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Highlights • Goodness of fit tests do not validate statistical models for reliability prediction. • Extreme-value models based on distribution tails inform statistical model validation. • Bayesian model updating with carefully constructed priors is needed with sparse data. Abstract Estimating the tails of probability distributions plays a key role in complex engineering systems where the goal is characterization of low probability, high consequence events. When data are collected using physical experimentation, statistical distributional assumptions are often used to extrapolate tail behavior to assess reliability, introducing risk due to extrapolation from an unvalidated (statistical) model. Existing tools to evaluate statistical model fit, such as probability plots and goodness of fit tests, fail to communicate the risk associated with this extrapolation. In this work, we develop a new statistical model validation metric and relate this metric to engineering-driven model validation metrics. The metric measures how consistent the parametric tail estimates are with a more flexible model that makes weaker assumptions about the distribution tails. An extreme-value based generalized Pareto distribution is used for the more flexible model. Models are updated using a Bayesian inference procedure that defaults to reasonably conservative inferences when data are sparse. Properties of the estimation procedure are evaluated in statistical simulation, and the effectiveness of the proposed metrics relative to the standard-of-practice statistical metrics is illustrated using a pedagogical example related to a real, but proprietary, engineering example. [ABSTRACT FROM AUTHOR]

We develop a novel approach for quantifying small effects in regression models. Our method is based on variation in the mean function, in contrast to methods that focus on regression coefficients. Our idea applies in diverse settings such as testing for a negligible trend and quantifying differences in regression functions across strata. Straightforward Bayesian methods are proposed for inference. Four examples are used to illustrate the ideas. [ABSTRACT FROM AUTHOR]

Lot quality assurance sampling (LQAS) surveys are commonly used for monitoring and evaluation in resource-limited settings. Recently several methods have been proposed to combine LQAS with cluster sampling for more timely and cost-effective data collection. For some of these methods, the standard binomial model can be used for constructing decision rules as the clustering can be ignored. For other designs, considered here, clustering is accommodated in the design phase. In this paper, we compare these latter cluster LQAS methodologies and provide recommendations for choosing a cluster LQAS design. We compare technical differences in the three methods and determine situations in which the choice of method results in a substantively different design. We consider two different aspects of the methods: the distributional assumptions and the clustering parameterization. Further, we provide software tools for implementing each method and clarify misconceptions about these designs in the literature. We illustrate the differences in these methods using vaccination and nutrition cluster LQAS surveys as example designs. The cluster methods are not sensitive to the distributional assumptions but can result in substantially different designs (sample sizes) depending on the clustering parameterization. However, none of the clustering parameterizations used in the existing methods appears to be consistent with the observed data, and, consequently, choice between the cluster LQAS methods is not straightforward. Further research should attempt to characterize clustering patterns in specific applications and provide suggestions for best-practice cluster LQAS designs on a setting-specific basis. [ABSTRACT FROM AUTHOR]

Lot quality assurance sampling (LQAS) has a long history of applications in industrial quality control. LQAS is frequently used for rapid surveillance in global health settings, with areas classified as poor or acceptable performance on the basis of the binary classification of an indicator. Historically, LQAS surveys have relied on simple random samples from the population; however, implementing two-stage cluster designs for surveillance sampling is often more cost-effective than simple random sampling. By applying survey sampling results to the binary classification procedure, we develop a simple and flexible nonparametric procedure to incorporate clustering effects into the LQAS sample design to appropriately inflate the sample size, accommodating finite numbers of clusters in the population when relevant. We use this framework to then discuss principled selection of survey design parameters in longitudinal surveillance programs. We apply this framework to design surveys to detect rises in malnutrition prevalence in nutrition surveillance programs in Kenya and South Sudan, accounting for clustering within villages. By combining historical information with data from previous surveys, we design surveys to detect spikes in the childhood malnutrition rate. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Lot Quality Assurance Sampling (LQAS) surveys have become increasingly popular in global health care applications. Incorporating Bayesian ideas into LQAS survey design, such as using reasonable prior beliefs about the distribution of an indicator, can improve the selection of design parameters and decision rules. In this paper, a joint frequentist and Bayesian framework is proposed for evaluating LQAS classification accuracy and informing survey design parameters. Simple software tools are provided for calculating the positive and negative predictive value of a design with respect to an underlying coverage distribution and the selected design parameters. These tools are illustrated using a data example from two consecutive LQAS surveys measuring Oral Rehydration Solution (ORS) preparation. Using the survey tools, the dependence of classification accuracy on benchmark selection and the width of the 'grey region' are clarified in the context of ORS preparation across seven supervision areas. Following the completion of an LQAS survey, estimation of the distribution of coverage across areas facilitates quantifying classification accuracy and can help guide intervention decisions. [ABSTRACT FROM AUTHOR]

