Assessing and communicating heterogeneity of treatment effects for patient subpopulations: Panel discussion on considerations in design and analysis. (28th October 2020)

• Record Type: Journal Article
• Authors: Rothmann, Mark; Crown, William; Louis, Thomas A.; Permutt, Thomas; Ruberg, Stephen; Segal, Jodi; Scott, John
• Other Names: Scott John guestEditor.
• Is Part Of: Pharmaceutical statistics. Volume 20:Number 5(2021)
• Page Start: 952
• Page End: 964
• Publication Date: 2020-10-28
• Subjects: heterogeneity of treatment effects -- patient subpopulations -- precision medicine; Pharmacy -- Statistical methods -- Periodicals; Pharmacy -- Statistics -- Periodicals; 615.10727
• DOI: 10.1002/pst.2077
• Languages: English
• ISSNs: 1539-1604

Abstract

SUMMARY: Clinical trials are primarily conducted to understand the average effects treatments have on patients. However, patients are heterogeneous in the severity of the condition and in ways that affect what treatment effect they can expect. It is therefore important to understand and characterize how treatment effects vary. The design and analysis of clinical studies play critical roles in evaluating and characterizing heterogeneous treatment effects. This panel discussed considerations in design and analysis under the recognition that there are heterogeneous treatment effects across subgroups of patients. Panel members discussed many questions including: What is a good estimate of the treatment effect in me, a 65‐year‐old, bald, Caucasian‐American, male patient? What magnitude of heterogeneity of treatment effects (HTE) is sufficiently large to merit attention? What role can prior evidence about HTE play in confirmatory trial design and analysis? Is there anything described in the 21st Century Cures Act that would benefit from greater attention to HTE? An example of a Bayesian approach addressing multiplicity when testing for treatment effects in subgroups will be provided. We can do more or better at understanding heterogeneous treatment effects and providing the best information on heterogeneous treatment effects.