Marginal Models for Censored Longitudinal Cost Data: Appropriate Working Variance Matrices in Inverse-Probability-Weighted GEEs Can Improve Precision

Abstract

When cost data are collected in a clinical study, interest centers on the between-treatment difference in mean cost. When censoring is present, the resulting loss of information can be limited by collecting cost data for several pre-specified time intervals, leading to censored longitudinal cost data. Most models for marginal costs stratify by time interval. However, in few other areas of biostatistics would we stratify by default. We argue that there are benefits to considering more general models: for example, in some settings, pooling regression coefficients across intervals can improve the precision of the estimated between-treatment difference in mean cost. Previous work has used inverse-probability-weighted GEEs coupled with an independent working variance to estimate parameters from these more general models. We show that the greatest precision benefits of non-stratified models are achieved by using more sophisticated working variance matrices.