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- W63553896 abstract "Adjusting for several unmeasured confounders is a challenging problem in the analysis of observationaldata. Information about unmeasured confounders is sometimes available from externalvalidation data, such as surveys or secondary samples drawn from the same source population.In principal, the validation permits us to recover information about the missing data, but thedifficulty is in eliciting a valid model for nuisance distribution of the unmeasured confounders.Motivated by a British study of the effects of trihalomethane exposure on full-term low birthweight,we describe a flexible Bayesian procedure for adjusting for a vector of unmeasuredconfounders using external validation data. We summarize the unmeasured confounders with ascalar summary score using the propensity score methodology of Rosenbaum and Rubin. Thescore has the property that it breaks the dependence between the exposure and unmeasuredconfounders within levels of measured confounders. To adjust for unmeasured confounding in aBayesian analysis, we need only update and adjust for the summary score during Markov chainMonte Carlo simulation. We demonstrate that trihalomethane exposure is associated with increasedrisk of full-term low birthweight, and this association persists even after adjusting foreight unmeasured confounders. Empirical results from simulation illustrate that our proposedmethod eliminates bias from several unmeasured confounders, even in small samples." @default.
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- W63553896 date "2008-03-01" @default.
- W63553896 modified "2023-09-28" @default.
- W63553896 title "Propensity Score Adjustment for UnmeasuredConfounding in Observational Studies" @default.
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