Using Targeted Maximum Likelihood Estimation to Estimate Treatment Effect with Longitudinal Continuous or Binary Data: A Systematic Evaluation of 28 Diabetes Clinical Trials

TL;DR

This study systematically compares targeted maximum likelihood estimation (TMLE) with traditional methods like MMRM and GLMM for analyzing longitudinal data in diabetes trials, highlighting TMLE's efficiency and practical advantages.

Contribution

It provides a comprehensive evaluation of TMLE against standard estimators through simulations and real data, demonstrating its effectiveness for longitudinal binary and continuous outcomes.

Findings

TMLE outperforms GLMM in binary outcome analysis.

Adjusted estimators are generally more efficient than unadjusted ones.

In continuous outcomes, MMRM with interactions performs well, but TMLE is competitive.

Abstract

The primary analysis of clinical trials in diabetes therapeutic area often involves a mixed-model repeated measure (MMRM) approach to estimate the average treatment effect for longitudinal continuous outcome, and a generalized linear mixed model (GLMM) approach for longitudinal binary outcome. In this paper, we considered another estimator of the average treatment effect, called targeted maximum likelihood estimator (TMLE). This estimator can be a one-step alternative to model either continuous or binary outcome. We compared those estimators by simulation studies and by analyzing real data from 28 diabetes clinical trials. The simulations involved different missing data scenarios, and the real data sets covered a wide range of possible distributions of the outcome and covariates in real-life clinical trials for diabetes drugs with different mechanisms of action. For all the settings, adjusted estimators tended to be more efficient than the unadjusted one. In the setting of longitudinal continuous outcome, the MMRM approach with visits and baseline variables interaction appeared to dominate the performance of the MMRM considering the main effects only for the baseline variables while showing better or comparable efficiency to the TMLE estimator in both simulations and data applications. For modeling longitudinal binary outcome, TMLE generally outperformed GLMM in terms of relative efficiency, and its avoidance of the cumbersome covariance fitting procedure from GLMM makes TMLE a more advantageous estimator.