Literature DB >> 23221975

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

Cuiling Wang1, Xiaonan Xue2.   

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.
© The Author(s) 2012.

Entities:  

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

Mesh:

Year:  2012        PMID: 23221975      PMCID: PMC3883797          DOI: 10.1177/0962280212465163

Source DB:  PubMed          Journal:  Stat Methods Med Res        ISSN: 0962-2802            Impact factor:   3.021


  24 in total

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5.  Sample size calculations for evaluating mediation.

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6.  A simple method of sample size calculation for linear and logistic regression.

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7.  The power to detect differences in average rates of change in longitudinal studies.

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8.  Estimation of the mediation effect with a binary mediator.

Authors:  Yan Li; Julie A Schneider; David A Bennett
Journal:  Stat Med       Date:  2007-08-15       Impact factor: 2.373

9.  Random-effects models for longitudinal data.

Authors:  N M Laird; J H Ware
Journal:  Biometrics       Date:  1982-12       Impact factor: 2.571

10.  POWER ANALYSIS FOR COMPLEX MEDIATIONAL DESIGNS USING MONTE CARLO METHODS.

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  3 in total

1.  When the test of mediation is more powerful than the test of the total effect.

Authors:  Holly P O'Rourke; David P MacKinnon
Journal:  Behav Res Methods       Date:  2015-06

2.  Sample Size for Joint Testing of Indirect Effects.

Authors:  Eric Vittinghoff; Torsten B Neilands
Journal:  Prev Sci       Date:  2015-11

3.  Sample size determination for mediation analysis of longitudinal data.

Authors:  Haitao Pan; Suyu Liu; Danmin Miao; Ying Yuan
Journal:  BMC Med Res Methodol       Date:  2018-03-27       Impact factor: 4.615

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