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Improving the mixed model for repeated measures to robustly increase precision in randomized trials

  • Bingkai Wang ORCID logo EMAIL logo und Yu Du
Veröffentlicht/Copyright: 29. November 2023
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The International Journal of Biostatistics
Aus der Zeitschrift The International Journal of Biostatistics Band 20 Heft 2

Abstract

In randomized 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 primary endpoint. MMRM, however, can suffer from bias and precision loss when it models intermediate outcomes incorrectly, and hence fails to use the post-randomization information harmlessly. This paper proposes an extension of the commonly used MMRM, called IMMRM, that improves the robustness and optimizes the precision gain from covariate adjustment, stratified randomization, and adjustment for intermediate outcome measures. Under regularity conditions and missing completely at random, we prove that the IMMRM estimator for the average treatment effect is robust to arbitrary model misspecification and is asymptotically equal or more precise than the analysis of covariance (ANCOVA) estimator and the MMRM estimator. Under missing at random, IMMRM is less likely to be misspecified than MMRM, and we demonstrate via simulation studies that IMMRM continues to have less bias and smaller variance. Our results are further supported by a re-analysis of a randomized trial for the treatment of diabetes.

Keywords: ANCOVA; heterogeneity; heteroscedasticity; repeated outcomes
Corresponding author: Bingkai Wang, The Statistics and Data Science Department of the Wharton School, University of Pennsylvania, Philadelphia, PA, USA, E-mail:

Acknowledgment

We sincerely thank the comments from the editor, Dr. Ashkan Ertefaie, and the anonymous reviewer to help us improve the paper. We also thank Drs. Yongming Qu, Michael Rosenblum, Ting Ye, and Yanyao Yi for their input in the early stage of this paper.

  1. Research ethics: Not applicable.

  2. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: The authors state no conflict of interest.

  4. Research funding: None declared.

  5. Data availability: The raw data contain sensitive information and are not publicly available.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/ijb-2022-0101).

Received: 2022-08-23
Accepted: 2023-08-12
Published Online: 2023-11-29

© 2023 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

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https://doi.org/10.1515/ijb-2022-0101
Schlagwörter für diesen Artikel
ANCOVA; heterogeneity; heteroscedasticity; repeated outcomes

Artikel in diesem Heft

  1. Frontmatter
  2. Research Articles
  3. Random forests for survival data: which methods work best and under what conditions?
  4. Flexible variable selection in the presence of missing data
  5. An interpretable cluster-based logistic regression model, with application to the characterization of response to therapy in severe eosinophilic asthma
  6. MBPCA-OS: an exploratory multiblock method for variables of different measurement levels. Application to study the immune response to SARS-CoV-2 infection and vaccination
  7. Detecting differentially expressed genes from RNA-seq data using fuzzy clustering
  8. Hypothesis testing for detecting outlier evaluators
  9. Response to comments on ‘sensitivity of estimands in clinical trials with imperfect compliance’
  10. Commentary
  11. Comments on “sensitivity of estimands in clinical trials with imperfect compliance” by Chen and Heitjan
  12. Research Articles
  13. Optimizing personalized treatments for targeted patient populations across multiple domains
  14. Statistical models for assessing agreement for quantitative data with heterogeneous random raters and replicate measurements
  15. History-restricted marginal structural model and latent class growth analysis of treatment trajectories for a time-dependent outcome
  16. Revisiting incidence rates comparison under right censorship
  17. Ensemble learning methods of inference for spatially stratified infectious disease systems
  18. The survival function NPMLE for combined right-censored and length-biased right-censored failure time data: properties and applications
  19. Hybrid classical-Bayesian approach to sample size determination for two-arm superiority clinical trials
  20. Estimation of a decreasing mean residual life based on ranked set sampling with an application to survival analysis
  21. Improving the mixed model for repeated measures to robustly increase precision in randomized trials
  22. Bayesian second-order sensitivity of longitudinal inferences to non-ignorability: an application to antidepressant clinical trial data
  23. A modified rule of three for the one-sided binomial confidence interval
  24. Kalman filter with impulse noised outliers: a robust sequential algorithm to filter data with a large number of outliers
  25. Bayesian estimation and prediction for network meta-analysis with contrast-based approach
  26. Testing for association between ordinal traits and genetic variants in pedigree-structured samples by collapsing and kernel methods
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