Literature DB >> 15083478

Allowing for imprecision of the intracluster correlation coefficient in the design of cluster randomized trials.

Rebecca M Turner1, A Toby Prevost, Simon G Thompson.   

Abstract

The sample size required for a cluster randomized trial depends on the magnitude of the intracluster correlation coefficient (ICC). The usual sample size calculation makes no allowance for the fact that the ICC is not known precisely in advance. We develop methods which allow for the uncertainty in a previously observed ICC, using a variety of distributional assumptions. Distributions for the power are derived, reflecting this uncertainty. Further, the observed ICC in a future study will not equal its true value, and we consider the impact of this on power. We implement calculations within a Bayesian simulation approach, and provide one simplification that can be performed using simple simulation within spreadsheet software. In our examples, recognizing the uncertainty in a previous ICC estimate decreases expected power, especially when the power calculated naively from the ICC estimate is high. To protect against the possibility of low power, sample sizes may need to be very substantially increased. Recognizing the variability in the future observed ICC has little effect if prior uncertainty has already been taken into account. We show how our method can be extended to the case in which multiple prior ICC estimates are available. The methods presented in this paper can be used by applied researchers to protect against loss of power, or to choose a design which reduces the impact of uncertainty in the ICC. Copyright 2004 John Wiley & Sons, Ltd.

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Year:  2004        PMID: 15083478     DOI: 10.1002/sim.1721

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  13 in total

1.  Sample size considerations when groups are the appropriate unit of analysis.

Authors:  Georgia Robins Sadler; Celine Marie Ko; Jennifer Alisangco; Bradley P Rosbrook; Eric Miller; Judith Fullerton
Journal:  Appl Nurs Res       Date:  2007-08       Impact factor: 2.257

Review 2.  Best (but oft-forgotten) practices: designing, analyzing, and reporting cluster randomized controlled trials.

Authors:  Andrew W Brown; Peng Li; Michelle M Bohan Brown; Kathryn A Kaiser; Scott W Keith; J Michael Oakes; David B Allison
Journal:  Am J Clin Nutr       Date:  2015-05-27       Impact factor: 7.045

Review 3.  Review of Recent Methodological Developments in Group-Randomized Trials: Part 1-Design.

Authors:  Elizabeth L Turner; Fan Li; John A Gallis; Melanie Prague; David M Murray
Journal:  Am J Public Health       Date:  2017-04-20       Impact factor: 9.308

4.  Intraclass correlation estimates for cancer screening outcomes: estimates and applications in the design of group-randomized cancer screening studies.

Authors:  Erinn M Hade; David M Murray; Michael L Pennell; Dale Rhoda; Electra D Paskett; Victoria L Champion; Benjamin F Crabtree; Allen Dietrich; Mark B Dignan; Melissa Farmer; Joshua J Fenton; Susan Flocke; Robert A Hiatt; Shawna V Hudson; Michael Mitchell; Patrick Monahan; Salma Shariff-Marco; Stacey L Slone; Kurt Stange; Susan L Stewart; Pamela A Ohman Strickland
Journal:  J Natl Cancer Inst Monogr       Date:  2010

5.  A new dependence parameter approach to improve the design of cluster randomized trials with binary outcomes.

Authors:  Catherine M Crespi; Weng Kee Wong; Sheng Wu
Journal:  Clin Trials       Date:  2011-11-02       Impact factor: 2.486

6.  Sample size considerations in the design of cluster randomized trials of combination HIV prevention.

Authors:  Rui Wang; Ravi Goyal; Quanhong Lei; M Essex; Victor De Gruttola
Journal:  Clin Trials       Date:  2014-06       Impact factor: 2.486

7.  Using second-order generalized estimating equations to model heterogeneous intraclass correlation in cluster-randomized trials.

Authors:  Catherine M Crespi; Weng Kee Wong; Shiraz I Mishra
Journal:  Stat Med       Date:  2009-02-28       Impact factor: 2.373

8.  Relative efficiency of equal versus unequal cluster sizes in cluster randomized trials with a small number of clusters.

Authors:  Jingxia Liu; Chengjie Xiong; Lei Liu; Guoqiao Wang; Luo Jingqin; Feng Gao; Ling Chen; Yan Li
Journal:  J Biopharm Stat       Date:  2020-09-24       Impact factor: 1.051

9.  Intracluster correlation coefficients and coefficients of variation for perinatal outcomes from five cluster-randomised controlled trials in low and middle-income countries: results and methodological implications.

Authors:  Christina Pagel; Audrey Prost; Sonia Lewycka; Sushmita Das; Tim Colbourn; Rajendra Mahapatra; Kishwar Azad; Anthony Costello; David Osrin
Journal:  Trials       Date:  2011-06-14       Impact factor: 2.279

10.  A priori postulated and real power in cluster randomized trials: mind the gap.

Authors:  Lydia Guittet; Bruno Giraudeau; Philippe Ravaud
Journal:  BMC Med Res Methodol       Date:  2005-08-18       Impact factor: 4.615

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