Advances in risk-benefit evaluation using probabilistic simulation methods: an application to the prophylaxis of deep vein thrombosis

Abstract

ObjectiveTo demonstrate the use of probabilistic simulation modeling to estimate the joint density of therapeutic risks and benefits. Published data are used to introduce the risk–benefit acceptability curve as a novel method of illustrating risk–benefit analysis. Study design and settingUsing published data, we performed a second-order Monte Carlo simulation to estimate the joint density of major bleeding and deep vein thrombosis (DVT) secondary to enoxaparin or unfractionated heparin. Within a Bayesian framework, beta-distributions for the probabilities of experiencing a DVT and major bleed were derived from the clinical trial, and incremental probabilities were calculated. ResultsThe incremental risk–benefit pairs from 3,000 simulations are presented on a risk–benefit plane. To accommodate different risk preferences, the results are also illustrated using a risk–benefit acceptability curve, which incorporates different risk–benefit acceptability thresholds (μ), or the number of major bleeds one is willing to accept in order to avert one DVT. Finally, a net-benefit curve is used to illustrate the risk–benefit ratio and the derivation of 95% confidence intervals around the ratio. ConclusionModern simulation methods permit the estimation of the joint density of risks and benefits with their associated uncertainty, and within a Bayesian framework, facilitate the estimation of the probability that a therapy is net-beneficial over different preference thresholds for risk–benefit trade-offs.