title: Comparing predictions among competing risks models with time-dependent covariates.
abstract: Prediction of cumulative incidences is often a primary goal in clinical studies with several endpoints. We compare predictions among competing risks models with time-dependent covariates. For a series of landmark time points, we study the predictive accuracy of a multi-state regression model, where the time-dependent covariate represents an intermediate state, and two alternative landmark approaches. At each landmark time point, the prediction performance is measured as the t-year expected Brier score where pseudovalues are constructed in order to deal with right-censored event times. We apply the methods to data from a bone marrow transplant study where graft versus host disease is considered a time-dependent covariate for predicting relapse and death in remission.
authors.affiliation: Department of Statistical Sciences, University of Padua, Via Cesare Battisti 241, 35121 Padua, Italy. gcortese@stat.unipd.it
authors.forename: Giuliana
authors.initials: G
authors.lastname: Cortese
authors.forename: Thomas A
authors.initials: TA
authors.lastname: Gerds
authors.forename: Per K
authors.initials: PK
authors.lastname: Andersen
mesh_terms: D016026:Bone Marrow Transplantation / Q000009:adverse effects; D006086:Graft vs Host Disease / Q000209:etiology; D006801:Humans; D015233:Models, Statistical; D009364:Neoplasm Recurrence, Local / Q000209:etiology; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000628:therapy; D011237:Predictive Value of Tests; D012306:Risk
keywords: Brier score; bone marrow transplant studies; competing risks; prediction models; pseudovalues; time-dependent covariates