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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
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    References listed on IDEAS

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    1. 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.
    2. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769.
    3. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, January.
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