Dark Mode Light Mode

Forecasting brand share after LOE in the chronic disease market using machine learning

Spread the love


  • Global, EY: Accelerate commercial success by effectively navigating loss of exclusivity. technology. EY Representative (2024)

    Google Scholar

  • Deloitte, LLP: Exploring a pharmaceutical odyssey. technology. Deloitte Representative (2024)

    Google Scholar

  • Conti, RM, Berndt, ER: In: Measurement and Modeling of Healthcare Costs (University of Chicago Press, 2018). https://doi.org/10.7208/chicago/9780226530994.001.0001

  • Castanheira, M., Ornaghi, C., Siotis, G.: Unexpected consequences of generic entry. J. Health Economics. 68102243(2019). https://doi.org/10.1016/j.jhealeco.2019.102243

    article
    pubmed

    Google Scholar

  • Health Union, LLC: Strategic Healthcare Marketing Budget: The Complete Guide with Benchmarks (2024)

  • Hemphill, CS, Sampat, BN: Evergreening, patent issues and effective market life in pharmaceuticals. J. Health Economics. 31(2), 327(2012). https://doi.org/10.1016/j.jhealeco.2012.01.004

    article
    pubmed

    Google Scholar

  • Grabowski, H., Long, G., Mortimer, R., Boyo, A.: Recent trends in U.S. brand and generic drug competition. J. Med. ikon. 19(9), 836(2016). https://doi.org/10.1080/13696998.2016.1176578

    article
    pubmed

    Google Scholar

  • Wouters, OJ, Kanavos, PG, McKEE, M.: Comparison of generic drug markets in Europe and the United States: prices, volumes, and expenditures. Milbank Q. 95(3), 554(2017). https://doi.org/10.1111/1468-0009.12279

    article
    pubmed
    PubMed Central

    Google Scholar

  • Acosta, A., Ciapponi, A., Aaserud, M., Vietto, V., Austvoll-Dahlgren, A., Kösters, JP, Vacca, C., Machado, M., Diaz Ayala, D.H., Oxman, AD: Pharmaceutical policy: reference prices; Impact of other pricing and purchasing policies. Cochrane Database Systematic Rev. 2019(8) (2014). https://doi.org/10.1002/14651858.CD005979.pub2

  • Moorkens, E., Godman, B., Huys, I., Hoxha, I., Malaj, A., Circuit, Stockinger, St., Multinhuber, S., Dimitrova, M., Tachkov, K., Vončina, L., Palčevsky, VV, G., Slab, J., J., J., J., J., J., J., J., Popelková, L., Kohutova, K., D., Laius, O., Martikainen, J.E., Selke, Kurafalos, V., Magn., Einarsdóttir, Gubrijanov, I., Vella Bonnno, P., Sutorp, V., Melien, Oy, Plisko, R., Mardare, I., Meshkov, D., Novakovic, T G., R., Vulto, A.G. Front. Pharmacol. 11 https://doi.org/10.3389/fphar.2020.591134

  • Veeraraghavan, B., Bakthavachalam, YD, Sahni, RD, Malhotra, S., Bansal, N., Walia, K.: Loss of ceftazidime/avibactam exclusivity in low- and middle-income countries: a test of antibiotic stewardship practices. Lancet Regional Health – Southeast Asia 15100225(2023). https://doi.org/10.1016/j.lansea.2023.100225

    article
    pubmed
    PubMed Central

    Google Scholar

  • Nguyen, NX, Sheingold, SH, Tarazi, W., Bosworth, A.: The impact of competition on generic drug prices. Application Health Economy. health policy 20(2), 243(2022). https://doi.org/10.1007/s40258-021-00705-w

    article
    pubmed

    Google Scholar

  • IQVIA: Global pharmaceutical use to 2024. technology. CEO of IQVIA (2024)

    Google Scholar

  • OECD: Health at a glance 2023: Generics and biosimilars. OECD, technology. Representative (2023)

    book

    Google Scholar

  • Rizzo, J.A., Zeckhauser, R.: Generic script sharing and the pricing of branded drugs: The role of consumer choice. International J. Healthcare Finance Economics. 9(3), 291(2009). https://doi.org/10.1007/s10754-008-9052-0

    article
    pubmed

  • Hua, LH, Hersh, C.M., Morten, P., Kusel, J., Lin, F., Cave, J., Varga, S., Herrera, V., Ko, JJ: Impact of price reductions after loss of exclusivity in a cost-effectiveness analysis: Fingolimod versus Fingolimod for the treatment of relapsing multiple sclerosis. Interferon Beta-1a. J. Managed Care and Specialty Pharmacy 25(4), 490(2019). https://doi.org/10.18553/jmcp.2019.25.4.490

  • Aitken, ML, Berndt, ER, Bosworth, B., Cockburn, IM, Frank, R., Kleinrock, M., Shapiro, BT: In: Measuring and Modeling Healthcare Costs (University of Chicago Press, 2018). https://doi.org/10.7208/chicago/9780226530994.001.0001

  • Hong, SH, Shepherd, MD, Scoones, D., Wan, TT: Product line expansion and pricing strategies for branded pharmaceuticals facing patent expiration. J. Managed care pharmacy 11(9), 746(2005). https://doi.org/10.18553/jmcp.2005.11.9.746

