Statistical Methods for Evaluating Markers for Treatment Selection

评估治疗选择标志物的统计方法

• 批准号: 8586307
• 负责人: Holly Janes
• 金额: $ 36.7万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8586307
• 关键词: Biological; Biological Markers; Blinded; Characteristics; Clinical; Clinical Markers; Clinical Trials Design; Collaborations; Decision Aid; Decision Making; Development; Diagnosis; Disease; Ensure; Evaluation; Goals; Hormones; Individual; International; Intervention; Intervention Trial; Measurement; Measures; Medical; Methods; Outcome; Paper; Participant; Patients; Performance; Policies; Population; Prevention; Procedures; Protocols documentation; ROC Curve; Recommendation; Research; Research Design; Research Personnel; Review Literature; Selection for Treatments; Specimen; Specimen Handling; Statistical Methods; Stratification; Subgroup; Testing; Therapeutic; Writing; base; candidate marker; case control; clinical practice; cost; cost effective; design; improved; method development; new technology; novel; novel marker; outcome forecast; public health relevance; randomized trial; research study; response; sample collection; screening; treatment effect; treatment strategy

DESCRIPTION (provided by applicant): Markers for treatment selection have the potential to improve patient outcomes and decrease medical costs. When a treatment benefits only a subset of patients, a marker that identifies these subjects could be used to spare others unnecessary treatment. If a therapy is particularly harmful to certain individuals, a useful marker would identify these subjects to avoid treating them. New technologies are producing an abundance of candidate markers. However, the standards for their evaluation, which are essential for making decisions regarding marker advancement and regulatory approval, are sorely lacking. This application proposes to contribute to the development of these standards. Aim 1 ("Measures of Performance") demonstrates the inadequacy of the current approach to evaluating treatment selection markers, and develops three novel statistical measures of marker performance: (i) Marker-by-treatment predictiveness curves display the treatment effect at each marker value; ii) Treatment selection ROC curves show the accuracy with which the marker discriminates between individuals who do and do not benefit from treatment; and iii) The selection impact curve describes the population impact of using the marker to select treatment. Aim 2 ("Comparing Markers") builds on this approach to develop methods for comparing the performance of two candidate markers. Comparisons at fixed and optimized marker thresholds, as well as global summaries of marker performance, are proposed. Aim 3 ("Covariate-Specific Performance") develops an approach to evaluating how marker performance varies with factors such as patient characteristics or aspects of the marker measurement procedure. Because marker combinations are commonly sought with the hopes of improving performance, Aim 4 ("Combining Markers") develops an approach to combining multiple markers and evaluating the performance of the combination. This also leads to a method for assessing the increment in performance gained by adding a new marker to existing markers or clinical information. Aim 5 ("Study Design") considers the implications of these new methods for study design. While the ideal design is a blinded and randomized trial where the marker is measured at baseline on all participants, careful selection of a subset of trial subjects in which to measure the marker (eg a nested case-control design) may yield similar efficiency. Approaches to the design of both of these types of studies, including power calculations and recommendations regarding matching and stratification, will be developed. Methods for evaluating markers in designs that measure the marker on a subset of trial participants will also be provided. This research will be conducted in collaboration with international leaders in the fields of marker evaluation and clinical trial design and analysis. The methods will be applied to several important intervention trials where markers have been measured for treatment selection.

描述(由申请人提供)：治疗选择的标记具有改善患者结果和降低医疗成本的潜力。当一种治疗只使一部分患者受益时，识别这些受试者的标记可以用来免除其他人不必要的治疗。如果一种疗法对某些人特别有害，一个有用的标记将识别这些受试者，以避免对他们进行治疗。新技术正在产生大量的候选标记。然而，他们的评估标准，对于做出关于市场进步和监管批准的决定是必不可少的，严重缺乏。这项申请旨在为这些标准的发展做出贡献。目的1(“绩效衡量”)显示了当前评估治疗选择标记的方法的不足，并发展了三种新的标记绩效统计度量：(I)逐个治疗的标记预测性曲线显示每个标记值处的治疗效果；ii)治疗选择ROC曲线显示标记区分从治疗中受益和不从治疗中受益的个人的准确性；以及iii)选择影响曲线描述使用标记选择治疗对人群的影响。目标2(“比较标记”)建立在这种方法的基础上，以开发比较两个候选标记的性能的方法。在固定和优化的标记阈值下的比较以及标记性能的全局总结被提出。目标3(“协变量特定表现”)开发了一种方法来评估标记表现如何随诸如患者特征或标记测量过程的各个方面等因素而变化。由于人们通常希望寻找标记组合以提高性能，因此目标4(“组合标记”)开发了一种组合多个标记并评估组合性能的方法。这也导致了一种评估通过向现有标记物或临床信息添加新标记物而获得的性能增量的方法。目标5(“研究设计”)考虑了这些新方法对研究设计的影响。虽然理想的设计是盲法和随机试验，在所有参与者的基线上测量标志物，但仔细选择试验受试者的子集来测量标志物(例如嵌套病例对照设计)可能会产生类似的效率。将制定设计这两类研究的方法，包括功率计算和关于匹配和分层的建议。还将提供在试验参与者子集上测量标记的设计中评估标记的方法。这项研究将与标记物评估和临床试验设计和分析领域的国际领先者合作进行。这些方法将应用于几项重要的干预试验，在这些试验中，标记物已被测量用于治疗选择。