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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Improving the mixed model for repeated measures to robustly increase precision in randomized trials.
1The Statistics and Data Science Department of the Wharton School, University of Pennsylvania, Philadelphia, PA, USA.
The International Journal of Biostatistics
|November 28, 2023
Summary
A new statistical model, IMMRM, enhances randomized trial analysis by improving robustness and precision. This method optimizes the use of repeated outcome measures, offering better treatment effect estimates than traditional models.
Area of Science:
- Biostatistics
- Clinical Trials Methodology
- Statistical Modeling
Background:
- Repeated outcome measures are common in randomized trials.
- The mixed model for repeated measures (MMRM) is frequently used for primary analysis.
- MMRM can be biased or imprecise if intermediate outcomes are misspecified.
Purpose of the Study:
- To propose an enhanced mixed model for repeated measures (IMMRM).
- To improve robustness and optimize precision gain from covariate adjustment, stratification, and intermediate outcome adjustment.
- To provide a more reliable estimation of the average treatment effect.
Main Methods:
- Developing an extension of the MMRM, termed IMMRM.
- Proving robustness to model misspecification under regularity conditions and missing completely at random.
- Conducting simulation studies to compare IMMRM with ANCOVA and MMRM under missing at random.
Main Results:
- IMMRM is robust to arbitrary model misspecification.
- IMMRM is asymptotically equal or more precise than ANCOVA and MMRM estimators.
- Simulation studies show IMMRM has less bias and smaller variance under missing at random.
- A diabetes treatment trial re-analysis supports the findings.
Conclusions:
- IMMRM offers improved robustness and precision for analyzing repeated measures in randomized trials.
- The model effectively utilizes post-randomization information, addressing MMRM limitations.
- IMMRM provides a more reliable estimation of average treatment effects, particularly in complex trial designs.
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