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The primary analysis of clinical trials in diabetes therapeutic area ofteninvolves a mixed-model repeated measure (MMRM) approach to estimate the averagetreatment effect for longitudinal continuous outcome, and a generalized linearmixed model (GLMM) approach for longitudinal binary outcome. In this paper, weconsidered another estimator of the average treatment effect, called targetedmaximum likelihood estimator (TMLE). This estimator can be a one-stepalternative to model either continuous or binary outcome. We compared thoseestimators by simulation studies and by analyzing real data from 28 diabetesclinical trials. The simulations involved different missing data scenarios, andthe real data sets covered a wide range of possible distributions of theoutcome and covariates in real-life clinical trials for diabetes drugs withdifferent mechanisms of action. For all the settings, adjusted estimatorstended to be more efficient than the unadjusted one. In the setting oflongitudinal continuous outcome, the MMRM approach with visits and baselinevariables interaction appeared to dominate the performance of the MMRMconsidering the main effects only for the baseline variables while showingbetter or comparable efficiency to the TMLE estimator in both simulations anddata applications. For modeling longitudinal binary outcome, TMLE generallyoutperformed GLMM in terms of relative efficiency, and its avoidance of thecumbersome covariance fitting procedure from GLMM makes TMLE a moreadvantageous estimator.

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