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Confounding adjustment methods in longitudinal observational data with a time-varying treatment: a mapping review
Stan R W Wijn1, Maroeska M Rovers2, Gerjon Hannink3
1Radboud University Medical Center, Radboud Institute for Health Sciences, Department of Operating Rooms, Radboudumc, Nijmegen, The Netherlands stan.wijn@radboudumc.nl.
BMJ Open
|March 19, 2022
Summary
Propensity score matching (PSM) is common for confounding in longitudinal studies, but often inappropriately used for time-varying treatments. Advanced methods are underutilized despite their necessity for accurate treatment effect estimation.
Area of Science:
- Epidemiology
- Biostatistics
- Health Services Research
Background:
- Confounding is a major challenge in observational studies.
- Propensity score matching (PSM) is a common method to adjust for confounding.
- PSM may be insufficient for longitudinal data with time-varying treatments.
Purpose of the Study:
- To review confounding adjustment methods used in longitudinal observational studies.
- To assess the appropriateness of PSM for time-varying treatments.
- To identify trends in method selection over time.
Main Methods:
- A mapping review of studies using PubMed from inception to January 2021.
- Included studies evaluated treatment effects using longitudinal observational data.
- Studies were categorized by treatment timing (baseline vs. time-varying) and confounding adjustment method.
Main Results:
- 764 studies were included.
- PSM was most common for baseline treatments (82%), while inverse probability weighting (IPW) was most common for time-varying treatments (31%).
- 25% of time-varying treatment studies potentially misused PSM with only baseline covariates; advanced methods were used in only 45% of these studies.
Conclusions:
- PSM is frequently used but often inappropriately applied in longitudinal studies with time-varying treatments.
- There is a need for greater adoption of advanced confounding adjustment methods suitable for time-varying exposures.
- The use of PSM over IPW has increased in recent years for time-varying treatments, potentially leading to biased estimates.
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