2010
DOI: 10.1097/mlr.0b013e3181c1328f
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The Multiple Propensity Score as Control for Bias in the Comparison of More Than Two Treatment Arms

Abstract: Our results indicate that the multiple PS method is a feasible method to adjust for observed pretreatment differences in nonrandomized studies where the number of pretreatment differences is large and multiple treatments are compared.

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Cited by 204 publications
(191 citation statements)
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“…All variables significantly related to a specific outcome were used to estimate the multiple propensity scores in a multinomial regression analysis, with group membership as a dependent variable (see table 1 for the variables included in the GSI propensity score; a complete list of potential/identified confounders for all outcome variables is available upon request). A major advantage of the propensity score method, as compared to other correction techniques, is the fact that the overlap in propensity score distributions (and thus the overlap in relevant variables) between treatment groups can be easily judged and visualised [63]. From looking at the overlap between the 3 treatment groups, it appeared that in spite of some differences, these groups were readily comparable.…”
Section: Methodsmentioning
confidence: 99%
“…All variables significantly related to a specific outcome were used to estimate the multiple propensity scores in a multinomial regression analysis, with group membership as a dependent variable (see table 1 for the variables included in the GSI propensity score; a complete list of potential/identified confounders for all outcome variables is available upon request). A major advantage of the propensity score method, as compared to other correction techniques, is the fact that the overlap in propensity score distributions (and thus the overlap in relevant variables) between treatment groups can be easily judged and visualised [63]. From looking at the overlap between the 3 treatment groups, it appeared that in spite of some differences, these groups were readily comparable.…”
Section: Methodsmentioning
confidence: 99%
“…The PS was derived from multinomial logistic regression using the same covariates as in the adjusted analysis to control for potential selection bias caused by nonrandom assignment of induction treatments. We specifically used the inverse probability of treatment weight, in which the weights were calculated as the inverse of the PS (13,(15)(16)(17)(18). Details regarding calculation of PS can be found in Supplemental Material and our prior publication.…”
Section: Statistical Analysesmentioning
confidence: 99%
“…We utilized multinomial logistic regression to estimate the PS as the conditional probability of a patient receiving a certain induction treatment given pretreatment covariates including donor (age, sex, and race), recipient (age, sex, race, diabetes status, cardiovascular comorbidities, retransplant status, dialysis before transplant, and panel reactive antibodies [PRAs]), and transplant factors (donor/ recipient weight ratio, HLA mismatch, and transplant year) (12). Several adjustment methods integrating the estimated PS have been suggested, including matching (13), regression adjustment (14), and weighting (12,15). In this analysis, we utilized the inverse probability of treatment weight (IPTW), in which the weights were calculated as the inverse of the PS (15).…”
Section: Propensity Score Analysesmentioning
confidence: 99%