IDEAS home Printed from https://ideas.repec.org/a/spr/aphecp/v18y2020i1d10.1007_s40258-019-00533-z.html
   My bibliography  Save this article

Forecasting Ontario Oncology Drug Expenditures: A Hybrid Approach to Improving Accuracy

Author

Listed:
  • Paula M. Murray

    (Children’s Hospital Los Angeles)

  • Yusuf A. Shalaby

    (University of Toronto)

  • Luciano Ieraci

    (Cancer Care Ontario)

  • Emmett Borg

    (Cancer Care Ontario)

  • Daphne Sniekers

    (Ontario Renal Network)

  • Ali Vahit Esensoy

    (Klick Labs, Klick Health)

  • Jessica Arias

    (Cancer Care Ontario)

Abstract

Background The Provincial Drug Reimbursement Program (PDRP) at Cancer Care Ontario (CCO) is responsible for monitoring actual and projected outpatient intravenous cancer drug spending in the province. We developed a hybrid forecasting approach combining automated time-series forecasting with expert-customizable input. Objective Our objectives were to provide a flexible tool in which to incorporate multiple forecasts and to improve the accuracy of the resulting forecast. Methods The approach employed linear and non-linear time-series techniques and a combined hybrid model incorporating both approaches. We developed an interactive tool that incorporated the statistical models and identified the best performing forecast according to standard goodness-of-fit measures. Model selection procedures considered both the amount of historical expenditure data available per drug policy and the individual policy contributions to the overall budget. The user was allowed to customize forecasts based on knowledge of external factors related to policy or price changes and new drugs that come to market Results A comparison of 2016/17 fiscal year expenditures showed that all policies with a significant contribution to the overall budget were forecast with

