IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v44y2024i3p283-295.html

Causal Estimation of Long-term Intervention Cost-effectiveness Using Genetic Instrumental Variables: An Application to Cancer

Author

Listed:
  • Padraig Dixon

    (Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
    MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK)

  • Richard M. Martin

    (MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
    Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
    NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol, Bristol, UK)

  • Sean Harrison

    (MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
    UK Health Security Agency)

Abstract

Background This article demonstrates a means of assessing long-term intervention cost-effectiveness in the absence of data from randomized controlled trials and without recourse to Markov simulation or similar types of cohort simulation. Methods Using a Mendelian randomization study design, we developed causal estimates of the genetically predicted effect of bladder, breast, colorectal, lung, multiple myeloma, ovarian, prostate, and thyroid cancers on health care costs and quality-adjusted life-years (QALYs) using outcome data drawn from the UK Biobank cohort. We then used these estimates in a simulation model to estimate the cost-effectiveness of a hypothetical population-wide preventative intervention based on a repurposed class of antidiabetic drugs known as sodium-glucose cotransporter-2 (SGLT2) inhibitors very recently shown to reduce the odds of incident prostate cancer. Results Genetic liability to prostate cancer and breast cancer had material causal impacts on either or both health care costs and QALYs. Mendelian randomization results for the less common cancers were associated with considerable uncertainty. SGLT2 inhibition was unlikely to be a cost-effective preventative intervention for prostate cancer, although this conclusion depended on the price at which these drugs would be offered for a novel anticancer indication. Implications Our new causal estimates of cancer exposures on health economic outcomes may be used as inputs into decision-analytic models of cancer interventions such as screening programs or simulations of longer-term outcomes associated with therapies investigated in randomized controlled trials with short follow-ups. Our method allowed us to rapidly and efficiently estimate the cost-effectiveness of a hypothetical population-scale anticancer intervention to inform and complement other means of assessing long-term intervention value. Highlights The article demonstrates a novel method of assessing long-term intervention cost-effectiveness without relying on randomized controlled trials or cohort simulations. Mendelian randomization was used to estimate the causal effects of certain cancers on health care costs and quality-adjusted life-years (QALYs) using data from the UK Biobank cohort. Given causal data on the association of different cancer exposures on costs and QALYs, it was possible to simulate the cost-effectiveness of an anticancer intervention. Genetic liability to prostate cancer and breast cancer significantly affected health care costs and QALYs, but the hypothetical intervention using SGLT2 inhibitors for prostate cancer may not be cost-effective, depending on the drug’s price for the new anticancer indication. The methods we propose and implement can be used to efficiently estimate intervention cost-effectiveness and to inform decision making in all manner of preventative and therapeutic contexts.

