceived to have poor health were more likely to engage in leisure activities only or leisure and family role activities. Promotion of a healthy lifestyle at a younger age encourages every person to monitor and take responsibility for their own health, which is a necessary strategy to ensure active participation at an older age, and thus improve their well-being.

Adult Children’s Education and Parents’ Functional Limitations in Mexico

PubMed Central

Yahirun, Jenjira J.; Sheehan, Connor M.; Hayward, Mark D.

2016-01-01

This article asks how adult children’s education influences older parents’ physical health in Mexico, a context where older adults often lack access to institutional resources and rely on kin, primarily children, as a main source of support. Using logistic and negative binomial regression models and data from the first wave of the Mexican Health and Aging Study (N = 9,661), we find that parents whose children all completed high school are less likely to report any functional limitations as well as fewer limitations compared to parents with no children who completed high school. This association remains significant even after accounting for parent and offspring-level characteristics, including parents’ income that accounts for children’s financial transfers to parents. Future research should aim to understand the mechanisms that explain the association between adult children’s education and changes to parents’ health over time. PMID:26966254

Bio-Ecology of the Louse, Upupicola upupae, Infesting the Common Hoopoe, Upupa epops

PubMed Central

Agarwal, G. P; Ahmad, Aftab; Rashmi, Archna; Arya, Gaurav; Bansal, Nayanci; Saxena, A.K.

2011-01-01

The population characteristics of the louse, Upupicola upupae (Shrank) (Mallophaga: Philopteridae: Ishnocera), infesting the Common Hoopae, Upupa epops L. (Aves: Upupiformes), were recorded during 2007–08 in District Rampur, Uttar Pradesh India. The pattern of frequency distribution of the louse conformed to the negative binomial model. The lice and its nits were reared in vitro at 35 ± 1° C, 75–82 % RH, on a feather diet. The data obtained was used to construct the life table and to determine the intrinsic rate of natural increase (0.035 female/day), the net reproductive rate was 3.67 female eggs/female, the generation time was 37 days, and the doubling time of the population was 19 days. The chaetotaxy of the three nymphal instars has also been noted to record their diagnostic characteristics. Information on egg morphology and antennal sensilla is also presented. PMID:21861650

Assaults on Days of Campaign Rallies During the 2016 US Presidential Election.

PubMed

Morrison, Christopher N; Ukert, Benjamin; Palumbo, Aimee; Dong, Beidi; Jacoby, Sara F; Wiebe, Douglas J

2018-07-01

This study investigates whether assault frequency increased on days and in cities where candidates Donald Trump and Hillary Clinton held campaign rallies prior to the 2016 US Presidential election. We calculated city-level counts of police-reported assaults for 31 rallies for Donald Trump and 38 rallies for Hillary Clinton. Negative binomial models estimated the assault incidence on rally days (day 0) relative to that on eight control days for the same city (days -28, -21, -14, -7, +7, +14, +21, and +28). Cities experienced an increase in assaults (incidence rate ratio [IRR] = 1.12, 95% CI: 1.03-1.22) on the days of Donald Trump's rallies, and no change in assaults on the days of Hillary Clinton's rallies (IRR = 1.00; 95% CI: 0.94-1.06). Assaults increased on days when cities hosted Donald Trump's rallies during the 2016 Presidential election campaign.

Does the Organized Sexual Murderer Better Delay and Avoid Detection?

PubMed

Beauregard, Eric; Martineau, Melissa

2016-01-01

According to the organized-disorganized model, organized sexual murderers adopt specific behaviors during the commission of their crimes that contribute to avoiding police detection. The current study examines the effect of sexual murderers' organized behaviors on their ability to both delay and/or avoid police detection. Using a combination of negative binomial and logistic regression analyses on a sample of 350 sexual murder cases, findings showed that although both measures of delaying and avoiding detection are positively correlated, different behavioral patterns were observed. For instance, offenders who moved the victim's body were more likely to avoid detection but the victim's body was likely to be recovered faster. Moreover, victim characteristics have an impact on both measures; however, this effect disappears for the measure of delaying detection once the organized behaviors are introduced. Implications of the findings are discussed. © The Author(s) 2014.

Atlas-based segmentation technique incorporating inter-observer delineation uncertainty for whole breast

NASA Astrophysics Data System (ADS)

Bell, L. R.; Dowling, J. A.; Pogson, E. M.; Metcalfe, P.; Holloway, L.

