Evaluating heterogeneity of treatment effects. Issue 1 (1st January 2020)

• Record Type: Journal Article
• Authors: Vijan, Sandeep
• Is Part Of: Biostatistics & epidemiology. Volume 4:Issue 1(2020)
• Page Start: 98
• Page End: 104
• Publication Date: 2020-01-01
• Subjects: Heterogeneity -- treatment -- randomized clinical trials -- statistical analysis -- risk prediction -- subgroup analysis; Epidemiology -- Periodicals; Biometry -- Periodicals; 614.42
• DOI: 10.1080/24709360.2020.1724003
• Languages: English
• ISSNs: 2470-9360

Abstract

Abstract : Evaluation of treatment effects in randomized clinical trials typically focuses on the average difference in outcomes between arms of a trial. While this approach is the gold standard for establishing a causal relationship between treatment and outcome, reporting of average effects can mask important differences in benefits across various subpopulations, a phenomenon known as heterogeneity of treatment effects (HTE). The presence of HTE has been demonstrated in many settings and lack of consideration of HTE can lead to inappropriate treatment (or lack of treatment) for many patients. This paper describes approaches to analyzing and reporting trials with explicit consideration of heterogeneity, in order to improve our ability to treat individual patients more effectively.