Hongyuan Cao, PhD, Department of Statistics, Florida State University
Extended follow-up with longitudinal data is common in many medical investigations. In regression analyses, a longitudinal covariate may be omitted, often because it is not measured synchronously with the longitudinal response. Naive approcach that simply ingores the omitted longitudinal covariate can lead to biased estimators. In this article, we establish conditions under which estimation is unbiased with an omitted longitudinal covariate and propose unbiased estimation methods to accommodate omitted longitudinal covariate. A second stage estimator is presented for inference about the asynchronous longitudinal covariates, when such covariates are observed. Extensive simulation studies provide numerical support for the theoretical findings. We illustrate the performance of our method on dataset from an HIV study.