Background: The Uzbekistan 1996 Demographic Health Survey reported 60.4% of women of reproductive age (WRA) had low hemoglobin concentrations (<120 g/L), and anemia was an important public health problem. Fortification of wheat flour was identified as an appropriate intervention to address anemia due to the ubiquitous consumption of wheat flour. A National Flour Fortification Program (NFFP) was implemented in 2005. Methodology/Principal Findings: After 3-years of the NFFP, a national survey using large country-lot quality assurance sampling was carried out to assess iron, folate, hemoglobin and inflammation status of WRA; the coverage and knowledge of the fortified first grade UzDonMakhsulot (UDM) flour/grey loaf program; and consumption habits of women to investigate the dietary factors associated with anemia. Estimated anemia prevalence was 34.4% (95% CI: 32.0, 36.7), iron depletion 47.5% (95% CI: 45.1, 49.9) and folate deficiency 28.8% (95% CI: 26.8, 30.8); the effect of inflammation was minimal (4% with CRP >5 mg/L). Severe anemia was more prevalent among folate deficient than iron depleted WRA. Presence of UDM first grade flour or the grey loaf was reported in 71.3% of households. Among WRA, 32.1% were aware of UDM fortification; only 3.7% mentioned the benefits of fortification and 12.5% understood causes of anemia. Consumption of heme iron-containing food (91%) and iron absorption enhancers (97%) was high, as was the consumption of iron absorption inhibitors (95%). Conclusions/Significance: The NFFP coincided with a substantial decline in the prevalence of anemia. Folate deficiency was a stronger predictor of severe anemia than iron depletion. However, the prevalence of iron depletion was high, suggesting that women are not eating enough iron or iron absorption is inhibited. Fortified products were prevalent throughout Uzbekistan, though UDM flour must be adequately fortified and monitored in the future. Knowledge of fortification and anemia was low, suggesting consumer education should be prioritized. [ABSTRACT FROM AUTHOR]

Many HIV prevalence surveys are plagued by the problem that a sizeable number of surveyed individuals do not consent to contribute blood samples for testing. One can ignore this problem, as is often done, but the resultant bias can be of sufficient magnitude to invalidate the results of the survey, especially if the number of non-responders is high and the reason for refusing to participate is related to the individual's HIV status. One reason for refusing to participate may be for reasons of privacy. For those individuals, we suggest offering the option of being tested in a pool. This form of testing is less certain than individual testing, but, if it convinces more people to submit to testing, it should reduce the potential for bias and give a cleaner answer to the question of prevalence. This paper explores the logistics of implementing a combined individual and pooled testing approach and evaluates the analytical advantages to such a combined testing strategy. We quantify improvements in a prevalence estimator based on this combined testing strategy, relative to an individual testing only approach and a pooled testing only approach. Minimizing non-response is key for reducing bias, and, if pooled testing assuages privacy concerns, offering a pooled testing strategy has the potential to substantially improve HIV prevalence estimates. [ABSTRACT FROM AUTHOR]

