IDEAS home Printed from https://ideas.repec.org/a/spr/pharme/v41y2023i10d10.1007_s40273-023-01288-1.html
   My bibliography  Save this article

Modeling Monthly Migraine-Day Distributions Using Longitudinal Regression Models and Linking Quality of Life to Inform Cost-Effectiveness Analysis: A Case Study of Fremanezumab in Japanese-Korean Migraine Patients

Author

Listed:
  • Xinyu Wang

    (Otsuka Pharmaceuticals Co., Ltd., Medical Affairs HEOR/RWE)

  • Kentaro Yamato

    (Otsuka Pharmaceuticals Co., Ltd., Medical Affairs HEOR/RWE)

  • Yoshitsugu Kojima

    (Otsuka Pharmaceuticals Co., Ltd., Medical Affairs HEOR/RWE)

  • Josef J. Paris

    (OPEN Health, Evidence & Access, Chatsworth House)

  • Elisabeth F. P. Peterse

    (OPEN Health, Evidence & Access)

  • Martijn J. H. G. Simons

    (OPEN Health, Evidence & Access)

  • Craig Bennison

    (OPEN Health, Evidence & Access)

Abstract

Background and Objectives As regression approaches have been used more recently to model the effectiveness and health-related quality of life (HRQOL) of novel migraine treatments, an example is provided for fremanezumab. The objective is to estimate the distribution of mean monthly migraine days (MMD) as a continuous variable and corresponding migraine-specific utility values as a function of the MMD, to inform health states in a cost-effectiveness model (CEM). Methods Three longitudinal regression models (zero-adjusted gamma [ZAGA], zero-inflated beta-binomial [ZIBB], and zero-inflated negative binomial [ZINBI]) were fitted to Japanese-Korean clinical trial data of episodic (EM) and chronic migraine (CM) patients treated with fremanezumab or placebo, to estimate MMD over a period of 12 months. The EQ-5D-5L and the migraine-specific quality-of-life (MSQ), mapped to the EQ-5D-3L, questionnaires were used to measure HRQOL. Migraine-specific utility values were estimated as a function of MMD using a linear mixed effects model. Results The ZIBB models fitted the data best in estimating the distribution of mean MMD over time. MSQ-derived values were more sensitive than the EQ-5D-5L values for the effect of the number of MMD on HRQOL, with higher values for less MMD and more time on treatment. Conclusions Using longitudinal regression models to estimate MMD distributions and linking utility values as a function is an appropriate method to inform CEMs and capture inter-patient heterogeneity. The observed distribution shifts demonstrated fremanezumab’s effect at reducing MMD for both EM and CM patients, while treatment effect on HRQOL was captured by MMD and time on treatment.