2017-01-01

Accurate, efficient auto-segmentation methods are essential for the clinical efficacy of adaptive radiotherapy delivered with highly conformal techniques. Current atlas based auto-segmentation techniques are adequate in this respect, however fail to account for inter-observer variation. An atlas-based segmentation method that incorporates inter-observer variation is proposed. This method is validated for a whole breast radiotherapy cohort containing 28 CT datasets with CTVs delineated by eight observers. To optimise atlas accuracy, the cohort was divided into categories by mean body mass index and laterality, with atlas’ generated for each in a leave-one-out approach. Observer CTVs were merged and thresholded to generate an auto-segmentation model representing both inter-observer and inter-patient differences. For each category, the atlas was registered to the left-out dataset to enable propagation of the auto-segmentation from atlas space. Auto-segmentation time was recorded. The segmentation was compared to the gold-standard contour using the dice similarity coefficient (DSC) and mean absolute surface distance (MASD). Comparison with the smallest and largest CTV was also made. This atlas-based auto-segmentation method incorporating inter-observer variation was shown to be efficient (<4min) and accurate for whole breast radiotherapy, with good agreement (DSC>0.7, MASD <9.3mm) between the auto-segmented contours and CTV volumes.

A semi-nonparametric Poisson regression model for analyzing motor vehicle crash data.

PubMed

Ye, Xin; Wang, Ke; Zou, Yajie; Lord, Dominique

2018-01-01

This paper develops a semi-nonparametric Poisson regression model to analyze motor vehicle crash frequency data collected from rural multilane highway segments in California, US. Motor vehicle crash frequency on rural highway is a topic of interest in the area of transportation safety due to higher driving speeds and the resultant severity level. Unlike the traditional Negative Binomial (NB) model, the semi-nonparametric Poisson regression model can accommodate an unobserved heterogeneity following a highly flexible semi-nonparametric (SNP) distribution. Simulation experiments are conducted to demonstrate that the SNP distribution can well mimic a large family of distributions, including normal distributions, log-gamma distributions, bimodal and trimodal distributions. Empirical estimation results show that such flexibility offered by the SNP distribution can greatly improve model precision and the overall goodness-of-fit. The semi-nonparametric distribution can provide a better understanding of crash data structure through its ability to capture potential multimodality in the distribution of unobserved heterogeneity. When estimated coefficients in empirical models are compared, SNP and NB models are found to have a substantially different coefficient for the dummy variable indicating the lane width. The SNP model with better statistical performance suggests that the NB model overestimates the effect of lane width on crash frequency reduction by 83.1%.

Evaluation of the Use of Zero-Augmented Regression Techniques to Model Incidence of Campylobacter Infections in FoodNet.

PubMed

Tremblay, Marlène; Crim, Stacy M; Cole, Dana J; Hoekstra, Robert M; Henao, Olga L; Döpfer, Dörte

2017-10-01

The Foodborne Diseases Active Surveillance Network (FoodNet) is currently using a negative binomial (NB) regression model to estimate temporal changes in the incidence of Campylobacter infection. FoodNet active surveillance in 483 counties collected data on 40,212 Campylobacter cases between years 2004 and 2011. We explored models that disaggregated these data to allow us to account for demographic, geographic, and seasonal factors when examining changes in incidence of Campylobacter infection. We hypothesized that modeling structural zeros and including demographic variables would increase the fit of FoodNet's Campylobacter incidence regression models. Five different models were compared: NB without demographic covariates, NB with demographic covariates, hurdle NB with covariates in the count component only, hurdle NB with covariates in both zero and count components, and zero-inflated NB with covariates in the count component only. Of the models evaluated, the nonzero-augmented NB model with demographic variables provided the best fit. Results suggest that even though zero inflation was not present at this level, individualizing the level of aggregation and using different model structures and predictors per site might be required to correctly distinguish between structural and observational zeros and account for risk factors that vary geographically.

