title: Personalized dynamic prediction of death according to tumour progression and high-dimensional genetic factors: Meta-analysis with a joint model.
abstract: Developing a personalized risk prediction model of death is fundamental for improving patient care and touches on the realm of personalized medicine. The increasing availability of genomic information and large-scale meta-analytic data sets for clinicians has motivated the extension of traditional survival prediction based on the Cox proportional hazards model. The aim of our paper is to develop a personalized risk prediction formula for death according to genetic factors and dynamic tumour progression status based on meta-analytic data. To this end, we extend the existing joint frailty-copula model to a model allowing for high-dimensional genetic factors. In addition, we propose a dynamic prediction formula to predict death given tumour progression events possibly occurring after treatment or surgery. For clinical use, we implement the computation software of the prediction formula in the joint.Cox R package. We also develop a tool to validate the performance of the prediction formula by assessing the prediction error. We illustrate the method with the meta-analysis of individual patient data on ovarian cancer patients.
authors.affiliation: 1 Graduate Institute of Statistics, National Central University, Taoyuan City, Taiwan.
authors.forename: Takeshi
authors.initials: T
authors.lastname: Emura
authors.affiliation: 2 Statistical Analysis Section, Center for Advanced Medicine and Clinical Research, Nagoya University Hospital, Nagoya, Japan.
authors.forename: Masahiro
authors.initials: M
authors.lastname: Nakatochi
authors.affiliation: 3 Department of Biostatistics, Nagoya University Graduate School of Medicine, Nagoya, Japan.
authors.forename: Shigeyuki
authors.initials: S
authors.lastname: Matsui
authors.affiliation: 4 Department of Clinical Medicine (Biostatistics), School of Pharmacy, Kitasato University, Tokyo, Japan.
authors.forename: Hirofumi
authors.initials: H
authors.lastname: Michimae
authors.affiliation: 5 INSERM CR1219 (Biostatistic), Université de Bordeaux, Bordeaux Cedex, France.
authors.forename: Virginie
authors.initials: V
authors.lastname: Rondeau
mesh_terms: D000465:Algorithms; D015982:Bias; D018450:Disease Progression; D005260:Female; D005544:Forecasting; D006801:Humans; D010051:Ovarian Neoplasms / Q000235:genetics* / Q000401:mortality*; D016016:Proportional Hazards Models
keywords: Compound covariate; copula; dependent censoring; risk prediction; semi-competing risk; surrogate endpoint