Semi-parametric Analysis of Binary Outcomes in Longitudinal Studies with Outcome-dependent Observation Times


Conventional longitudinal data analysis methods assume that outcomes are independent of the data-collection schedule. However, the independence assumption may be violated, for example, when adverse events trigger additional visits in between prescheduled follow-ups. Outcome-dependent observation times may introduce bias when estimating the effect of covariates on outcomes using a standard longitudinal regression model. Recent research has focused on the development of joint modeling approaches that combine a semi-parametric regression model for a continuous outcome with a recurrent event model for the observation times. One such approach is based on estimating observation-level visit-intensity weights, which are used to reweight the model for longitudinal outcomes. We extend this approach to the analysis of binary outcomes. Computational limitations motivate us to consider a parametric, yet flexible, adjustment for temporal trends in the outcome model. We examine the performance of our methods via simulation studies, and illustrate their application to a randomized controlled trial regarding interventions to improve anti-coagulation control among patients treated with warfarin.


This program is for the research community.

Date & Time(s)


Memorial Sloan Kettering Cancer Center
307 East 63rd Street
Third Floor Conference Room
New York, NY


Department of Epidemiology and Biostatistics


Benjamin French
Department of Biostatistics and Epidemiology
Perelman School of Medicine, University of Pennsylvania