Informant similarities, twin studies, and the assessment of externalizing behavior: A meta-analysis.

The purpose of this study was to examine similarity within informant ratings of the externalizing behavior of monozygotic (MZ) and dizygotic (DZ) twin pairs. To do this, we conducted a meta-analysis of correlations within ratings completed by mothers, fathers, teachers, and youth. We retrieved n=204 correlations for MZ twins and n=267 correlations for DZ twins from n=54 studies containing n=55 samples. Results indicated that all four informants were significant negative predictors of within-informant correlations in their ratings of MZ, but not DZ twins. In the case of longitudinal studies and as the age of MZ twins increased, similarity within ratings by mothers was significantly greater than similarity within ratings by fathers. Among participant characteristics, we found that (a) age was a significant negative predictor of similarity within ratings for MZ twins; (b) race was a significant predictor of similarity within ratings for both MZ and DZ twins, but in the opposite direction; and (c) DZ opposite sex twins were a significant negative predictor of within-rating similarity. Among study characteristics for MZ twins, participant group and longitudinal study were significant negative predictors of within-rating similarity, and for both MZ and DZ twin pairs, non-independence in the data was a significant negative predictor of within-rating similarity. For DZ twins, multiple informants were significant positive predictors of within-rating similarity, and in longitudinal studies with DZ twins, similarity within ratings by mothers was significantly greater than similarity within ratings by fathers, and similarity within ratings by fathers was significantly less than similarity within ratings by teachers and youth. For both MZ and DZ twins, the following study characteristics were significant positive predictors of similarity within ratings: study group, number of time points, and multiple constructs. All four informants appeared equally skilled at predicting within-informant correlations for MZ (but not DZ) twins, with participant characteristics having different predictive effects for MZ compared to DZ twins, and study characteristics having comparable predictive effects for both twin types. Overall, these findings suggest effective discrimination on the part of four informants who rated the externalizing behavior of MZ and DZ twins.

On Bayesian Testing of Additive Conjoint Measurement Axioms Using Synthetic Likelihood.

This article introduces a Bayesian method for testing the axioms of additive conjoint measurement. The method is based on an importance sampling algorithm that performs likelihood-free, approximate Bayesian inference using a synthetic likelihood to overcome the analytical intractability of this testing problem. This new method improves upon previous methods because it provides an omnibus test of the entire hierarchy of cancellation axioms, beyond double cancellation. It does so while accounting for the posterior uncertainty that is inherent in the empirical orderings that are implied by these axioms, together. The new method is illustrated through a test of the cancellation axioms on a classic survey data set, and through the analysis of simulated data.

A Bayesian nonparametric test of significance chasing biases.

There is a growing concern that much of the published research literature is distorted by the pursuit of statistically significant results. In a seminal article, Ioannidis and Trikalinos (2007, Clinical Trials) proposed an omnibus (I&T) test for significance chasing (SC) biases. This test compares the observed number of studies that report statistically significant results, against their expected number based on study power, assuming a common effect size across studies. The current article extends this approach by developing a Bayesian nonparametric (BNP) meta-regression model and test of SC bias, which can diagnose bias at the individual study level. This new BNP test is based on a flexible model of the predictive distribution of study power, conditionally on study-level covariates which account for study diversity, including diversity due to heterogeneous effect sizes across studies. A test of SC bias proceeds by comparing each study's significant outcome report indicator against its estimated posterior predictive distribution of study power, conditionally on the study's covariates. The BNP model and SC bias test are illustrated through the analyses of 3 meta-analytic data sets and through a simulation study. Software code for the BNP model and test, and the data sets, are provided as Supporting Information.

A Bayesian Robust IRT Outlier-Detection Model.

In psychometric practice, the parameter estimates of a standard item-response theory (IRT) model can become biased when item-response data, of persons' individual responses to test items, contain outliers relative to the model. Also, the manual removal of outliers can be a time-consuming and difficult task. Besides, removing outliers leads to data information loss in parameter estimation. To address these concerns, a Bayesian IRT model that includes person and latent item-response outlier parameters, in addition to person ability and item parameters, is proposed and illustrated, and is defined by item characteristic curves (ICCs) that are each specified by a robust, Student's t-distribution function. The outlier parameters and the robust ICCs enable the model to automatically identify item-response outliers, and to make estimates of the person ability and item parameters more robust to outliers. Hence, under this IRT model, it is unnecessary to remove outliers from the data analysis. Our IRT model is illustrated through the analysis of two data sets, involving dichotomous- and polytomous-response items, respectively.

A menu-driven software package of Bayesian nonparametric (and parametric) mixed models for regression analysis and density estimation.

