The proportion of randomized controlled trials that inform clinical practice
- McGill University, Canada
- Brigham and Women's Hospital, United States
Abstract
Prior studies suggest that clinical trials are often hampered by problems in design, conduct and reporting that limit their uptake in clinical practice. We have described 'informativeness' as the ability of a trial to guide clinical, policy or research decisions. Little is known about the proportion of initiated trials that inform clinical practice. We created a cohort of randomized interventional clinical trials in three disease areas (ischemic heart disease, diabetes mellitus and lung cancer), that were initiated between 1 January 2009 and 31 December 2010 using ClinicalTrials.gov. We restricted inclusion to trials aimed at answering a clinical question related to the treatment or prevention of disease. Our primary outcome was the proportion of clinical trials fulfilling four conditions of informativeness: importance of the clinical question, trial design, feasibility, and reporting of results. Our study included 125 clinical trials. The proportion meeting four conditions for informativeness was 26.4% (95% CI 18.9 - 35.0). Sixty-seven percent of participants were enrolled in informative trials. The proportion of informative trials did not differ significantly between our three disease areas. Our results suggest that the majority of clinical trials designed to guide clinical practice possess features that may compromise their ability to do so. This highlights opportunities to improve the scientific vetting of clinical research.
Data availability
The data set is available online on Open Science Framework (DOI 10.17605/OSF.IO/3EGKU) (reference 18 in the manuscript).
Article and author information
Author details
Jonathan Kimmelman
Funding
Fonds de Recherche du Québec - Santé
- Nora Hutchinson
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2022, Hutchinson et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,657
- views
-
- 331
- downloads
-
- 14
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
The proportion of randomized controlled trials that inform clinical practice
eLife 11:e79491.
https://doi.org/10.7554/eLife.79491
Further reading
-
- Medicine
Background:
Identification of individuals with prediabetes who are at high risk of developing diabetes allows for precise interventions. We aimed to determine the role of nuclear magnetic resonance (NMR)-based metabolomic signature in predicting the progression from prediabetes to diabetes.
Methods:
This prospective study included 13,489 participants with prediabetes who had metabolomic data from the UK Biobank. Circulating metabolites were quantified via NMR spectroscopy. Cox proportional hazard (CPH) models were performed to estimate the associations between metabolites and diabetes risk. Supporting vector machine, random forest, and extreme gradient boosting were used to select the optimal metabolite panel for prediction. CPH and random survival forest (RSF) models were utilized to validate the predictive ability of the metabolites.
Results:
During a median follow-up of 13.6 years, 2525 participants developed diabetes. After adjusting for covariates, 94 of 168 metabolites were associated with risk of progression to diabetes. A panel of nine metabolites, selected by all three machine-learning algorithms, was found to significantly improve diabetes risk prediction beyond conventional risk factors in the CPH model (area under the receiver-operating characteristic curve, 1 year: 0.823 for risk factors + metabolites vs 0.759 for risk factors, 5 years: 0.830 vs 0.798, 10 years: 0.801 vs 0.776, all p < 0.05). Similar results were observed from the RSF model. Categorization of participants according to the predicted value thresholds revealed distinct cumulative risk of diabetes.
Conclusions:
Our study lends support for use of the metabolite markers to help determine individuals with prediabetes who are at high risk of progressing to diabetes and inform targeted and efficient interventions.
Funding:
Shanghai Municipal Health Commission (2022XD017). Innovative Research Team of High-level Local Universities in Shanghai (SHSMU-ZDCX20212501). Shanghai Municipal Human Resources and Social Security Bureau (2020074). Clinical Research Plan of Shanghai Hospital Development Center (SHDC2020CR4006). Science and Technology Commission of Shanghai Municipality (22015810500).
-
- Medicine
Three senior figures at the US National Institutes of Health explain why the agency remains committed to supporting basic science and research.
