Improving small-sample inference in group randomized trials with binary outcomes
dc.contributor.author | Westgate, Philip Michael | en_US |
dc.contributor.author | Braun, Thomas M. | en_US |
dc.date.accessioned | 2011-02-02T18:00:35Z | |
dc.date.available | 2012-03-05T15:30:01Z | en_US |
dc.date.issued | 2011-02-10 | en_US |
dc.identifier.citation | Westgate, Philip M.; Braun, Thomas M. (2011). "Improving small-sample inference in group randomized trials with binary outcomes." Statistics in Medicine 30(3): 201-210. <http://hdl.handle.net/2027.42/79433> | en_US |
dc.identifier.issn | 0277-6715 | en_US |
dc.identifier.issn | 1097-0258 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/79433 | |
dc.description.abstract | Group Randomized Trials (GRTs) randomize groups of people to treatment or control arms instead of individually randomizing subjects. When each subject has a binary outcome, over-dispersed binomial data may result, quantified as an intra-cluster correlation (ICC). Typically, GRTs have a small number, bin , of independent clusters, each of which can be quite large. Treating the ICC as a nuisance parameter, inference for a treatment effect can be done using quasi-likelihood with a logistic link. A Wald statistic, which, under standard regularity conditions, has an asymptotic standard normal distribution, can be used to test for a marginal treatment effect. However, we have found in our setting that the Wald statistic may have a variance less than 1, resulting in a test size smaller than its nominal value. This problem is most apparent when marginal probabilities are close to 0 or 1, particularly when n is small and the ICC is not negligible. When the ICC is known, we develop a method for adjusting the estimated standard error appropriately such that the Wald statistic will approximately have a standard normal distribution. We also propose ways to handle non-nominal test sizes when the ICC is estimated. We demonstrate the utility of our methods through simulation results covering a variety of realistic settings for GRTs. Copyright © 2010 John Wiley & Sons, Ltd. | en_US |
dc.format.extent | 159594 bytes | |
dc.format.extent | 3118 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.publisher | John Wiley & Sons, Ltd. | en_US |
dc.subject.other | Mathematics and Statistics | en_US |
dc.title | Improving small-sample inference in group randomized trials with binary outcomes | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Medicine (General) | en_US |
dc.subject.hlbsecondlevel | Statistics and Numeric Data | en_US |
dc.subject.hlbsecondlevel | Public Health | en_US |
dc.subject.hlbtoplevel | Health Sciences | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.subject.hlbtoplevel | Social Sciences | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, U.S.A. ; Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, U.S.A. | en_US |
dc.contributor.affiliationum | Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, U.S.A. | en_US |
dc.identifier.pmid | 21213338 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/79433/1/4101_ftp.pdf | |
dc.identifier.doi | 10.1002/sim.4101 | en_US |
dc.identifier.source | Statistics in Medicine | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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