Document Detail


Propensity score methods for estimating relative risks in cluster randomized trials with low-incidence binary outcomes and selection bias.
MedLine Citation:
PMID:  24771662     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Despite randomization, selection bias may occur in cluster randomized trials. Classical multivariable regression usually allows for adjusting treatment effect estimates with unbalanced covariates. However, for binary outcomes with low incidence, such a method may fail because of separation problems. This simulation study focused on the performance of propensity score (PS)-based methods to estimate relative risks from cluster randomized trials with binary outcomes with low incidence. The results suggested that among the different approaches used (multivariable regression, direct adjustment on PS, inverse weighting on PS, and stratification on PS), only direct adjustment on the PS fully corrected the bias and moreover had the best statistical properties. Copyright © 2014 John Wiley & Sons, Ltd.
Authors:
Clémence Leyrat; Agnès Caille; Allan Donner; Bruno Giraudeau
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2014-4-27
Journal Detail:
Title:  Statistics in medicine     Volume:  -     ISSN:  1097-0258     ISO Abbreviation:  Stat Med     Publication Date:  2014 Apr 
Date Detail:
Created Date:  2014-4-28     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8215016     Medline TA:  Stat Med     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
Copyright © 2014 John Wiley & Sons, Ltd.
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