Propensity score calibration in the absence of surrogacy.

ABSTRACT: Propensity score calibration (PSC) can be used to adjust for unmeasured confounders using a cross-sectional validation study that lacks information on the disease outcome (Y), under a strong surrogacy assumption. Using directed acyclic graphs and path analysis, the authors developed a formula to predict the presence and magnitude of the bias of PSC in the simplest setting of a binary exposure (T) and 1 confounder (X) that are observed in the main study and 1 confounder (C) that is observed in the validation study only. PSC bias is predicted on the basis of parameters that can be estimated from the data and a single unidentifiable parameter, the relative risk (RR) associated with C (RR(CY)). The authors simulated 1,000 cohort studies each with a Poisson-distributed outcome Y, varying parame

SUBMITTER: Lunt M 

PROVIDER: S-EPMC3491974 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature