Genetic matching for time-dependent treatments: a longitudinal extension and simulation study.

ABSTRACT:

Background

Longitudinal matching can mitigate confounding in observational, real-world studies of time-dependent treatments. To date, these methods have required iterative, manual re-specifications to achieve covariate balance. We propose a longitudinal extension of genetic matching, a machine learning approach that automates balancing of covariate histories. We examine performance by comparing the proposed extension against baseline propensity score matching and time-dependent propensity score matching.

Methods

To evaluate comparative performance, we developed a Monte Carlo simulation framework that reflects a static treatment assigned at multiple time points. Data generation considers a treatment assignment model, a continuous outcome model, and underlying covariates. In s

SUBMITTER: Weymann D 

PROVIDER: S-EPMC10413721 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature