Treatment effects in randomized longitudinal trials with different types of nonignorable dropout.

Abstract

Randomized longitudinal designs are commonly used in psychological and medical studies to investigate the treatment effect of an intervention or an experimental drug. Traditional linear mixed-effects models for randomized longitudinal designs are limited to maximum-likelihood methods that assume data are missing at random (MAR). In practice, because longitudinal data are often likely to be missing not at random (MNAR), the traditional mixed-effects model might lead to biased estimates of treatment effects. In such cases, an alternative approach is to utilize pattern-mixture models. In this article, a Monte Carlo simulation study compares the traditional mixed-effects model and 2 different approaches to pattern-mixture models (i.e., the differencing-averaging method and the averaging-differencing method) across different missing mechanisms (i.e., MAR, random-coefficient-dependent MNAR, or outcome-dependent MNAR) and different types of treatment-condition-based missingness. Results suggest that the traditional mixed-effects model is well suited for analyzing data with the MAR mechanism whereas the proposed pattern-mixture averaging-differencing model has the best overall performance for analyzing data with the MNAR mechanism. No method was found that could provide unbiased estimates under every missing mechanism, leading to a practical suggestion that researchers need to consider why data are missing and should also consider performing a sensitivity analysis to ascertain the extent to which their results are consistent across various missingness assumptions. Applications of different estimation methods are also illustrated using a real-data example.