Advancing Analysis and Interpretation ofAdverse Events and PROs in Cancer Clinical Trials

在这个应用和研究计划中，我们将与NRG肿瘤学统计中心合作 制定分析策略，研究评估治疗耐受性的新方法，并建立模型 使用来自随机NSABP试验的数据呈现数据的新方法 不良事件(CTCAE)数据和高质量患者报告结果(PRO)数据的术语标准。 随后，我们将把这些新的分析方法和其他方法应用到NRG肿瘤学第三阶段 包括支持CTCAE的项目的临床试验，以评估与免疫治疗相关的治疗毒性。 在最新一代免疫治疗试验中包括支持CTCAE的项目尤其重要，因为 来自早期免疫治疗研究的PRO数据有限，耐受性可能是一个关键问题 在辅助治疗或早期转移性疾病环境中的患者，即这些患者的人群 审判。我们之前开发了一种综合衡量标准，毒性指数(TI)，根据以下因素区分患者 他们的整体中毒经历。根据分级的AE对每个受试者的毒性数据进行汇总 CTCAE.有毒物质包括所有观察到的毒性等级，而不是传统意义上的最严重的毒性等级。 搞定了。由于其对总体毒性差异的敏感性，TI很可能也可用于识别 治疗相关毒性的预测因子。除了这里描述的其他新方法外，我们还将使用 TI及其扩展或改进，以支持PRO和相关不良反应的新的和改进的方法 事件数据。本RFA中解决的问题非常适合用TI方法进行部分解决。我们 还建议与肿瘤学家、专业专家和患者权益倡导者合作对其进行修改，以解决 AEs的持续时间和频率，以及其他专业CTCAE数据的特殊需要。虽然我们会集中精力 在开发新的技术统计方法方面，我们将作为一个由专业专家、肿瘤学家、数据 科学家和临床试验专家将以患者为中心和临床试验相关的发展为基础 透视。本申请的具体目标是：目标1：将TI和其他方法应用和扩展到 描述毒性并开发模型以确定不良反应的风险因素。(A)开发新的图形方法以 描述毒性；(B)开发新的纵向模型，说明丢失的数据，以确定风险因素 (C)将我们的新方法与现有的方法如最大等级/最大时间、TAME和ToxT进行比较； (D)提炼、扩展并将TI应用于PRO-CTCAE以模拟CTCAE数据。目标2：开发预测模型 根据个别患者的特点和情况限制剂量毒性、治疗完成率和疗效 由TI和PRO-TI定义的毒性分布。(A)制定完成时间和效力的预测性模型 事件结果；(B)使用基于以下因素的各种耐受性定义，开发最佳剂量的预测模型 CTCAE和支持CTCAE；(C)开发和传播网络应用程序，以实施所制定的方法； (D)利用多学科专家和患者权益倡导者审查和指导制定的方法。

In this application and program of research, we will collaborate with the NRG Oncology Statistical Center to develop analytic strategies to investigate novel methods for assessing treatment tolerability, as well as to model new approaches for data presentation using data from randomized NSABP trials that contain both Common Terminology Criteria for Adverse Events (CTCAE) data and high quality patient reported outcomes (PRO) data. Subsequently, we will apply these new analytic approaches and other methods to NRG Oncology phase III clinical trials that include PRO-CTCAE items to assess treatment toxicity associated with immunotherapy. Inclusion of PRO-CTCAE items in this newest generation of immunotherapy trials is particularly important, as there are limited PRO data from early phase immunotherapy studies, and tolerability may be a critical issue for patients in the adjuvant therapy or early metastatic disease settings that are the patient populations in these trials. We previously developed a summary measure, the toxicity index (TI), to discriminate patients based on their overall toxicity experiences. Toxicity data are summarized for each subject from graded AE according to CTCAE. TI accounts for all observed toxicity grades rather than only the most severe one, as is conventionally done. Because of its sensitivity to differences in the overall toxicity, the TI is likely to be useful also for identifying predictors of treatment-related toxicity. In addition to the other novel methods described herein, we will employ the TI and extensions or refinements of it to support new and improved methods for PRO and related adverse event data. The problems addressed in this RFA are very amenable to partial solution by the TI approach. We also propose to modify it in collaboration with oncologists, PRO experts and patient advocates to address the duration and frequency of AEs, and other special needs of PRO-CTCAE data. While we will focus much effort on developing new technical statistical methods, we will work as a team of PRO experts, oncologists, data scientists, and clinical trial experts to keep the developments grounded in patient-centric and clinical trial relevant perspectives. The specific aims of this application are: Aim 1: To apply and extend TI and other methods to describe toxicity and develop models to determine risk factors for AEs. (a) Develop new graphical methods to describe toxicity; (b) Develop new longitudinal models accounting for missing data to determine risk factors for AEs; (c) Compare our new methods with existing approaches such as max-grade/max-time, TAME, and ToxT; (d) Refine, extend, and apply the TI to PRO-CTCAE to model CTCAE data. Aim 2: To develop predictive models for limiting dose toxicity, treatment completion, and efficacy based on individual patient characteristics and toxicity profiles defined by TI and PRO-TI. (a) Develop predictive models for completion and efficacy as time to event outcomes; (b) Develop predictive models for optimal dose using various definitions of tolerability based on CTCAE and PRO-CTCAE; (c) Develop and disseminate web applications to implement the methods developed; (d) Use multi-disciplinary experts and patient advocates to review and guide the methods developed.