IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v32y2012i3p507-516.html
   My bibliography  Save this article

Do Different Methods of Modeling Statin Treatment Effectiveness Influence the Optimal Decision?

Author

Listed:
  • Bob J. H. van Kempen
  • Bart S. Ferket
  • Albert Hofman
  • Sandra Spronk
  • Ewout Steyerberg
  • M. G. Myriam Hunink

Abstract

Purpose. Modeling studies that evaluate statin treatment for the prevention of cardiovascular disease (CVD) use different methods to model the effect of statins. The aim of this study was to evaluate the impact of using different modeling methods on the optimal decision found in such studies. Methods. We used a previously developed and validated Monte Carlo–Markov model based on the Rotterdam study (RISC model). The RISC model simulates coronary heart disease (CHD), stroke, cardiovascular death, and death due to other causes. Transition probabilities were based on 5-year risks predicted by Cox regression equations, including (among others) total and high-density lipoprotein (HDL) cholesterol as covariates. In a cost-effectiveness analysis of implementing the ATP-III guidelines, we evaluated the impact of using 3 different modeling methods of statin effectiveness: 1) through lipid level modification: statins lower total cholesterol and increase HDL cholesterol, which through the covariates in the Cox regression equations leads to a lower incidence of CHD and stroke events; 2) fixed risk reduction of CVD events: statins decrease the odds of CHD and stroke with an associated odds ratio that is assumed to be the same for each individual; 3) risk reduction of CVD events proportional to individual change in low-density lipoprotein (LDL) cholesterol: the relative risk reduction with statin therapy on the incidence of CHD and stroke was assumed to be proportional to the absolute reduction in LDL cholesterol levels for each individual. The probability that the ATP-III strategy was cost-effective, compared to usual care as observed in the Rotterdam study, was calculated for each of the 3 modeling methods for varying willingness-to-pay thresholds. Results. Incremental cost-effectiveness ratios for the ATP-III strategy compared with the reference strategy were €56,642/quality-adjusted life year (QALY), €21,369/QALY, and €22,131/QALY for modeling methods 1, 2, and 3, respectively. At a willingness-to-pay threshold of €50,000/QALY, the probability that the ATP-III strategy was cost-effective was about 40% for modeling method 1 and more than 90% for both methods 2 and 3. Differences in results between the modeling methods were sensitive to both the time horizon modeled and age distribution of the target population. Conclusions. Modeling the effect of statins on CVD through the modification of lipid levels produced different results and associated uncertainty than modeling it directly through a risk reduction of events. This was partly attributable to the modeled effect of cholesterol on the incidence of stroke.

Suggested Citation

  • Bob J. H. van Kempen & Bart S. Ferket & Albert Hofman & Sandra Spronk & Ewout Steyerberg & M. G. Myriam Hunink, 2012. "Do Different Methods of Modeling Statin Treatment Effectiveness Influence the Optimal Decision?," Medical Decision Making, , vol. 32(3), pages 507-516, May.
  • Handle: RePEc:sae:medema:v:32:y:2012:i:3:p:507-516
    DOI: 10.1177/0272989X12439754
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X12439754
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0272989X12439754?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
    ---><---

    References listed on IDEAS

    as
    1. Weinstein, M.C. & Coxson, P.G. & Williams, L.W. & Pass, T.M. & Stason, W.B. & Goldman, L., 1987. "Forecasting coronary heart disease incidence, mortality, and cost: The coronary heart disease policy model," American Journal of Public Health, American Public Health Association, vol. 77(11), pages 1417-1426.
    2. Hugh Gravelle & Werner Brouwer & Louis Niessen & Maarten Postma & Frans Rutten, 2007. "Discounting in economic evaluations: stepping forward towards optimal decision rules," Health Economics, John Wiley & Sons, Ltd., vol. 16(3), pages 307-317, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. David Epstein & Leticia García-Mochón & Stephen Kaptoge & Simon G. Thompson, 2016. "Modeling the costs and long-term health benefits of screening the general population for risks of cardiovascular disease: a review of methods used in the literature," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 17(8), pages 1041-1053, November.

