A critical review of graphics for subgroup analyses in clinical trials. (25th March 2020)

• Record Type: Journal Article
• Authors: Ballarini, Nicolás M.; Chiu, Yi‐Da; König, Franz; Posch, Martin; Jaki, Thomas
• Is Part Of: Pharmaceutical statistics. Volume 19:Number 5(2020)
• Page Start: 541
• Page End: 560
• Publication Date: 2020-03-25
• Subjects: contour plot -- data visualisation -- exploratory data analysis -- forest plot -- Galbraith plot -- STEPP -- treatment effect heterogeneity -- UpSet plot; Pharmacy -- Statistical methods -- Periodicals; Pharmacy -- Statistics -- Periodicals; 615.10727
• DOI: 10.1002/pst.2012
• Languages: English
• ISSNs: 1539-1604

Abstract

SUMMARY: Subgroup analyses are a routine part of clinical trials to investigate whether treatment effects are homogeneous across the study population. Graphical approaches play a key role in subgroup analyses to visualise effect sizes of subgroups, to aid the identification of groups that respond differentially, and to communicate the results to a wider audience. Many existing approaches do not capture the core information and are prone to lead to a misinterpretation of the subgroup effects. In this work, we critically appraise existing visualisation techniques, propose useful extensions to increase their utility and attempt to develop an effective visualisation approach. We focus on forest plots, UpSet plots, Galbraith plots, subpopulation treatment effect pattern plot, and contour plots, and comment on other approaches whose utility is more limited. We illustrate the methods using data from a prostate cancer study.