ArticleAmerican journal of epidemiology2023
Eliminating Ambiguous Treatment Effects Using Estimands.
Article in American journal of epidemiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed.
- Defining estimands in anaesthesia clinical trials: a scoping review and framework.British journal of anaesthesia · 2026Article
- CRT-Estimands Framework: consensus based extension of the ICH E9(R1) addendum for cluster randomised trials.BMJ (Clinical research ed.) · 2026Article
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- A Fully-Integrated Bayesian Approach for the Imputation and Analysis of Derived Outcome Variables With Missingness.Statistics in medicine · 2026Article
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- Estimating treatment effects in trials with outcome data truncated by death: A case study on aligning estimators with estimands.Clinical trials (London, England) · 2025Article
- Article
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- Review
- Using the ICH estimand framework to improve the interpretation of treatment effects in internet interventions.NPJ digital medicine · 2025Review
- A New Generation of Clinical Endpoints for Critical Care Trials.American journal of respiratory and critical care medicine · 2025Article
- Development of a consensus extension of the estimands framework for cluster randomised trials (CRT-estimands): results from an international Delphi study.medRxiv : the preprint server for health sciences · 2025Article
- Causal clarity in statistical software.International journal of epidemiology · 2025Article
- Applying the Estimands Framework to Non-Inferiority Trials: Guidance on Choice of Hypothetical Estimands for Non-Adherence and Comparison of Estimation Methods.Statistics in medicine · 2025Article
- Guidance for protocol content and reporting of factorial randomised trials: explanation and elaboration of the CONSORT 2010 and SPIRIT 2013 extensions.BMJ (Clinical research ed.) · 2025Article
- Is inverse probability of censoring weighting a safer choice than per-protocol analysis in clinical trials?Statistical methods in medical research · 2025Article
- Article
- Advancing Delirium Treatment Trials in Older Adults: Recommendations for Future Trials From the Network for Investigation of Delirium: Unifying Scientists (NIDUS).Critical care medicine · 2025Article
- Four targets: an enhanced framework for guiding causal inference from observational data.International journal of epidemiology · 2024Article
- Causal interpretation of the hazard ratio in randomized clinical trials.Clinical trials (London, England) · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
Most reported treatment effects in medical research studies are ambiguously defined, which can lead to misinterpretation of study results. This is because most authors 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 challenging, because 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 in which 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.