Confounding adjustment methods in longitudinal observational data with a time-varying treatment: a mapping review

• Published in: BMJ open Vol. 12; no. 3; p. e058977
• Main Authors: Wijn, Stan R W, Rovers, Maroeska M, Hannink, Gerjon
• Format: Journal Article
• Language: English
• Published: England British Medical Journal Publishing Group 18.03.2022; BMJ Publishing Group LTD; BMJ Publishing Group
• Series: Original research
• Subjects: Arthritis; Bias; epidemiology; Humans; orthopaedic & trauma surgery; Osteoarthritis; Patients; Performance evaluation; Propensity Score; Research Design; Research Methods; Statistical methods; statistics & research methods; Steroids; Surgery; epidemiology; orthopaedic & trauma surgery; statistics & research methods
• ISSN: 2044-6055; 2044-6055
• DOI: 10.1136/bmjopen-2021-058977

ObjectivesTo adjust for confounding in observational data, researchers use propensity score matching (PSM), but more advanced methods might be required when dealing with longitudinal data and time-varying treatments as PSM might not include possible changes that occurred over time. This study aims to explore which confounding adjustment methods have been used in longitudinal observational data to estimate a treatment effect and identify potential inappropriate use of PSM.DesignMapping review.Data sourcesWe searched PubMed, from inception up to January 2021, for studies in which a treatment was evaluated using longitudinal observational data.Eligibility criteriaMethodological, non-medical and cost-effectiveness papers were excluded, as were non-English studies and studies that did not study a treatment effect.Data extraction and synthesisStudies were categorised based on time of treatment: at baseline (interventions performed at start of follow-up) or time-varying (interventions received asynchronously during follow-up) and sorted based on publication year, time of treatment and confounding adjustment method. Cumulative time series plots were used to investigate the use of different methods over time. No risk-of-bias assessment was performed as it was not applicable.ResultsIn total, 764 studies were included that met the eligibility criteria. PSM (165/201, 82%) and inverse probability weighting (IPW; 154/502, 31%) were most common for studies with a treatment at baseline (n=201) and time-varying treatment (n=502), respectively. Of the 502 studies with a time-varying treatment, 123 (25%) used PSM with baseline covariates, which might be inappropriate. In the past 5 years, the proportion of studies with a time-varying treatment that used PSM over IPW increased.ConclusionsPSM is the most frequently used method to correct for confounding in longitudinal observational data. In studies with a time-varying treatment, PSM was potentially inappropriately used in 25% of studies. Confounding adjustment methods designed to deal with a time-varying treatment and time-varying confounding are available, but were only used in 45% of the studies with a time-varying treatment.