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2010•Disability and RehabilitationRequires access

Statistical methods in epidemiology. IX. Survival (failure-time) models

Alan S. Rigby, Jufen Zhang, Kevin M Goode

Open publisher page 1 citations

Abstract

PURPOSE: This article introduces readers to survival (failure-time) models, with a focus on Kaplan-Meier curves, Cox regression and sample size estimation. METHODS: An example is used to show readers how to calculate a Kaplan-Meier curve from first principles. RESULTS: What makes survival data unique is censoring. Readers should understand censoring before undertaking an analysis of survival data. CONCLUSION: The Cox model continues to set the standard for survival models, and will continue well into the future.

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What this paper is about

PURPOSE: This article introduces readers to survival (failure-time) models, with a focus on Kaplan-Meier curves, Cox regression and sample size estimation. METHODS: An example is used to show readers how to calculate a Kaplan-Meier curve from first principles. RESULTS: What makes survival data unique is censoring. Readers should understand censoring before undertaking an analysis of survival data. CONCLUSION: The Cox model continues to set the standard for survival models, and will continue well into the future.

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Available abstract

PURPOSE: This article introduces readers to survival (failure-time) models, with a focus on Kaplan-Meier curves, Cox regression and sample size estimation. METHODS: An example is used to show readers how to calculate a Kaplan-Meier curve from first principles. RESULTS: What makes survival data unique is censoring. Readers should understand censoring before undertaking an analysis of survival data. CONCLUSION: The Cox model continues to set the standard for survival models, and will continue well into the future.

Key concepts: Censoring (clinical trials), Proportional hazards model, Survival analysis, Accelerated failure time model, Statistics, Econometrics, Regression analysis, Covariate

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