title: Incorporating short-term outcome information to predict long-term survival with discrete markers.
abstract: In disease screening and prognosis studies, an important task is to determine useful markers for identifying high-risk subgroups. Once such markers are established, they can be incorporated into public health practice to provide appropriate strategies for treatment or disease monitoring based on each individual's predicted risk. In the recent years, genetic and biological markers have been examined extensively for their potential to signal progression or risk of disease. In addition to these markers, it has often been argued that short-term outcomes may be helpful in making a better prediction of disease outcomes in clinical practice. In this paper we propose model-free non-parametric procedures to incorporate short-term event information to improve the prediction of a long-term terminal event. We include the optional availability of a single discrete marker measurement and assess the additional information gained by including the short-term outcome. We focus on the semi-competing risk setting where the short-term event is an intermediate event that may be censored by the terminal event while the terminal event is only subject to administrative censoring. Simulation studies suggest that the proposed procedures perform well in finite samples. Our procedures are illustrated using a data set of post-dialysis patients with end-stage renal disease.
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA. lparast@hsph.harvard.edu
authors.forename: Layla
authors.initials: L
authors.lastname: Parast
authors.forename: Su-Chun
authors.initials: SC
authors.lastname: Cheng
authors.forename: Tianxi
authors.initials: T
authors.lastname: Cai
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D015415:Biomarkers / Q000378:metabolism*; D003198:Computer Simulation; D004921:Erythropoietin / Q000378:metabolism; D006801:Humans; D007676:Kidney Failure, Chronic / Q000401:mortality / Q000628:therapy; D015233:Models, Statistical; D011379:Prognosis; D011634:Public Health; D006435:Renal Dialysis / Q000379:methods; D012306:Risk; D013534:Survival; D016896:Treatment Outcome
chemical_list: D015415:Biomarkers; D004921:Erythropoietin