Crash Frequency Modeling Using Real-Time Environmental and Traffic Data and Unbalanced Panel Data Models

PubMed Central

Chen, Feng; Chen, Suren; Ma, Xiaoxiang

2016-01-01

Traffic and environmental conditions (e.g., weather conditions), which frequently change with time, have a significant impact on crash occurrence. Traditional crash frequency models with large temporal scales and aggregated variables are not sufficient to capture the time-varying nature of driving environmental factors, causing significant loss of critical information on crash frequency modeling. This paper aims at developing crash frequency models with refined temporal scales for complex driving environments, with such an effort providing more detailed and accurate crash risk information which can allow for more effective and proactive traffic management and law enforcement intervention. Zero-inflated, negative binomial (ZINB) models with site-specific random effects are developed with unbalanced panel data to analyze hourly crash frequency on highway segments. The real-time driving environment information, including traffic, weather and road surface condition data, sourced primarily from the Road Weather Information System, is incorporated into the models along with site-specific road characteristics. The estimation results of unbalanced panel data ZINB models suggest there are a number of factors influencing crash frequency, including time-varying factors (e.g., visibility and hourly traffic volume) and site-varying factors (e.g., speed limit). The study confirms the unique significance of the real-time weather, road surface condition and traffic data to crash frequency modeling. PMID:27322306

Adjusting for overdispersion in piecewise exponential regression models to estimate excess mortality rate in population-based research.

PubMed

Luque-Fernandez, Miguel Angel; Belot, Aurélien; Quaresma, Manuela; Maringe, Camille; Coleman, Michel P; Rachet, Bernard

2016-10-01

In population-based cancer research, piecewise exponential regression models are used to derive adjusted estimates of excess mortality due to cancer using the Poisson generalized linear modelling framework. However, the assumption that the conditional mean and variance of the rate parameter given the set of covariates x i are equal is strong and may fail to account for overdispersion given the variability of the rate parameter (the variance exceeds the mean). Using an empirical example, we aimed to describe simple methods to test and correct for overdispersion. We used a regression-based score test for overdispersion under the relative survival framework and proposed different approaches to correct for overdispersion including a quasi-likelihood, robust standard errors estimation, negative binomial regression and flexible piecewise modelling. All piecewise exponential regression models showed the presence of significant inherent overdispersion (p-value <0.001). However, the flexible piecewise exponential model showed the smallest overdispersion parameter (3.2 versus 21.3) for non-flexible piecewise exponential models. We showed that there were no major differences between methods. However, using a flexible piecewise regression modelling, with either a quasi-likelihood or robust standard errors, was the best approach as it deals with both, overdispersion due to model misspecification and true or inherent overdispersion.

Allogeneic transplantation provides durable remission in a subset of DLBCL patients relapsing after autologous transplantation.

PubMed

Fenske, Timothy S; Ahn, Kwang W; Graff, Tara M; DiGilio, Alyssa; Bashir, Qaiser; Kamble, Rammurti T; Ayala, Ernesto; Bacher, Ulrike; Brammer, Jonathan E; Cairo, Mitchell; Chen, Andy; Chen, Yi-Bin; Chhabra, Saurabh; D'Souza, Anita; Farooq, Umar; Freytes, Cesar; Ganguly, Siddhartha; Hertzberg, Mark; Inwards, David; Jaglowski, Samantha; Kharfan-Dabaja, Mohamed A; Lazarus, Hillard M; Nathan, Sunita; Pawarode, Attaphol; Perales, Miguel-Angel; Reddy, Nishitha; Seo, Sachiko; Sureda, Anna; Smith, Sonali M; Hamadani, Mehdi

2016-07-01

For diffuse large B-cell lymphoma (DLBCL) patients progressing after autologous haematopoietic cell transplantation (autoHCT), allogeneic HCT (alloHCT) is often considered, although limited information is available to guide patient selection. Using the Center for International Blood and Marrow Transplant Research (CIBMTR) database, we identified 503 patients who underwent alloHCT after disease progression/relapse following a prior autoHCT. The 3-year probabilities of non-relapse mortality, progression/relapse, progression-free survival (PFS) and overall survival (OS) were 30, 38, 31 and 37% respectively. Factors associated with inferior PFS on multivariate analysis included Karnofsky performance status (KPS) <80, chemoresistance, autoHCT to alloHCT interval <1-year and myeloablative conditioning. Factors associated with worse OS on multivariate analysis included KPS<80, chemoresistance and myeloablative conditioning. Three adverse prognostic factors were used to construct a prognostic model for PFS, including KPS<80 (4 points), autoHCT to alloHCT interval <1-year (2 points) and chemoresistant disease at alloHCT (5 points). This CIBMTR prognostic model classified patients into four groups: low-risk (0 points), intermediate-risk (2-5 points), high-risk (6-9 points) or very high-risk (11 points), predicting 3-year PFS of 40, 32, 11 and 6%, respectively, with 3-year OS probabilities of 43, 39, 19 and 11% respectively. In conclusion, the CIBMTR prognostic model identifies a subgroup of DLBCL patients experiencing long-term survival with alloHCT after a failed prior autoHCT. © 2016 John Wiley & Sons Ltd.

