title: Bayesian nonparametric nonproportional hazards survival modeling.
abstract: We develop a dependent Dirichlet process model for survival analysis data. A major feature of the proposed approach is that there is no necessity for resulting survival curve estimates to satisfy the ubiquitous proportional hazards assumption. An illustration based on a cancer clinical trial is given, where survival probabilities for times early in the study are estimated to be lower for those on a high-dose treatment regimen than for those on the low dose treatment, while the reverse is true for later times, possibly due to the toxic effect of the high dose for those who are not as healthy at the beginning of the study.
authors.affiliation: Department of Epidemiology and Public Health, Imperial College London W2 1PG, United Kingdom. m.deiorio@ic.ac.uk
authors.forename: Maria
authors.initials: M
authors.lastname: De Iorio
authors.forename: Wesley O
authors.initials: WO
authors.lastname: Johnson
authors.forename: Peter
authors.initials: P
authors.lastname: Müller
authors.forename: Gary L
authors.initials: GL
authors.lastname: Rosner
mesh_terms: D001499:Bayes Theorem; D001699:Biometry / Q000379:methods*; D002986:Clinical Trials as Topic; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D023381:Endpoint Determination / Q000379:methods*; D015233:Models, Statistical; D016016:Proportional Hazards Models; D018570:Risk Assessment; D012307:Risk Factors; D016019:Survival Analysis; D015996:Survival Rate