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.
| Original language | English (US) |
|---|---|
| Article number | 20 |
| Journal | BMC Medical Research Methodology |
| Volume | 19 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 23 2019 |
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Keywords
- Beta-binomial
- Erenumab
- Migraine
- Migraine frequency
- Modelling
- Negative binomial
ASJC Scopus subject areas
- Epidemiology
- Health Informatics
Cite this
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.
In: BMC Medical Research Methodology, Vol. 19, No. 1, 20, 23.01.2019.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Migraine day frequency in migraine prevention
T2 - Longitudinal modelling approaches
AU - Di Tanna, Gian Luca
AU - Porter, Joshua K.
AU - Lipton, Richard B.
AU - Brennan, Alan
AU - Palmer, Stephen
AU - Hatswell, Anthony J.
AU - Sapra, Sandhya
AU - Villa, Guillermo
PY - 2019/1/23
Y1 - 2019/1/23
N2 - 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.
AB - 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.
KW - Beta-binomial
KW - Erenumab
KW - Migraine
KW - Migraine frequency
KW - Modelling
KW - Negative binomial
UR - http://www.scopus.com/inward/record.url?scp=85060372469&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060372469&partnerID=8YFLogxK
U2 - 10.1186/s12874-019-0664-5
DO - 10.1186/s12874-019-0664-5
M3 - Article
C2 - 30674285
AN - SCOPUS:85060372469
VL - 19
JO - BMC Medical Research Methodology
JF - BMC Medical Research Methodology
SN - 1471-2288
IS - 1
M1 - 20
ER -