Generalized binomial τ-leap method for biochemical kinetics incorporating both delay and intrinsic noise

NASA Astrophysics Data System (ADS)

Leier, André; Marquez-Lago, Tatiana T.; Burrage, Kevin

2008-05-01

The delay stochastic simulation algorithm (DSSA) by Barrio et al. [Plos Comput. Biol. 2, 117(E) (2006)] was developed to simulate delayed processes in cell biology in the presence of intrinsic noise, that is, when there are small-to-moderate numbers of certain key molecules present in a chemical reaction system. These delayed processes can faithfully represent complex interactions and mechanisms that imply a number of spatiotemporal processes often not explicitly modeled such as transcription and translation, basic in the modeling of cell signaling pathways. However, for systems with widely varying reaction rate constants or large numbers of molecules, the simulation time steps of both the stochastic simulation algorithm (SSA) and the DSSA can become very small causing considerable computational overheads. In order to overcome the limit of small step sizes, various τ-leap strategies have been suggested for improving computational performance of the SSA. In this paper, we present a binomial τ-DSSA method that extends the τ-leap idea to the delay setting and avoids drawing insufficient numbers of reactions, a common shortcoming of existing binomial τ-leap methods that becomes evident when dealing with complex chemical interactions. The resulting inaccuracies are most evident in the delayed case, even when considering reaction products as potential reactants within the same time step in which they are produced. Moreover, we extend the framework to account for multicellular systems with different degrees of intercellular communication. We apply these ideas to two important genetic regulatory models, namely, the hes1 gene, implicated as a molecular clock, and a Her1/Her 7 model for coupled oscillating cells.

Mixture models for estimating the size of a closed population when capture rates vary among individuals

USGS Publications Warehouse

Dorazio, R.M.; Royle, J. Andrew

2003-01-01

We develop a parameterization of the beta-binomial mixture that provides sensible inferences about the size of a closed population when probabilities of capture or detection vary among individuals. Three classes of mixture models (beta-binomial, logistic-normal, and latent-class) are fitted to recaptures of snowshoe hares for estimating abundance and to counts of bird species for estimating species richness. In both sets of data, rates of detection appear to vary more among individuals (animals or species) than among sampling occasions or locations. The estimates of population size and species richness are sensitive to model-specific assumptions about the latent distribution of individual rates of detection. We demonstrate using simulation experiments that conventional diagnostics for assessing model adequacy, such as deviance, cannot be relied on for selecting classes of mixture models that produce valid inferences about population size. Prior knowledge about sources of individual heterogeneity in detection rates, if available, should be used to help select among classes of mixture models that are to be used for inference.

A Taxonomic Reduced-Space Pollen Model for Paleoclimate Reconstruction

NASA Astrophysics Data System (ADS)

Wahl, E. R.; Schoelzel, C.

2010-12-01

Paleoenvironmental reconstruction from fossil pollen often attempts to take advantage of the rich taxonomic diversity in such data. Here, a taxonomically "reduced-space" reconstruction model is explored that would be parsimonious in introducing parameters needing to be estimated within a Bayesian Hierarchical Modeling context. This work involves a refinement of the traditional pollen ratio method. This method is useful when one (or a few) dominant pollen type(s) in a region have a strong positive correlation with a climate variable of interest and another (or a few) dominant pollen type(s) have a strong negative correlation. When, e.g., counts of pollen taxa a and b (r >0) are combined with pollen types c and d (r <0) to form ratios of the form (a + b) / (a + b + c + d), an appropriate estimation form is the binomial logistic generalized linear model (GLM). The GLM can readily model this relationship in the forward form, pollen = g(climate), which is more physically realistic than inverse models often used in paleoclimate reconstruction [climate = f(pollen)]. The specification of the model is: rnum Bin(n,p), where E(r|T) = p = exp(η)/[1+exp(η)], and η = α + β(T); r is the pollen ratio formed as above, rnum is the ratio numerator, n is the ratio denominator (i.e., the sum of pollen counts), the denominator-specific count is (n - rnum), and T is the temperature at each site corresponding to a specific value of r. Ecological and empirical screening identified the model (Spruce+Birch) / (Spruce+Birch+Oak+Hickory) for use in temperate eastern N. America. α and β were estimated using both "traditional" and Bayesian GLM algorithms (in R). Although it includes only four pollen types, the ratio model yields more explained variation ( 80%) in the pollen-temperature relationship of the study region than a 64-taxon modern analog technique (MAT). Thus, the new pollen ratio method represents an information-rich, reduced space data model that can be efficiently employed in a BHM framework. The ratio model can directly reconstruct past temperature by solving the GLM equations for T as a function of α, β, and E(r|T): T = {ln[E(r|T)/{1-E(r|T)}]-α}/β. To enable use in paleoreconstruction, the observed r values from fossil pollen data are, by assumption, treated as unbiased estimators of the true r value at each time sampled, which can be substituted for E(r|T). Uncertainty in this reconstruction is systematically evaluated in two parts: 1) the observed r values and their corresponding n values are input as parameters into the binomial distribution, Monte Carlo random pollen count draws are made, and a new ratio value is determined for each iteration; and 2) in the "traditional" GLM the estimated SEs for α and β are used with the α and β EV estimates to yield Monte Carlo random draws for each binomial draw (assuming α and β are Gaussian), in the Bayesian GLM random draws for α and β are taken directly from their estimated posterior distribution. Both methods yield nearly identical reconstructions from varved lakes in Wisconsin where the model has been tested; slightly narrower uncertainty ranges are produced by the Bayesian model. The Little Ice Age is readily identified. Pine:Oak and Fir:Oak versions of the model used in S. California show differences from MAT-based reconstructions.

