Generalised decision curve analysis for explicit comparison of treatment effects.

Autor: Hozo I; Department of Mathematics, Indiana University Northwest, Gary, Indiana, USA., Djulbegovic B; Division of Medical Hematology and Oncology, Department of Medicine, Medical University of South Carolina, Charleston, South Carolina, USA.
Jazyk: angličtina
Zdroj: Journal of evaluation in clinical practice [J Eval Clin Pract] 2023 Dec; Vol. 29 (8), pp. 1271-1278. Date of Electronic Publication: 2023 Aug 25.
DOI: 10.1111/jep.13915
Abstrakt: Rationale: Decision 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 Objectives: To 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.
Methods: We 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.
Results: We 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.
Conclusion: We 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.
(© 2023 The Authors. Journal of Evaluation in Clinical Practice published by John Wiley & Sons Ltd.)
Databáze: MEDLINE
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