ArticleStroke2021
Use of the Estimand Framework to Manage the Disruptive Effects of COVID-19 on Stroke Clinical Trials.
Article in Stroke, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Using the ICH estimand framework to improve the interpretation of treatment effects in internet interventions.NPJ digital medicine · 2025Review
- Therapeutic Trends of Cerebrovascular Disease during the COVID-19 Pandemic and Future Perspectives.Journal of stroke · 2022Review
- Tranexamic acid for intracerebral haemorrhage within 2 hours of onset: protocol of a phase II randomised placebo-controlled double-blind multicentre trial.Stroke and vascular neurology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has presented unique challenges to stroke care and research internationally. In particular, clinical trials in stroke are vulnerable to the impacts of the pandemic at multiple stages, including design, recruitment, intervention, follow-up, and interpretation of outcomes. A carefully considered approach is required to ensure the appropriate conduct of stroke trials during the pandemic and to maintain patient and participant safety. This has been recently addressed by the International Council for Harmonisation which, in November 2019, released an addendum to the Statistical Principles for Clinical Trials guidelines entitled Estimands and Sensitivity Analysis in Clinical Trials. In this article, we present the International Council for Harmonisation estimand framework for the design and conduct of clinical trials, with a specific focus on its application to stroke clinical trials. This framework aims to align the clinical and scientific objectives of a trial with its design and end points. It also encourages the prospective consideration of potential postrandomization intercurrent events which may occur during a trial and either impact the ability to measure an end point or its interpretation. We describe the different categories of such events and the proposed strategies for dealing with them, specifically focusing on the COVID-19 pandemic as a source of intercurrent events. We also describe potential practical impacts posed by the COVID-19 pandemic on trials, health systems, study groups, and participants, all of which should be carefully reviewed by investigators to ensure an adequate practical and statistical strategy is in place to protect trial integrity. We provide examples of the implementation of the estimand framework within hypothetical stroke trials in intracerebral hemorrhage and stroke recovery. While the focus of this article is on COVID-19 impacts, the strategies and principles proposed are well suited for other potential events or issues, which may impact clinical trials in the field of stroke.
Indexed as
Identifiers
What OpenQuestion holds
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.