Abstract
The primary analysis of Alzheimer's disease clinical trials often involves a mixed-model repeated measure (MMRM) approach. We consider another estimator of the average treatment effect, called targeted minimum loss based estimation (TMLE). This estimator is more robust to violations of assumptions about missing data than MMRM.
We compare TMLE versus MMRM by analyzing data from a completed Alzheimer's disease trial data set and by simulation studies. The simulations involved different missing data distributions, where loss to followup at a given visit could depend on baseline variables, treatment assignment, and the outcome measured at previous visits. The TMLE generally has improved robustness in our simulated settings, i.e., less bias and mean squared error, and better confidence interval coverage probability. The robustness is due to the TMLE correctly modeling the dropout distribution. We illustrate the tradeoffs between these estimators and give recommendations for how to use these estimators in practice.
Disciplines
Biostatistics | Statistical Methodology
Suggested Citation
Rosenblum, Michael; McDermont, Aidan; and Colantuoni, Elizabeth, "ROBUST ESTIMATION OF THE AVERAGE TREATMENT EFFECT IN ALZHEIMER'S DISEASE CLINICAL TRIALS" (March 2018). Johns Hopkins University, Dept. of Biostatistics Working Papers. Working Paper 291.
https://biostats.bepress.com/jhubiostat/paper291
Media Format
flash_audio