Use of Internet Viral Marketing to Promote Smoke-Free Lifestyles among Chinese Adolescents

PubMed Central

Ip, Patrick; Lam, Tai-Hing; Chan, Sophia Siu-Chee; Ho, Frederick Ka-Wing; Lo, Lewis A.; Chiu, Ivy Wing-Sze; Wong, Wilfred Hing-Sang; Chow, Chun-Bong

2014-01-01

Purpose Youth smoking is a global public health concern. Health educators are increasingly using Internet-based technologies, but the effectiveness of Internet viral marketing in promoting health remains uncertain. This prospective pilot study assessed the efficacy of an online game-based viral marketing campaign in promoting a smoke-free attitude among Chinese adolescents. Methods One hundred and twenty-one Hong Kong Chinese adolescents aged 10 to 24 were invited to participate in an online multiple-choice quiz game competition designed to deliver tobacco-related health information. Participants were encouraged to refer others to join. A zero-inflated negative binomial model was used to explore the factors contributing to the referral process. Latent transition analysis utilising a pre- and post-game survey was used to detect attitudinal changes toward smoking. Results The number of participants increased almost eightfold from 121 to 928 (34.6% current or ex-smokers) during the 22-day campaign. Participants exhibited significant attitudinal change, with 73% holding negative attitudes toward smoking after the campaign compared to 57% before it. The transition probabilities from positive to negative and neutral to negative attitudes were 0.52 and 0.48, respectively. It was also found that attempting every 20 quiz questions was associated with lower perceived smoking decision in future (OR  = 0.95, p-value <0.01). Conclusions Our online game-based viral marketing programme was effective in reaching a large number of smoking and non-smoking participants and changing their attitudes toward smoking. It constitutes a promising practical and cost-effective model for engaging young smokers and promulgating smoking-related health information among Chinese adolescents. PMID:24911010

Jump-and-return sandwiches: A new family of binomial-like selective inversion sequences with improved performance

NASA Astrophysics Data System (ADS)

Brenner, Tom; Chen, Johnny; Stait-Gardner, Tim; Zheng, Gang; Matsukawa, Shingo; Price, William S.

2018-03-01

A new family of binomial-like inversion sequences, named jump-and-return sandwiches (JRS), has been developed by inserting a binomial-like sequence into a standard jump-and-return sequence, discovered through use of a stochastic Genetic Algorithm optimisation. Compared to currently used binomial-like inversion sequences (e.g., 3-9-19 and W5), the new sequences afford wider inversion bands and narrower non-inversion bands with an equal number of pulses. As an example, two jump-and-return sandwich 10-pulse sequences achieved 95% inversion at offsets corresponding to 9.4% and 10.3% of the non-inversion band spacing, compared to 14.7% for the binomial-like W5 inversion sequence, i.e., they afforded non-inversion bands about two thirds the width of the W5 non-inversion band.

