Migraine day frequency in migraine prevention: longitudinal modelling approaches
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 ch...
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Published in | BMC medical research methodology Vol. 19; no. 1; p. 20 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
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BioMed Central Ltd
23.01.2019
BioMed Central BMC |
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Abstract | 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.
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.
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.
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. |
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AbstractList | 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. 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. 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. 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. Abstract 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. 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. 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. Keywords: Erenumab, Migraine, Migraine frequency, Modelling, Negative binomial, Beta-binomial BACKGROUNDHealth 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.METHODSMMD 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.RESULTSUsing 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.CONCLUSIONSThis 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. 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. 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. 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. 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. |
ArticleNumber | 20 |
Audience | Academic |
Author | Lipton, Richard B Di Tanna, Gian Luca Brennan, Alan Palmer, Stephen Sapra, Sandhya Villa, Guillermo Hatswell, Anthony J Porter, Joshua K |
Author_xml | – sequence: 1 givenname: Gian Luca orcidid: 0000-0002-5470-3567 surname: Di Tanna fullname: Di Tanna, Gian Luca email: gditanna@amgen.com organization: Economic Modelling Center of Excellence, Amgen Europe GmbH, Suurstoffi 22, P.O. Box 94, CH-6343, Rotkreuz, Switzerland. gditanna@amgen.com – sequence: 2 givenname: Joshua K surname: Porter fullname: Porter, Joshua K organization: Economic Modelling Center of Excellence, Amgen Europe GmbH, Suurstoffi 22, P.O. Box 94, CH-6343, Rotkreuz, Switzerland – sequence: 3 givenname: Richard B surname: Lipton fullname: Lipton, Richard B organization: Albert Einstein College of Medicine, New York, NY, 10461, USA – sequence: 4 givenname: Alan surname: Brennan fullname: Brennan, Alan organization: ScHARR, University of Sheffield, Sheffield, UK – sequence: 5 givenname: Stephen surname: Palmer fullname: Palmer, Stephen organization: Centre for Health Economics, University of York, York, UK – sequence: 6 givenname: Anthony J surname: Hatswell fullname: Hatswell, Anthony J organization: Delta Hat Limited, Nottingham, UK – sequence: 7 givenname: Sandhya surname: Sapra fullname: Sapra, Sandhya organization: Amgen Inc., Thousand Oaks, CA, 91320, USA – sequence: 8 givenname: Guillermo surname: Villa fullname: Villa, Guillermo organization: Economic Modelling Center of Excellence, Amgen Europe GmbH, Suurstoffi 22, P.O. Box 94, CH-6343, Rotkreuz, Switzerland |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30674285$$D View this record in MEDLINE/PubMed |
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CitedBy_id | crossref_primary_10_3390_jcm13041130 crossref_primary_10_36469_jheor_2023_87790 crossref_primary_10_1007_s12325_022_02386_w crossref_primary_10_1177_0333102421989247 crossref_primary_10_1097_GOX_0000000000005620 crossref_primary_10_1007_s40273_023_01288_1 crossref_primary_10_1080_13696998_2020_1754840 crossref_primary_10_36469_001c_87790 |
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Keywords | Migraine frequency Negative binomial Erenumab Modelling Beta-binomial Migraine |
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Snippet | Health economic models are critical tools to inform reimbursement agencies on health care interventions. Many clinical trials report outcomes using the... Background Health economic models are critical tools to inform reimbursement agencies on health care interventions. Many clinical trials report outcomes using... BACKGROUNDHealth economic models are critical tools to inform reimbursement agencies on health care interventions. Many clinical trials report outcomes using... Abstract Background Health economic models are critical tools to inform reimbursement agencies on health care interventions. Many clinical trials report... |
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SubjectTerms | Accounting Antibodies, Monoclonal, Humanized - therapeutic use Beta-binomial Binomial Distribution Calcitonin Gene-Related Peptide Receptor Antagonists - therapeutic use Clinical trials Cost analysis Data Interpretation, Statistical Economic models Erenumab Frequency distribution Headaches Humans Medical research Methods Migraine Migraine Disorders - drug therapy Migraine Disorders - prevention & control Migraine frequency Modelling Models, Statistical Monoclonal antibodies Negative binomial Patients Prevention Quality of life Survival analysis Testing Time Factors |
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Title | Migraine day frequency in migraine prevention: longitudinal modelling approaches |
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