Literature DB >> 14998798

Sample size estimation in research with dependent measures and dichotomous outcomes.

Kevin L Delucchi1.   

Abstract

I reviewed sample estimation methods for research designs involving nonindependent data and a dichotomous response variable to examine the importance of proper sample size estimation and the need to align methods of sample size estimation with planned methods of statistical analysis. Examples and references to published literature are provided in this article. When the method of sample size estimation is not in concert with the method of planned analysis, poor estimates may result. The effects of multiple measures over time also need to be considered. Proper sample size estimation is often overlooked. Alignment of the sample size estimation method with the planned analysis method, especially in studies involving nonindependent data, will produce appropriate estimates.

Mesh:

Year:  2004        PMID: 14998798      PMCID: PMC1448260          DOI: 10.2105/ajph.94.3.372

Source DB:  PubMed          Journal:  Am J Public Health        ISSN: 0090-0036            Impact factor:   9.308


  37 in total

Review 1.  Sample size and statistical power in clinical orthopaedic research.

Authors:  K B Freedman; J Bernstein
Journal:  J Bone Joint Surg Am       Date:  1999-10       Impact factor: 5.284

2.  Comparison of telephone and postal survey modes on respiratory symptoms and risk factors.

Authors:  Jan Brøgger; Per Bakke; Geir E Eide; Amund Gulsvik
Journal:  Am J Epidemiol       Date:  2002-03-15       Impact factor: 4.897

3.  Sample size requirements for stratified cluster randomization designs.

Authors:  A Donner
Journal:  Stat Med       Date:  1992-04       Impact factor: 2.373

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Authors:  V Clark
Journal:  Plast Reconstr Surg       Date:  1991-03       Impact factor: 4.730

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Authors:  H C Kraemer
Journal:  Psychopharmacol Bull       Date:  1991

6.  On the appropriateness of marginal models for repeated measurements in clinical trials.

Authors:  J K Lindsey; P Lambert
Journal:  Stat Med       Date:  1998-02-28       Impact factor: 2.373

7.  Mantel-Haenszel test statistics for correlated binary data.

Authors:  J Zhang; D D Boos
Journal:  Biometrics       Date:  1997-12       Impact factor: 2.571

Review 8.  Size and quality of randomised controlled trials in head injury: review of published studies.

Authors:  K Dickinson; F Bunn; R Wentz; P Edwards; I Roberts
Journal:  BMJ       Date:  2000-05-13

9.  Estimation and sample size considerations for clustered binary responses.

Authors:  E W Lee; N Dubin
Journal:  Stat Med       Date:  1994-06-30       Impact factor: 2.373

10.  The association between secondhand smoke and the risk of developing acute coronary syndromes, among non-smokers, under the presence of several cardiovascular risk factors: The CARDIO2000 case-control study.

Authors:  Demosthenes B Panagiotakos; Christina Chrysohoou; Christos Pitsavos; Ioanna Papaioannou; John Skoumas; Christodoulos Stefanadis; Pavlos Toutouzas
Journal:  BMC Public Health       Date:  2002-05-24       Impact factor: 3.295

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  2 in total

1.  Evaluating interventions to promote routine preventive screenings: a comparison of analytical outcomes.

Authors:  Akshay Sharma; Brent A Johnson; Patrick S Sullivan
Journal:  Contemp Clin Trials       Date:  2015-01-29       Impact factor: 2.226

2.  At Odds: Concerns Raised by Using Odds Ratios for Continuous or Common Dichotomous Outcomes in Research on Physical Activity and Obesity.

Authors:  Gina S Lovasi; Lindsay J Underhill; Darby Jack; Catherine Richards; Christopher Weiss; Andrew Rundle
Journal:  Open Epidemiol J       Date:  2012
  2 in total

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