Advancing Analysis and Interpretation of Adverse 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

基本信息

批准号：
10884827
负责人：
PATRICIA A. GANZ
金额：
$ 35万
依托单位：
CEDARS-SINAI MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-07-08 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10884827
关键词：
Adverse event Advocate Benchmarking CYP2D6 gene Clinical Clinical Trials Collaborations Common Terminology Criteria for Adverse Events Data Data Collection Decision Making Evaluation Focus Groups Funding Future Genotype Health Professional Immunotherapy Individual Integration Host Factors Measurement Measures Methods Modeling National Surgical Adjuvant Breast and Bowel Project Oncology Outcome Patient Outcomes Assessments Patients Performance Phase III Clinical Trials Plasma Postmenopause Prediction of Response to Therapy Qualitative Evaluations Randomized Reporting Research Sampling Symptoms Tamoxifen Testing Time Toxic effect Treatment-related toxicity Update Visualization Voice Woman Work cancer clinical trial cancer therapy experience hormone therapy indexing malignant breast neoplasm multidisciplinary novel novel strategies predictive modeling programs recruit risk prediction model statistical center tool treatment trial

项目摘要

ABSTRACT In this application and program of research, we will collaborate with the NRG Oncology Statistical Center to develop analytic and graphical 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. We have applied these new analytic approaches and other methods to NSABP and NRG Oncology phase III clinical trials that include PRO-CTCAE items to assess treatment toxicity associated with immunotherapy and other treatments. During the first four years of the funding period, we developed a novel summary measure, the toxicity index (TI), to discriminate patients based on their overall toxicity experiences as assessed by AE grades according to CTCAE and PRO-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, we showed that the TI can identify predictors of treatment-related toxicity better than conventional summary scores such as max grade and average grade. In this program of research, we plan to (1) apply our methods for evaluation of endocrine therapy toxicity by use of PRO data to associations between CYP2D6 genotype and tamoxifen discontinuation in the NSABP P-1 clinical trial using plasma samples for CYP2D6 genotyping that are expected within the next six months, (2) continue our analysis of the feasibility of frequent assessment of ePRO data test the added value of weekly measurements of ePROs relative to data collection of ePROs every cycle using clinician’s CTCAE assessment as a benchmark, (3) further evaluate and disseminate the Breast Cancer Symptom Explorer visualization online tool by updating the content, functionality, technical features by returning to original focus groups and recruiting additional focus groups for further qualitative evaluation, (4) continue our analysis of symptom trajectories among postmenopausal women by exploring patient host factors associated with membership in the individual trajectories and how these impact treatment discontinuation and other outcomes, (5) continue our work on developing and building dynamic risk prediction models for treatment discontinuation and efficacy using longitudinal PROs, clinician’s assessed CTCAE, and baseline clinical and demographic data from the NSABP B-35 phase III clinical trial. We will test the added value of including longitudinal clinician’s assessed CTCAE data in addition to longitudinal PROs in the predictive performance of the dynamic model, assess and compare the predictive performance when CTCAE are summarized with our novel TI relative to average grade, and max grade, and build real-time calculators based on these new predictive models in Shiny app to aid healthcare professionals in decision making.

抽象的在此应用和研究计划中，我们将与 NRG 肿瘤统计中心合作开发分析和图形策略来研究评估治疗耐受性的新方法，例如以及使用随机 NSABP 试验的数据建模新的数据呈现方法，其中包含不良事件通用术语标准 (CTCAE) 数据和高质量患者报告我们已将这些新的分析方法和其他方法应用于 NSABP 和 NRG。肿瘤学 III 期临床试验，包括 PRO-CTCAE 项目，用于评估与在资助期的前四年，我们开发了一种新型疗法。总结性测量，毒性指数（TI），根据患者的总体毒性经历来区分患者根据 CTCAE 和 PRO-CTCAE 的 AE 等级评估，考虑了所有观察到的毒性等级。而不仅仅是最严重的一种，因为它对差异的敏感性。总体毒性，我们表明 TI 可以比传统方法更好地识别治疗相关毒性的预测因子总结分数，例如最高成绩和平均成绩在本研究计划中，我们计划 (1) 应用我们的方法。通过使用 PRO 数据与 CYP2D6 之间的关联来评估内分泌治疗毒性的方法使用 CYP2D6 血浆样本的 NSABP P-1 临床试验中的基因型和他莫昔芬停药预计在未来六个月内进行基因分型，（2）继续分析频繁进行基因分型的可行性 ePRO 数据评估测试每周测量 ePRO 相对于数据收集的附加值 ePRO 每个周期都以临床医生的 CTCAE 评估为基准，(3) 进一步评估和传播通过更新内容、功能、技术，乳腺癌症状浏览器可视化在线工具通过返回原来的焦点小组并招募额外的焦点小组来进一步定性评估，（4）继续分析绝经后妇女的症状轨迹，探索与个人轨迹中的成员资格相关的患者宿主因素以及这些因素如何影响治疗终止和其他结果，(5) 继续我们开发和建立动态风险预测的工作使用纵向 PRO、临床医生评估的 CTCAE 和疗效模型我们将测试来自 NSABP B-35 III 期临床试验的基线临床和人口统计数据。除了纵向 PRO 之外，还包括纵向临床医生评估的 CTCAE 数据动态模型的性能，评估和比较 CTCAE 时的预测性能用我们新颖的 TI 相对于平均成绩和最高成绩进行总结，并基于此构建实时计算器 Shiny 应用程序中的这些新预测模型可帮助医疗保健专业人员做出决策。

项目成果

期刊论文数量（2）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Applying the Toxicity Index to Patient-Reported Symptom Data: An Example Using the European Organization for Research and Treatment of Cancer Colorectal Cancer-Specific Quality of Life Questionnaire.

将毒性指数应用于患者报告的症状数据：使用欧洲癌症研究和治疗组织结直肠癌特定生活质量调查问卷的示例。

DOI：
10.1016/j.clinthera.2021.05.011
发表时间：
2021
期刊：
Clinical therapeutics
影响因子：
3.2
作者：
Hays,RonD;Ganz,PatriciaA;Spritzer,KarenL;Rogatko,André
通讯作者：
Rogatko,André

Evaluating the Association of Adverse Events and Patient-Reported Symptoms to Endocrine Therapy Tolerability.

评估不良事件和患者报告的症状与内分泌治疗耐受性的关联。

DOI：
10.1200/jco.21.02387
发表时间：
2022
期刊：
Journal of clinical oncology : official journal of the American Society of Clinical Oncology
影响因子：
0
作者：
Henry,NLynn;Rogatko,André;Ganz,PatriciaA
通讯作者：
Ganz,PatriciaA