Suggested Citation

  • Paula M. Murray & Yusuf A. Shalaby & Luciano Ieraci & Emmett Borg & Daphne Sniekers & Ali Vahit Esensoy & Jessica Arias, 2020. "Forecasting Ontario Oncology Drug Expenditures: A Hybrid Approach to Improving Accuracy," Applied Health Economics and Health Policy, Springer, vol. 18(1), pages 127-137, February.
  • Handle: RePEc:spr:aphecp:v:18:y:2020:i:1:d:10.1007_s40258-019-00533-z
    DOI: 10.1007/s40258-019-00533-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40258-019-00533-z
    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/s40258-019-00533-z?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. Dangerfield, Byron J. & Morris, John S., 1992. "Top-down or bottom-up: Aggregate versus disaggregate extrapolations," International Journal of Forecasting, Elsevier, vol. 8(2), pages 233-241, October.
    2. S. P. Thi颡ut & T. Barnay & B. Ventelou, 2013. "Ageing, chronic conditions and the evolution of future drugs expenditure: a five-year micro-simulation from 2004 to 2029," Applied Economics, Taylor & Francis Journals, vol. 45(13), pages 1663-1672, May.
    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. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    2. Hyndman, Rob J. & Ahmed, Roman A. & Athanasopoulos, George & Shang, Han Lin, 2011. "Optimal combination forecasts for hierarchical time series," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2579-2589, September.
    3. Li, Han & Hyndman, Rob J., 2021. "Assessing mortality inequality in the U.S.: What can be said about the future?," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 152-162.
    4. Sbrana, Giacomo & Silvestrini, Andrea, 2013. "Forecasting aggregate demand: Analytical comparison of top-down and bottom-up approaches in a multivariate exponential smoothing framework," International Journal of Production Economics, Elsevier, vol. 146(1), pages 185-198.
    5. C. GEAY & M. KOUBI & G. de LAGASNERIE, 2015. "Evolution of outpatient healthcare expenditure, a dynamic micro-simulation using the Destinie model," Documents de Travail de l'Insee - INSEE Working Papers g2015-15, Institut National de la Statistique et des Etudes Economiques.
    6. Chun-Cheng Lin & Rou-Xuan He & Wan-Yu Liu, 2018. "Considering Multiple Factors to Forecast CO 2 Emissions: A Hybrid Multivariable Grey Forecasting and Genetic Programming Approach," Energies, MDPI, vol. 11(12), pages 1-25, December.
    7. Moon, Seongmin & Hicks, Christian & Simpson, Andrew, 2012. "The development of a hierarchical forecasting method for predicting spare parts demand in the South Korean Navy—A case study," International Journal of Production Economics, Elsevier, vol. 140(2), pages 794-802.
    8. Phill O’Neill & Jorge Mestre-Ferrandiz & Ruth Puig-Peiro & Jon Sussex, 2013. "Projecting Expenditure on Medicines in the UK NHS," PharmacoEconomics, Springer, vol. 31(10), pages 933-957, October.
    9. Moon, Seongmin & Simpson, Andrew & Hicks, Christian, 2013. "The development of a classification model for predicting the performance of forecasting methods for naval spare parts demand," International Journal of Production Economics, Elsevier, vol. 143(2), pages 449-454.
    10. Han Lin Shang, 2017. "Reconciling Forecasts of Infant Mortality Rates at National and Sub-National Levels: Grouped Time-Series Methods," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 36(1), pages 55-84, February.
    11. Huddleston, Samuel H. & Porter, John H. & Brown, Donald E., 2015. "Improving forecasts for noisy geographic time series," Journal of Business Research, Elsevier, vol. 68(8), pages 1810-1818.
    12. Athanasopoulos, George & Ahmed, Roman A. & Hyndman, Rob J., 2009. "Hierarchical forecasts for Australian domestic tourism," International Journal of Forecasting, Elsevier, vol. 25(1), pages 146-166.
    13. Han Lin Shang & Yang Yang, 2021. "Forecasting Australian subnational age-specific mortality rates," Journal of Population Research, Springer, vol. 38(1), pages 1-24, March.
    14. Spiliotis, Evangelos & Petropoulos, Fotios & Kourentzes, Nikolaos & Assimakopoulos, Vassilios, 2018. "Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption," MPRA Paper 91762, University Library of Munich, Germany.
    15. Babai, M. Zied & Ali, Mohammad M. & Nikolopoulos, Konstantinos, 2012. "Impact of temporal aggregation on stock control performance of intermittent demand estimators: Empirical analysis," Omega, Elsevier, vol. 40(6), pages 713-721.
    16. J. Scott Armstrong & Kesten C. Green, 2005. "Demand Forecasting: Evidence-based Methods," Monash Econometrics and Business Statistics Working Papers 24/05, Monash University, Department of Econometrics and Business Statistics.
    17. repec:hal:spmain:info:hdl:2441/5cg3fnvgpv8u5peaglp6lrkkaq is not listed on IDEAS
    18. George Athanasopoulos & Rob J Hyndman & Nikolaos Kourentzes & Anastasios Panagiotelis, 2023. "Forecast Reconciliation: A Review," Monash Econometrics and Business Statistics Working Papers 8/23, Monash University, Department of Econometrics and Business Statistics.
    19. Renuga Nagarajan & Aurora A.C. Teixeira & Sandra T. Silva, 2013. "The impact of population ageing on economic growth: an in-depth bibliometric analysis," FEP Working Papers 505, Universidade do Porto, Faculdade de Economia do Porto.
    20. Pennings, Clint L.P. & van Dalen, Jan, 2017. "Integrated hierarchical forecasting," European Journal of Operational Research, Elsevier, vol. 263(2), pages 412-418.
    21. Silva, Felipe L.C. & Souza, Reinaldo C. & Cyrino Oliveira, Fernando L. & Lourenco, Plutarcho M. & Calili, Rodrigo F., 2018. "A bottom-up methodology for long term electricity consumption forecasting of an industrial sector - Application to pulp and paper sector in Brazil," Energy, Elsevier, vol. 144(C), pages 1107-1118.

    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:aphecp:v:18:y:2020:i:1:d:10.1007_s40258-019-00533-z. 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.