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

• Published in: BMC medical research methodology Vol. 23; no. 1; pp. 4 - 16
• Main Authors: Kalia, Sumeet, Saarela, Olli, Escobar, Michael, Moineddin, Rahim, Greiver, Michelle
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 07.01.2023; BioMed Central; BMC
• Subjects: Analysis; Calibrated weights; Computer Simulation; Constrained optimization; Diabetes Mellitus, Type 2 - drug therapy; Electronic health records; Electronic records; Glucose - therapeutic use; Glycated Hemoglobin; Glycosylated hemoglobin; Health aspects; Humans; Longitudinal data analysis; Marginal structural models; Measurement; Medical records; Models, Statistical; Models, Structural; Monte Carlo method; Primary care; Primary health care; Probability; Services; Canada; Marginal structural models; Calibrated weights; Electronic health records; Primary care; Constrained optimization; Longitudinal data analysis
• DOI: 10.1186/s12874-022-01831-2
• ISSN: 1471-2288

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 estimators using time-dependent treatment assignment and irregular visits with subject-specific unmeasured confounders. The simulation study showed that the longitudinal weights with calibrated restrictions improved the finite sample bias when compared to the stabilized weights. The application of the calibrated weights is demonstrated using the exposure of glucose lowering medications and the longitudinal outcome of Hemoglobin A1c. Our results support the effectiveness of glucose lowering medications in reducing Hemoglobin A1c among type II diabetes patients with elevated glycemic index ( $$\ge 8.5 \%$$ ≥ 8.5 % ) using stabilized and calibrated weights.