Power and sample size calculations for evaluating mediation effects in longitudinal studies

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Current methods of power and sample size calculations for the design of longitudinal studies to evaluate mediation effects are mostly based on simulation studies and do not provide closed-form formulae. A further challenge due to the longitudinal study design is the consideration of missing data, which almost always occur in longitudinal studies due to staggered entry or drop out. In this article, we consider the product of coefficients as a measure for the longitudinal mediation effect and evaluate three methods for testing the hypothesis on the longitudinal mediation effect: the joint significant test, the normal approximation and the test of b methods. Formulae for power and sample size calculations are provided under each method while taking into account missing data. Performance of the three methods under limited sample size are examined using simulation studies. An example from the Einstein aging study is provided to illustrate the methods.

Original languageEnglish (US)
Pages (from-to)686-705
Number of pages20
JournalStatistical Methods in Medical Research
Volume25
Issue number2
DOIs
StatePublished - 2012

Fingerprint

Sample Size Calculation
Mediation
Longitudinal Study
Sample Size
Longitudinal Studies
Missing Data
Simulation Study
Normal Approximation
Evaluate
Drop out
Albert Einstein
Closed-form
Joints
Testing
Coefficient

Keywords

  • Drop out
  • joint significance test
  • linear mixed effects model
  • missing data
  • power analysis
  • product of coefficients

ASJC Scopus subject areas

  • Epidemiology
  • Health Information Management
  • Statistics and Probability

Cite this

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abstract = "Current methods of power and sample size calculations for the design of longitudinal studies to evaluate mediation effects are mostly based on simulation studies and do not provide closed-form formulae. A further challenge due to the longitudinal study design is the consideration of missing data, which almost always occur in longitudinal studies due to staggered entry or drop out. In this article, we consider the product of coefficients as a measure for the longitudinal mediation effect and evaluate three methods for testing the hypothesis on the longitudinal mediation effect: the joint significant test, the normal approximation and the test of b methods. Formulae for power and sample size calculations are provided under each method while taking into account missing data. Performance of the three methods under limited sample size are examined using simulation studies. An example from the Einstein aging study is provided to illustrate the methods.",
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AB - Current methods of power and sample size calculations for the design of longitudinal studies to evaluate mediation effects are mostly based on simulation studies and do not provide closed-form formulae. A further challenge due to the longitudinal study design is the consideration of missing data, which almost always occur in longitudinal studies due to staggered entry or drop out. In this article, we consider the product of coefficients as a measure for the longitudinal mediation effect and evaluate three methods for testing the hypothesis on the longitudinal mediation effect: the joint significant test, the normal approximation and the test of b methods. Formulae for power and sample size calculations are provided under each method while taking into account missing data. Performance of the three methods under limited sample size are examined using simulation studies. An example from the Einstein aging study is provided to illustrate the methods.

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