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MMRM vs. LOCF: a comprehensive comparison based on simulation study and 25 NDA datasets.

Ohidul Siddiqui1, H M James Hung, Robert O'Neill

  • 1Office of Biostatistics, Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland 20993, USA. ohidul.siddiqui@fda.hhs.gov

Journal of Biopharmaceutical Statistics
|February 13, 2009
PubMed
Summary

Last Observation Carried Forward (LOCF) analysis in clinical trials can cause bias and inflate Type I error rates. Mixed-Effect Model Repeated Measure (MMRM) analysis offers a superior alternative, controlling bias and error rates effectively with incomplete data.

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Data Analysis

Background:

  • The Last Observation Carried Forward (LOCF) method for handling missing data in clinical trials is increasingly criticized.
  • Likelihood-based modeling approaches, such as the Mixed-Effect Model Repeated Measure (MMRM), are proposed as alternatives.

Purpose of the Study:

  • To compare the performance of LOCF and MMRM in analyzing incomplete clinical trial data.
  • To evaluate empirical bias and Type I error rates under different missing data paradigms.

Main Methods:

  • Two extensive simulation studies were conducted.
  • Sensitivity analysis on 48 clinical trial datasets from neurological and psychiatric drug products.
  • Evaluation of bias and Type I error rates for LOCF and MMRM estimators and tests.

Related Experiment Videos

Main Results:

  • LOCF analysis demonstrated substantial bias in treatment effect estimators and inflated Type I error rates.
  • MMRM analysis yielded estimators with minimal bias and controlled Type I error rates under missing completely at random (MCAR), missing at random (MAR), and some missing not at random (MNAR) scenarios.
  • MMRM outperformed LOCF ANCOVA in controlling Type I error rates and minimizing bias in real-world clinical trial datasets.

Conclusions:

  • MMRM is a superior approach to LOCF for analyzing incomplete data in clinical trials.
  • MMRM effectively controls bias and Type I error rates, even with various missing data patterns.
  • No clear evidence of MNAR missingness was found in the analyzed neurological and psychiatric drug trial datasets.