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Sample size estimation using a latent variable model for mixed outcome co-primary, multiple primary and composite

Martina E McMenamin1,2, Jessica K Barrett1, Anna Berglind3

  • 1MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.

Statistics in Medicine
|February 24, 2022
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Summary
This summary is machine-generated.

This study introduces latent variable models for sample size estimation in clinical trials with mixed outcome endpoints. These methods provide accurate power calculations for co-primary, multiple primary, and composite endpoints, guiding efficient trial design.

Keywords:
latent variable modelingmixed outcome endpointssample size estimation

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Modeling

Background:

  • Mixed outcome endpoints, combining continuous and discrete data, are common in clinical trials.
  • These endpoints can be co-primary, multiple primary, or composite, each with distinct interpretation rules.
  • Accurate sample size estimation is crucial for the validity and efficiency of trials using these complex endpoints.

Purpose of the Study:

  • To propose and validate methods for sample size estimation in clinical trials with mixed outcome endpoints using latent variable models.
  • To provide power calculations and hypothesis testing frameworks for co-primary, multiple primary, and composite endpoints.
  • To demonstrate the practical application and efficiency gains of these methods.

Main Methods:

  • Utilized latent variable models to jointly model individual outcomes within mixed endpoints.
  • Developed sample size estimation techniques tailored for co-primary, multiple primary, and composite endpoint scenarios.
  • Performed power calculations and simulated empirical power using a four-dimensional endpoint example.
  • Assessed the control of family-wise error rate (FWER) with Bonferroni correction and alternative adjustments.

Main Results:

  • Sample size for co-primary endpoints exceeded that for the smallest individual effect size.
  • Sample size for multiple primary endpoints approximated that for the largest individual effect size.
  • Latent variable models achieved empirical power and controlled FWER effectively, with Bonferroni suitable for correlations < 0.5.
  • Demonstrated efficiency gains in the composite endpoint setting through empirical illustration.

Conclusions:

  • Latent variable models offer a robust framework for sample size estimation with mixed outcome endpoints in clinical trials.
  • The proposed methods provide essential tools for designing efficient trials with complex outcome structures.
  • Understanding the relationship between endpoint types and required sample sizes is critical for trial planning.