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Brief introduction to parametric time to event model

This tutorial explains the basic concept of parametric time to event (TTE) models, focusing on commonly used exponential, Weibull, and log-logistic model. TTE data is commonly used as endpoint for treatment effect of a drug or prognosis of diseases. Although non-parametric Kaplan-Meier analysis has...

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Autor principal: Lim, Hyeong-Seok
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korean Society for Clinical Pharmacology and Therapeutics 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8020361/
https://www.ncbi.nlm.nih.gov/pubmed/33854996
http://dx.doi.org/10.12793/tcp.2021.29.e7
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author Lim, Hyeong-Seok
author_facet Lim, Hyeong-Seok
author_sort Lim, Hyeong-Seok
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description This tutorial explains the basic concept of parametric time to event (TTE) models, focusing on commonly used exponential, Weibull, and log-logistic model. TTE data is commonly used as endpoint for treatment effect of a drug or prognosis of diseases. Although non-parametric Kaplan-Meier analysis has been widely used for TTE data analysis, parametric modeling analysis has its own advantages such as ease of simulation, and evaluation of continuous covariate. Accelerated failure time model is introduced as a covariate model for TTE data together with proportional hazard model. Compared to proportional hazard model, accelerated failure time model provides more intuitive results on covariate effect since it states that covariates change TTE whereas in proportional hazard model covariates affect hazard.
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spelling pubmed-80203612021-04-13 Brief introduction to parametric time to event model Lim, Hyeong-Seok Transl Clin Pharmacol Tutorial This tutorial explains the basic concept of parametric time to event (TTE) models, focusing on commonly used exponential, Weibull, and log-logistic model. TTE data is commonly used as endpoint for treatment effect of a drug or prognosis of diseases. Although non-parametric Kaplan-Meier analysis has been widely used for TTE data analysis, parametric modeling analysis has its own advantages such as ease of simulation, and evaluation of continuous covariate. Accelerated failure time model is introduced as a covariate model for TTE data together with proportional hazard model. Compared to proportional hazard model, accelerated failure time model provides more intuitive results on covariate effect since it states that covariates change TTE whereas in proportional hazard model covariates affect hazard. Korean Society for Clinical Pharmacology and Therapeutics 2021-03 2021-03-25 /pmc/articles/PMC8020361/ /pubmed/33854996 http://dx.doi.org/10.12793/tcp.2021.29.e7 Text en Copyright © 2021 Translational and Clinical Pharmacology https://creativecommons.org/licenses/by-nc/4.0/ It is identical to the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/).
spellingShingle Tutorial
Lim, Hyeong-Seok
Brief introduction to parametric time to event model
title Brief introduction to parametric time to event model
title_full Brief introduction to parametric time to event model
title_fullStr Brief introduction to parametric time to event model
title_full_unstemmed Brief introduction to parametric time to event model
title_short Brief introduction to parametric time to event model
title_sort brief introduction to parametric time to event model
topic Tutorial
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8020361/
https://www.ncbi.nlm.nih.gov/pubmed/33854996
http://dx.doi.org/10.12793/tcp.2021.29.e7
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