The Homogenized Energy Model (HEM) for Characterizing Polarization and Strains in Hysteretic Ferroelectric Materials: Material Properties and Uniaxial Model Development

DTIC Science & Technology

2012-01-01

auto focusing mechanisms, energy harvesting , and pico air vehicle design. Their advantages include nanometer positioning resolution, broadband frequency...high speed camera shutters and auto focusing mechanisms energy harvesting , and pico air vehicle design. Their advantages include nanometer positioning...THIS PAGE unclassified Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 Nomenclature d Piezoelectric constant (m/V = C/N) h

Positronium ions and molecules

NASA Technical Reports Server (NTRS)

Ho, Y. K.

1990-01-01

Recent theoretical studies on positronium ions and molecules are discussed. A positronium ion is a three particle system consisting of two electrons in singlet spin state, and a positron. Recent studies include calculations of its binding energy, positron annihilation rate, and investigations of its doubly excited resonant states. A positronium molecule is a four body system consisting of two positrons and two electrons in an overall singlet spin state. The recent calculations of its binding energy against the dissociation into two positronium atoms, and studies of auto-detaching states in positronium molecules are discussed. These auto-dissociating states, which are believed to be part of the Rydberg series as a result of a positron attaching to a negatively charged positronium ion, Ps-, would appear as resonances in Ps-Ps scattering.

Monthly streamflow forecasting with auto-regressive integrated moving average

NASA Astrophysics Data System (ADS)

Nasir, Najah; Samsudin, Ruhaidah; Shabri, Ani

2017-09-01

Forecasting of streamflow is one of the many ways that can contribute to better decision making for water resource management. The auto-regressive integrated moving average (ARIMA) model was selected in this research for monthly streamflow forecasting with enhancement made by pre-processing the data using singular spectrum analysis (SSA). This study also proposed an extension of the SSA technique to include a step where clustering was performed on the eigenvector pairs before reconstruction of the time series. The monthly streamflow data of Sungai Muda at Jeniang, Sungai Muda at Jambatan Syed Omar and Sungai Ketil at Kuala Pegang was gathered from the Department of Irrigation and Drainage Malaysia. A ratio of 9:1 was used to divide the data into training and testing sets. The ARIMA, SSA-ARIMA and Clustered SSA-ARIMA models were all developed in R software. Results from the proposed model are then compared to a conventional auto-regressive integrated moving average model using the root-mean-square error and mean absolute error values. It was found that the proposed model can outperform the conventional model.

Use of clinical practice guidelines by dentists: findings from the Japanese dental practice-based research network.

PubMed

Kakudate, Naoki; Yokoyama, Yoko; Sumida, Futoshi; Matsumoto, Yuki; Gordan, Valeria V; Gilbert, Gregg H

2017-02-01

The objectives of this study were to: (1) examine differences in the use of dental clinical practice guidelines among Japanese dentists, and (2) identify characteristics associated with the number of guidelines used by participating dentists. We conducted a cross-sectional study consisting of a questionnaire survey in Japan between July 2014 and May 2015. The study queried dentists working in outpatient dental practices who are affiliated with the Dental Practice-Based Research Network Japan (n = 148). They were asked whether they have used each of 15 Japanese dental clinical guidelines. Associations between the number of guidelines used by participants and specific characteristics were analysed via negative binomial regression analysis. The mean number of guidelines used by participating dentists was 2.5 ± 2.9 [standard deviation (SD)]. Rate of use of guidelines showed substantial variation, from 5% to 34% among dentists. The proportion of dentists that used guidelines was the highest among oral medicine specialists, who had the highest proportion for 10 of 15 guidelines. Negative binomial regression analysis identified three factors significantly associated with the number of guidelines used: 'years since graduation from dental school', 'specialty practice' and 'practice busyness'. These results suggest that the use of clinical practice guidelines by Japanese dentists may still be inadequate. Training in the use of the guidelines could be given to dental students as undergraduate education and to young clinicians as continuing education. © 2016 John Wiley & Sons, Ltd.

Mapping the Personality Psychopathology Five domains onto DSM-IV personality disorders in Dutch clinical and forensic samples: implications for DSM-5.

PubMed

Sellbom, Martin; Smid, Wineke; de Saeger, Hilde; Smit, Naomi; Kamphuis, Jan H

2014-01-01

The Personality Psychopathology Five (PSY-5) model represents 5 broadband dimensional personality domains that align with the originally proposed DSM-5 personality trait system, which was eventually placed in Section III for further study. The main objective of this study was to examine the associations between the PSY-5 model and personality disorder criteria. More specifically, we aimed to determine if the PSY-5 domain scales converged with the alternative DSM-5 Section III model for personality disorders, with a particular emphasis on the personality trait profiles proposed for each of the specific personality disorder types. Two samples from The Netherlands consisting of clinical patients from a personality disorder treatment program (n = 190) and forensic psychiatric hospital (n = 162) were used. All patients had been administered the MMPI-2 (from which MMPI-2-RF PSY-5 scales were scored) and structured clinical interviews to assess personality disorder criteria. Results based on Poisson or negative binomial regression models showed statistically significant and meaningful associations for the hypothesized PSY-5 domains for each of the 6 personality disorders, with a few minor exceptions that are discussed in detail. Implications for these findings are also discussed.

