Optimal and lead-in adaptive allocation for binary outcomes: A comparison of Bayesian methods

We compare posterior and predictive estimators and probabilities in response-adaptive randomization designs for two- and three-group clinical trials with binary outcomes. Adaptation based upon posterior estimates are discussed, as are two predictive probability algorithms: one using the traditional definition, the other using a skeptical distribution. Optimal and natural lead-in designs are covered. Simulation studies show that efficacy comparisons lead to more adaptation than center comparisons, though at some power loss, skeptically predictive efficacy comparisons and natural lead-in approaches lead to less adaptation but offer reduced allocation variability. Though nuanced, these results help clarify the power-adaptation trade-off in adaptive randomization.     

Gender and racial/ethnic differences in predicting adolescent sexual risk: athletic participation versus exercise

Though often conflated, informal physical exercise and organized athletic participation have very different implications for adolescent sexual risk outcomes. The purpose of this research is to disaggregate strenuous exercise from sports, examine how each is associated with sexual risk, and explain the observed differences using the conceptual lens of cultural resource theory. Using a nationally representative sample of over 16,000 public and private high school students, we employ logistic regression to test hypotheses about the gender-specific and race-specific effects of strenuous exercise and athletic participation on adolescent sexual risk behavior. The results show that both forms of physical activity buffer sexual risk for girls. Strenuous exercise is associated with increased odds of sexual risk for boys. Sports and race interact to influence boys' sexual risk outcomes: Athletic participation is associated with lowered odds of sexual risk for white male adolescents, but heightened odds of sexual risk for black male adolescents.     

Jocks, gender, race, and adolescent problem drinking

Alcohol remains the drug of choice for many adolescents; however, the nature of the relationship between athletic involvement and alcohol misuse remains ambiguous. In this article, we used a longitudinal sample of over 600 Western New York adolescents and their families to explore the gender-specific and race-specific relationships between identification with the "jock" label and adolescent alcohol consumption, specifically problem drinking. Operationalization of problem drinking included frequency measures of heavy drinking, binge drinking, and social problems related to alcohol (e.g., trouble with family, friends, school officials over drinking). Self-identified adolescent "jocks" were more likely to engage in problem drinking than their non-jock counterparts, even after controlling for gender, age, race, socioeconomic status, physical maturity, social maturity, and frequency of athletic activity. Jock identity was strongly associated with higher binge drinking frequency in Black adolescent girls. This study underscores the need to distinguish between objective and subjective meanings of athletic involvement when assessing the relationship between sport and adolescent health-risk behavior.     

Men's health studies: origins and trends

This article provides a brief outline of the development of men's health studies in the United States. Research on men's health is discussed within critical feminist theories that highlight the reciprocality of gender relations as well as power differences between men and women and among male subgroups. A relational theory of gender and health is used to identify both positive-gendered and negative-gendered health synergies that influence the health processes and outcomes of men and women. Several examples of gendered health synergies are presented to illustrate key concepts. Finally, some directions for future research and advocacy with reference to men's health are outlined.     

A natural lead-in approach to response-adaptive allocation for continuous outcomes

Response-adaptive (RA) allocation designs can skew the allocation of incoming subjects toward the better performing treatment group based on the previously accrued responses. While unstable estimators and increased variability can adversely affect adaptation in early trial stages, Bayesian methods can be implemented with decreasingly informative priors (DIP) to overcome these difficulties. DIPs have been previously used for binary outcomes to constrain adaptation early in the trial, yet gradually increase adaptation as subjects accrue. We extend the DIP approach to RA designs for continuous outcomes, primarily in the normal conjugate family by functionalizing the prior effective sample size to equal the unobserved sample size. We compare this effective sample size DIP approach to other DIP formulations. Further, we considered various allocation equations and assessed their behavior utilizing DIPs. Simulated clinical trials comparing the behavior of these approaches with traditional Frequentist and Bayesian RA as well as balanced designs show that the natural lead-in approaches maintain improved treatment with lower variability and greater power.     

Estimating a uniform distribution when data are measured with a normal additive error with unknown variance

The problem of estimating the width of a symmetric uniform distribution on the line together with the error variance, when data are measured with normal additive error, is considered. The main purpose is to analyse the maximum-likelihood (ML) estimator and to compare it with the moment-method estimator. It is shown that this two-parameter model is regular so that the ML estimator is asymptotically efficient. Necessary and sufficient conditions are given for the existence of the ML estimator. As numerical problems are known to frequently occur while computing the ML estimator in this model, useful suggestions for computing the ML estimator are also given.     

Estimation Methods for an Autoregressive Familial Correlation Structure

In this article, we apply an autoregressive correlation structure to the analysis of balanced familial clustered data in the one-parent case with homogeneous intra-class variance. We use the quasi-least squares procedure to derive estimators of the correlation parameters and compare them with maximum likelihood and moment estimators. Asymptotically, the quasi-least squares estimators are nearly as efficient as the maximum likelihood estimators. The small-sample case is analyzed through simulation, and the quasi-least squares estimators are found to be more robust than the maximum likelihood estimators. To show the application of the estimation procedures, data provided in Katapa (1993 Katapa , R. S. ( 1993 ). A test of hypothesis on familial correlations . Biometrics 49 : 569 – 576 . [Google Scholar]) are re-analyzed. For non stationary unbalanced familial data, we outline general correlation models which are natural extensions of the structure studied in this article.     

Providing Consulting Experiences Through Role Playing in a Graduate Statistics Course

An effective method for improving the communication skills of graduate students in statistics and biostatistics is to provide consultations with non‐statistical researchers. Unfortunately, those experiences can be difficult to arrange or occur too infrequently to be reliable. The current study sought to help students develop both written and oral communication skills within an existing graduate biostatistics course by having students partake in role‐playing consultations. Though the class size was small, the students felt these activities helped improve their oral and written communication skills, and made them more aware of a biostatistician's role in consulting. There was also modest improvement in the students perceived function as a consulting biostatistician. Simulated consultations can be an effective educational tool for promoting the development of soft skills necessary for developing successful statisticians, can be implemented in existing courses, and do not require reliance upon external collaborators. Embedding these types of exercises within an existing curriculum can also be a cost‐effective alternative for programs that do not have formal consulting training.     

Hypothesis Testing for Various Familial Dependence Structures

This work compares various hypothesis testing procedures in the case of familial clustered data. Specifically, we use likelihood ratio and Wald's tests for maximum likelihood estimators, and Wald-type tests for moment and quasi-least squares estimators. Using simulations, we estimate significance levels for various hypotheses concerning the one-parent auto-regressive and two-parent equi-correlated dependence structures. We show that the likelihood ratio test performs best for certain simple hypotheses in the one-parent case, whereas the Wald-type test for the quasi-least squares procedure is optimal in the more complex two-parent case.     

Border estimation of a two-dimensional uniform distribution if data are measured with additive error

The paper considers estimation of the boundary of an elliptical domain when the data without a measurement error are distributed uniformly on this domain but are superimposed by random errors. The problem is solved in two phases. In the first phase the domain is subdivided into thin slices and the endpoints of these slices are estimated within the framework of a corresponding one-dimensional problem. In the second phase the estimated endpoints are used to estimate the boundary using the total least-squares curve fitting procedure.