Premium
Analyzing time to event outcomes with a Cox regression model
Author(s) -
Walters Stephen J.
Publication year - 2012
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.1197
Subject(s) - proportional hazards model , regression analysis , statistics , regression diagnostic , regression , linear regression , variables , survival analysis , econometrics , hazard , hazard ratio , event (particle physics) , accelerated failure time model , variable (mathematics) , marginal model , mathematics , polynomial regression , confidence interval , chemistry , organic chemistry , physics , quantum mechanics , mathematical analysis
Survival analysis is concerned with studying the time between entry to a study and a subsequent event (such as death). Survival times now often refer to the development of a particular symptom or to relapse after remission of a disease, as well as to the time to death. A Cox regression model is a statistical technique for exploring the relationship between the survival of a patient and several explanatory variables. A Cox regression model provides an estimate of the treatment effect on survival after adjustment for other explanatory variables. Also, it allows us to estimate the hazard or risk of death for an individual given their prognostic variables. A Cox regression model must be fitted using an appropriate computer program (such as R, S‐Plus, SAS, STATA, or SPSS). The final model from a Cox regression analysis yields an equation for the hazard as a function of several explanatory variables. Interpreting the Cox regression model involves examining the coefficients for each explanatory variable. A positive regression coefficient sign for an explanatory variable means that the hazard is higher and thus the prognosis worse. Conversely, a negative regression coefficient implies a worse prognosis for patients with higher values of that variable. WIREs Comput Stat 2012, 4:310–315. doi: 10.1002/wics.1197 This article is categorized under: Applications of Computational Statistics > Clinical Trials Statistical and Graphical Methods of Data Analysis > Reliability, Survivability, and Quality Control