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Evaluating heterogeneity of treatment effects

Evaluating heterogeneity of treatment effects 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biostatistics & Epidemiology Taylor & Francis

Evaluating heterogeneity of treatment effects

Vijan, Sandeep
Biostatistics & Epidemiology , Volume 4 (1): 7 – Jan 1, 2020
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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...
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Publisher
Taylor & Francis
Copyright
This work was authored as part of the Contributor's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 USC. 105, no copyright protection is available for such works under US Law.
ISSN
2470-9379
eISSN
2470-9360
DOI
10.1080/24709360.2020.1724003
Publisher site
See Article on Publisher Site

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.

Journal

Biostatistics & Epidemiology – Taylor & Francis

Published: Jan 1, 2020

Keywords: Heterogeneity; treatment; randomized clinical trials; statistical analysis; risk prediction; subgroup analysis

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