Comparison of parametric and non-parametric survival methods using simulated clinical data

John W. Gamel; Robert L. Vogel

doi:10.1002/(SICI)1097-0258(19970730)16:14<1629::AID-SIM594>3.0.CO;2-C

Comparison of parametric and non-parametric survival methods using simulated clinical data

John W. Gamel
, Robert L. Vogel

Biostatistics, Epidemiology & Environmental Health Sciences

University of Louisville
Mercer University

Research output: Contribution to journal › Article › peer-review

19 Scopus citations

Abstract

<div class="line" id="line-5"> We derived three parametric survival models (the log&hyphen;normal, log logit, and Weibull) from the clinical data of chemotherapy trials for stage II breast cancer. We then used these models to generate simulated survival data, which we analysed using both parametric (log&hyphen;normal) and non&hyphen;parametric (logrank, Gray–Tsiatis and Laska–Meisner) methods. With limited follow&hyphen;up (5 years), the non&hyphen;parametric tests had greater power than the log&hyphen;normal model. This advantage diminished, however, with extended follow&hyphen;up (15 years). Furthermore, only the log&hyphen;normal model could distinguish reliably a survival advantage due to an increase in cured fraction from an advantage due to an increase in time to failure.</div>

Original language	English
Pages (from-to)	1629-1643
Number of pages	15
Journal	Statistics in Medicine
Volume	16
Issue number	14
DOIs	https://doi.org/10.1002/(SICI)1097-0258(19970730)16:14<1629::AID-SIM594>3.0.CO;2-C
State	Published - Jul 30 1997

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Scopus Subject Areas

Epidemiology
Statistics and Probability

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10.1002/(SICI)1097-0258(19970730)16:14<1629::AID-SIM594>3.0.CO;2-C

Cite this

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title = "Comparison of parametric and non-parametric survival methods using simulated clinical data",

abstract = " We derived three parametric survival models (the log\&hyphen;normal, log logit, and Weibull) from the clinical data of chemotherapy trials for stage II breast cancer. We then used these models to generate simulated survival data, which we analysed using both parametric (log\&hyphen;normal) and non\&hyphen;parametric (logrank, Gray\–Tsiatis and Laska\–Meisner) methods. With limited follow\&hyphen;up (5 years), the non\&hyphen;parametric tests had greater power than the log\&hyphen;normal model. This advantage diminished, however, with extended follow\&hyphen;up (15 years). Furthermore, only the log\&hyphen;normal model could distinguish reliably a survival advantage due to an increase in cured fraction from an advantage due to an increase in time to failure.",

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N2 - We derived three parametric survival models (the log&hyphen;normal, log logit, and Weibull) from the clinical data of chemotherapy trials for stage II breast cancer. We then used these models to generate simulated survival data, which we analysed using both parametric (log&hyphen;normal) and non&hyphen;parametric (logrank, Gray–Tsiatis and Laska–Meisner) methods. With limited follow&hyphen;up (5 years), the non&hyphen;parametric tests had greater power than the log&hyphen;normal model. This advantage diminished, however, with extended follow&hyphen;up (15 years). Furthermore, only the log&hyphen;normal model could distinguish reliably a survival advantage due to an increase in cured fraction from an advantage due to an increase in time to failure.

AB - We derived three parametric survival models (the log&hyphen;normal, log logit, and Weibull) from the clinical data of chemotherapy trials for stage II breast cancer. We then used these models to generate simulated survival data, which we analysed using both parametric (log&hyphen;normal) and non&hyphen;parametric (logrank, Gray–Tsiatis and Laska–Meisner) methods. With limited follow&hyphen;up (5 years), the non&hyphen;parametric tests had greater power than the log&hyphen;normal model. This advantage diminished, however, with extended follow&hyphen;up (15 years). Furthermore, only the log&hyphen;normal model could distinguish reliably a survival advantage due to an increase in cured fraction from an advantage due to an increase in time to failure.

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JO - Statistics in Medicine

JF - Statistics in Medicine

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ER -

Comparison of parametric and non-parametric survival methods using simulated clinical data

Abstract

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