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Value of Personalized Risk Information

个性化风险信息的价值

基本信息

批准号：
8796340
负责人：
DAVID M KENT
金额：
$ 9.86万
依托单位：
TUFTS MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-06-01 至 2018-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8796340
关键词：
Acute Address Base Ratios Biological Markers Calibration Caring Chronic Clinical Clinical Trials Cost Effectiveness Analysis Costs and Benefits Decision Making Diagnostic Discrimination Future Genomics Health Health Benefit Healthcare Heterogeneity Image Incentives Individual Intervention Laboratories Literature Measures Medical Medicine Methods Modeling Outcome Patients Population Population Study Prevalence Probability Relative (related person)Research Resources Risk Risk Estimate Risk Factors Risk Marker Risk Reduction Simulate Stratification Testing Therapeutic Translations Work base clinical care clinical decision-making clinical practice clinical risk cost cost effective cost effectiveness economic impact health economics heuristics improved indexing novel novel marker policy implication preference programs public health relevance research clinical testing screening tool

项目摘要

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.

更好地了解干预措施对个体的益处，对改善临床具有巨大的潜力