Value of Personalized Risk Information

更好地了解个体干预的益处具有巨大的潜力，可以改善临床 做决定。然而，成本效益分析(CEA)几乎总是基于平均增量成本 以及在团体中发现的平均增量收益。由于卫生保健资源是由决策分配的 对于个别患者，使用平均成本效益(CE)比率可能是不合适的， 误导性的。平均而言具有成本效益的干预措施对许多人(甚至对大多数人)来说可能并不具有成本效益 患有指标性疾病和-相反-名义上成本效益不高的干预措施的患者可能 在某些方面非常有价值。对应用平均人口CE比率不适当的关注 根据临床总结结果，同时关注对个别患者最好的治疗方法 审判。我们先前使用风险模型所做的工作表明，基线风险之间的显著差异是普遍存在的。 在具有相同索引条件的个人之间。这种风险异质性导致了大量的、通常 临床上有意义的治疗益处的差异--特别是当考虑到 绝对量表，临床决策和CEA最相关的衡量标准。临床预测模型 (CPMS)可以跨研究和实践领域使用，以解决这种风险异质性，并 丰富的文学作品。尽管对平均效果和平均CE的使用有重要的担忧 CPM的比率和可获得性、更好地个体化风险的潜在健康和经济影响 有关临床决策的信息在很大程度上仍未得到审查。此外，就像CEA通常会忽略 人口风险分层的潜力、用于评估CPM的传统方法和新的风险生物标志物 通常忽略应用预测的决策环境，转而关注“无效用” 统计准确性的衡量标准。不足为奇的是，这些措施往往很难预测最终的临床结果。 预测性信息的有用性。 因此，我们的具体目标是：目标1：检查基于风险的方法的预期价值 在广泛的医疗干预措施中实现个性化护理和成本效益；目标2：制定 并测试评估预测模型的适当方法，以及风险预测的增量改进， 基于评估改善的健康和经济影响的决策分析框架 个性化医疗决策；目标3： 探讨使用以风险为基础的 个性化护理的方法：(A)模拟以激励为基础的方案的影响，以及(B)参与 利益攸关方对现实世界的实施。 本项目将：1)阐明靶向治疗的整体价值 使用基于风险的方法；2)帮助我们了解这种方法可能出现的情况 特别有用；3)提供启发式方法和工具，以加快对CPM和新的风险生物标志物的评估； 4)帮助我们了解如何最好地激励他们转化为临床实践。

Better information about the benefits of interventions in individuals has enormous potential to improve clinical decision making. Yet cost effectiveness analyses (CEA) are almost always based on average incremental cost and average incremental benefits found in groups. Since health care resources are allocated by decisions made by and for individual patients, use of average cost effectiveness (CE) ratios can be inappropriate and misleading. Interventions that are cost effective on average may not be cost effective for many (even for most) patients with the index condition and-conversely-interventions that are nominally cost-ineffective may be highly worthwhile in some. Concerns about the inappropriateness of applying average population CE ratios parallel concerns about what treatment is best for an individual patient based on summary results of clinical trials. Our prior work using risk models has shown that substantial differences in baseline risk are ubiquitous across individuals with the same index condition. This risk heterogeneity gives rise to substantial, and often clinically meaningful, differences in therapeutic benefits--particularly when benefits are considered on the absolute scale, the most relevant measure for clinical decision making and CEA. Clinical Prediction Models (CPMs) can be used across research and practice domains to address this risk heterogeneity and are abundant in the literature. Despite the important concerns about the use of average effects and average CE ratios and the availability of CPMs, the potential health and economic impact of better individualization of risk information on clinical decisions remains largely unexamined. Further, just as CEAs typically ignore the potential for population risk stratification, traditional measures used to evaluate CPM and novel risk biomarkers typically ignore the decisional context in which the predictions are applied, and focus instead on "utility-free" measures of statistical accuracy. Not surprisingly, these measures often poorly anticipate the ultimate clinical usefulness of the predictive information. Thus, our specific aims are: Aim 1: To examine the expected value of a risk-based approach to individualizing care and cost effectiveness across a broad range of medical interventions; Aim 2: To develop and test appropriate methods to assess prediction models, and incremental improvements in risk prediction, based on a decision analytic framework that estimates the health and economic impact of improved individualized medical decision-making; Aim 3: To explore the policy implications of using a risk-based approach to individualize care by: (a) simulating the impact of incentive-based programs, and (b) engaging stakeholders on real-world implementation. This project will: 1) elucidate the overall value of targeting therapy using a risk-based approach; 2) help us understand the circumstances in which such an approach might be especially useful; 3) provide heuristics and tools to expedite the evaluation of CPMs and novel risk biomarkers; and 4) help us understand how best to incentivize their translation into clinical practice.