Most of applied statistics involves regression analysis of data. In practice, it is important to specify a regression model that has minimal assumptions which are not violated by data, to ensure that statistical inferences from the model are informative and not misleading. This paper presents a stand-alone and menu-driven software package, Bayesian Regression: Nonparametric and Parametric Models, constructed from MATLAB Compiler. Currently, this package gives the user a choice from 83 Bayesian models for data analysis. They include 47 Bayesian nonparametric (BNP) infinite-mixture regression models; 5 BNP infinite-mixture models for density estimation; and 31 normal random effects models (HLMs), including normal linear models. Each of the 78 regression models handles either a continuous, binary, or ordinal dependent variable, and can handle multi-level (grouped) data. All 83 Bayesian models can handle the analysis of weighted observations (e.g., for meta-analysis), and the analysis of left-censored, right-censored, and/or interval-censored data. Each BNP infinite-mixture model has a mixture distribution assigned one of various BNP prior distributions, including priors defined by either the Dirichlet process, Pitman-Yor process (including the normalized stable process), beta (two-parameter) process, normalized inverse-Gaussian process, geometric weights prior, dependent Dirichlet process, or the dependent infinite-probits prior. The software user can mouse-click to select a Bayesian model and perform data analysis via Markov chain Monte Carlo (MCMC) sampling. After the sampling completes, the software automatically opens text output that reports MCMC-based estimates of the model's posterior distribution and model predictive fit to the data. Additional text and/or graphical output can be generated by mouse-clicking other menu options. This includes output of MCMC convergence analyses, and estimates of the model's posterior predictive distribution, for selected functionals and values of covariates. The software is illustrated through the BNP regression analysis of real data.

A Bayesian nonparametric meta-analysis model.

In a meta-analysis, it is important to specify a model that adequately describes the effect-size distribution of the underlying population of studies. The conventional normal fixed-effect and normal random-effects models assume a normal effect-size population distribution, conditionally on parameters and covariates. For estimating the mean overall effect size, such models may be adequate, but for prediction, they surely are not if the effect-size distribution exhibits non-normal behavior. To address this issue, we propose a Bayesian nonparametric meta-analysis model, which can describe a wider range of effect-size distributions, including unimodal symmetric distributions, as well as skewed and more multimodal distributions. We demonstrate our model through the analysis of real meta-analytic data arising from behavioral-genetic research. We compare the predictive performance of the Bayesian nonparametric model against various conventional and more modern normal fixed-effects and random-effects models.

An instrument to evaluate pharmacists' patient counseling on herbal and dietary supplements.

OBJECTIVE: To develop a measure of pharmacists' patient counseling on herbal and dietary supplements. METHODS: A systematic process was used for item generation, testing, and validation of a measure of pharmacists counseling on herbal and dietary supplements. Because a pharmacist-patient encounter may or may not identify an indication for taking an herb or dietary supplement, the instrument was bifurcated into 2 distinct components: (1) patient counseling in general; and (2) patient counseling related to herbal and dietary supplements. RESULTS: The instrument demonstrated high reliability and desirable construct validity. After adjusting for item difficulty, we found that pharmacists tended to provide more general patient counseling than counseling related to herbal and dietary supplements. CONCLUSION: This instrument can be applied to assess the quality of counseling provided by pharmacists and pharmacy students, and the outcomes of pharmacist and pharmacy student education on herbal and dietary supplements.

Scaling sexual behavior or "sexual risk propensity" among men at risk for HIV in Kisumu, Kenya.

We present a scale to measure sexual risk behavior or "sexual risk propensity" to evaluate risk compensation among men engaged in a randomized clinical trial of male circumcision. This statistical approach can be used to represent each respondent's level of sexual risk behavior as the sum of his responses on multiple dichotomous and rating scale (i.e. ordinal) items. This summary "score" can be used to summarize information on many sexual behaviors or to evaluate changes in sexual behavior with respect to an intervention. Our 18 item scale demonstrated very good reliability (Cronbach's alpha of 0.87) and produced a logical, unidimensional continuum to represent sexual risk behavior. We found no evidence of differential item function at different time points (except for reporting a concurrent partners when comparing 6 and 12 month follow-up visits) or with respect to the language with which the instrument was administered. Further, we established criterion validity by demonstrating a statistically significant association between the risk scale and the acquisition of incident sexually transmitted infections (STIs) at the 6 month follow-up and HIV at the 12 month follow-up visits. This method has broad applicability to evaluate sexual risk behavior in the context of other HIV and STI prevention interventions (e.g. microbicide or vaccine trials), or in response to treatment provision (e.g., anti-retroviral therapy).

Coherent psychometric modelling with Bayesian nonparametrics.

