Bayesian Decision Curve Analysis with bayesDCA

TL;DR

This paper introduces bayesDCA, a Bayesian decision curve analysis method that quantifies uncertainty and incorporates prior evidence to improve clinical decision-making and policy evaluation.

Contribution

It develops a Bayesian framework for decision curve analysis, addressing key concerns like strategy usefulness, comparison, and uncertainty quantification, with software implementation in R.

Findings

Bayesian DCA provides probabilistic interpretation of decision strategies.

Simulation studies demonstrate the method's effectiveness.

Case study illustrates practical application in clinical settings.

Abstract

Clinical decisions are often guided by clinical prediction models or diagnostic tests. Decision curve analysis (DCA) combines classical assessment of predictive performance with the consequences of using these strategies for clinical decision-making. In DCA, the best decision strategy is the one that maximizes the so-called net benefit: the net number of true positives (or negatives) provided by a given strategy. In this decision-analytic approach, often only point estimates are published. If uncertainty is reported, a risk-neutral interpretation is recommended: it motivates further research without changing the conclusions based on currently-available data. However, when it comes to new decision strategies, replacing the current Standard of Care must be carefully considered -- prematurely implementing a suboptimal strategy poses potentially irrecoverable costs. In this risk-averse setting, quantifying uncertainty may also inform whether the available data provides enough evidence to change current clinical practice. Here, we employ Bayesian approaches to DCA addressing four fundamental concerns when evaluating clinical decision strategies: (i) which strategies are clinically useful, (ii) what is the best available decision strategy, (iii) pairwise comparisons between strategies, and (iv) the expected net benefit loss associated with the current level of uncertainty. While often consistent with frequentist point estimates, fully Bayesian DCA allows for an intuitive probabilistic interpretation framework and the incorporation of prior evidence. We evaluate the methods using simulation and provide a comprehensive case study. Software implementation is available in the bayesDCA R package. Ultimately, the Bayesian DCA workflow may help clinicians and health policymakers adopt better-informed decisions.