A bias correction in testing treatment efficacy under informative dropout in clinical trials

Abstract

In clinical trials of drug development, patients are often followed for a certain period of time, and the outcome variables are measured at scheduled time intervals. The main interest of the trial is the treatment efficacy at a prespecified time point, which is often the last visit. In such trials, patient dropout is often the major source for missing data. With possible informative patient dropout, the missing information often causes biases in the inference of treatment efficacy. In this article, for a time-saturated treatment effect model and an informative dropout scheme that depends on the unobserved outcomes only through the random coefficients, we propose a grouping method to correct the biases in the estimation of treatment effect. The asymptotic variance estimator is also obtained for statistical inference. In a simulation study, we compare the new method with the traditional methods of the observed case (OC) analysis, the last observation carried forward (LOCF) analysis, and the mixed model repeated measurement (MMRM) approach, and find it improves the current methods and gives more stable results in the treatment efficacy inferences.