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Handling baselines in repeated measures analyses with missing data at random.

Phillip Dinh1, Peiling Yang

  • 1Division of Biometrics 1, Office of Biostatistics/Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA. Phillip.Dinh@fda.hhs.gov

Journal of Biopharmaceutical Statistics
|March 11, 2011
PubMed
Summary
This summary is machine-generated.

For longitudinal clinical studies, mixed models for repeated measures (MMRM) analyze symptom development. Strategies 2 and 5, which retain baseline responses and assume equal baseline means or use baseline as a covariate with differing slopes, are recommended for missing at random data.

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Longitudinal Data Analysis

Background:

  • Longitudinal clinical studies track subjects over time post-randomization.
  • Mixed models for repeated measures (MMRM) are suitable for analyzing such data.
  • Handling baseline responses in MMRM is crucial for accurate analysis.

Purpose of the Study:

  • To evaluate five distinct approaches for handling baseline responses in MMRM analyses.
  • To assess these methods under conditions of missing at random data.
  • To determine the most reliable strategies for bias and confidence interval coverage.

Main Methods:

  • The study examined five methods for incorporating baseline data into MMRM.
  • Methods included retaining baselines, subtracting baselines, or using baselines as covariates.
  • Performance was evaluated based on estimate bias and confidence interval coverage accuracy.

Main Results:

  • Two strategies demonstrated superior performance in the presence of missing at random data.
  • Strategy 2: Retaining baseline responses with assumed equal group means at baseline.
  • Strategy 5: Using baseline responses as a covariate with differing regression slopes between groups.

Conclusions:

  • Strategies 2 and 5 are recommended for MMRM analyses with missing at random data.
  • These approaches offer better bias and confidence interval accuracy.
  • Proper handling of baseline data is critical for robust longitudinal study findings.