Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
Med Decis Making. 2024 Apr;44(3):283-295. doi: 10.1177/0272989X241232607. Epub 2024 Mar 1.
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.
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.
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.
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.
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.
本文展示了一种在缺乏随机对照试验数据的情况下评估长期干预成本效益的方法，且无需使用马尔可夫模拟或类似的队列模拟。
使用孟德尔随机化研究设计，我们使用来自英国生物库队列的结果数据，针对膀胱癌、乳腺癌、结直肠癌、肺癌、多发性骨髓瘤、卵巢癌、前列腺癌和甲状腺癌的遗传预测效应，开发了对医疗成本和质量调整生命年（QALY）的因果估计。然后，我们使用这些估计值在模拟模型中，根据最近证明可降低前列腺癌发病风险的一类已重新用于治疗的抗糖尿病药物（即钠-葡萄糖共转运蛋白 2 [SGLT2] 抑制剂），对基于假设人群的预防性干预的成本效益进行估计。
前列腺癌和乳腺癌的遗传易感性对医疗成本和 QALY 都有实质性的因果影响。较少见癌症的孟德尔随机化结果与相当大的不确定性相关。SGLT2 抑制不太可能成为前列腺癌的一种具有成本效益的预防干预措施，尽管这一结论取决于这些药物用于新的抗癌适应症的价格。
我们对癌症暴露与健康经济结果之间关系的新因果估计，可以作为癌症干预（如筛查计划）的决策分析模型的输入，或者作为与短期随访的随机对照试验中所研究的治疗方法相关的长期结果的模拟。我们的方法使我们能够快速有效地估计假设的人群规模抗癌干预的成本效益，以补充其他评估长期干预价值的方法。
本文展示了一种新的方法，在不依赖于随机对照试验或队列模拟的情况下评估长期干预的成本效益。孟德尔随机化用于使用英国生物库队列的数据估计某些癌症对医疗成本和质量调整生命年（QALY）的因果效应。根据不同癌症暴露与成本和 QALY 之间的关联的因果数据，可以模拟抗癌干预的成本效益。前列腺癌和乳腺癌的遗传易感性显著影响医疗成本和 QALY，但使用 SGLT2 抑制剂治疗前列腺癌的假设干预可能没有成本效益，这取决于该药物用于新的抗癌适应症的价格。我们提出并实施的方法可用于有效地估计干预成本效益，并在各种预防和治疗环境中为决策提供信息。
