Handling Dropouts in Longitudinal Clinical Trials Aata: Likelihood-based Analysis Versus Inverse Probability Weighting PDF

Keywords

Incomplete longitudinal data; Likelihood-based analysis; Inverse probability weighted (IPW); Dropout; Missing at random (MAR); Linear mixed model (LMM).

Abstract

Dropout is a pervasive problem in longitudinal clinical trials, and it is the result mainly of non-responses due to individuals who leave the study and are therefore lost to follow-up. The current paper deals with incomplete longitudinal clinical trials data when there are dropout. Statistical methods that ignore the mechanism for dropouts are susceptible to biased inference. This article focuses on dropouts missing at random (MAR). The study demonstrates application and the performance of likelihood-based and inverse probability weighting (IPW) in handling dropout in longitudinal continuous responses. The main objective of this paper is to compare the performance of these methods under different dropout rates. Data from a study with individual heart rate as the outcome is used to investigate the performance of the considered methods. Based on this longitudinal clinical trial data, results from IPW will be compared with those obtained from likelihood-based analysis. The performance of these methods are compared in terms of bias and efficiency.