Temporal boundary misalignment occurs when area boundaries shift across time (e.g., census tract boundaries change at each census year), complicating the modeling of temporal trends across space. Large area-level datasets with temporal boundary misalignment are becoming increasingly common in practice. The few existing approaches for temporally misaligned data do not account for correlation in spatial random effects over time. To overcome issues associated with temporal misalignment, we construct a geostatistical model for aggregate count data by assuming that an underlying continuous risk surface induces spatial correlation between areas. We implement the model within the framework of a generalized linear mixed model using radial basis splines. Using this approach, boundary misalignment becomes a nonissue. Additionally, this disease-mapping framework facilitates fast, easy model fitting by using a penalized quasilikelihood approximation to maximum likelihood estimation. We anticipate that the method will also be useful for large disease-mapping datasets for which fully Bayesian approaches are infeasible. We apply our method to assess socioeconomic trends in breast cancer incidence in Los Angeles between the periods 1988-1992 and 1998-2002. [ABSTRACT FROM AUTHOR]

Background: The number of children with prenatal polysubstance exposure is increasing. Supportive mother-child interaction is a protective factor, which can ameliorate adverse effects of prenatal polysubstance exposure on developmental outcomes.Aim: To examine the role of maternal verbal scaffolding on cognitive and language development in children with prenatal polysubstance exposure.Study Design: Pregnant women were recruited, and we prospectively followed mother-child dyads to 20 months of age. This analysis included 66 dyads (33 healthy controls and 33 with prenatal polysubstance exposure). Multivariable linear regression modelling was used to examine the cross-sectional association between maternal scaffolding and Bayley Scales of Infant and Toddler Development (BSID-III) score, as well as an interaction between the study group and scaffolding score.Outcome Measures: The BSID-III cognitive and language score was used. Videotaped mother-child play was coded to obtain a maternal verbal scaffolding score. Effect sizes were measured using average differences in scores between groups.Results: There was no evidence of an association between study group and maternal scaffolding scores. Children in the polysubstance exposure group had lower cognitive and language scores compared to controls, but this association was not statistically significant after controlling for maternal education. Maternal scaffolding was predictive of language scores, with scores increasing by 1.24 points on average (95% CI: 0.42, 2.06) for every 1-point increase in scaffolding score after adjustment for covariates. There was no evidence of a study group-by-scaffolding interaction with respect to the language or cognitive scores.Conclusions: Maternal scaffolding during play was associated with language development in children with and without prenatal polysubstance exposure. [ABSTRACT FROM AUTHOR]

• Understanding reliability often requires causal, rather than predictive, inferences. • Structural causal modeling is a framework for thinking causally about reliability. • Sensitivity studies investigating data bias can improve decisions about reliability. Causality in an engineered system pertains to how a system output changes due to a controlled change or intervention on the system or system environment. Engineered systems designs reflect a causal theory regarding how a system will work, and predicting the reliability of such systems typically requires knowledge of this underlying causal structure. The aim of this work is to introduce causal modeling tools that inform reliability predictions based on biased data sources. We present a novel application of the popular structural causal modeling (SCM) framework to reliability estimation in an engineering application, illustrating how this framework can inform whether reliability is estimable and how to estimate reliability given a set of data and assumptions about the subject matter and data generating mechanism. When data are insufficient for estimation, sensitivity studies based on problem-specific knowledge can inform how much reliability estimates can change due to biases in the data and what information should be collected next to provide the most additional information. We apply the approach to a pedagogical example related to a real, but proprietary, engineering application, considering how two types of biases in data can influence a reliability calculation. [ABSTRACT FROM AUTHOR]

