Title: Novel metrics for assessing importance of new biomarkers for competing outcomes
Authors: Yu Cheng - University of Pittsburgh (United States) [presenting]
Zheng Wang - University of Pittsburgh (United States)
Eric Seaberg - John Hopkins University (United States)
James Becker - University of Pittsburgh (United States)
Abstract: The net reclassification improvement (NRI) and the integrated discrimination improvement (IDI) were originally proposed to characterize accuracy improvement in predicting a binary outcome, when new biomarkers are added to regression models. These two indices have been extended from dichotomous outcomes to multi-categorical and survival outcomes. Working on an AIDS study where the onset of cognitive impairment is competing risks censored by death, we extend the NRI and the IDI to competing risk outcomes, by using cumulative incidence functions to quantify cumulative risks of competing events, and adopting the definitions of the two indices for multi-category outcomes. The ``missing'' category due to independent censoring is handled through inverse probability weighting. Various competing risks models are considered, such as the Fine and Gray, multistate, and multinomial logistic models. Estimation methods for the NRI and the IDI from competing risks data are presented. The inference for the NRI is constructed based on asymptotic normality of its estimator, and the bias-corrected and accelerated bootstrap procedure is applied for the IDI inference. Simulations demonstrate that the proposed inferential procedures perform very well. The Multicenter AIDS Cohort Study is used to illustrate the practical utility of the extended NRI and IDI for competing risks outcomes.