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Sample Size for Joint Testing of Indirect Effects.

Eric Vittinghoff1, Torsten B Neilands2

  • 1Department of Epidemiology and Biostatistics, University of California San Francisco, 550 16th Street, 2nd Floor, San Francisco, CA, 94158, USA. eric.vittinghoff@ucsf.edu.

Prevention Science : the Official Journal of the Society for Prevention Research
|November 25, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces accurate methods for calculating sample sizes needed to evaluate mediation effects in statistical models. The approach uses simulations and accommodates various data types for robust mediation analysis.

Keywords:
Generalized linear models.Indirect pathwayMediationPowerSample size

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Statistical Modeling

Background:

  • Mediation analysis is crucial for understanding indirect effects in exposure-mediator-outcome pathways.
  • Accurate sample size calculation is essential for the statistical power and validity of mediation studies.
  • Existing methods may not adequately address the complexities of various data types and potential confounders.

Purpose of the Study:

  • To present and validate methods for determining the required sample size in mediation analysis.
  • To develop a flexible approach accommodating diverse exposure, mediator, and outcome variable types.
  • To provide a practical tool for researchers conducting mediation studies.

Main Methods:

  • Sample size calculations based on joint testing of two links in the indirect pathway.
  • Utilizes simulations of the data structure under the assumption of asymptotically independent test statistics.
  • Accommodates continuous, binary, count (with over-dispersion), and survival outcomes.

Main Results:

  • Simulation results demonstrate the accuracy of the proposed sample size calculation methods.
  • The methods are robust to confounding in both exposure-mediator and mediator-outcome relationships.
  • Design effects are also incorporated, enhancing applicability in complex study designs.

Conclusions:

  • The presented methods provide a reliable approach for sample size determination in mediation analysis.
  • The flexibility in accommodating various data types and study complexities makes these methods widely applicable.
  • An R program is available to facilitate the implementation of these calculations in research.