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Power for balanced linear mixed models with complex missing data processes.

Kevin P Josey1, Brandy M Ringham2, Anna E Barón1

  • 1Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver, Denver, Colorado, USA.

Communications in Statistics: Theory and Methods
|February 6, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces new power approximations for repeated measures studies with missing data. The method accurately estimates statistical power for both complete-case and observed-case analyses, crucial for study design.

Keywords:
Powerlongitudinalmissing datamixed modelmultilevel

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Area of Science:

  • Statistics
  • Biostatistics
  • Clinical Trial Design

Background:

  • Missing outcome data in repeated measures studies can significantly impact statistical power.
  • The pattern and amount of missingness, including correlations across measurements, are critical factors.
  • Physiotherapy studies, like those for Parkinson's disease, often exhibit intermittent dropout leading to missing functional measurements.

Purpose of the Study:

  • To develop accurate power approximations for balanced linear mixed models with Gaussian responses under missing completely at random (MCAR) data.
  • To provide methods for calculating power for both complete-case and observed-case analyses.
  • To evaluate the accuracy of the proposed power approximations using Monte Carlo simulations.

Main Methods:

  • Proposed noncentral F power approximations for the Wald test in linear mixed models.
  • Utilized moments of missing data summary statistics derived from a conditional linear missingness process.
  • Assessed accuracy through Monte Carlo simulations for small sample sizes.

Main Results:

  • The proposed noncentral F power approximations accurately estimate power for repeated measures studies with MCAR data.
  • The method provides reliable power calculations for both complete-case and observed-case analyses.
  • Simulations confirmed the accuracy of the approximations, even in small sample sizes.

Conclusions:

  • The developed power approximations are valuable tools for researchers designing repeated measures studies with missing data.
  • The method enhances the ability to plan studies, such as those in physiotherapy for Parkinson's disease, by providing accurate power estimates.
  • Accurate power calculations are essential for ensuring adequate sample sizes and reliable study outcomes.