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

Sep 21, 2026

Causal Estimation of Long-term Intervention Cost-effectiveness Using Genetic Instrumental Variables develops a way to assess long-term intervention value when randomized controlled trial data are unavailable and Markov or cohort simulations are not used. Using Mendelian randomization with UK Biobank outcomes, the study estimates how genetically predicted cancers affect healthcare costs and quality-adjusted life-years (QALYs). A simulation then evaluates a population-wide preventive scenario using SGLT2 inhibitors for prostate cancer. Results show material causal impacts for prostate and breast cancer, substantial uncertainty for rarer cancers, and an overall unlikely cost-effectiveness for the hypothetical intervention depending on drug price and the new indication.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Background

Methods

Causal estimation via Mendelian randomization

Simulation of a hypothetical preventive intervention

Results

Implications

Original Research Article Causal Estimation of Long-term Intervention Cost-effectiveness Using Genetic Instrumental Variables: An Application to Cancer Medical Decision Making 2024, Vol. 44(3) 283–295 􀀂 The Author(s) 2024 Article reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)[DOI: 10.1177/0272989X241232607](DOI: 10.1177/0272989X241232607)[ ](DOI: 10.1177/0272989X241232607)[journals.sagepub.com/home/mdm](journals.sagepub.com/home/mdm) Padraig Dixon*, Richard M. Martin, and Sean Harrison* 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 cancerson 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 sodiumglucose 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 healthcare 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. Corresponding Author Padraig Dixon, Nuffield Department of Primary Care Health Sciences, University of Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter, Woodstock Rd, Oxford, OX2 6GG, UK; ([padraig.dixon@phc.ox.ac.uk](padraig.dixon@phc.ox.ac.uk)) . 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. Keywords genetics, cancer, instrumental variables, health care costs, cost-effectiveness, quality of life, Mendelian randomization Date received: July 29, 2023; accepted: January 23, 2024 Nuffield Department of Primary Car

FAQs

How does the article estimate long-term intervention cost-effectiveness without randomized controlled trials?

It uses Mendelian randomization to derive causal estimates for genetically predicted cancer effects on costs and QALYs, then feeds these estimates into a simulation model for a hypothetical intervention scenario.

Which data source is used to estimate the causal effects of cancers on costs and QALYs?

Outcome data are drawn from the UK Biobank cohort to support the Mendelian randomization design.

What do the results indicate about the cost-effectiveness of SGLT2 inhibitors for preventing prostate cancer?

SGLT2 inhibition is unlikely to be cost-effective as a preventive intervention for prostate cancer, and the conclusion depends on the drug price offered for the new anticancer indication.

Sep 21, 2026

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