Suggested Citation

  • Padraig Dixon & Richard M. Martin & Sean Harrison, 2024. "Causal Estimation of Long-term Intervention Cost-effectiveness Using Genetic Instrumental Variables: An Application to Cancer," Medical Decision Making, , vol. 44(3), pages 283-295, April.
    • Handle: RePEc:sae:medema:v:44:y:2024:i:3:p:283-295
    DOI: 10.1177/0272989X241232607
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X241232607
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0272989X241232607?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. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769, December.
    2. Sara Lindström & Deborah J. Thompson & Andrew D. Paterson & Jingmei Li & Gretchen L. Gierach & Christopher Scott & Jennifer Stone & Julie A. Douglas & Isabel dos-Santos-Silva & Pablo Fernandez-Navarro, 2014. "Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk," Nature Communications, Nature, vol. 5(1), pages 1-8, December.
    3. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881.
    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. Anna Petherick & Guilherme A. Ramos & Rodrigo Furst & Rafael Goldszmidt & Eduardo B. Andrade, 2026. "Dynamics of sociopolitical polarization and effects of misperception-correcting information around the 2022 Brazilian elections," Nature Communications, Nature, vol. 17(1), pages 1-15, December.
    2. Dennis Shen & Peng Ding & Jasjeet Sekhon & Bin Yu, 2022. "Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel Data," Papers 2207.14481, arXiv.org, revised Oct 2022.
    3. Art B. Owen & Hal Varian, 2018. "Optimizing the tie-breaker regression discontinuity design," Papers 1808.07563, arXiv.org, revised Jul 2020.
    4. Cristian Mardones & Pablo Herreros, 2023. "Ex post evaluation of voluntary environmental policies on the energy intensity in Chilean firms," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(9), pages 9111-9136, September.
    5. Tymon Słoczyński, 2022. "Interpreting OLS Estimands When Treatment Effects Are Heterogeneous: Smaller Groups Get Larger Weights," The Review of Economics and Statistics, MIT Press, vol. 104(3), pages 501-509, May.
    6. John A. List, 2024. "Field Experiments: Here Today Gone Tomorrow?," The American Economist, Sage Publications, vol. 69(2), pages 214-234, October.
    7. Lutfah Ariana & Rimawan Pradiptyo & Evi Noor Afifah, 2026. "Assessing the impact of public–private partnership adoption on regional economic growth in Asia," International Economics and Economic Policy, Springer, vol. 23(2), pages 1-26, May.
    8. Colnet Bénédicte & Josse Julie & Varoquaux Gaël & Scornet Erwan, 2022. "Causal effect on a target population: A sensitivity analysis to handle missing covariates," Journal of Causal Inference, De Gruyter, vol. 10(1), pages 372-414, January.
    9. Zikun Ye & Zhiqi Zhang & Dennis J. Zhang & Heng Zhang & Renyu Zhang, 2026. "Deep Learning-Based Causal Inference for Large-Scale Combinatorial Experiments: Theory and Empirical Evidence," Management Science, INFORMS, vol. 72(7), pages 5611-5634, July.
    10. Sloczynski, Tymon, 2018. "A General Weighted Average Representation of the Ordinary and Two-Stage Least Squares Estimands," IZA Discussion Papers 11866, IZA Network @ LISER.
    11. Hao, Shiming, 2021. "True structure change, spurious treatment effect? A novel approach to disentangle treatment effects from structure changes," MPRA Paper 108679, University Library of Munich, Germany.
    12. Byard, Donal & Darrough, Masako & Suh, Jangwon, 2019. "There is No Evidence that Mandatory IFRS Adoption Significantly Decreased IPO Underpricing," SocArXiv b56u2, Center for Open Science.
    13. Karel Janda & Oleg Kravtsov, 2022. "Regulatory Stress Tests and Bank Responses: Heterogeneous Treatment Effect in Dynamic Settings," International Journal of Central Banking, International Journal of Central Banking, vol. 18(2), pages 1-49, June.
    14. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
    15. Stefano Carattini & Suphi Sen, 2019. "Carbon Taxes and Stranded Assets: Evidence from Washington State," CESifo Working Paper Series 7785, CESifo.
    16. Capuno, Joseph & Kraft, Aleli & O'Donnell, Owen, 2021. "Effectiveness of clinic-based cardiovascular disease prevention: A randomized encouragement design experiment in the Philippines," Social Science & Medicine, Elsevier, vol. 283(C).
    17. Rustam Romaniuc & Odile Séré de Lanauze & Lisette Hafkamp‐Ibanez & Sébastien Roussel, 2026. "Field Evidence on Nudging High School Students Towards Pro‐Environmental Behavior," Bulletin of Economic Research, Wiley Blackwell, vol. 78(2), pages 328-333, April.
    18. Wei, Wei & Young, Alex, 2025. "Beyond Russell reconstitution: A re-examination of methodologies for natural experiments," Journal of Corporate Finance, Elsevier, vol. 91(C).
    19. Aditya Ghosh & Dominik Rothenhausler, 2025. "Which Covariates to Adjust for? Specification-robust Causal Inference in Observational Studies," Papers 2505.08729, arXiv.org, revised Mar 2026.
    20. Claudio A. Agostini & Eduardo Engel & Andrea Repetto & Damián Vergara, 2018. "Using small businesses for individual tax planning: evidence from special tax regimes in Chile," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 25(6), pages 1449-1489, December.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    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:sae:medema:v:44:y:2024:i:3:p:283-295. 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.