Three-step matching algorithm to enhance between-group comparability and minimize confounding in comparative effectiveness studies

• Published in: Scientific reports Vol. 12; no. 1; p. 214
• Main Authors: Yang, Chen-Yi, Kuo, Shihchen, Lai, Edward Chia-Cheng, Ou, Huang-Tz
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 07.01.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 692/308/174; 692/308/409; Algorithms; Comparative studies; Diabetes mellitus (non-insulin dependent); Glucagon; Glucagon-like peptide 1; Humanities and Social Sciences; multidisciplinary; Science; Science (multidisciplinary); Sulfonylurea
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-04014-z

We developed a three-step matching algorithm to enhance the between-group comparability for comparative drug effect studies involving prevalent new-users of the newer study drug versus older comparator drug(s). The three-step matching scheme is to match on: (1) index date of initiating the newer study drug to align the cohort entry time between study groups, (2) medication possession ratio measures that consider prior exposure to all older comparator drugs, and (3) propensity scores estimated from potential confounders. Our approach is illustrated with a comparative cardiovascular safety study of glucagon-like peptide-1 receptor agonist (GLP-1ra) versus sulfonylurea (SU) in type 2 diabetes patients using Taiwan’s National Health Insurance Research Database 2003–2015. 66% of 3195 GLP-1ra users had previously exposed to SU. The between-group comparability was well-achieved after implementing the matching algorithm (i.e., standardized mean difference < 0.2 for all baseline patient characteristics). Compared to SU, the use of GLP-1ra yielded a significantly reduced risk of the primary composite cardiovascular events (hazard ratio [95% confidence interval]: 0.71 [0.54–0.95], p  = 0.022). Our matching scheme can enhance the between-group comparability in prevalent new-user cohort designs to minimize time-related bias, improve confounder adjustment, and ensure the reliability and validity of study findings.