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Modeling and Simulation of Count Data
Count data, or number of events per time interval, are discrete data arising from repeated time to event observations. Their mean count, or piecewise constant event rate, can be evaluated by discrete probability distributions from the Poisson model family. Clinical trial data characterization often...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group
2014
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4150925/ https://www.ncbi.nlm.nih.gov/pubmed/25116273 http://dx.doi.org/10.1038/psp.2014.27 |
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author | Plan, E L |
author_facet | Plan, E L |
author_sort | Plan, E L |
collection | PubMed |
description | Count data, or number of events per time interval, are discrete data arising from repeated time to event observations. Their mean count, or piecewise constant event rate, can be evaluated by discrete probability distributions from the Poisson model family. Clinical trial data characterization often involves population count analysis. This tutorial presents the basics and diagnostics of count modeling and simulation in the context of pharmacometrics. Consideration is given to overdispersion, underdispersion, autocorrelation, and inhomogeneity. |
format | Online Article Text |
id | pubmed-4150925 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-41509252014-09-04 Modeling and Simulation of Count Data Plan, E L CPT Pharmacometrics Syst Pharmacol Tutorial Count data, or number of events per time interval, are discrete data arising from repeated time to event observations. Their mean count, or piecewise constant event rate, can be evaluated by discrete probability distributions from the Poisson model family. Clinical trial data characterization often involves population count analysis. This tutorial presents the basics and diagnostics of count modeling and simulation in the context of pharmacometrics. Consideration is given to overdispersion, underdispersion, autocorrelation, and inhomogeneity. Nature Publishing Group 2014-08 2014-08-13 /pmc/articles/PMC4150925/ /pubmed/25116273 http://dx.doi.org/10.1038/psp.2014.27 Text en Copyright © 2014 American Society for Clinical Pharmacology and Therapeutics http://creativecommons.org/licenses/by-nc-nd/3.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/ |
spellingShingle | Tutorial Plan, E L Modeling and Simulation of Count Data |
title | Modeling and Simulation of Count Data |
title_full | Modeling and Simulation of Count Data |
title_fullStr | Modeling and Simulation of Count Data |
title_full_unstemmed | Modeling and Simulation of Count Data |
title_short | Modeling and Simulation of Count Data |
title_sort | modeling and simulation of count data |
topic | Tutorial |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4150925/ https://www.ncbi.nlm.nih.gov/pubmed/25116273 http://dx.doi.org/10.1038/psp.2014.27 |
work_keys_str_mv | AT planel modelingandsimulationofcountdata |