Regression modeling of longitudinal data with outcome‐dependent observation times: extensions and comparative evaluation

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 a specific treatment necessitates a different follow‐up schedule than the control arm or when adverse events trigger additional physician visits in between prescheduled follow‐ups. Dependence between outcomes and observation times may introduce bias when estimating the marginal association of covariates on outcomes using a standard longitudinal regression model. We formulate a framework of outcome‐observation dependence mechanisms to describe conditional independence given observed observation‐time process covariates or shared latent variables. We compare four recently developed semi‐parametric methods that accommodate one of these mechanisms. To allow greater flexibility, we extend these methods to accommodate a combination of mechanisms. In simulation studies, we show how incorrectly specifying the outcome‐observation dependence may yield biased estimates of covariate‐outcome associations and how our proposed extensions can accommodate a greater number of dependence mechanisms. We illustrate the implications of different modeling strategies in an application to bladder cancer data. In longitudinal studies with potentially outcome‐dependent observation times, we recommend that analysts carefully explore the conditional independence mechanism between the outcome and observation‐time processes to ensure valid inference regarding covariate‐outcome associations. Copyright © 2014 John Wiley & Sons, Ltd.