Invited Review Series: Modern Statistical Methods in Respiratory Medicine
Survival analysis of time-to-event data in respiratory health research studies
- The Authors: Dr Jessica Kasza, BSc, PhD, a research fellow in biostatistics at the Department of Epidemiology and Preventive Medicine at Monash University, has research interests that include health-care provider comparison and the estimation of causal effects. Dr Darren Wraith, BMath, PhD, a research fellow in biostatistics at the School of Population and Global Health at Melbourne University, has broad research interests in biostatistics. Dr Karen Lamb, BSc, PhD, a biostatistician and research fellow at the Murdoch Children's Research Institute at the Royal Children's Hospital, has broad biostatistical research interests, with a focus on measures for describing treatment effects in survival analysis and appropriate methods for dealing with correlation in the analysis of neighbourhood effects on health. Prof. Rory Wolfe, BSc, PhD, professor of biostatistics at the School of Public Health and Preventive Medicine, has broad research interests in biostatistics.
- Series Editors: Rory Wolfe and Michael Abramson
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.