Causal estimation of time-varying treatments in observational studies: a scoping review of methods, applications, and missing data practices.

ABSTRACT: Estimating causal effects of time-varying treatments or exposures in observational studies is challenging due to time-dependent confounding and missing data, necessitating advanced statistical approaches for accurate inference. Previous reviews indicate that singly robust methods are prevalent in epidemiological studies despite the availability of more robust alternatives that better handle time-varying confounding. Although common in longitudinal studies, missing data are often inadequately reported and addressed, potentially compromising the validity of estimates. Whether this dependence on less robust methods and inadequate handling of missing data persists in time-varying treatment settings remains unclear. This review aimed to identify current practices, methodological trends, and gap

SUBMITTER: Rop M 

PROVIDER: S-EPMC12382241 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature