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: 10477392
• 负责人: PATRICIA A. GANZ
• 金额: $ 65.03万
• 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 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10477392
• 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.

在此应用程序和研究计划中，我们将与NRG肿瘤学统计中心合作 制定分析策略来研究评估治疗耐受性的新方法，并建模 使用随机NSABP试验的数据，用于数据显示的新方法，这些方法包含两个常见 不良事件的术语标准（CTCAE）数据和高质量的患者报告的结果（PRO）数据。 随后，我们将将这些新的分析方法和其他方法应用于NRG肿瘤学阶段 临床试验，包括促进症的项目，以评估与免疫疗法相关的治疗毒性。 在这一最新一代的免疫疗法试验中纳入亲CTCAE项目尤为重要，因为 来自早期免疫疗法研究的PRO数据有限，耐受性可能是一个关键问题 辅助治疗或早期转移性疾病环境中的患者是患者的患者 试验。我们以前制定了一项摘要措施，即毒性指数（TI），以根据基于患者进行区分 他们的整体毒性经历。根据根据AE分级AE的每个受试者总结毒性数据 ctcae。 TI占所有观察到的毒性等级，而不是最严重的毒性等级 完毕。由于其对整体毒性差异的敏感性，TI可能也对识别也很有用 预测与治疗相关的毒性的指标。除了本文所述的其他新方法外，我们还将采用 IT的TI和扩展或改进，以支持Pro和相关不利的新方法和改进的方法 事件数据。通过TI方法，此RFA中解决的问题非常适合部分解决方案。我们 还建议与肿瘤学家，专家专家和患者倡导者合作修改它，以解决 AES的持续时间和频率以及Pro-CTCAE数据的其他特殊需求。虽然我们将集中精力 在开发新的技术统计方法时，我们将成为专家，肿瘤学家，数据的团队 科学家和临床试验专家，以保持发展中以患者为中心和临床试验的发展相关 观点。此应用程序的具体目的是：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，以确定缺少数据以确定的风险因素 aes; （c）将我们的新方法与现有方法进行比较，例如最高级/最大时间，TAME和TOXT； （d）改进，扩展并将Ti应用于Pro-CTCAE，以建模CTCAE数据。目标2：开发预测模型 限制剂量毒性，治疗完成和基于个体患者特征的功效 Ti和Pro-Ti定义的毒性曲线。 （a）为完成和效力作为时间的预测模型 事件成果； （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Web应用程序以实施开发的方法； （d）使用多学科专家和患者倡导者审查和指导开发的方法。