  • Scott Morton, F., Kyle, M.: In: Handbook of Health Economics, vol. 2(Elsevier, 2011), pp. 763-823. https://doi.org/10.1016/B978-0-444-53592-4.00012-8

  • Federal Trade Commission: Approved Generic Drugs: Short-Term and Long-Term Effects. technology. Representative, Federal Trade Commission, FTC Report (2011)

  • Grabowski, H., Long, G., Mortimer, R.: Recent trends in brand-name and generic drug competition. J. Med. ikon. 17(3), 207(2014). https://doi.org/10.3111/13696998.2013.873723

    article
    pubmed

    Google Scholar

  • Scott Morton, FM: Barriers to entry, brand advertising, and general barriers to entry in the U.S. pharmaceutical industry. International J. Industrial Institute. 18(7), 1085(2000). https://doi.org/10.1016/S0167-7187(98)00057-5

    article

    Google Scholar

  • Regan, TL: General entry, price competition, and market segmentation in the prescription drug market. 국제 J. Ind. Organ. 26(4), 930(2008). https://doi.org/10.1016/j.ijindorg.2007.08.004

    article

    Google Scholar

  • Nikolopoulos, K., Buxton, S., Khammash, M., Stern, P.: Forecasting branded and generic drugs. International J. Forecast. 32(2), 344(2016). https://doi.org/10.1016/j.ijforecast.2015.08.001

    article

    Google Scholar

  • Box, GE, Jenkins, GM, Reinsel, GC, Ljung, GM: Time Series Analysis: Forecasting and Control (John Wiley & Sons, 2015).

  • Hochreiter, S., Schmidhuber, J.: Long-term short-term memory. Neural Computing. 9(8), 1735 (1997). https://doi.org/10.1162/neco.1997.9.8.1735

    article
    Cass
    pubmed

    Google Scholar

  • Lim, B., Arik, S.O., Loeff, N., Pfister, T.: A temporal fusion transformer for interpretable multi-horizontal time series forecasting. progress. 국제 J. 예측. 371748(2021). https://doi.org/10.1016/j.ijforecast.2021.03.012

    article

    Google Scholar

  • Orishkin, B., Carpov, D., Chapad, N., Bengio, Y.: In: International Conference on Learning Representations (ICLR) (2020). https://openreview.net/forum?id=r1ecqn4YwB

  • Li, S., Zhang, W., Wang, L., et al.: Emergency room occupancy prediction using advanced time series models: a comparative study. International J. Med. information 179105380(2023).

    article

    Google Scholar

  • Kim, M., Park, J., Lee, S.: Chatgpt-assisted deep learning model for influenza-like disease surveillance: a comparative study of lstm, n-beats, and tft. J. Med. 국제 결의안. 27e74423(2025). https://doi.org/10.2196/74423

    article

    Google Scholar

  • Wang, arXiv:2311.04770 (2024)

  • Mozoori, H., Jafari, Z., Rahmani, A.: Model-agnostic careful and interpretable vital sign prediction: a comparative study of n-beats, n-hits and tft. We are conducting a CEUR workshop. 3793176(2024)

    Google Scholar

  • Lundberg, SM, Lee, SI: In: Advances in Neural Information Processing Systems 30 (2017)

  • Shapley, LS: In: Game Theory II, vol. 28 (Princeton University Press, 1953), pp. 307–317

  • Rodríguez-Pérez, R.: Interpreting machine learning models using Shapley values: Application to prediction of composite efficacy and multi-target activity. J. Computer-Aided Molecular Design (2020)

  • Ward, IR: Explainable artificial intelligence for pharmacovigilance: what features are important when predicting adverse events? Computing, Methods Program Biomed (2021)

    Google Scholar

  • Ye, Z., Yang, W., Yang, Y., Ouyang, D.: An interpretable machine learning method for in vitro drug formulation development. Food front. pp 195–207 (2021). https://doi.org/10.1002/fft2.78

  • Jaganathan, K., Tayara, H., Chong, K.T.: An explainable supervised machine learning model for predicting respiratory toxicity of chemicals using optimal molecular descriptors. Pharmaceutical Sciences (2022)

  • Lundberg, SM, Nair, B., Vavilala, MS, Horibe, M., Eisses, MJ, Adams, T., Liston, DE, Low, D., Newman, SF, Kim, J., et al.: Explainable machine learning predictions for intraoperative hypoxemia prevention. Nat. Biomed. english 2(10), 749(2018). https://doi.org/10.1038/s41551-018-0304-0

    article
    pubmed
    PubMed Central

    Google Scholar

  • Molnar, C., Casalicchio, G., Bischl, B.: Surrogate models for explainability: a survey; arXiv:2008.08268



  • Source link

    Keep Up to Date with the Most Important News

    By pressing the Subscribe button, you confirm that you have read and are agreeing to our Privacy Policy and Terms of Use
    Add a comment Add a comment

    Leave a Reply

    Previous Post

    RAISE Guidelines – Maintaining SLR Standards in an AI-Enabled Future

    Next Post

    Help fight flu by taking part in a citizen science project – Department of Health and Safety in the UK