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Survival analysis

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Survival analysis. / Flynn, Robert.

In: Journal of Clinical Nursing, 2012.

Research output: Contribution to journalArticle

Harvard

Flynn, R 2012, 'Survival analysis' Journal of Clinical Nursing.

APA

Flynn, R. (2012). Survival analysis. Journal of Clinical Nursing, doi: 10.1111/j.1365-2702.2011.04023.x

Vancouver

Flynn R. Survival analysis. Journal of Clinical Nursing. 2012.

Author

Flynn, Robert / Survival analysis.

In: Journal of Clinical Nursing, 2012.

Research output: Contribution to journalArticle

Bibtex - Download

@article{078f1330692249848a2ba22b18e87696,
title = "Survival analysis",
author = "Robert Flynn",
year = "2012",
journal = "Journal of Clinical Nursing",
issn = "0962-1067",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Survival analysis

A1 - Flynn,Robert

AU - Flynn,Robert

PY - 2012

Y1 - 2012

N2 - Aims and objectives. This paper describes when and why survival analysis is used and describes the use and interpretation of the techniques most commonly encountered in medical literature. This is performed using examples taken from core medical journals. Background. Survival analysis is widely used in clinical and epidemiological research: in randomised clinical trials for comparing the efficacy of treatments and in observational (non-randomised) research to determine and test the existence of epidemiological association. Design. This paper introduces the principles, practice and terminology of survival analysis. Methods. References are made to examples from open-access medical journals. Results. Survival analysis is a well-established series of methodologies that are widely encountered in medical literature for both observational and randomised studies. Conclusions. Survival analysis represents a more efficient use of clinical data than other forms of analysis which rely on fixed time periods. One of the most widely used techniques is that developed by Kaplan and Meier. This involves the creation of life tables and the plotting of survival curves with comparison made between two or more groups. The log-rank test is commonly used to establish whether there is a statistically significant difference between these groups. The Multivariate Cox proportional hazards extend this approach to give an estimate of effect size (the Hazards Ratio) and can adjust for any potential confounding variables. In this model, the assumption of proportional hazards is of key importance and should always be checked. More advanced techniques are the use of time-dependent variables and the less widely used parametric survival techniques. Care should always be taken when considering the assumptions involved when using such methods. Relevance to clinical practice. As survival analysis is widely used in clinical research, it is important that readers can critically evaluate the use of this technique. © 2011 Blackwell Publishing Ltd.

AB - Aims and objectives. This paper describes when and why survival analysis is used and describes the use and interpretation of the techniques most commonly encountered in medical literature. This is performed using examples taken from core medical journals. Background. Survival analysis is widely used in clinical and epidemiological research: in randomised clinical trials for comparing the efficacy of treatments and in observational (non-randomised) research to determine and test the existence of epidemiological association. Design. This paper introduces the principles, practice and terminology of survival analysis. Methods. References are made to examples from open-access medical journals. Results. Survival analysis is a well-established series of methodologies that are widely encountered in medical literature for both observational and randomised studies. Conclusions. Survival analysis represents a more efficient use of clinical data than other forms of analysis which rely on fixed time periods. One of the most widely used techniques is that developed by Kaplan and Meier. This involves the creation of life tables and the plotting of survival curves with comparison made between two or more groups. The log-rank test is commonly used to establish whether there is a statistically significant difference between these groups. The Multivariate Cox proportional hazards extend this approach to give an estimate of effect size (the Hazards Ratio) and can adjust for any potential confounding variables. In this model, the assumption of proportional hazards is of key importance and should always be checked. More advanced techniques are the use of time-dependent variables and the less widely used parametric survival techniques. Care should always be taken when considering the assumptions involved when using such methods. Relevance to clinical practice. As survival analysis is widely used in clinical research, it is important that readers can critically evaluate the use of this technique. © 2011 Blackwell Publishing Ltd.

UR - http://www.scopus.com/inward/record.url?partnerID=yv4JPVwI&eid=2-s2.0-84864510084&md5=1f83df77a8f62812479f4358ba408fd4

U2 - 10.1111/j.1365-2702.2011.04023.x

DO - 10.1111/j.1365-2702.2011.04023.x

M1 - Article

JO - Journal of Clinical Nursing

JF - Journal of Clinical Nursing

SN - 0962-1067

ER -

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