Application of models for multivariate mixed outcomes to medical device trials: coronary artery stenting

The approval process for some medical devices involves a single-arm trial in which the outcomes associated with the new device are compared to the expected outcomes associated with approved devices, the latter denoted the objective performance criterion (OPC). In this paper, models for multivariate mixed outcomes are applied to derive the OPC for a medical device to be used in clinical evaluations of the same type of device. We illustrate the techniques by determining the OPC for coronary artery stents, metal cages used to widen blocked coronary arteries in patients with coronary artery disease, using data from seven randomized trials of stents approved for use in the U.S.A. involving 5806 patients. The OPC is based on two 9-month endpoints: target lesion revascularization, a binary outcome, and proportion diameter stenosis, a continuous outcome. To account for the correlation between mixed outcomes we consider factorization of the likelihood into marginal and conditional components, or adoption of a latent variable model. Because the models have different structural forms, standard methods for model comparison (such as the AIC and BIC) cannot be used. We discuss how model identifiability and valid inference are achieved, and then adapt the deviance information criterion (DIC) and the pseudo-Bayes factor (PSBF) to select the best model. Nine months post-stenting, we find that the average posterior probability (standard deviation) of target lesion revascularization ranges from 0.086 (0.008) for non-diabetics with one diseased vessel to 0.163 (0.022) for diabetics with three diseased vessels. When considering proportion diameter stenosis, the corresponding posterior means are 0.375 (0.020) and 0.427 (0.030). The correlation coefficient of the components of the OPC lies in the range 0.042 to 0.116.