Survival Analysis Resources
Survival Analysis
Survival analysis methods focus on the time until the occurrence of an event of interest. Key elements to perform a survival analysis are:
- Subjects are followed over time.
- A censoring time is defined for the study.
- Either censoring time or time to the event is known for each subject.
The two most frequently used methods for survival analysis are the Kaplan Meier method and Cox proportional hazards regression.
The Kaplan Meier method provides estimates of survival probabilities as a function of time. In graphical from it is the familiar Kaplan Meier curve. Median survival time is often reported and between group comparisons of survival probabilities is usually performed with the the log-rank test.
Cox proportional hazards regression models the hazard of an outcome between two or more groups and also allows for covariate adjustment. The resulting hazard ratio is the ratio of the hazard for the two groups given survival up until that time. A key assumption of Cox proportional hazard is that the hazard ratio between two groups is constant over the course of the survival time.
Survival Analysis Additional Resources |
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For a quick overview of survival analysis see: |
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For an in depth look at survival consult this excellent series of papers from the British Journal of Cancer: Survival Analysis Part I: Basic concepts and first analyses TG Clark, MJ Bradburn, SB Love and DG Altman. UK British Journal of Cancer (2003) 89, 232 – 238. Keywords: survival analysis; statistical methods; Kaplan-Meier |
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Survival Analysis Part II: Multivariate data analysis – an introduction to concepts and methods MJ Bradburn, TG Clark , SB Love and DG Altman. UK British Journal of Cancer (2003) 89, 431 – 436. Keywords: survival analysis; Cox model; AFT model; model selection |
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Survival Analysis Part III: Multivariate data analysis – choosing a model and assessing its adequacy and fit MJ Bradburn, TG Clark, SB Love and DG Altman. UK British Journal of Cancer (2003) 89, 605 – 611. Keywords: survival analysis; Cox model; AFT model; model checking; choice of coavriates; goodness of fit |
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Survival Analysis Part IV: Further concepts and methods in survival analysis TG Clark, MJ Bradburn, SB Love and DG Altman. UK British Journal of Cancer (2003) 89, 781 – 786. Keywords: survival analysis; missing data; validation; repeated event |
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Please use the following text to acknowledge the CTSI Study Design and Biostatistics Center:
"This investigation was supported by Translational Research: Implementation, Analysis and Design (TRIAD), with funding in part from the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UM1TR004409. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health."
"This investigation was supported by the Study Design and Biostatistics Center (SDBC), with funding in part from the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UM1TR004409. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health."