Survival analysis in public health research.
This paper reviews the common statistical techniques employed to analyze survival data in public health research. Due to the presence of censoring, the data are not amenable to the usual method of analysis. The improvement in statistical computing and wide accessibility of personal computers led to the rapid development and popularity of nonparametric over parametric procedures. The former required less stringent conditions. But, if the assumptions for parametric methods hold, the resulting estimates have smaller standard errors and are easier to interpret. Nonparametric techniques include the Kaplan-Meier method for estimating the survival function and the Cox proportional hazards model to identify risk factors and to obtain adjusted risk ratios. In cases where the assumption of proportional hazards is not tenable, the data can be stratified and a model fitted with different baseline functions in each stratum. Parametric modeling such as the accelerated failure time model also may be used. Hazard functions for the exponential, Weibull, gamma, Gompertz, lognormal, and log-logistic distributions are described. Examples from published literature are given to illustrate the various methods. The paper is intended for public health professionals who are interested in survival data analysis.
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College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City 73190, USA. elisa-lee@uokhsc.edu
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D003627:Data Interpretation, Statistical; D006801:Humans; D016015:Logistic Models; D015233:Models, Statistical; D016016:Proportional Hazards Models; D011634:Public Health; D012106:Research; D012307:Risk Factors; D018709:Statistics, Nonparametric; D016019:Survival Analysis