Survival analysis of time-to-event data in respiratory health research studies

Jessica Kasza; Darren Wraith; Karen Lamb; Rory Wolfe

doi:10.1111/resp.12281

Survival analysis of time-to-event data in respiratory health research studies

Respirology. 2014 May;19(4):483-92. doi: 10.1111/resp.12281. Epub 2014 Apr 1.

Authors

Jessica Kasza¹, Darren Wraith, Karen Lamb, Rory Wolfe

Affiliation

¹ Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia; Victorian Centre for Biostatistics (ViCBiostat), Melbourne, Victoria, Australia.

PMID: 24689901
DOI: 10.1111/resp.12281

Abstract

This article provides a review of techniques for the analysis of survival data arising from respiratory health studies. Popular techniques such as the Kaplan-Meier survival plot and the Cox proportional hazards model are presented and illustrated using data from a lung cancer study. Advanced issues are also discussed, including parametric proportional hazards models, accelerated failure time models, time-varying explanatory variables, simultaneous analysis of multiple types of outcome events and the restricted mean survival time, a novel measure of the effect of treatment.

Keywords: Kaplan-Meier estimate; accelerated failure time model; proportional hazards model; survival analysis.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Australia
Biomedical Research / methods
Clinical Trials as Topic / classification
Clinical Trials as Topic / methods
Humans
Kaplan-Meier Estimate
Karnofsky Performance Status
Lung Neoplasms* / diagnosis
Lung Neoplasms* / mortality
Lung Neoplasms* / psychology
Lung Neoplasms* / therapy
Outcome Assessment, Health Care / methods
Outcome Assessment, Health Care / trends
Proportional Hazards Models
Quality of Life*
Time Factors
Treatment Outcome