Evaluating heterogeneity of treatment effects

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.

KEYWORDS:

• Heterogeneity
• treatment
• randomized clinical trials
• statistical analysis
• risk prediction
• subgroup analysis

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by U.S. Department of Veterans Affairs (US).

Notes on contributors

Sandeep Vijan

Dr Sandeep Vijan is a professor of internal medicine, investigator, and director of quality analytics at the Ann Arbor VA Health System and the University of Michigan.