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

在这项研究申请和计划中，我们将与NRG肿瘤统计中心合作， 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试验的数据展示数据的新方法， 不良事件术语标准（CTCAE）数据和高质量患者报告结局（PRO）数据。 随后，我们将把这些新的分析方法和其他方法应用于NRG肿瘤III期 包括PRO-CTCAE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纳入PRO-CTCAE项目尤为重要，因为 早期免疫治疗研究的PRO数据有限，耐受性可能是 辅助治疗或早期转移性疾病背景中的患者，即这些患者人群 审判我们以前开发了一个概括性的衡量标准，毒性指数（TI），根据患者的 他们的整体毒性体验。根据AE分级总结每例受试者的毒性数据， CTCAE。TI解释了所有观察到的毒性等级，而不是传统上仅解释最严重的毒性等级 完了由于其对总体毒性差异的敏感性，TI也可能用于识别 治疗相关毒性的预测因子。除了本文所述的其他新方法之外，我们将采用 TI及其扩展或改进，以支持PRO和相关不良反应的新方法和改进方法 事件数据。本RFA中解决的问题非常适合采用TI方法部分解决。我们 我还建议与肿瘤学家，PRO专家和患者倡导者合作修改它，以解决 AE的持续时间和频率，以及PRO-CTCAE数据的其他特殊需求。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PRO专家，肿瘤学家，数据 科学家和临床试验专家，以保持以患者为中心和临床试验相关的发展基础 视角本申请的具体目标是：目标1：应用和扩展TI和其他方法， 描述毒性并开发模型以确定AE的风险因素。(a)开发新的图形方法， （B）开发新的纵向模型，说明缺失数据，以确定 （c）将我们的新方法与现有方法（如最大等级/最大时间，驯服和ToxT）进行比较; (d)将TI细化、扩展并应用于PRO-CTCAE，以对CTCAE数据建模。目标2：建立预测模型 基于个体患者特征限制剂量毒性、治疗完成和疗效， 由TI和PRO-TI定义的毒性特征。(a)开发完成和有效性的预测模型， 事件结局;（B）使用基于以下的各种耐受性定义，开发最佳剂量的预测模型： （c）开发和传播网络应用程序，以实施所开发的方法; (d)使用多学科专家和患者倡导者来审查和指导所开发的方法。

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.