ArticleContemporary clinical trials2020
Selecting appropriate endpoints for assessing treatment effects in comparative clinical studies for COVID-19.
Article in Contemporary clinical trials, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Association between intensive blood pressure lowering and stroke-free survival among patients with and without Diabetes.Scientific reports · 2024Trial
- Brief report on the development of hemorrhagic pericardial effusion after thoracic surgery for traumatic injuries.Trauma surgery & acute care open · 2023Article
- Quantifying Treatment Effects in Trials with Multiple Event-Time Outcomes.NEJM evidence · 2022Article
- Evaluating Primary Endpoints for COVID-19 Therapeutic Trials to Assess Recovery.American journal of respiratory and critical care medicine · 2022Article
- SARS-CoV-2 vaccines: Clinical endpoints and psychological perspectives: A literature review.Journal of infection and public health · 2022Review
- Design aspects of COVID-19 treatment trials: Improving probability and time of favorable events.Biometrical journal. Biometrische Zeitschrift · 2022Article
- Article
- Guest editors-in-chief note to COVID-19 special issue.Contemporary clinical trials · 2021Article
- Patient and Citizen Participation in the Identification of Ethical Considerations Aiming to Address Uncertainty in the Evaluation of Promising Interventions in a Pandemic Context.Frontiers in medical technology · 2021Article
- Causal inference methods for small non-randomized studies: Methods and an application in COVID-19.Contemporary clinical trials · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
To evaluate the efficacy and safety of a new treatment for COVID-19 vs. standard care, certain key endpoints are related to the duration of a specific event, such as hospitalization, ICU stay, or receipt of supplemental oxygen. However, since patients may die in the hospital during study follow-up, using, for example, the duration of hospitalization to assess treatment efficacy can be misleading. If the treatment tends to prolong patients' survival compared with standard care, patients in the new treatment group may spend more time in hospital. This can lead to a "survival bias" issue, where a treatment that is effective for preventing death appears to prolong an undesirable outcome. On the other hand, by using hospital-free survival time as the endpoint, we can circumvent the survival bias issue. In this article, we use reconstructed data from a recent, large clinical trial for COVID-19 to illustrate the advantages of this approach. For the analysis of ICU stay or oxygen usage, where the initiating event is potentially an outcome of treatment, standard survival analysis techniques may not be appropriate. We also discuss issues with analyzing the durations of such events.
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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.