Literature DB >> 25418811

Sample Size for Joint Testing of Indirect Effects.

Eric Vittinghoff1, Torsten B Neilands2.   

Abstract

This paper presents methods to calculate sample size for evaluating mediation by joint testing of both links in an indirect pathway from exposure to mediator to outcome. Calculations rely on simulations of the underlying data structure, with testing of the two links performed under the simplifying assumption that the two test statistics are asymptotically independent. Simulations show that the proposed methods are accurate. Continuous and binary exposures and mediators, as well as continuous, binary, count, and survival outcomes are accommodated, along with over-dispersion of count outcomes, design effects, and confounding of the exposure-mediator and mediator-outcome relationships. An illustrative example is provided, and a documented R program implementing the calculations is available online.

Entities:  

Keywords:  Generalized linear models.; Indirect pathway; Mediation; Power; Sample size

Mesh:

Year:  2015        PMID: 25418811      PMCID: PMC4442753          DOI: 10.1007/s11121-014-0528-5

Source DB:  PubMed          Journal:  Prev Sci        ISSN: 1389-4986


  22 in total

1.  Sample size considerations for the evaluation of prognostic factors in survival analysis.

Authors:  C Schmoor; W Sauerbrei; M Schumacher
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2.  Sample-size calculations for the Cox proportional hazards regression model with nonbinary covariates.

Authors:  F Y Hsieh; P W Lavori
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3.  Fallibility in estimating direct effects.

Authors:  Stephen R Cole; Miguel A Hernán
Journal:  Int J Epidemiol       Date:  2002-02       Impact factor: 7.196

4.  Sample size for studying intermediate endpoints within intervention trails or observational studies.

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5.  A practical approach to computing power for generalized linear models with nominal, count, or ordinal responses.

Authors:  Robert H Lyles; Hung-Mo Lin; John M Williamson
Journal:  Stat Med       Date:  2007-03-30       Impact factor: 2.373

6.  Sample size determination for logistic regression revisited.

Authors:  Eugene Demidenko
Journal:  Stat Med       Date:  2007-08-15       Impact factor: 2.373

7.  The intermediate endpoint effect in logistic and probit regression.

Authors:  D P MacKinnon; C M Lockwood; C H Brown; W Wang; J M Hoffman
Journal:  Clin Trials       Date:  2007       Impact factor: 2.486

8.  Sample size calculations for evaluating mediation.

Authors:  E Vittinghoff; S Sen; C E McCulloch
Journal:  Stat Med       Date:  2009-02-15       Impact factor: 2.373

9.  Marginal structural models for the estimation of direct and indirect effects.

Authors:  Tyler J VanderWeele
Journal:  Epidemiology       Date:  2009-01       Impact factor: 4.822

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

Authors:  F Y Hsieh; D A Bloch; M D Larsen
Journal:  Stat Med       Date:  1998-07-30       Impact factor: 2.373

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  5 in total

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Journal:  BMJ Open       Date:  2017-07-03       Impact factor: 2.692

5.  Mediation analysis with a time-to-event outcome: a review of use and reporting in healthcare research.

Authors:  Lauren Lapointe-Shaw; Zachary Bouck; Nicholas A Howell; Theis Lange; Ani Orchanian-Cheff; Peter C Austin; Noah M Ivers; Donald A Redelmeier; Chaim M Bell
Journal:  BMC Med Res Methodol       Date:  2018-10-29       Impact factor: 4.615

  5 in total

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