Adjusting for unmeasured confounding in nonrandomized longitudinal studies: a methodological review.

ABSTRACT:

Objectives

Motivated by recent calls to use electronic health records for research, we reviewed the application and development of methods for addressing the bias from unmeasured confounding in longitudinal data.

Study design and setting

Methodological review of existing literature. We searched MEDLINE and EMBASE for articles addressing the threat to causal inference from unmeasured confounding in nonrandomized longitudinal health data through quasi-experimental analysis.

Results

Among the 121 studies included for review, 84 used instrumental variable analysis (IVA), of which 36 used lagged or historical instruments. Difference-in-differences (DiD) and fixed effects (FE) models were found in 29 studies. Five of these combined IVA with DiD or FE to try to mitigate for

SUBMITTER: Streeter AJ 

PROVIDER: S-EPMC5589113 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature