Statistical Methods for Evaluating Markers for Treatment Selection

描述（由申请人提供）：治疗选择的标记具有改善患者预后和降低医疗费用的潜力。当一种治疗方法只对一小部分患者有益时，识别这些受试者的标记物可以用来避免其他不必要的治疗。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，对市场开发和监管审批决策至关重要的评估标准却严重缺乏。本应用程序建议为这些标准的发展做出贡献。目标1（“绩效指标”）证明了目前评估治疗选择指标的方法的不足，并开发了三种新的指标绩效统计指标：(i)按治疗指标预测曲线显示每个标记值的治疗效果；ii)治疗选择ROC曲线显示标记区分从治疗中获益和未从治疗中获益的个体的准确性；iii)选择影响曲线描述了使用标记选择治疗的总体影响。目标2（“比较标记”）以这种方法为基础，开发比较两个候选标记的性能的方法。提出了固定和优化标记阈值的比较，以及标记性能的全局摘要。目标3（“协变量特异性表现”）开发了一种评估标志物表现如何随患者特征或标志物测量程序方面等因素而变化的方法。由于寻求标记组合通常是为了提高性能，Aim 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。目的5（“研究设计”）考虑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记物，但仔细选择一组试验受试者来测量标记物（例如嵌套病例对照设计）可能产生类似的效率。将拟订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的子集上测量标记的设计中评估标记的方法。这项研究将与市场评估、临床试验设计和分析领域的国际领先企业合作进行。这些方法将应用于几个重要的干预试验，其中已经测量了治疗选择的标记物。

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. PUBLIC HEALTH RELEVANCE: Interventions for disease treatment and prevention can potentially be made more cost-effective by using markers to identify in advance the individuals most likely to benefit from the treatment, and thus avoid treating those unlikely to benefit. This proposal will develop methods to help realize this potential, by developing standards for evaluating candidate markers. These standards will help distinguish the good markers from the bad, optimize how the markers are used to select treatment, and ensure that research studies are designed so that the markers can be properly evaluated.