title: Cut-off estimation and medical decision making based on a continuous prognostic factor: the prediction of kidney graft failure.
abstract: The determination of a cut-off value for a continuous prognostic test is an important problem, which is statistically challenging and practically important for risk assessment. We propose in this paper a method to estimate the optimal cut-off from this type of longitudinal data with censored failure times. The principle is to combine the prognostic error rates of false positives and false negatives with a cost function, which has the advantages to be statistically convenient and to be directly associated with the decision-making. Simulations were performed and the results demonstrate the interest of our approach compared to a reference method. The method is also illustrated by predicting the long-term survival of kidney transplant recipients from the 1-year creatinine clearance.
authors.affiliation: Institut of Transplantation and Research in Transplantation, Department of Biostatistics, Nantes University.
authors.forename: Yohann
authors.initials: Y
authors.lastname: Foucher
authors.forename: Magali
authors.initials: M
authors.lastname: Giral
authors.forename: Jean-Paul
authors.initials: JP
authors.lastname: Soulillou
authors.forename: Jean-Pierre
authors.initials: JP
authors.lastname: Daurès
mesh_terms: D015415:Biomarkers / Q000378:metabolism; D056808:Biostatistics; D003198:Computer Simulation; D003404:Creatinine / Q000378:metabolism; D016208:Databases, Factual; D003657:Decision Making; D006085:Graft Survival; D006801:Humans; D053208:Kaplan-Meier Estimate; D016030:Kidney Transplantation / Q000009:adverse effects* / Q000502:physiology*; D015233:Models, Statistical; D011237:Predictive Value of Tests; D011379:Prognosis; D012372:ROC Curve; D013997:Time Factors
chemical_list: D015415:Biomarkers; D003404:Creatinine