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Activity Number: 555 - Novel Methods in Estimation Enhancement
Type: Contributed
Date/Time: Thursday, August 11, 2022 : 10:30 AM to 12:20 PM
Sponsor: Biopharmaceutical Section
Abstract #322134
Title: Using Targeted Maximum Likelihood Estimation to Estimate Treatment Effect with Longitudinal Continuous or Binary Data: A Systematic Evaluation of 28 Clinical Trials
Author(s): Lingjing Jiang*
Companies: Johnson & Johnson
Keywords: targeted maximum likelihood estimation; MMRM; pharmaceutical; randomized trial; longitudinal; doubly robust
Abstract:

The primary analysis of clinical trials 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. We considered another estimator of the average treatment effect, called targeted maximum likelihood estimator (TMLE). This doubly robust estimator can be a one-step alternative to model either continuous or binary outcome. We compared different estimators by simulation studies and by analyzing real data from 28 clinical trials. For all the settings, adjusted estimators tended to be more efficient than the unadjusted one. In the setting of longitudinal continuous outcome, the modified MMRM approach appeared to dominate the performance of the traditional MMRM, 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.


Authors who are presenting talks have a * after their name.

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