ArticleJournal of evaluation in clinical practice2023
Generalised decision curve analysis for explicit comparison of treatment effects.
Article in Journal of evaluation in clinical practice, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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Who cites it
6 citing papers in PubMed.
- Predicting major amputation risk in diabetic foot ulcers using comparative machine learning models for enhanced clinical decision-making.Scientific reports · 2025Article
- Improving Guideline Development Processes: Integrating Evidence Estimation and Decision-Analytical Frameworks.Journal of evaluation in clinical practice · 2025Article
- Predicting responsiveness to fixed-dose methylene blue in adult patients with septic shock using interpretable machine learning: a retrospective study.Scientific reports · 2025Article
- Biomarker-derived fast-and-frugal decision tree for preemption of veno-occlusive disease/sinusoidal obstructive syndrome.Blood advances · 2024Article
- Decision theoretical foundations of clinical practice guidelines: an extension of the ASH thrombophilia guidelines.Blood advances · 2024Article
- Converting IMPROVE bleeding and VTE risk assessment models into a fast-and-frugal decision tree for optimal hospital VTE prophylaxis.Blood advances · 2024Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
rationaleDecision curve analysis (DCA) helps integrate prediction models with treatment assessments to guide personalised therapeutic choices among multiple treatment options. However, the current versions of DCA do not explicitly model treatment effects in the analysis but implicitly or holistically assess therapeutic benefits and harms. In addition, the existing DCA cannot allow the comparison of multiple treatments using a standard metric. AIMS AND
objectivesTo develop a generalised version of DCA (gDCA) by decomposing holistically assessed net benefits and harms into patient preferences versus empirical evidence (as obtained in the trials, meta-analyses of clinical studies, etc.) to allow individualised comparison of single or multiple treatments using a common metric.
methodsWe reformulated DCA by (1) decomposing holistic, implicit utilities into specific utilities related to treatment effects and patient's relative values (RV) about disease outcomes versus treatment harms, (2) explicitly modelling each treatment effect at the level of probabilities and/or utilities (outcomes) in a decision tree, and (3) avoiding scaling effects employed in the original DCA to enable comparison of treatment effects against the common metrics. We used data from a published network meta-analysis of randomised trials to inform the use of statin treatment according to Framingham Risk Model.
resultsWe illustrate the analysis by modelling the effects of three statins in the primary prevention of cardiovascular disease. We performed simultaneous comparisons against standard metrics (RV) for all treatments. We examined for which RV values, a predictive model for guiding personalised treatment, outperformed the strategies of treating everyone or treating no one. We found that the magnitude of benefits (efficacy) seems more important than the simple ratio of efficacy/harms.
conclusionWe describe gDCA for evaluating single or multiple treatments to help tailor therapy toward individual risk characteristics. gDCA further helps integrate the principles of evidence-based medicine with decision analysis.
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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.