statistics review 12 survival analysis统计评估12生存分析.pdf

Available online /content/8/5/389 Review Statistics review 12: Survival analysis 1 1 2 Viv Bewick , Liz Cheek and Jonathan Ball 1Senior Lecturer, School of Computing, Mathematical and Information Sciences, University of Brighton, Brighton, UK 2Senior Registrar in ICU, Liverpool Hospital, Sydney, Australia Correspondence: Viv Bewick, v.bewick@brighton.ac.uk Published online: 6 September 2004 Critical Care 2004, 8:389-394 (DOI 10.1186/cc2955) This article is online at /content/8/5/389 © 2004 BioMed Central Ltd Abstract This review introduces methods of analyzing data arising from studies where the response variable is the length of time taken to reach a certain end-point, often death. The Kaplan–Meier methods, log rank test and Cox’s proportional hazards model are described. Keywords Cox’s proportional-hazards model, cumulative hazard function H(t), hazard ratio, Kaplan–Meier method, log rank test, survival function S(t) Introduction The graph of S(t) against t is called the survival curve. The Survival times are data that measure follow-up time from a Kaplan–Meier method can be used to estimate this curve defined starting point to the occurrence of a given event, for from the observed survival times without the assumption of an example the time from the beginning to the end of a remission underlying probability distribution. The method is based on period or the time fro