Suggested Citation

  • Xinyu Wang & Kentaro Yamato & Yoshitsugu Kojima & Josef J. Paris & Elisabeth F. P. Peterse & Martijn J. H. G. Simons & Craig Bennison, 2023. "Modeling Monthly Migraine-Day Distributions Using Longitudinal Regression Models and Linking Quality of Life to Inform Cost-Effectiveness Analysis: A Case Study of Fremanezumab in Japanese-Korean Migr," PharmacoEconomics, Springer, vol. 41(10), pages 1263-1274, October.
    • Handle: RePEc:spr:pharme:v:41:y:2023:i:10:d:10.1007_s40273-023-01288-1
    DOI: 10.1007/s40273-023-01288-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40273-023-01288-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40273-023-01288-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. R. A. Rigby & D. M. Stasinopoulos, 2005. "Generalized additive models for location, scale and shape," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 507-554, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yixuan Wang & Jianzhu Li & Ping Feng & Rong Hu, 2015. "A Time-Dependent Drought Index for Non-Stationary Precipitation Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5631-5647, December.
    2. Nathaniel Geiger & Bryan McLaughlin & John Velez, 2021. "Not all boomers: temporal orientation explains inter- and intra-cultural variability in the link between age and climate engagement," Climatic Change, Springer, vol. 166(1), pages 1-20, May.
    3. Panayi, Efstathios & Peters, Gareth W. & Danielsson, Jon & Zigrand, Jean-Pierre, 2018. "Designating market maker behaviour in limit order book markets," Econometrics and Statistics, Elsevier, vol. 5(C), pages 20-44.
    4. Gauss Cordeiro & Josemar Rodrigues & Mário Castro, 2012. "The exponential COM-Poisson distribution," Statistical Papers, Springer, vol. 53(3), pages 653-664, August.
    5. Christian Kleiber & Achim Zeileis, 2016. "Visualizing Count Data Regressions Using Rootograms," The American Statistician, Taylor & Francis Journals, vol. 70(3), pages 296-303, July.
    6. Chen, Shu & Shao, Dongguo & Tan, Xuezhi & Gu, Wenquan & Lei, Caixiu, 2017. "An interval multistage classified model for regional inter- and intra-seasonal water management under uncertain and nonstationary condition," Agricultural Water Management, Elsevier, vol. 191(C), pages 98-112.
    7. Riccardo De Bin & Vegard Grødem Stikbakke, 2023. "A boosting first-hitting-time model for survival analysis in high-dimensional settings," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(2), pages 420-440, April.
    8. Matteo Malavasi & Gareth W. Peters & Pavel V. Shevchenko & Stefan Truck & Jiwook Jang & Georgy Sofronov, 2021. "Cyber Risk Frequency, Severity and Insurance Viability," Papers 2111.03366, arXiv.org, revised Mar 2022.
    9. Joanna Baj-Korpak & Marian Jan Stelmach & Kamil Zaworski & Piotr Lichograj & Marek Wochna, 2022. "Assessment of Motor Abilities and Physical Fitness in Youth in the Context of Talent Identification—OSF Test," IJERPH, MDPI, vol. 19(21), pages 1-19, November.
    10. Youxin Wang & Tao Peng & Qingxia Lin & Vijay P. Singh & Xiaohua Dong & Chen Chen & Ji Liu & Wenjuan Chang & Gaoxu Wang, 2022. "A New Non-stationary Hydrological Drought Index Encompassing Climate Indices and Modified Reservoir Index as Covariates," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(7), pages 2433-2454, May.
    11. Lucio Masserini & Matilde Bini & Monica Pratesi, 2017. "Effectiveness of non-selective evaluation test scores for predicting first-year performance in university career: a zero-inflated beta regression approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 693-708, March.
    12. Kerstin Hotte, 2021. "Demand-pull, technology-push, and the direction of technological change," Papers 2104.04813, arXiv.org, revised Jan 2023.
    13. Simon N. Wood & Natalya Pya & Benjamin Säfken, 2016. "Smoothing Parameter and Model Selection for General Smooth Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1548-1563, October.
    14. Dominique Guegan & Bertrand K. Hassani, 2011. "Operational risk: a Basel II++ step before Basel III," Documents de travail du Centre d'Economie de la Sorbonne 11053, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    15. Tong, Edward N.C. & Mues, Christophe & Thomas, Lyn, 2013. "A zero-adjusted gamma model for mortgage loan loss given default," International Journal of Forecasting, Elsevier, vol. 29(4), pages 548-562.
    16. Alexander Silbersdorff & Kai Sebastian Schneider, 2019. "Distributional Regression Techniques in Socioeconomic Research on the Inequality of Health with an Application on the Relationship between Mental Health and Income," IJERPH, MDPI, vol. 16(20), pages 1-28, October.
    17. Menghao Wang & Shanhu Jiang & Liliang Ren & Chong-Yu Xu & Linyong Wei & Hao Cui & Fei Yuan & Yi Liu & Xiaoli Yang, 2022. "The Development of a Nonstationary Standardised Streamflow Index Using Climate and Reservoir Indices as Covariates," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1377-1392, March.
    18. Tong, Edward N.C. & Mues, Christophe & Brown, Iain & Thomas, Lyn C., 2016. "Exposure at default models with and without the credit conversion factor," European Journal of Operational Research, Elsevier, vol. 252(3), pages 910-920.
    19. Ding, Hui & Zhang, Jian & Zhang, Riquan, 2022. "Nonparametric variable screening for multivariate additive models," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    20. Alexander März & Nadja Klein & Thomas Kneib & Oliver Musshoff, 2016. "Analysing farmland rental rates using Bayesian geoadditive quantile regression," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 43(4), pages 663-698.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:pharme:v:41:y:2023:i:10:d:10.1007_s40273-023-01288-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.