Temporal acceleration of spatially distributed kinetic Monte Carlo simulations

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

Chatterjee, Abhijit; Vlachos, Dionisios G.

The computational intensity of kinetic Monte Carlo (KMC) simulation is a major impediment in simulating large length and time scales. In recent work, an approximate method for KMC simulation of spatially uniform systems, termed the binomial {tau}-leap method, was introduced [A. Chatterjee, D.G. Vlachos, M.A. Katsoulakis, Binomial distribution based {tau}-leap accelerated stochastic simulation, J. Chem. Phys. 122 (2005) 024112], where molecular bundles instead of individual processes are executed over coarse-grained time increments. This temporal coarse-graining can lead to significant computational savings but its generalization to spatially lattice KMC simulation has not been realized yet. Here we extend the binomial {tau}-leapmore » method to lattice KMC simulations by combining it with spatially adaptive coarse-graining. Absolute stability and computational speed-up analyses for spatial systems along with simulations provide insights into the conditions where accuracy and substantial acceleration of the new spatio-temporal coarse-graining method are ensured. Model systems demonstrate that the r-time increment criterion of Chatterjee et al. obeys the absolute stability limit for values of r up to near 1.« less

CYLD Proteolysis Protects Macrophages from TNF-Mediated Auto-necroptosis Induced by LPS and Licensed by Type I IFN.

PubMed

Legarda, Diana; Justus, Scott J; Ang, Rosalind L; Rikhi, Nimisha; Li, Wenjing; Moran, Thomas M; Zhang, Jianke; Mizoguchi, Emiko; Zelic, Matija; Kelliher, Michelle A; Blander, J Magarian; Ting, Adrian T

2016-06-14

Tumor necrosis factor (TNF) induces necroptosis, a RIPK3/MLKL-dependent form of inflammatory cell death. In response to infection by Gram-negative bacteria, multiple receptors on macrophages, including TLR4, TNF, and type I IFN receptors, are concurrently activated, but it is unclear how they crosstalk to regulate necroptosis. We report that TLR4 activates CASPASE-8 to cleave and remove the deubiquitinase cylindromatosis (CYLD) in a TRIF- and RIPK1-dependent manner to disable necroptosis in macrophages. Inhibiting CASPASE-8 leads to CYLD-dependent necroptosis caused by the TNF produced in response to TLR4 ligation. While lipopolysaccharides (LPS)-induced necroptosis was abrogated in Tnf(-/-) macrophages, a soluble TNF antagonist was not able to do so in Tnf(+/+) macrophages, indicating that necroptosis occurs in a cell-autonomous manner. Surprisingly, TNF-mediated auto-necroptosis of macrophages requires type I IFN, which primes the expression of key necroptosis-signaling molecules, including TNFR2 and MLKL. Thus, the TNF necroptosis pathway is regulated by both negative and positive crosstalk. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

Modeling work zone crash frequency by quantifying measurement errors in work zone length.

PubMed

Yang, Hong; Ozbay, Kaan; Ozturk, Ozgur; Yildirimoglu, Mehmet

2013-06-01

Work zones are temporary traffic control zones that can potentially cause safety problems. Maintaining safety, while implementing necessary changes on roadways, is an important challenge traffic engineers and researchers have to confront. In this study, the risk factors in work zone safety evaluation were identified through the estimation of a crash frequency (CF) model. Measurement errors in explanatory variables of a CF model can lead to unreliable estimates of certain parameters. Among these, work zone length raises a major concern in this analysis because it may change as the construction schedule progresses generally without being properly documented. This paper proposes an improved modeling and estimation approach that involves the use of a measurement error (ME) model integrated with the traditional negative binomial (NB) model. The proposed approach was compared with the traditional NB approach. Both models were estimated using a large dataset that consists of 60 work zones in New Jersey. Results showed that the proposed improved approach outperformed the traditional approach in terms of goodness-of-fit statistics. Moreover it is shown that the use of the traditional NB approach in this context can lead to the overestimation of the effect of work zone length on the crash occurrence. Copyright © 2013 Elsevier Ltd. All rights reserved.

