Confounder adjustment in observational studies investigating multiple risk factors: a methodological study.

Background: Confounder adjustment is critical for accurate causal inference in observational studies. However, the appropriateness of methods for confounder adjustment in studies investigating multiple risk factors, where the factors are not simply mutually confounded, is often overlooked. This study aims to summarise the methods for confounder adjustment and the related issues in studies investigating multiple risk factors.

Methods: A methodological study was performed. We searched PubMed from January 2018 to March 2023 to identify cohort and case-control studies investigating multiple risk factors for three chronic diseases (cardiovascular disease, diabetes and dementia). Study selection and data extraction were conducted independently by two reviewers. The study objectives were grouped into two categories: widely exploring potential risk factors and examining specific risk factors. The methods for confounder adjustment were classified based on a summarisation of the included studies, identifying six categories: (1) each risk factor was adjusted for potential confounders separately (the recommended method); (2) all risk factors were mutually adjusted (i.e. including all factors in a multivariable model); (3) all risk factors were adjusted for the same confounders separately; (4) all risk factors were adjusted for the same confounders with some factors being mutually adjusted; (5) all risk factors were adjusted for the same confounders with mutual adjustment among them being unclear; and (6) unable to judge. All data were descriptively analysed.

Results: A total of 162 studies were included, with 88 (54.3%) exploring potential risk factors and 74 (45.7%) examining specific risk factors. The current status of confounder adjustment was unsatisfactory: only ten studies (6.2%) used the recommended method, all of which aimed at examining several specific risk factors; in contrast, mutual adjustment was adopted in over 70% of the studies. The remaining studies either adjusted for the same confounders across all risk factors, or unable to judge.

Conclusions: There is substantial variation in the methods for confounder adjustment among studies investigating multiple risk factors. Mutual adjustment was the most commonly adopted method, which might lead to overadjustment bias and misleading effect estimates. Future research should avoid indiscriminately including all risk factors in a multivariable model to prevent inappropriate adjustment.