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Calculating statistical power in Mendelian randomization studies.

Marie-Jo A Brion1, Konstantin Shakhbazov, Peter M Visscher

  • 1Broad Institute of MIT & Harvard, Cambridge, MA USA, MRC Centre for Causal Analyses in Translational Epidemiology, School of Social and Community Medicine, University of Bristol, Bristol, UK, Queensland Brain Institute, QLD, Australia and University of Queensland Diamantina Institute, University of Queensland, Brisbane, QLD, Australia.

International Journal of Epidemiology
|October 26, 2013
PubMed
Summary
This summary is machine-generated.

Statistical power in Mendelian randomization (MR) studies is often limited. This study provides equations to calculate power based on genetic variation and causal association, enhancing two-stage least squares (2SLS) MR analyses.

Keywords:
Mendelian randomizationPowerinstrumental variablenon-centrality parameter

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Area of Science:

  • Statistical Genetics
  • Epidemiology

Background:

  • Mendelian randomization (MR) studies use genetic variants to infer causal relationships.
  • Statistical power is a key concern in MR due to limited genetic influence on traits.
  • Existing power estimates for two-stage least squares (2SLS) MR are simulation-based and lack generalizable equations.

Purpose of the Study:

  • To derive theoretical equations for calculating statistical power in 2SLS MR analyses.
  • To provide a framework for power calculations based on asymptotic theory.
  • To develop practical tools for assessing MR study power.

Main Methods:

  • Utilized asymptotic theory to develop power equations for 2SLS MR with continuous variables.
  • Demonstrated power is a function of the proportion of variation explained by genetic predictors and the true causal association.
  • Derived power calculations using the non-centrality parameter (NCP) of the statistical test for the 2SLS coefficient.

Main Results:

  • Developed general equations for calculating statistical power in 2SLS MR.
  • Showed that simulation-based power estimates align with the NCP-based theoretical approach.
  • Implemented power calculations in a web-based application for user accessibility.

Conclusions:

  • The NCP-based approach provides a theoretical foundation for MR power calculations.
  • The derived equations and application facilitate better study design and power assessment in MR.
  • This work addresses the need for practical tools to determine adequate statistical power in MR studies.