title: Penalized likelihood in Cox regression.
abstract: In a Cox regression model, instability of the estimated regression coefficients can be reduced by maximizing a penalized partial log-likelihood, where a penalty function of the regression coefficients is substracted from the partial log-likelihood. In this paper, we choose the optimal weight of the penalty function by maximizing the predictive value of the model, as measured by the crossvalidated partial log-likelihood. Our methods are illustrated by a study of ovarian cancer survival and by a study of centre-effects in kidney graft survival.
authors.affiliation: Department of Medical Statistics, Leiden University, The Netherlands.
authors.forename: P J
authors.initials: PJ
authors.lastname: Verweij
authors.forename: H C
authors.initials: HC
authors.lastname: Van Houwelingen
mesh_terms: D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D005260:Female; D006085:Graft Survival; D006801:Humans; D016030:Kidney Transplantation / Q000706:statistics & numerical data; D016013:Likelihood Functions; D016015:Logistic Models; D015337:Multicenter Studies as Topic / Q000706:statistics & numerical data; D010051:Ovarian Neoplasms / Q000401:mortality; D016016:Proportional Hazards Models; D015203:Reproducibility of Results; D016019:Survival Analysis