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MMRM versus MI in dealing with missing data--a comparison based on 25 NDA data sets.

Ohidul Siddiqui¹

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

|March 29, 2011

Summary

For incomplete clinical trial data, the mixed-effects model repeated measures (MMRM) approach is superior to multiple imputation (MI) for maintaining statistical properties during drug efficacy testing.

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Area of Science:

Clinical Trials
Biostatistics
Drug Development

Background:

Incomplete clinical trial data analysis is critical for drug development.
Last-observation-carried-forward (LOCF) is a traditional but often suboptimal method.
Multiple imputation (MI) and mixed-effects model repeated measures (MMRM) are advanced alternatives.

Purpose of the Study:

To compare the performance of MI and MMRM in analyzing incomplete clinical trial data.
To evaluate their robustness in controlling type I error rates and statistical power.
To determine the optimal approach for hypothesis testing in drug efficacy studies.

Main Methods:

Simulated incomplete data sets were analyzed.
25 New Drug Application (NDA) data sets from neuropsychiatric drug products were utilized.
Comparative analysis focused on type I error rate and statistical power.

Main Results:

The MMRM approach demonstrated superior performance compared to MI.
MMRM effectively maintained the statistical properties of hypothesis testing.
MI showed less favorable results in the analyzed datasets.

Conclusions:

The MMRM approach is recommended over MI for analyzing ignorable missing data in clinical trials.
MMRM offers better control over statistical properties for drug efficacy determination.
This finding has significant implications for clinical trial data analysis and drug approval processes.