Abstract| Volume 56, ISSUE 9, P920, September 2003

Effect of matching for propensity on the balance of “unmeasured” covariates

      Propensity score methods effectively balance the distribution of measured covariates that are associated with an exposure, but their effect on the balance of unmeasured variables is less clear. We undertook an analysis of this by examining variables that were measured but un-used- “pseudo-unmeasured variables” -in a large study of the effect of right heart catheter (RHC) use in seriously ill patients. Subjects included 5,735 seriously ill adults admitted to an ICU in one of 5 U.S. teaching hospitals (1989–1994; SUPPORT study). We used the published model of propensity for RHC that used 88 variables to generate 1008 matched pairs. Matching effectively balanced the measured covariates across exposure groups (RHC+ or RHC−), yielding estimates of RHC's effect on outcomes, assuming no hidden bias.
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