Survival analysis: Part I - Analysis of time-to-event

Junyong In; Dong Kyu Lee

doi:10.4097/kja.d.18.00067

Survival analysis: Part I - Analysis of time-to-event

Junyong In, Dong Kyu Lee

Research output: Contribution to journal › Review article › peer-review

23 Citations (Scopus)

Abstract

Length of time is a variable often encountered during data analysis. Survival analysis provides simple, intuitive results concerning time-to-event for events of interest, which are not confined to death. This review introduces methods of analyzing time-to-event. The Kaplan-Meier survival analysis, log-rank test, and Cox proportional hazards regression modeling method are described with examples of hypothetical data.

Original language	English
Pages (from-to)	182-191
Number of pages	10
Journal	Korean journal of anesthesiology
Volume	71
Issue number	3
DOIs	https://doi.org/10.4097/kja.d.18.00067
Publication status	Published - 2018 Jun

Keywords

Censored data
Cox regression
Hazard ratio
Kaplan-Meier method
Log-rank test
Medical statistics
Power analysis
Proportional hazards
Sample size
Survival analysis

ASJC Scopus subject areas

Anesthesiology and Pain Medicine

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10.4097/kja.d.18.00067

Cite this

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title = "Survival analysis: Part I - Analysis of time-to-event",

abstract = "Length of time is a variable often encountered during data analysis. Survival analysis provides simple, intuitive results concerning time-to-event for events of interest, which are not confined to death. This review introduces methods of analyzing time-to-event. The Kaplan-Meier survival analysis, log-rank test, and Cox proportional hazards regression modeling method are described with examples of hypothetical data.",

keywords = "Censored data, Cox regression, Hazard ratio, Kaplan-Meier method, Log-rank test, Medical statistics, Power analysis, Proportional hazards, Sample size, Survival analysis",

author = "Junyong In and Lee, {Dong Kyu}",

note = "Publisher Copyright: {\textcopyright} The Korean Society of Anesthesiologists, 2018.",

year = "2018",

month = jun,

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language = "English",

volume = "71",

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AU - In, Junyong

AU - Lee, Dong Kyu

N1 - Publisher Copyright: © The Korean Society of Anesthesiologists, 2018.

PY - 2018/6

Y1 - 2018/6

N2 - Length of time is a variable often encountered during data analysis. Survival analysis provides simple, intuitive results concerning time-to-event for events of interest, which are not confined to death. This review introduces methods of analyzing time-to-event. The Kaplan-Meier survival analysis, log-rank test, and Cox proportional hazards regression modeling method are described with examples of hypothetical data.

AB - Length of time is a variable often encountered during data analysis. Survival analysis provides simple, intuitive results concerning time-to-event for events of interest, which are not confined to death. This review introduces methods of analyzing time-to-event. The Kaplan-Meier survival analysis, log-rank test, and Cox proportional hazards regression modeling method are described with examples of hypothetical data.

KW - Censored data

KW - Cox regression

KW - Hazard ratio

KW - Kaplan-Meier method

KW - Log-rank test

KW - Medical statistics

KW - Power analysis

KW - Proportional hazards

KW - Sample size

KW - Survival analysis

UR - http://www.scopus.com/inward/record.url?scp=85049589503&partnerID=8YFLogxK

U2 - 10.4097/kja.d.18.00067

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M3 - Review article

C2 - 29768911

AN - SCOPUS:85049589503

SN - 2005-6419

VL - 71

SP - 182

EP - 191

JO - Korean Journal of Anesthesiology

JF - Korean Journal of Anesthesiology

IS - 3

ER -

Survival analysis: Part I - Analysis of time-to-event

Abstract

Keywords

ASJC Scopus subject areas

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