The development of prognostic time to event models is an area of active research, notably in the fields of cardiology, oncology and intensive care medicine. These models try to find a link between patient covariates and an event at a later time point, and are used for example for therapy assignment, risk stratification or interhospital quality assurance. Increasingly, interest is not only in prognostic models with one possible type of event (e.g. death), but in models that distinguish between several competing endpoints. However, research into methods for the evaluation of the prognostic potential of these models is still needed, as most proposed methods measure either discrimination or calibration of models, but do not examine both simultaneously. We adapt the prediction error proposal of Graf et al. (1999) and Gerds and Schumacher (2006) to handle models with more than one possible event type and introduce a consistent estimator. A simulation study investigating the behaviour of the estimator in small sample size situations and for different levels of censoring together with a real data application follows, highlighting the usefulness of the proposed approach for quantifying effects of model misspecification in summary models for a competing risks setting.

Prognostic models in survival analysis typically aim to describe the association between patient covariates and future outcomes. More recently, efforts have been made to include covariate information that is updated over time. However, there exists as yet no standard approach to assess the predictive accuracy of such updated predictions. In this article, proposals from the literature are discussed and a conditional loss function approach is suggested, illustrated by a publicly available data set.

We performed the largest randomized, placebo-controlled clinical trial to date (N = 112, 12-week intervention) to investigate the effects and safety of resveratrol supplementation on liver fat content and cardiometabolic risk parameters in overweight and obese and insulin-resistant subjects. At baseline the variability in liver fat content was very large, ranging from 0.09% to 37.55% (median, 7.12%; interquartile range, 3.85%-12.94%). Mean (SD) liver fat content was 9.22 (6.85) % in the placebo group and 9.91 (7.76) % in the resveratrol group. During the study liver fat content decreased in the placebo group (-0.7%) but not in the resveratrol group (-0.03%) (differences between groups: P = .018 for the intention-to-treat [ITT] population; N = 54, resveratrol, N = 54, placebo and P = .0077 for the per protocol [PP] population). No effects of resveratrol supplementation on cardiometabolic risk parameters were observed. Resveratrol supplementation was well tolerated and safe. In conclusion, these data suggest that resveratrol supplementation is safe and that it does not considerably impact liver fat content or cardiometabolic risk parameters in humans.