In this paper we argue that model selection, as commonly practised in psychometrics, violates certain principles of coherence. On the other hand, we show that Bayesian nonparametrics provides a coherent basis for model selection, through the use of a 'nonparametric' prior distribution that has a large support on the space of sampling distributions. We illustrate model selection under the Bayesian nonparametric approach, through the analysis of real questionnaire data. Also, we present ways to use the Bayesian nonparametric framework to define very flexible psychometric models, through the specification of a nonparametric prior distribution that supports all distribution functions for the inverse link, including the standard logistic distribution functions. The Bayesian nonparametric approach provides a coherent method for model selection that can be applied to any statistical model, including psychometric models. Moreover, under a 'non-informative' choice of nonparametric prior, the Bayesian nonparametric approach is easy to apply, and selects the model that maximizes the log likelihood. Thus, under this choice of prior, the approach can be extended to non-Bayesian settings where the parameters of the competing models are estimated by likelihood maximization, and it can be used with any psychometric software package that routinely reports the model log likelihood.

Mixed-effects Poisson regression analysis of adverse event reports: the relationship between antidepressants and suicide.

A new statistical methodology is developed for the analysis of spontaneous adverse event (AE) reports from post-marketing drug surveillance data. The method involves both empirical Bayes (EB) and fully Bayes estimation of rate multipliers for each drug within a class of drugs, for a particular AE, based on a mixed-effects Poisson regression model. Both parametric and semiparametric models for the random-effect distribution are examined. The method is applied to data from Food and Drug Administration (FDA)'s Adverse Event Reporting System (AERS) on the relationship between antidepressants and suicide. We obtain point estimates and 95 per cent confidence (posterior) intervals for the rate multiplier for each drug (e.g. antidepressants), which can be used to determine whether a particular drug has an increased risk of association with a particular AE (e.g. suicide). Confidence (posterior) intervals that do not include 1.0 provide evidence for either significant protective or harmful associations of the drug and the adverse effect. We also examine EB, parametric Bayes, and semiparametric Bayes estimators of the rate multipliers and associated confidence (posterior) intervals. Results of our analysis of the FDA AERS data revealed that newer antidepressants are associated with lower rates of suicide adverse event reports compared with older antidepressants. We recommend improvements to the existing AERS system, which are likely to improve its public health value as an early warning system.

Measuring and comparing higher education quality between countries worldwide.

The purpose of this investigation is to establish a unidimensional interval scale for measuring each country on the quality of higher education, based on indicators (items) characterizing various aspects of a country's quality. The data from these indicators are publicly available through the United Nations Educational, Scientific, and Cultural Organization (UNESCO), for all countries worldwide. Currently, a country's quality of higher education is summarized by simple descriptive statistics of these many indicators, and there seems to be a need to combine these results into a single measure of quality. A single measure of quality can facilitate a country's education quality to be monitored over time, enable a comparison of education quality between different countries, and help decision making relative to improving a country's quality of education. The results show that, with the Rasch partial credit model, it is possible to represent each country's quality of higher education by a single measure. Possible implications and extensions of this new scale are discussed.

The use of the Minimum Data Set to identify depression in the elderly.

OBJECTIVE: To determine whether depression in the elderly in institutionalized settings could be identified using the mood indicators in the Minimum Data Set (MDS) 2.0 (Section E1, Items A-P). DESIGN: Descriptive study. SETTING: Three nursing homes in the southeastern part of the country. PARTICIPANTS: Residents aged 65 and above. MEASUREMENTS: The items in "Indicators of Depression, Anxiety and Sad Mood" on the MDS 2.0 were used to identify observable features of depression in the elderly. The Cornell Scale for Depression in Dementia (CSDD) was used to validate the MDS indicators. Consensus analysis, which controls raters' bias, raters' ability, and item difficulty, was used to analyze data. RESULTS: No depressive patterns were detected using the MDS indicators. On the CSDD, distinct depressive features were identified: anxiety, sadness, lack of reaction to pleasant events, irritability, agitation, multiple physical complaints, loss of interest, appetite loss, and lack of energy. CONCLUSION: The incongruent findings on the MDS indicators the CSDD may be reflective of the assessment process used with the MDS rather than its ability to identify features of elderly depression. The practice of allowing nondirect caregivers to complete the MDS may have serious implications for the accuracy of the data collected.

A student-initiated elective on end-of-life care: a unique perspective.

Traditionally, curriculum change is a faculty responsibility. However, a first-year medical student, inspired by previous interactions with cancer patients and disillusioned with her education on the physician's role at the end of life, successfully initiated and sustained an end-of-life curriculum change. This article briefly describes the Preceptorship on End of Life Care and then shifts focus to five key dilemmas associated with student-led curriculum change. These dilemmas include articulating the benefits of student-initiated curriculum change, securing resources for curriculum change, the use of peer versus faculty facilitators, determining whether to create an elective or required curriculum, when to offer the course, and how to transition to new student leadership. Recommendations for students/residents seeking to initiate curriculum change are provided, highlighting the need for a collaborative approach of faculty, community affiliates, and students for sustained success.