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Migraine day frequency in migraine prevention: longitudinal modelling approaches

BACKGROUND: Health economic models are critical tools to inform reimbursement agencies on health care interventions. Many clinical trials report outcomes using the frequency of an event over a set period of time, for example, the primary efficacy outcome in most clinical trials of migraine preventio...

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Autores principales: Di Tanna, Gian Luca, Porter, Joshua K., Lipton, Richard B., Brennan, Alan, Palmer, Stephen, Hatswell, Anthony J., Sapra, Sandhya, Villa, Guillermo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6343253/
https://www.ncbi.nlm.nih.gov/pubmed/30674285
http://dx.doi.org/10.1186/s12874-019-0664-5
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author Di Tanna, Gian Luca
Porter, Joshua K.
Lipton, Richard B.
Brennan, Alan
Palmer, Stephen
Hatswell, Anthony J.
Sapra, Sandhya
Villa, Guillermo
author_facet Di Tanna, Gian Luca
Porter, Joshua K.
Lipton, Richard B.
Brennan, Alan
Palmer, Stephen
Hatswell, Anthony J.
Sapra, Sandhya
Villa, Guillermo
author_sort Di Tanna, Gian Luca
collection PubMed
description BACKGROUND: Health economic models are critical tools to inform reimbursement agencies on health care interventions. Many clinical trials report outcomes using the frequency of an event over a set period of time, for example, the primary efficacy outcome in most clinical trials of migraine prevention is mean change in the frequency of migraine days (MDs) per 28 days (monthly MDs [MMD]) relative to baseline for active treatment versus placebo. Using these cohort-level endpoints in economic models, accounting for variation among patients is challenging. In this analysis, parametric models of change in MMD for migraine preventives were assessed using data from erenumab clinical studies. METHODS: MMD observations from the double-blind phases of two studies of erenumab were used: one in episodic migraine (EM) (NCT02456740) and one in chronic migraine (CM) (NCT02066415). For each trial, two longitudinal regression models were fitted: negative binomial and beta binomial. For a thorough comparison we also present the fitting from the standard multilevel Poisson and the zero inflated negative binomial. RESULTS: Using the erenumab study data, both the negative binomial and beta-binomial models provided unbiased estimates relative to observed trial data with well-fitting distribution at various time points. CONCLUSIONS: This proposed methodology, which has not been previously applied in migraine, has shown that these models may be suitable for estimating MMD frequency. Modelling MMD using negative binomial and beta-binomial distributions can be advantageous because these models can capture intra- and inter-patient variability so that trial observations can be modelled parametrically for the purposes of economic evaluation of migraine prevention. Such models have implications for use in a wide range of disease areas when assessing repeated measured utility values. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12874-019-0664-5) contains supplementary material, which is available to authorized users.
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spelling pubmed-63432532019-01-24 Migraine day frequency in migraine prevention: longitudinal modelling approaches Di Tanna, Gian Luca Porter, Joshua K. Lipton, Richard B. Brennan, Alan Palmer, Stephen Hatswell, Anthony J. Sapra, Sandhya Villa, Guillermo BMC Med Res Methodol Research Article BACKGROUND: Health economic models are critical tools to inform reimbursement agencies on health care interventions. Many clinical trials report outcomes using the frequency of an event over a set period of time, for example, the primary efficacy outcome in most clinical trials of migraine prevention is mean change in the frequency of migraine days (MDs) per 28 days (monthly MDs [MMD]) relative to baseline for active treatment versus placebo. Using these cohort-level endpoints in economic models, accounting for variation among patients is challenging. In this analysis, parametric models of change in MMD for migraine preventives were assessed using data from erenumab clinical studies. METHODS: MMD observations from the double-blind phases of two studies of erenumab were used: one in episodic migraine (EM) (NCT02456740) and one in chronic migraine (CM) (NCT02066415). For each trial, two longitudinal regression models were fitted: negative binomial and beta binomial. For a thorough comparison we also present the fitting from the standard multilevel Poisson and the zero inflated negative binomial. RESULTS: Using the erenumab study data, both the negative binomial and beta-binomial models provided unbiased estimates relative to observed trial data with well-fitting distribution at various time points. CONCLUSIONS: This proposed methodology, which has not been previously applied in migraine, has shown that these models may be suitable for estimating MMD frequency. Modelling MMD using negative binomial and beta-binomial distributions can be advantageous because these models can capture intra- and inter-patient variability so that trial observations can be modelled parametrically for the purposes of economic evaluation of migraine prevention. Such models have implications for use in a wide range of disease areas when assessing repeated measured utility values. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12874-019-0664-5) contains supplementary material, which is available to authorized users. BioMed Central 2019-01-23 /pmc/articles/PMC6343253/ /pubmed/30674285 http://dx.doi.org/10.1186/s12874-019-0664-5 Text en © The Author(s). 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 unrestricted use, distribution, and reproduction in any medium, provided 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. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Di Tanna, Gian Luca
Porter, Joshua K.
Lipton, Richard B.
Brennan, Alan
Palmer, Stephen
Hatswell, Anthony J.
Sapra, Sandhya
Villa, Guillermo
Migraine day frequency in migraine prevention: longitudinal modelling approaches
title Migraine day frequency in migraine prevention: longitudinal modelling approaches
title_full Migraine day frequency in migraine prevention: longitudinal modelling approaches
title_fullStr Migraine day frequency in migraine prevention: longitudinal modelling approaches
title_full_unstemmed Migraine day frequency in migraine prevention: longitudinal modelling approaches
title_short Migraine day frequency in migraine prevention: longitudinal modelling approaches
title_sort migraine day frequency in migraine prevention: longitudinal modelling approaches
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6343253/
https://www.ncbi.nlm.nih.gov/pubmed/30674285
http://dx.doi.org/10.1186/s12874-019-0664-5
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