Survival Analysis, Kaplan-Meier Curves, and Cox Regression: Basic Concepts

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Abstract

Survival analysis is used to analyze data from patients who are followed for different periods of time and in whom the outcome of interest, a dichotomous event, may or may not have occurred at the time the study is halted; data from all patients are used in the analysis, including data from patients who dropped out, regardless of the duration of follow-up. This article discusses basic concepts in survival analysis, explains technical terms such as censoring, and provides reasons why ordinary methods of analysis cannot be applied to such data. The Kaplan-Meier survival curve is described, as is the Cox proportional hazards regression and the hazard ratio. Supplementary information includes a data file, graphs with explanations, and additional discussions; these are provided to enhance the reader’s experience and understanding.

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Survival Analysis and Interpretation of Time-to-Event Data: The Tortoise and the Hare

Patrick Schober, Thomas R. Vetter (2018)

Survival analysis, or more generally, time-to-event analysis, refers to a set of methods for analyzing the length of time until the occurrence of a well-defined end point of interest. A unique feature of survival data is that typically not all patients experience the event (eg, death) by the end of the observation period, so the actual survival times for some patients are unknown. This phenomenon, referred to as censoring, must be accounted for in the analysis to allow for valid inferences. Moreover, survival times are usually skewed, limiting the usefulness of analysis methods that assume a normal data distribution. As part of the ongoing series in Anesthesia & Analgesia, this tutorial reviews statistical methods for the appropriate analysis of time-to-event data, including nonparametric and semiparametric methods—specifically the Kaplan-Meier estimator, log-rank test, and Cox proportional hazards model. These methods are by far the most commonly used techniques for such data in medical literature. Illustrative examples from studies published in Anesthesia & Analgesia demonstrate how these techniques are used in practice. Full parametric models and models to deal with special circumstances, such as recurrent events models, competing risks models, and frailty models, are briefly discussed.

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Stayin' alive: an introduction to survival analysis.

D. Streiner (1995)

In some studies, the outcome of interest is the time until some event occurs: readmission to hospital, the next manic episode, or even death. Survival analysis is a technique which can be used to analyze such data. It has added usefulness because it allows us to use data from subjects who drop out of sight over the course of the follow-up period as well as from those who do not experience the event by the time the study ends. This article introduces this technique and provides some guidelines for designing follow-up trials.

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Author and article information

Journal

Journal ID (nlm-ta): Indian J Psychol Med

Journal ID (iso-abbrev): Indian J Psychol Med

Journal ID (publisher-id): SZJ

Journal ID (hwp): spszj

Title: Indian Journal of Psychological Medicine

Publisher: SAGE Publications (Sage India: New Delhi, India )

ISSN (Print): 0253-7176

ISSN (Electronic): 0975-1564

Publication date (Electronic): 11 June 2023

Publication date (Print): July 2023

Volume: 45

Issue: 4

Pages: 434-435

Affiliations

[1 ] Dept. of Clinical Psychopharmacology and Neurotoxicology, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India.

Author notes

[*]Chittaranjan Andrade, Dept. of Clinical Psychopharmacology and Neurotoxicology, National Institute of Mental Health and Neurosciences, Benguluru 560029, Karnataka, India. E-mail: andradec@ 123456gmail.com

Author information

Chittaranjan Andrade https://orcid.org/0000-0003-1526-567X

Article

Publisher ID: 10.1177_02537176231176986

DOI: 10.1177/02537176231176986

PMC ID: 10357905

PubMed ID: 37483572

SO-VID: 0673eb72-24ca-410f-ab71-8dcb70c2e05d

License:

This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

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ScienceOpen disciplines: Clinical Psychology & Psychiatry

Keywords: survival analysis,censoring,kaplan-meier curve,cox proportional hazards regression,hazard ratio

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ScienceOpen disciplines: Clinical Psychology & Psychiatry

Keywords: survival analysis, censoring, kaplan-meier curve, cox proportional hazards regression, hazard ratio

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