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Multivariate test power approximations for balanced linear mixed models in studies with missing data.

Brandy M Ringham1, Sarah M Kreidler2, Keith E Muller3

  • 1Department of Biostatistics and Informatics, University of Colorado Denver, Aurora, CO, U.S.A.

Statistics in Medicine
|November 26, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a method to approximate statistical power in multilevel and longitudinal studies with missing data. Adjusting power calculations using the expected number of complete cases provides the most accurate results for the Catellier and Muller multivariate test.

Keywords:
Hotelling-Lawley trace power approximationbalanced linear mixed modelsdata missing completely at randommultilevel and longitudinal studies

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Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Multivariate Statistics

Background:

  • Missing data is common in multilevel and longitudinal studies, such as oral cancer biomarker research.
  • Existing methods like Catellier and Muller's address missing at random data but require power approximation adjustments.
  • Assay failures in biomarker studies can lead to missing data, often assumed missing completely at random.

Purpose of the Study:

  • To propose and evaluate methods for approximating statistical power in multilevel and longitudinal studies with missing data.
  • To adapt the Catellier and Muller multivariate test for power calculations under missing data conditions.
  • To assess the accuracy of different power approximation strategies.

Main Methods:

  • Proposed power approximations using a modified non-central F statistic.
  • Evaluated approximations based on expected complete cases, non-missing pairs, and trimmed sample size.
  • Compared theoretical power approximations with Monte Carlo simulations for the Catellier and Muller test.

Main Results:

  • Adjusting power calculations with the expected number of complete cases yielded the closest approximation to empirical power.
  • The proposed method accurately estimates power for the Catellier and Muller multivariate test in the presence of missing data.
  • Demonstrated the utility with a power analysis for an oral cancer biomarkers study.

Conclusions:

  • The expected number of complete cases is the most effective adjustment for approximating power in studies with missing data.
  • The proposed method provides a practical approach for power analysis in complex longitudinal and multilevel studies.
  • Implementation guidance and example code are provided for using standard software.