Decision Curve Analysis for Personalized Treatment Choice between Multiple Options

Konstantina Chalkou, Andrew J. Vickers, Fabio Pellegrini, Andrea Manca and Georgia Salanti
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Konstantina Chalkou: Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
Andrew J. Vickers: Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
Fabio Pellegrini: BDH, Biogen Spain, Madrid, Spain
Andrea Manca: Centre for Health Economics, University of York, York, UK
Georgia Salanti: Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland

Medical Decision Making, 2023, vol. 43, issue 3, 337-349

Abstract: Background Decision curve analysis can be used to determine whether a personalized model for treatment benefit would lead to better clinical decisions. Decision curve analysis methods have been described to estimate treatment benefit using data from a single randomized controlled trial. Objectives Our main objective is to extend the decision curve analysis methodology to the scenario in which several treatment options exist and evidence about their effects comes from a set of trials, synthesized using network meta-analysis (NMA). Methods We describe the steps needed to estimate the net benefit of a prediction model using evidence from studies synthesized in an NMA. We show how to compare personalized versus one-size-fit-all treatment decision-making strategies, such as “treat none†or “treat all patients with a specific treatment†strategies. First, threshold values for each included treatment need to be defined (i.e., the minimum risk difference compared with control that renders a treatment worth taking). The net benefit per strategy can then be plotted for a plausible range of threshold values to reveal the most clinically useful strategy. We applied our methodology to an NMA prediction model for relapsing-remitting multiple sclerosis, which can be used to choose between natalizumab, dimethyl fumarate, glatiramer acetate, and placebo. Results We illustrated the extended decision curve analysis methodology using several threshold value combinations for each available treatment. For the examined threshold values, the “treat patients according to the prediction model†strategy performs either better than or close to the one-size-fit-all treatment strategies. However, even small differences may be important in clinical decision making. As the advantage of the personalized model was not consistent across all thresholds, improving the existing model (by including, for example, predictors that will increase discrimination) is needed before advocating its clinical usefulness. Conclusions This novel extension of decision curve analysis can be applied to NMA-based prediction models to evaluate their use to aid treatment decision making. Highlights Decision curve analysis is extended into a (network) meta-analysis framework. Personalized models predicting treatment benefit are evaluated when several treatment options are available and evidence about their effects comes from a set of trials. Detailed steps to compare personalized versus one-size-fit-all treatment decision-making strategies are outlined. This extension of decision curve analysis can be applied to (network) meta-analysis–based prediction models to evaluate their use to aid treatment decision making.

Keywords: decision curve analysis; network meta-analysis; prediction model; net benefit; clinical usefulness (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:43:y:2023:i:3:p:337-349

DOI: 10.1177/0272989X221143058

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