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

推进癌症临床试验中不良事件和 PRO 的分析和解释

• 批准号: 10241463
• 负责人: PATRICIA A. GANZ
• 金额: $ 66.64万
• 依托单位: CEDARS-SINAI MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-19 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10241463
• 关键词: Accounting; Address; Adjuvant Therapy; Adverse event; Advocate; Aftercare; Alkaline Phosphatase; Bilirubin; Characteristics; Chemoprevention; Clinical Investigator; Clinical Trials; Clinical assessments; Collaborations; Common Terminology Criteria for Adverse Events; Communities; Data; Data Analyses; Data Collection; Data Reporting; Data Scientist; Development; Disease; Dose; Dose-Limiting; Evaluation; Event; Frequencies; Funding; Future; Generations; Human Resources; Immunotherapy; Measures; Methods; Modeling; Morbidity - disease rate; National Surgical Adjuvant Breast and Bowel Project; Oncologist; Oncology; Outcome; Patient Outcomes Assessments; Patients; Phase; Phase III Clinical Trials; Psychometrics; Publishing; Randomized; Reporting; Research; Research Personnel; Resources; Risk Factors; Serum; Standardization; Statistical Methods; Therapy trial; Time; Toxic effect; Treatment-related toxicity; Voice; Work; base; cancer clinical trial; cancer site; cancer therapy; data management; docetaxel; experience; health related quality of life; immunotherapy trials; improved; indexing; individual patient; instrument; irinotecan; multidisciplinary; novel; novel strategies; patient population; predictive modeling; programs; secondary outcome; statistical center; treatment site; treatment trial; web app

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.

在此应用和研究计划中，我们将与 NRG 肿瘤统计中心合作 制定分析策略来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 试验的数据进行数据呈现的新方法，其中包含常见的 不良事件术语标准 (CTCAE) 数据和高质量患者报告结果 (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 Oncology III 期 包括 PRO-CTCAE 项目的临床试验，用于评估与免疫疗法相关的治疗毒性。 将 PRO-CTCAE 项目纳入最新一代的免疫治疗试验尤为重要，因为 早期免疫治疗研究的 PRO 数据有限，耐受性可能是一个关键问题 处于辅助治疗或早期转移性疾病环境中的患者，即这些患者群体 试验。我们之前开发了一种总结性测量方法，即毒性指数 (TI)，用于根据以下因素来区分患者： 他们的总体毒性经历。根据 AE 分级对每个受试者的毒性数据进行汇总 CTCAE。 TI 考虑了所有观察到的毒性等级，而不是像传统那样仅考虑最严重的毒性等级 完毕。由于其对总体毒性差异的敏感性，TI 也可能有助于识别 治疗相关毒性的预测因子。除了本文描述的其他新颖方法之外，我们还将采用 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通过 TI 方法部分解决。我们 还建议与肿瘤学家、PRO 专家和患者权益倡导者合作对其进行修改，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、数据专家组成的团队进行工作 科学家和临床试验专家保持以患者为中心和临床试验相关的发展基础 观点。本应用的具体目标是： 目标 1：应用和扩展 TI 和其他方法 描述毒性并开发模型来确定 AE 的危险因素。 (a) 开发新的图形方法 描述毒性； (b) 开发新的纵向模型来解释缺失数据，以确定风险因素 不良事件； (c) 将我们的新方法与现有方法（例如 max-grade/max-time、TAME 和 ToxT）进行比较； (d) 细化、扩展和应用 TI 到 PRO-CTCAE 以对 CTCAE 数据进行建模。目标 2：开发预测模型 根据患者个体特征和疗效来限制剂量毒性、治疗完成度和疗效 TI 和 PRO-TI 定义的毒性特征。 (a) 开发预测模型，以了解完成时间和功效 事件结果； (b) 使用基于耐受性的各种定义开发最佳剂量的预测模型 CTCAE 和 PRO-CTCAE； (c) 开发和传播网络应用程序以实施所开发的方法； (d) 利用多学科专家和患者权益倡导者来审查和指导所制定的方法。