A hierarchical algorithm for multicentric matched cohort study designs. (1st November 2020)

• Record Type: Journal Article
• Authors: Mayer, Benjamin; Tadler, Simone; Rothenbacher, Dietrich; Seeger, Julia; Wöhrle, Jochen
• Is Part Of: Current medical research and opinion. Volume 36:Number 11(2020)
• Page Start: 1889
• Page End: 1896
• Publication Date: 2020-11-01
• Subjects: Absolute standardized difference -- common support -- matching -- observational data -- optimal matching -- propensity score; Clinical medicine -- Periodicals; Therapeutics -- Periodicals; 615.5
• DOI: 10.1080/03007995.2020.1808453
• Languages: English
• ISSNs: 0300-7995

Abstract

Abstract: Objective: Lack of structural equality is a major issue to be addressed in observational studies. Their major disadvantage of these studies compared to randomized controlled trials is the vulnerability towards confounding, but they often better mirror real world patients and, therefore, entail an increased external validity. Numerous approaches have been developed to account for confounding in observational research, including multiple regression, subgroup analysis and matched cohort designs. The latter has been often described as a useful tool if large control data sets are available. Methods: In this paper we present a hierarchical matching algorithm entailing two stages which enables a multicentric matched cohort study to be conducted. In particular, the algorithm defines the matching strategy as a combination of exact matching and a subsequent consideration of further matching variables to be controlled using a distance measure (e.g. the propensity score). Results: The algorithm is applied to a study in interventional cardiology and demonstrates high flexibility and usefulness with regard to the aim of finding comparable cases of exposed and non-exposed patients from observational data. The algorithm increased structural equality by balancing the most important covariates which might be of different importance for the matching process. Conclusion: The implementation of the algorithm in the statistical software SAS offers high flexibility regarding an application to various data analysis projects. Specifically, it provides a broader range of features (e.g. diverse distance measures) when compared to other existing solutions for conducting matched cohort analyses.