Kahan, Brennan C;
Cro, Suzie;
Li, Fan;
Harhay, Michael O;
(2023)
Eliminating ambiguous treatment effects using estimands.
American Journal of Epidemiology
, 192
(6)
, Article kwad036. 10.1093/aje/kwad036.
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Abstract
Most reported treatment effects in medical research studies are ambiguously defined, which can lead to misinterpretation of study results. This is because most studies do not attempt to describe what the treatment effect represents, and instead require readers to deduce this based on the reported statistical methods. However, this approach is fraught, as many methods provide counterintuitive results. For example, some methods include data from all patients, yet the resulting treatment effect applies only to a subset of patients, whereas other methods will exclude certain patients while results will apply to everyone. Additionally, some analyses provide estimates pertaining to hypothetical settings where patients never die or discontinue treatment. Herein we introduce estimands as a solution to the aforementioned problem. An estimand is a clear description of what the treatment effect represents, thus saving readers the necessity of trying to infer this from study methods and potentially getting it wrong. We provide examples of how estimands can remove ambiguity from reported treatment effects and describe their current use in practice. The crux of our argument is that readers should not have to infer what investigators are estimating; they should be told explicitly.
| Type: | Article |
|---|---|
| Title: | Eliminating ambiguous treatment effects using estimands |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/aje/kwad036 |
| Publisher version: | https://doi.org/10.1093/aje/kwad036 |
| Language: | English |
| Additional information: | © The Author(s) 2023. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | Estimands, estimates, estimators, randomized trials, treatment effects |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology > MRC Clinical Trials Unit at UCL |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10165280 |
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