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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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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Korean Society for Clinical Pharmacology and Therapeutics
2021
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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 |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-8020361 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Korean Society for Clinical Pharmacology and Therapeutics |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT limhyeongseok briefintroductiontoparametrictimetoeventmodel |