• Corpus ID: 239024846

Robustly leveraging the post-randomization information to improve precision in the analyses of randomized clinical trials

@inproceedings{Wang2021RobustlyLT,
  title={Robustly leveraging the post-randomization information to improve precision in the analyses of randomized clinical trials},
  author={Bingkai Wang and Yu Du},
  year={2021}
}
  • Bingkai Wang, Yu Du
  • Published 18 October 2021
  • Mathematics
In randomized clinical trials, repeated measures of the outcome are routinely collected. The mixed model for repeated measures (MMRM) leverages the information from these repeated outcome measures, and is often used for the primary analysis to estimate the average treatment effect at the final visit. MMRM, however, can suffer from precision loss when it models the intermediate outcomes incorrectly, and hence fails to use the post-randomization information in a harmless way. In this paper, we… 

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