Avoiding pitfalls when combining multiple imputation and propensity scores.

ABSTRACT: Overcoming bias due to confounding and missing data is challenging when analyzing observational data. Propensity scores are commonly used to account for the first problem and multiple imputation for the latter. Unfortunately, it is not known how best to proceed when both techniques are required. We investigate whether two different approaches to combining propensity scores and multiple imputation (Across and Within) lead to differences in the accuracy or precision of exposure effect estimates. Both approaches start by imputing missing values multiple times. Propensity scores are then estimated for each resulting dataset. Using the Across approach, the mean propensity score across imputations for each subject is used in a single subsequent analysis. Alternatively, the Within approach uses p

SUBMITTER: Granger E 

PROVIDER: S-EPMC6856837 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature