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: 10051404
• 负责人: Holly Janes
• 金额: $ 7.13万
• 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 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10051404
• 关键词: Address; Adjuvant Chemotherapy; Biological; Biological Markers; Characteristics; Clinical; Clinical Research; Clinical Trials Design; Cohort Studies; Collaborations; Conduct Clinical Trials; Data; Development; Early Detection Research Network; Early Diagnosis; Ensure; Epithelial ovarian cancer; Estrogen receptor positive; Ethics; Evaluation; Excision; Gene Proteins; Genetic; Goals; Health; High Risk Woman; Hysterectomy; Individual; Intervention; Logistics; Mammalian Oviducts; Measurement; Measures; Medical; Medical Care Costs; Methodology; Methods; Neoplasm Circulating Cells; Oncology; Outcome; Patients; Performance; Phase; Population; Preventive treatment; Public Health; Randomized Controlled Trials; Recommendation; Research; Research Design; Research Personnel; Sample Size; Selection for Treatments; Southwest Oncology Group; Statistical Data Interpretation; Statistical Methods; Techniques; Therapeutic; Therapeutic Effect; Time; Toxic effect; Treatment Efficacy; Uncertainty; Variant; Woman; arm; base; biomarker discovery; biomarker evaluation; biomarker identification; biomarker performance; biomarker selection; cancer biomarkers; cancer clinical trial; cancer prevention; candidate marker; cell free DNA; clinically relevant; cohort; cytokine; design; disorder prevention; health economics; improved; innovation; interest; malignant breast neoplasm; neoplastic cell; novel; novel strategies; personalized medicine; potential biomarker; predictive marker; programs; prophylactic; randomized trial; research study; standard of care; treatment comparison; trial design; tumor

Project Summary Biomarkers that predict the effects of therapeutic or preventative treatments hold great promise for im- proving health outcomes and decreasing medical costs. For example, if a treatment is thought likely to benefit only a subset of subjects, a biomarker that identifies these subjects can be used to recommend the treatment to them, and allow others to pursue alternatives. Potential treatment selection biomarkers, also called predictive or prescriptive biomarkers, are being produced in abundance due to technological advancements and a heightened interest in “personalized medicine”, and yet a comprehensive statistical framework for study design and analysis of biomarker performance is lacking. We propose to build on a successful research program to develop novel statistical methods for marker identification and evaluation, with the ultimate goal of advancing such a framework. Aim 1 develops novel statistical analysis methods for discovering and evaluating markers for guiding treatment. The ideal setting for marker evaluation is a randomized and controlled trial. For this setting we will develop methods that address challenges not accommodated by existing methodology. For early-phase marker studies, which are typically not random- ized trials, methods for evaluating a biomarker's potential performance do not yet exist and our research will fill this gap. Methods will also be developed that incorporate medical cost data into the evaluation of a biomarker. Aim 2 will develop novel study designs for discovering and evaluating markers for guiding treatment. A basic first step in study design is identifying what is the desired performance of the biomarker, and we will develop techniques for this. Study designs will then be developed to assess whether a marker achieves this standard. Early-phase studies– either cohort studies or single arm trials– will be sized to evaluate a marker's potential performance. Novel randomized trial designs will be developed to definitively assess marker performance and will require smaller sample sizes than existing designs. A sequence of studies will be put forth for developing a biomarker, from early-phase studies of potential performance to late-phase studies of actual performance. The research will be conducted by an inter-disciplinary team of investigators with extensive expertise in biomarker evaluation, clinical trial design and analysis, health economics, and clinical research. Collaborations with cooperative groups such as the Early Detection Re- search Network, focused on biomarker discovery and evaluation, and SWOG, focused on clinical trials of cancer prevention and therapeutic strategies, will ensure that the research has immediate application and impact. Studies to which the methods will be applied include one that aims to identify women at high risk for epithelial ovarian cancer who can be recommended prophylactic removal of the fallopian tubes at the time of hysterectomy; and another that seeks to identify women with estrogen-receptor-positive breast cancer who are unlikely to benefit from adjuvant chemotherapy.

项目摘要 预测治疗性或预防性治疗效果的生物标志物为免疫治疗带来了巨大的希望。 改善健康状况，降低医疗成本。例如，如果一种治疗被认为可能 贝内仅使一部分受试者受益，则可以使用识别这些受试者的生物标志物来推荐 他们的治疗，并允许其他人寻求替代品。潜在的治疗选择生物标志物， 也被称为预测性或规定性生物标志物，由于技术进步， 进步和对“个性化医疗”的兴趣提高，但一个全面的统计 缺乏生物标志物性能的研究设计和分析框架。我们建议建立在一个 成功的研究计划，以开发新的统计方法， 最终目标是推进这样一个框架。Aim 1开发了新的统计分析方法 用于发现和评估用于指导治疗的标记。标记评估的理想设置是 一项随机对照试验在这种情况下，我们将开发解决挑战的方法， 采用现有的方法。对于早期标志物研究，这通常不是随机的- 目前尚不存在评估生物标志物潜在性能的方法，我们的研究 将填补这一空白。还将开发将医疗成本数据纳入评估的方法， 一种生物标志物目标2将开发新的研究设计，用于发现和评估用于指导 治疗研究设计的基本第一步是确定生物标志物的理想性能， 我们将为此开发技术。然后将制定研究设计，以评估标记物是否 达到这个标准。早期研究（队列研究或单组试验）的规模将 评估一个标记的潜在性能。将开发新的随机试验设计， 评估标记性能，需要的样本量小于现有设计。的序列 将提出研究开发生物标志物，从早期的潜在性能研究， 后期的实际性能研究。这项研究将由一个跨学科小组进行 研究者在生物标志物评价、临床试验设计和分析、健康 经济学和临床研究。与早期发现重新评估等合作团体的合作 搜索网络，专注于生物标志物的发现和评估，SWOG，专注于临床试验， 癌症预防和治疗战略，将确保研究立即应用， 冲击将应用这些方法的研究包括一项旨在确定高危妇女的研究。 卵巢上皮性癌患者可以建议在预防性切除输卵管的时候 另一项研究旨在确定雌激素受体阳性乳腺癌妇女 不太可能从辅助化疗中贝内。