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A comparison of methods for analysing multiple outcome measures in randomised controlled trials using a simulation

Victoria Vickerstaff1,2, Gareth Ambler2, Rumana Z Omar2

  • 1Division of Psychiatry, University College London, London, UK.

Biometrical Journal. Biometrische Zeitschrift
|December 14, 2020
PubMed
Summary

Analyzing multiple outcomes in randomized controlled trials (RCTs) requires careful consideration of correlations. Multivariate multilevel (MM) models offer advantages, especially with missing data, but gains are minimal with strong outcome correlations.

Keywords:
multiple endpointsmultiple outcomesmultivariate modelrandomised controlled trials

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Epidemiology

Background:

  • Randomized controlled trials (RCTs) often collect multiple primary outcomes to comprehensively assess intervention effects.
  • Analyzing multiple outcomes separately ignores potential correlations, potentially leading to inefficient analysis and issues with missing data.
  • Multivariate methods can account for outcome correlations and handle missing data more effectively.

Purpose of the Study:

  • To provide an overview of methods for analyzing multiple outcome measures in RCTs.
  • To evaluate the performance of different analytical methods, including multivariate multilevel (MM) models, through simulation studies.
  • To assess bias in intervention effect estimates and power for detecting true effects under various scenarios.

Main Methods:

  • Overview of statistical methods for analyzing multiple outcomes in RCTs.
  • Conducting simulation studies to compare univariate methods (with and without multiple imputation) against multivariate methods (MM models).
  • Scenarios varied number, type, and correlation of outcomes, along with missing data proportions and mechanisms.

Main Results:

  • Multivariate multilevel (MM) models showed small power gains compared to separate outcome analysis when outcome correlations were strong (ρ > .4).
  • When outcome correlations were weak (ρ < .4), univariate methods with multiple imputation demonstrated reduced power compared to analyzing outcomes separately.
  • Multivariate methods may be more efficient in the presence of missing data by modeling pairwise correlations.

Conclusions:

  • The choice of analytical method for multiple outcomes in RCTs depends on the correlation structure between outcomes and the presence of missing data.
  • While MM models offer advantages, particularly with missing data, their power gains over separate analyses are modest with highly correlated outcomes.
  • Univariate methods with multiple imputation may reduce power in scenarios with weakly correlated outcomes.