Revealing Word Order: Using Serial Position in Binomials to Predict Properties of the Speaker

ERIC Educational Resources Information Center

Iliev, Rumen; Smirnova, Anastasia

2016-01-01

Three studies test the link between word order in binomials and psychological and demographic characteristics of a speaker. While linguists have already suggested that psychological, cultural and societal factors are important in choosing word order in binomials, the vast majority of relevant research was focused on general factors and on broadly…

Examining the Relationship between Marijuana Use, Medical Marijuana Dispensaries, and Abusive and Neglectful Parenting

PubMed Central

Freisthler, Bridget; Gruenewald, Paul J.; Wolf, Jennifer Price

2015-01-01

The current study extends previous research by examining whether and how current marijuana use and the physical availability of marijuana are related to child physical abuse, supervisory neglect, or physical neglect by parents while controlling for child, caregiver, and family characteristics in a general population survey in California. Individual level data on marijuana use and abusive and neglectful parenting were collected during a telephone survey of 3,023 respondents living in 50 mid-size cities in California. Medical marijuana dispensaries and delivery services data were obtained via six websites and official city lists. Data were analyzed using negative binomial and linear mixed effects multilevel models with individuals nested within cities. Current marijuana use was positively related to frequency of child physical abuse and negatively related to physical neglect. There was no relationship between supervisory neglect and marijuana use. Density of medical marijuana dispensaries and delivery services was positively related to frequency of physical abuse. As marijuana use becomes more prevalent, those who work with families, including child welfare workers must screen for how marijuana use may affect a parent’s ability to provide for care for their children, particularly related to physical abuse. PMID:26198452

Spanish language proficiency among providers and Latino clients' engagement in substance abuse treatment.

PubMed

Guerrero, Erick G; Khachikian, Tenie; Kim, Tina; Kong, Yinfei; Vega, William A

2013-12-01

Quality of care, such as provision of services in Spanish, is a common factor believed to improve treatment engagement among Spanish-speaking Latinos in health care. However, there is little evidence that Spanish language proficiency among providers increases treatment access and retention in publicly funded substance abuse treatment. We analyzed client and program data collected in 2010-2011 from publicly funded treatment programs in Los Angeles County, California. An analytic sample of 1903 Latino clients nested within 40 treatment programs located in minority communities was analyzed using multilevel negative binomial regressions on days to initiate and spent in treatment. As hypothesized, Spanish language proficiency was negatively associated with client wait time and positively associated with retention in treatment, after controlling for individual and program characteristics. The path analysis models showed that Spanish language proficiency played a mediating role between professional accreditation and client wait time and retention. These preliminary findings provide an evidentiary base for the role of providers' Spanish language proficiency and Latino engagement in treatment for a population at high risk of treatment dropout. Implications related to health care reform legislation, which seeks to enhance linguistically competent care, are discussed. © 2013.

Health Communication in Social Media: Message Features Predicting User Engagement on Diabetes-Related Facebook Pages.

PubMed

Rus, Holly M; Cameron, Linda D

2016-10-01

Social media provides unprecedented opportunities for enhancing health communication and health care, including self-management of chronic conditions such as diabetes. Creating messages that engage users is critical for enhancing message impact and dissemination. This study analyzed health communications within ten diabetes-related Facebook pages to identify message features predictive of user engagement. The Common-Sense Model of Illness Self-Regulation and established health communication techniques guided content analyses of 500 Facebook posts. Each post was coded for message features predicted to engage users and numbers of likes, shares, and comments during the week following posting. Multi-level, negative binomial regressions revealed that specific features predicted different forms of engagement. Imagery emerged as a strong predictor; messages with images had higher rates of liking and sharing relative to messages without images. Diabetes consequence information and positive identity predicted higher sharing while negative affect, social support, and crowdsourcing predicted higher commenting. Negative affect, crowdsourcing, and use of external links predicted lower sharing while positive identity predicted lower commenting. The presence of imagery weakened or reversed the positive relationships of several message features with engagement. Diabetes control information and negative affect predicted more likes in text-only messages, but fewer likes when these messages included illustrative imagery. Similar patterns of imagery's attenuating effects emerged for the positive relationships of consequence information, control information, and positive identity with shares and for positive relationships of negative affect and social support with comments. These findings hold promise for guiding communication design in health-related social media.