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The Performance of Multivariate Methods for Two-Group Comparisons with Small Samples and Incomplete Data.

Keenan A Pituch1, Megha Joshi2, Molly E Cain2

  • 1Edson College of Nursing and Health Innovation, Arizona State University, Phoenix, AZ, USA.

Multivariate Behavioral Research
|September 26, 2019
PubMed
Summary

For intervention studies with small sample sizes and missing data, the Kenward-Roger correction within multivariate multilevel models (MVMMs) offers superior performance over traditional univariate tests for assessing group mean differences in multiple outcomes.

Keywords:
ANOVAKenward–Roger correctionmissing datamultivariate multilevel modelsmall samples

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Statistical Modeling

Background:

  • Intervention studies with multiple outcomes often use univariate tests (e.g., ANOVAs) for group mean differences.
  • Traditional methods may not adequately handle outcome correlations or missing data.
  • Multivariate multilevel models (MVMMs) offer an alternative by incorporating outcome correlations.

Purpose of the Study:

  • To compare the performance of separate independent samples t-tests (using ordinary least squares) against t-tests derived from MVMMs.
  • To evaluate these methods under conditions of small sample sizes and missing outcome data.
  • To identify the most effective statistical approach for analyzing multiple outcomes in intervention studies with incomplete data.

Main Methods:

  • Simulation study comparing independent samples t-tests (OLS) with MVMM-based t-tests.
  • Focus on two-group mean differences across multiple, correlated outcomes.
  • Conditions included small sample sizes (small N) and varying degrees of missing data.

Main Results:

  • Multivariate multilevel models (MVMMs) implemented with restricted maximum likelihood estimation and the Kenward-Roger correction demonstrated the best performance.
  • This approach outperformed traditional univariate methods and standard MVMM analyses.
  • The Kenward-Roger procedure was particularly effective in managing missing outcome data.

Conclusions:

  • For intervention studies with small sample sizes (small N) and normally distributed multivariate outcomes, the Kenward-Roger procedure is recommended.
  • This method is superior to traditional univariate approaches and conventional MVMM analyses, especially when dealing with incomplete datasets.
  • The findings advocate for the use of MVMMs with the Kenward-Roger correction for robust analysis of complex intervention data.