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dc.contributor.authorLiem, Ylian S
dc.contributor.authorWong, John B.
dc.contributor.authorHunink, Maria G M
dc.contributor.authorde Charro, Frank Th
dc.contributor.authorWinkelmayer, Wolfgang C
dc.date.accessioned2010-11-18T18:45:05Z
dc.date.issued2010
dc.identifier.citationLiem, Ylian S., John B. Wong, M. G. Myriam Hunink, Frank Th de Charro, and Wolfgang C. Winkelmayer. 2010. Propensity scores in the presence of effect modification: A case study using the comparison of mortality on hemodialysis versus peritoneal dialysis. Emerging Themes in Epidemiology 7:1.en_US
dc.identifier.issn1742-7622en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:4582570
dc.description.abstractPurpose: To control for confounding bias from non-random treatment assignment in observational data, both traditional multivariable models and more recently propensity score approaches have been applied. Our aim was to compare a propensity score-stratified model with a traditional multivariable-adjusted model, specifically in estimating survival of hemodialysis (HD) versus peritoneal dialysis (PD) patients. Methods: Using the Dutch End-Stage Renal Disease Registry, we constructed a propensity score, predicting PD assignment from age, gender, primary renal disease, center of dialysis, and year of first renal replacement therapy. We developed two Cox proportional hazards regression models to estimate survival on PD relative to HD, a propensity score-stratified model stratifying on the propensity score and a multivariable-adjusted model, and tested several interaction terms in both models. Results: The propensity score performed well: it showed a reasonable fit, had a good c-statistic, calibrated well and balanced the covariates. The main-effects multivariable-adjusted model and the propensity score-stratified univariable Cox model resulted in similar relative mortality risk estimates of PD compared with HD (0.99 and 0.97, respectively) with fewer significant covariates in the propensity model. After introducing the missing interaction variables for effect modification in both models, the mortality risk estimates for both main effects and interactions remained comparable, but the propensity score model had nearly as many covariates because of the additional interaction variables. Conclusion: Although the propensity score performed well, it did not alter the treatment effect in the outcome model and lost its advantage of parsimony in the presence of effect modification.en_US
dc.language.isoen_USen_US
dc.publisherBioMed Centralen_US
dc.relation.isversionofdoi:10.1186/1742-7622-7-1en_US
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC2890634/pdf/en_US
dash.licenseLAA
dc.titlePropensity Scores in the Presence of Effect Modification: A Case Study using the Comparison of Mortality on Hemodialysis Versus Peritoneal dialysisen_US
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden_US
dc.relation.journalEmerging Themes in Epidemiologyen_US
dash.depositing.authorHunink, Maria G M
dc.date.available2010-11-18T18:45:05Z
dash.affiliation.otherSPH^Health Policy and Managementen_US
dc.identifier.doi10.1186/1742-7622-7-1*
dash.contributor.affiliatedHunink, Maria


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