Modeling Correlated Binary Outcomes with Time-Dependent Covariates

Marginal modeling of multilevel binary data with time-varying covariates.

We propose and compare two approaches for regression analysis of multilevel binary data when clusters are not necessarily nested: a GEE method that relies on a working independence assumption coupled with a three-step method for obtaining empirical standard errors, and a likelihood-based method implemented using Bayesian computational techniques. Implications of time-varying endogenous covariat...

Space-time models with time-dependent covariates

Modeling treatment effects on binary outcomes with grouped-treatment variables and individual covariates.

During evaluation of treatment effects in observational studies, confounding is a constant threat because it is always possible that patients with a better prognosis, not adequately characterized by measured covariates, are chosen for a specific therapy. Ecologic analyses may avoid confounding that would be present in analysis at the individual level because variations in regional or hospital p...

Finding Optimal Cutpoints for Continuous Covariates with Binary and Time-to-Event Outcomes

Regression analysis of longitudinal binary data with time-dependent environmental covariates: bias and efficiency.

Generalized estimating equations (Liang and Zeger, 1986) is a widely used, moment-based procedure to estimate marginal regression parameters. However, a subtle and often overlooked point is that valid inference requires the mean for the response at time t to be expressed properly as a function of the complete past, present, and future values of any time-varying covariate. For example, with envi...