Confounding adjustment methods in longitudinal observational data with a time-varying treatment: a mapping review. Issue 3 (18th March 2022)

• Record Type: Journal Article
• Authors: Wijn, Stan R W; Rovers, Maroeska M; Hannink, Gerjon
• Is Part Of: BMJ open. Volume 12:Issue 3(2022)
• Publication Date: 2022-03-18
• Subjects: epidemiology -- statistics & research methods -- orthopaedic & trauma surgery; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2021-058977
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Objectives: To 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. Design: Mapping review. Data sources: We searched PubMed, from inception up to January 2021, for studies in which a treatment was evaluated using longitudinal observational data. Eligibility criteria: Methodological, 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 synthesis: Studies 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. Results: In 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. Conclusions: PSM 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.