Traditional lecture-centered classrooms are being challenged by active learning hybrid curricula. In small graduate programs with limited resources and primarily non-traditional students, exploring how to use online technology to optimize the role of the professor in the classroom is imperative. However, very little research exists in this area. In this study, the use of short statistical computing video tutorials was explored using a pilot study in a small Public Health Program at the University of New Mexico. The videos were implemented in two Master's-level biostatistics courses and student perception of the videos was assessed using quantitative surveys and qualitative focus groups. The results from 16 survey respondents and 12 focus group participants are presented across the two courses. Viewing rates for the videos were high, with 15 out of 16 respondents reporting usually or always viewing the videos. Overall perception of the videos as a learning tool was positive, with 14 out of 16 respondents agreeing that the videos offer advantages to them. Two prominent themes emerged in our analysis: (1) the usability and convenience of the videos and (2) the deeper learning facilitated by having the videos available. We conclude that the short video tutorials were a useful learning tool in our study population. [ABSTRACT FROM PUBLISHER]

Background: Traditional Lot Quality Assurance Sampling (LQAS) designs assume observations are collected using simple random sampling. Alternatively, randomly sampling clusters of observations and then individuals within clusters reduces costs but decreases the precision of the classifications. In this paper, we develop a general framework for designing the cluster(C)-LQAS system and illustrate the method with the design of data quality assessments for the community health worker program in Rwanda. Results: To determine sample size and decision rules forC-LQAS, we use the beta-binomial distribution to account for inflated risk of errors introduced by sampling clusters at the first stage. We present general theory and code for sample size calculations. The C-LQAS sample sizes provided in this paper constrain misclassification risks below user-specified limits. Multiple CLQAS systems meet the specified risk requirements, but numerous considerations, including per-cluster versus perindividual sampling costs, help identify optimal systems for distinct applications. Conclusions: We show the utility of C-LQAS for data quality assessments, but the method generalizes to numerous applications. This paper provides the necessary technical detail and supplemental code to support the design of C-LQAS for specific programs. [ABSTRACT FROM AUTHOR]

The article presents a study on the effect of concurrent sexual partnerships on the rate of new HIV infections in rural South Africa. It has been speculated that the concurrent sexual partnerships are one of the main drivers of the HIV epidemic. The findings suggest the relevance to reduce multiple partnerships in the hyperendemic sub-Saharan African settings despite the insufficient evidence on the effect of concurrent partnerships on HIV epidemic.

Background:Collaborative information exchange using the elicit-provide-elicit (EPE) technique is a recommended health communication strategy, though little empirical work has evaluated its merit. Methods:This study evaluated the impact of EPE on risky drinking knowledge, attitudes, and behavior. Undergraduate drinkers (n = 150) were randomized to receive information regarding risky drinking limits via collaborative exchange (EPE), provision only, or to a no information control group. Changes in drinking behavior from baseline to 2-week follow-up were assessed. In addition, knowledge of and attitudes towards drinking limits were measured at follow-up. Results:Drinks per drinking day, percent days abstinent, and peak consumption did not improve from baseline to follow-up in any group. At follow-up, the proportion of participants who exceeded daily drinking cutoffs significantly decreased in the EPE and provision-only conditions, but increased among control participants. EPE and provision participants were more likely to accurately recall drinking limits at follow-up, though only about half of these participants correctly recalled limits and knowledge was not associated with risky drinking behavior. Conclusions:Providing information regarding safe drinking limits may impact knowledge and behavior among this population; however, collaborative exchange via EPE as measured in the present study does not appear to provide added benefit when compared with non-collaborative information provision. [ABSTRACT FROM PUBLISHER]

Well-constructed epidemiologic studies provide information about environmental risks and inform interventions and public health policies. Using focus groups, this pilot project examined the attitudes, beliefs, and concerns of rural-dwelling adults toward participating in a longitudinal cohort study. Focus group participants who were 40 years or older, residents in the Grants Mining District, and had no previous diagnosis of diabetes (except gestational diabetes) were recruited from a local physician's office in the Grants Mining District. Participants provided insight into local health concerns, willingness to participate in long-term research and to provide biological specimens, and consent form expectations. For this population, the uranium mining legacy in the Grants Mining District is a contextual factor that can be addressed via community engagement and in the study design to minimize misinterpretation or bias and to maximize the ability to detect causal risk factors for health outcomes. [ABSTRACT FROM AUTHOR]