Applying parametric models to survival data: tradeoffs between statistical significance, biological plausibility, and common sense

Applying parametric models to survival data: tradeoffs between statistical significance,... Biogerontology https://doi.org/10.1007/s10522-018-9759-3 RESEARCH ARTICLE Applying parametric models to survival data: tradeoffs between statistical significance, biological plausibility, and common sense . . Alexey Golubev Andrei Panchenko Vladimir Anisimov Received: 12 February 2018 / Accepted: 30 May 2018 Springer Science+Business Media B.V., part of Springer Nature 2018 Abstract Parametric models for survival data help obtained by randomization of control animals. An to differentiate aging from other lifespan determi- apparent acceleration of aging associated with a nants. However, such inferences suffer from small decrease in the initial mortality is invalid if it is not sizes of experimental animal samples and variable greater than SMC suggests. This approach applied to animals handling by different labs. We analyzed published data suggests that the effects of calorie control data from a single laboratory where interven- restriction and of drugs believed to mimic it are tions in murine lifespan were studied over decades. different. SMC and CEM relevance to human survival The minimal Gompertz model (GM) was found to patterns is discussed. perform best with most murine strains. However, when several control datasets related to a particular Keywords Survival  Mortality; aging  Parametric strain are fitted to GM, strikingly rigid interdepen- model  Gompertz-Makeham law http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biogerontology Springer Journals

Applying parametric models to survival data: tradeoffs between statistical significance, biological plausibility, and common sense

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Publisher
Springer Netherlands
Copyright
Copyright © 2018 by Springer Science+Business Media B.V., part of Springer Nature
Subject
Life Sciences; Cell Biology; Geriatrics/Gerontology; Developmental Biology
ISSN
1389-5729
eISSN
1573-6768
D.O.I.
10.1007/s10522-018-9759-3
Publisher site
See Article on Publisher Site

Abstract

Biogerontology https://doi.org/10.1007/s10522-018-9759-3 RESEARCH ARTICLE Applying parametric models to survival data: tradeoffs between statistical significance, biological plausibility, and common sense . . Alexey Golubev Andrei Panchenko Vladimir Anisimov Received: 12 February 2018 / Accepted: 30 May 2018 Springer Science+Business Media B.V., part of Springer Nature 2018 Abstract Parametric models for survival data help obtained by randomization of control animals. An to differentiate aging from other lifespan determi- apparent acceleration of aging associated with a nants. However, such inferences suffer from small decrease in the initial mortality is invalid if it is not sizes of experimental animal samples and variable greater than SMC suggests. This approach applied to animals handling by different labs. We analyzed published data suggests that the effects of calorie control data from a single laboratory where interven- restriction and of drugs believed to mimic it are tions in murine lifespan were studied over decades. different. SMC and CEM relevance to human survival The minimal Gompertz model (GM) was found to patterns is discussed. perform best with most murine strains. However, when several control datasets related to a particular Keywords Survival  Mortality; aging  Parametric strain are fitted to GM, strikingly rigid interdepen- model  Gompertz-Makeham law

Journal

BiogerontologySpringer Journals

Published: Jun 4, 2018

References

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