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Bayesian approach to investigate a two-state mixed model of COPD exacerbations

Chronic obstructive pulmonary disease (COPD) is a chronic obstructive disease of the airways. An exacerbation of COPD is defined as shortness of breath, cough, and sputum production. New therapies for COPD exacerbations are examined in clinical trials frequently based on the number of exacerbations...

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Autores principales: Largajolli, Anna, Beerahee, Misba, Yang, Shuying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6848253/
https://www.ncbi.nlm.nih.gov/pubmed/31197640
http://dx.doi.org/10.1007/s10928-019-09643-6
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author Largajolli, Anna
Beerahee, Misba
Yang, Shuying
author_facet Largajolli, Anna
Beerahee, Misba
Yang, Shuying
author_sort Largajolli, Anna
collection PubMed
description Chronic obstructive pulmonary disease (COPD) is a chronic obstructive disease of the airways. An exacerbation of COPD is defined as shortness of breath, cough, and sputum production. New therapies for COPD exacerbations are examined in clinical trials frequently based on the number of exacerbations that implies long-term study due to the high variability in occurrence and duration of the events. In this work, we expanded the two-state model developed by Cook et al. where the patient transits from an asymptomatic (state 1) to a symptomatic state (state 2) and vice versa, through investigating different semi-Markov models in a Bayesian context using data from actual clinical trials. Of the four models tested, the log-logistic model was shown to adequately characterize the duration and number of COPD exacerbations. The patient disease stage was found a significant covariate with an effect of accelerating the transition from asymptomatic to symptomatic state. In addition, the best dropout model (log-logistic) was incorporated in the final two-state model to describe the dropout mechanism. Simulation based diagnostics such as posterior predictive check (PPC) and visual predictive check (VPC) were used to assess the behaviour of the model. The final model was applied in three clinical trial data to investigate its ability to detect the drug effect: the drug effect was captured in all three datasets and in both directions (from state 1 to state 2 and vice versa). A practical design investigation was also carried out and showed the limits of reducing the number of subjects and study length on the drug effect identification. Finally, clinical trial simulation confirmed that the model can potentially be used to predict medium term (6–12 months) clinical trial outcome using the first 3 months data, but at the expense of showing a non-significant drug effect. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10928-019-09643-6) contains supplementary material, which is available to authorized users.
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spelling pubmed-68482532019-12-03 Bayesian approach to investigate a two-state mixed model of COPD exacerbations Largajolli, Anna Beerahee, Misba Yang, Shuying J Pharmacokinet Pharmacodyn Original Paper Chronic obstructive pulmonary disease (COPD) is a chronic obstructive disease of the airways. An exacerbation of COPD is defined as shortness of breath, cough, and sputum production. New therapies for COPD exacerbations are examined in clinical trials frequently based on the number of exacerbations that implies long-term study due to the high variability in occurrence and duration of the events. In this work, we expanded the two-state model developed by Cook et al. where the patient transits from an asymptomatic (state 1) to a symptomatic state (state 2) and vice versa, through investigating different semi-Markov models in a Bayesian context using data from actual clinical trials. Of the four models tested, the log-logistic model was shown to adequately characterize the duration and number of COPD exacerbations. The patient disease stage was found a significant covariate with an effect of accelerating the transition from asymptomatic to symptomatic state. In addition, the best dropout model (log-logistic) was incorporated in the final two-state model to describe the dropout mechanism. Simulation based diagnostics such as posterior predictive check (PPC) and visual predictive check (VPC) were used to assess the behaviour of the model. The final model was applied in three clinical trial data to investigate its ability to detect the drug effect: the drug effect was captured in all three datasets and in both directions (from state 1 to state 2 and vice versa). A practical design investigation was also carried out and showed the limits of reducing the number of subjects and study length on the drug effect identification. Finally, clinical trial simulation confirmed that the model can potentially be used to predict medium term (6–12 months) clinical trial outcome using the first 3 months data, but at the expense of showing a non-significant drug effect. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10928-019-09643-6) contains supplementary material, which is available to authorized users. Springer US 2019-06-13 2019 /pmc/articles/PMC6848253/ /pubmed/31197640 http://dx.doi.org/10.1007/s10928-019-09643-6 Text en © Springer Science+Business Media, LLC, part of Springer Nature 2019, corrected publication 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution, and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
spellingShingle Original Paper
Largajolli, Anna
Beerahee, Misba
Yang, Shuying
Bayesian approach to investigate a two-state mixed model of COPD exacerbations
title Bayesian approach to investigate a two-state mixed model of COPD exacerbations
title_full Bayesian approach to investigate a two-state mixed model of COPD exacerbations
title_fullStr Bayesian approach to investigate a two-state mixed model of COPD exacerbations
title_full_unstemmed Bayesian approach to investigate a two-state mixed model of COPD exacerbations
title_short Bayesian approach to investigate a two-state mixed model of COPD exacerbations
title_sort bayesian approach to investigate a two-state mixed model of copd exacerbations
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6848253/
https://www.ncbi.nlm.nih.gov/pubmed/31197640
http://dx.doi.org/10.1007/s10928-019-09643-6
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