Propensity score methods for estimating relative risks in cluster randomized trials with low-incidence binary outcomes and selection bias.


Leyrat, C; Caille, A; Donner, A; Giraudeau, B; (2014) Propensity score methods for estimating relative risks in cluster randomized trials with low-incidence binary outcomes and selection bias. Statistics in medicine, 33 (20). pp. 3556-75. ISSN 0277-6715 DOI: https://doi.org/10.1002/sim.6185

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Abstract

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.

Item Type: Article
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Medical Statistics
PubMed ID: 24771662
Web of Science ID: 340423200009
URI: http://researchonline.lshtm.ac.uk/id/eprint/2534204

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