In population based genetic association studies, confounding due to population stratification (PS) arises when differences in both allele and disease frequencies exist in a population of mixed racial/ethnic subpopulations. Propensity scores are often used to address confounding in observational...

In this article, we describe a new software for modeling correlated binary data based on orthogonalized residuals (Zink and Qaqish, 2009), a recently developed estimating equations approach that includes, as a special case, alternating logistic regressions (Carey et al., 1993). The software is...

Deletion diagnostics are introduced for the regression analysis of clustered binary outcomes estimated with alternating logistic regressions, an implementation of generalized estimating equations (GEE) that estimates regression coefficients in a marginal mean model and in a model for the...

Frailties models, an extension of the proportional hazards model, are used to model clustered survival data. In some situations there may be competing risks within a cluster. When this happens the basic frailty model is no longer appropriate. Depending on the purpose of the analysis, either the...

Longitudinal censoring is a common artifact when evaluating biomarkers and an obstacle to overcome when jointly investigating the longitudinal nature of the data and the impact on the survival prognoses of a study population. To fully appreciate the complexity of this scenario one has to devise...

The joint effect of several variables is a prevailing statistical concept in biology. The public health importance of developing methods to better assess joint effects is evident when studying gene combinations that function together to produce a disease phenotype, or biomarker pairs that...

Survival analysis has been used to estimate underlying survival or failure probabilities and to estimate the effects of covariates on survival times. The Cox proportional hazards regression model is the most commonly used approach. However, in practical situations, the assumption of proportional...

Variable selection is a difficult problem in statistical model building. Identification of cost efficient diagnostic factors is very important to health researchers, but most variable selection methods do not take into account the cost of collecting data for the predictors. The trade off between...

Two-stage predictor substitution (2SPS) and the two-stage residual inclusion (2SRI) are two approaches to instrumental variable (IV) analysis. While 2SPS and 2SRI with linear models are well-studied methods of causal inference, the properties of 2SPS and 2SRI for logistic binary outcomes have...

Two-stage predictor substitution (2SPS) and the two-stage residual inclusion (2SRI) are two approaches to instrumental variable (IV) analysis. While 2SPS and 2SRI with linear models are well-studied methods of causal inference, the properties of 2SPS and 2SRI for logistic binary outcomes have...