Using Mendelian Randomization to model the causal effect of cancer on health economic outcomes and to simulate the cost-effectiveness of anti-cancer interventions

BACKGROUNDCancer is associated with significant economic impacts. Quantifying the scale of these impacts is challenged by confounding variables that jointly influence both cancer status and economic outcomes such as healthcare costs and quality of life. Moreover, the increasing costs attributed to cancer drug development complicate the cost-effective provision of cancer care. METHODSWe address both challenges in this paper by using germline genetic variation in the risk of incident cancer as instrumental variables in Mendelian Randomization analyses of eight cancers. We developed causal estimates of the genetically predicted effect of bladder, breast, colorectal, lung, multiple myeloma, ovarian, prostate and thyroid cancers on healthcare costs and quality adjusted life years (QALYs) using outcome data drawn from the UK Biobank cohort. We then used Mendelian Randomization to model a hypothetical population-wide preventative intervention based on a repurposed class of anti-diabetic drugs known as sodium-glucose co-transporter-2 (SGLT2) inhibitors very recently shown to reduce the odds of incident prostate cancer. RESULTSGenetic liability to prostate cancer and to breast cancer had material causal impacts on 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 anti-cancer indication. IMPLICATIONSOur 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 programmes or simulations of longer-term outcomes associated with therapies investigated in RCTs with short follow-ups. Our new method allows us to rapidly and efficiently estimate the cost-effectiveness of a hypothetical population-scale anti-cancer intervention to inform and complement other means of assessing long-term intervention cost-effectiveness.