    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. William S. Weintraub & Samuel S. Gidding, 2016. "PCSK9 Inhibitors: A Technology Worth Paying For?," PharmacoEconomics, Springer, vol. 34(3), pages 217-220, March.
    2. John Hutton, 2012. "‘Health Economics’ and the evolution of economic evaluation of health technologies," Health Economics, John Wiley & Sons, Ltd., vol. 21(1), pages 13-18, January.
    3. Chris Sampson & Bernarda Zamora & Sam Watson & John Cairns & Kalipso Chalkidou & Patricia Cubi-Molla & Nancy Devlin & Borja García-Lorenzo & Dyfrig A. Hughes & Ashley A. Leech & Adrian Towse, 2022. "Supply-Side Cost-Effectiveness Thresholds: Questions for Evidence-Based Policy," Applied Health Economics and Health Policy, Springer, vol. 20(5), pages 651-667, September.
    4. Michael F. Drummond;Adrian Towse, 1998. "From Efficacy to Cost-Effectiveness," Briefing 000438, Office of Health Economics.
    5. Attema, Arthur E. & Brouwer, Werner B.F., 2009. "The correction of TTO-scores for utility curvature using a risk-free utility elicitation method," Journal of Health Economics, Elsevier, vol. 28(1), pages 234-243, January.
    6. Yizhe Xu & Tom H. Greene & Adam P. Bress & Brian C. Sauer & Brandon K. Bellows & Yue Zhang & William S. Weintraub & Andrew E. Moran & Jincheng Shen, 2022. "Estimating the optimal individualized treatment rule from a cost‐effectiveness perspective," Biometrics, The International Biometric Society, vol. 78(1), pages 337-351, March.
    7. Ana Bobinac & Job Exel & Frans Rutten & Werner Brouwer, 2014. "The Value of a QALY: Individual Willingness to Pay for Health Gains Under Risk," PharmacoEconomics, Springer, vol. 32(1), pages 75-86, January.
    8. Mark Roberts & Louise B. Russell & A. David Paltiel & Michael Chambers & Phil McEwan & Murray Krahn, 2012. "Conceptualizing a Model," Medical Decision Making, , vol. 32(5), pages 678-689, September.
    9. Weatherly, Helen & Drummond, Michael & Claxton, Karl & Cookson, Richard & Ferguson, Brian & Godfrey, Christine & Rice, Nigel & Sculpher, Mark & Sowden, Amanda, 2009. "Methods for assessing the cost-effectiveness of public health interventions: Key challenges and recommendations," Health Policy, Elsevier, vol. 93(2-3), pages 85-92, December.
    10. Gary A. Zarkin & Laura J. Dunlap & Katherine A. Hicks & Daniel Mamo, 2005. "Benefits and costs of methadone treatment: results from a lifetime simulation model," Health Economics, John Wiley & Sons, Ltd., vol. 14(11), pages 1133-1150, November.
    11. Meltzer, David, 1997. "Accounting for future costs in medical cost-effectiveness analysis," Journal of Health Economics, Elsevier, vol. 16(1), pages 33-64, February.
    12. K Cooper & S C Brailsford & R Davies, 2007. "Choice of modelling technique for evaluating health care interventions," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(2), pages 168-176, February.
    13. Mike Paulden & Anthony J. Culyer, 2010. "Does Cost-Effectiveness Analysis Discriminate against Patients with Short Life Expectancy?," Working Paper series 41_10, Rimini Centre for Economic Analysis.
    14. Martin Hoyle & Rob Anderson, 2010. "Whose Costs and Benefits? Why Economic Evaluations Should Simulate Both Prevalent and All Future Incident Patient Cohorts," Medical Decision Making, , vol. 30(4), pages 426-437, July.
    15. Arthur E. Attema & Werner B. F. Brouwer & Karl Claxton, 2018. "Discounting in Economic Evaluations," PharmacoEconomics, Springer, vol. 36(7), pages 745-758, July.
    16. Tekeshe A Mekonnen & Michelle C Odden & Pamela G Coxson & David Guzman & James Lightwood & Y Claire Wang & Kirsten Bibbins-Domingo, 2013. "Health Benefits of Reducing Sugar-Sweetened Beverage Intake in High Risk Populations of California: Results from the Cardiovascular Disease (CVD) Policy Model," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-9, December.
    17. Arthur E. Attema & Werner B.F. Brouwer, 2014. "Deriving Time Discounting Correction Factors For Tto Tariffs," Health Economics, John Wiley & Sons, Ltd., vol. 23(4), pages 410-425, April.
    18. Werner Brouwer & Pieter Baal & Job Exel & Matthijs Versteegh, 2019. "When is it too expensive? Cost-effectiveness thresholds and health care decision-making," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(2), pages 175-180, March.
    19. Karen T. Hicklin & Julie S. Ivy & James R. Wilson & Fay Cobb Payton & Meera Viswanathan & Evan R. Myers, 2019. "Simulation model of the relationship between cesarean section rates and labor duration," Health Care Management Science, Springer, vol. 22(4), pages 635-657, December.
    20. Davies, Ruth & Roderick, Paul & Raftery, James, 2003. "The evaluation of disease prevention and treatment using simulation models," European Journal of Operational Research, Elsevier, vol. 150(1), pages 53-66, October.

    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:sae:medema:v:32:y:2012:i:3:p:507-516. 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: SAGE Publications (email available below). General contact details of provider: .

    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.