Survival analysis: Part II – applied clinical data analysis

Junyong In, Dong Kyu Lee

    Research output: Contribution to journalArticlepeer-review

    32 Citations (Scopus)


    As a follow-up to a previous article, this review provides several in-depth concepts regarding a survival analysis. Also, several codes for specific survival analysis are listed to enhance the understanding of such an analysis and to provide an applicable survival analysis method. A proportional hazard assumption is an important concept in survival analysis. Validation of this assumption is crucial for survival analysis. For this purpose, a graphical analysis method and a goodness-of-fit test are introduced along with detailed codes and examples. In the case of a violated proportional hazard assumption, the extended models of a Cox regression are required. Simplified concepts of a stratified Cox proportional hazard model and time-dependent Cox regression are also described. The source code for an actual analysis using an available statistical package with a detailed interpretation of the results can enable the realization of survival analysis with personal data. To enhance the statistical power of survival analysis, an evaluation of the basic assumptions and the interaction between variables and time is important. In doing so, survival analysis can provide reliable scientific results with a high level of confidence.

    Original languageEnglish
    Pages (from-to)441-457
    Number of pages17
    JournalKorean journal of anesthesiology
    Issue number5
    Publication statusPublished - 2019 Oct


    • Cox regression
    • Extended cox regression
    • Goodness of fit test
    • Log minus log plot
    • Proportional hazard assumption
    • Schoenfeld residual
    • Stratified cox regression
    • Survival analysis
    • Time-dependent coefficient
    • Time-dependent cox regression

    ASJC Scopus subject areas

    • Anesthesiology and Pain Medicine


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