Estimation of marginal structural models under irregular visits and unmeasured confounder: calibrated inverse probability weights.

ABSTRACT: Clinical information collected in electronic health records (EHRs) is becoming an essential source to emulate randomized experiments. Since patients do not interact with the healthcare system at random, the longitudinal information in large observational databases must account for irregular visits. Moreover, we need to also account for subject-specific unmeasured confounders which may act as a common cause for treatment assignment mechanism (e.g. glucose-lowering medications) while also influencing the outcome (e.g. Hemoglobin A1c). We used the calibration of longitudinal weights to improve the finite sample properties and to account for subject-specific unmeasured confounders. A Monte Carlo simulation study is conducted to evaluate the performance of calibrated inverse probability estimat

SUBMITTER: Kalia S 

PROVIDER: S-EPMC9825036 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature