Personalized Medicine in Comparative Effectiveness

个性化医疗的比较效果

• 批准号: 8035869
• 负责人: LEE-JEN WEI
• 金额: $ 87.54万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-30 至 2013-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8035869
• 关键词: AIDS clinical trial group; Adverse effects; Affect; Age; Benefits and Risks; Biological; Biological Markers; Biometry; Characteristics; Clinical; Clinical Trials; Clinical Trials Network; Clinical assessments; Colon; Data; Data Sources; Development; Disease; Disease Outcome; Early Diagnosis; Eastern Cooperative Oncology Group; Effectiveness; Effectiveness of Interventions; Ensure; Environment; Foundations; Future; Genetic Markers; Head; Individual; Lead; Malignant neoplasm of prostate; Medicine; Methods; Modality; Modeling; Observational Study; Outcome; Patients; Population; Positioning Attribute; Prevention strategy; Procedures; Public Health; Public Health Practice; Public Health Schools; Public Policy; Randomized Clinical Trials; Recommendation; Research; Research Infrastructure; Research Personnel; Risk; Science; Screening procedure; Selection for Treatments; Statistical Methods; Subgroup; Technology; Time; Toxic effect; Treatment Effectiveness; abstracting; base; clinical decision-making; clinical practice; comparative effectiveness; compare effectiveness; cost; disease diagnosis; effectiveness research; evidence base; high risk; improved; novel; novel diagnostics; novel marker; prognostic; response; standard care; success; treatment effect; treatment response

DESCRIPTION (provided by applicant): Traditionally CER has focused primarily on the average effects across broad populations. However, the effectiveness of interventions with respect to risk and/or benefit often varies by patient subgroups. Recent advancement of science and technology has led to the discovery of many biological and genetic markers associated with disease outcomes and treatment responses. These new markers combined with traditional clinical assessments hold great potential for identifying subgroups of patients who are most likely to benefit or are at high risk for toxicity from a particular therapy and thus may lead to personalized or tailored medicine. This project will develop statistical approaches to personalized medicine in CER. The methods can be used to guide and tailor the treatment or disease screening strategies for individual patients. These methods will enhance future CER and improve the quality of public health by providing the foundation for identifying the most effective clinical options for each individual patient. The specific aims of the proposal are: 1. To develop methods for assessing treatment effects at an individual level using data from randomized clinical trials with: (a) a single outcome, and (b) multi-dimensional outcomes that quantify both risks and benefits. We will develop systematic statistical procedures to identify future patients that would benefit from a new therapy vs. for example, the standard care. 2. To develop and apply stochastic models governing the early detection of disease to colon and prostate cancers. We will develop optimal screening examination strategies as a function of age and risk status. The screening strategies will involve risk-based recommendations rather than fixed-time recommendations. We will also investigate upper age limits for ending screening. 3. To develop and evaluate the patient-level incremental value of new diagnostic and prognostic modalities. We will develop quantitative methods for assessing how the incremental value of new predictive modalities may vary across sub-populations and for identifying sub-populations that benefit the most or the least from the new modalities using data from clinical trials or observational studies. 4. To develop methods to compare the effectiveness of treatments implemented in different studies. We will also develop patient-specific treatment selection strategies in this setting. The proposal is submitted by leading researchers with complimentary but integrated expertise in CER research from the Department of Biostatistics at the Harvard School of Public Health (HSPH) which provides a well- established infrastructure and environment for methodological development in CER. The researchers also have strong ties to prominent clinical trial networks (e.g., AIDS Clinical Trials Group and the Eastern Cooperative Oncology Group) and other data sources that can be utilized to apply the developed methods, putting HSPH in a unique position to ensure the success of the proposal. PUBLIC HEALTH RELEVANCE: We will develop novel statistical methods for personalized medicine in comparative effectiveness. These methods will allow more robust evidence-based decisions in clinical practice that are tailored to individual patients based on their personal characteristics, so that the best clinical decisions are made for individual patients and the efficiency in public health practice is optimized.

描述（由申请方提供）：传统上，CER主要关注广泛人群的平均效应。然而，干预措施在风险和/或获益方面的有效性通常因患者亚组而异。最近科学和技术的进步导致发现了许多与疾病结果和治疗反应相关的生物和遗传标记。这些新的标志物与传统的临床评估相结合，具有很大的潜力，可以识别出最有可能从特定治疗中获益或具有高毒性风险的患者亚组，从而可能导致个性化或定制化药物。该项目将在CER中开发个性化医疗的统计方法。这些方法可用于指导和定制个体患者的治疗或疾病筛查策略。这些方法将通过为每个患者确定最有效的临床选择提供基础，增强未来的CER并提高公共卫生质量。该提案的具体目标是：1。使用随机临床试验的数据，开发在个体水平评估治疗效果的方法：（a）单一结局，（B）量化风险和获益的多维结局。我们将开发系统的统计程序，以确定未来的患者将受益于新的治疗与例如，标准护理。 2.开发和应用随机模型管理结肠癌和前列腺癌的早期疾病检测。我们将根据年龄和风险状况制定最佳的筛查策略。 筛查策略将涉及基于风险的建议，而不是固定时间的建议。我们还将调查结束筛查的年龄上限。 3.开发和评估新的诊断和预后模式的患者水平增量价值。我们将开发定量方法来评估新的预测模式的增量值如何在亚群中变化，并使用临床试验或观察性研究的数据来确定从新模式中受益最多或最少的亚群。 4.开发方法来比较不同研究中实施的治疗的有效性。我们还将在这种情况下制定针对患者的治疗选择策略。该提案由哈佛公共卫生学院（HSPH）生物统计学系的主要研究人员提交，这些研究人员在CER研究方面具有互补但综合的专业知识，为CER的方法开发提供了完善的基础设施和环境。研究人员还与著名的临床试验网络（例如，艾滋病临床试验组和东部肿瘤协作组）和其他数据源，可以用来应用开发的方法，使HSPH在一个独特的位置，以确保该提案的成功。 公共卫生相关性：我们将开发新的统计方法，用于比较有效性的个性化医疗。这些方法将允许在临床实践中根据个人特征为个体患者量身定制更强大的循证决策，以便为个体患者做出最佳临床决策，并优化公共卫生实践的效率。