Confronting “confounding by health system use” in Medicare Part D: comparative effectiveness of propensity score approaches to confounding adjustment

PROPENSITy SCORE fOR CONfOuNDING ADjuSTMENT IN HEALTH CARE DATABASES Propensity score methodology for confounding control in health care utilization databases

Propensity score (PS) methodology is a common approach to control for confounding in nonexperimental studies of treatment effects using health care utilization databases. This methodology offers researchers many advantages compared with conventional multivariate models: it directly focuses on the determinants of treatment choice, facilitating the understanding of the clinical decision-making pr...

Propensity Score Adjustment for Unmeasured Confounding in Observational Studies

Adjusting for several unmeasured confounders is a challenging problem in the analysis of observational data. Information about unmeasured confounders is sometimes available from external validation 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 the difficulty is in elici...

Propensity score methods for confounding control in nonexperimental research.

Confounding adjustment in comparative effectiveness research conducted within distributed research networks.

BACKGROUND A distributed research network (DRN) of electronic health care databases, in which data reside behind the firewall of each data partner, can support a wide range of comparative effectiveness research (CER) activities. An essential component of a fully functional DRN is the capability to perform robust statistical analyses to produce valid, actionable evidence without compromising pat...

Using propensity score modeling to minimize the influence of confounding risks related to prenatal tobacco exposure.

INTRODUCTION Despite efforts to control for confounding variables using stringent sampling plans, selection bias typically exists in observational studies, resulting in unbalanced comparison groups. Ignoring selection bias can result in unreliable or misleading estimates of the causal effect. METHODS Generalized boosted models were used to estimate propensity scores from 42 confounding variab...