Advancing Analysis and Interpretation of Adverse Events and PROs in Cancer Clinical Trials

摘要 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 开发分析和图表策略，研究评估治疗耐受性的新方法，如 以及使用来自随机NSABP试验的数据来为数据呈现的新方法建模，该试验包括 不良事件(CTCAE)数据和高质量患者报告的通用术语标准 结果(PRO)数据。我们已经将这些新的分析方法和其他方法应用于NSABP和NRG 肿瘤学第三阶段临床试验，包括CTCAE前项目，以评估与以下疾病相关的治疗毒性 免疫疗法和其他治疗方法。在资助期的前四年，我们开发了一部小说 毒性指数(TI)，根据患者的总体毒性经历来区分患者，如下所示 根据CTCAE和PRO-CTCAE进行AE分级。钛占所有观察到的毒性等级 而不是像通常所做的那样只是最严重的一次。因为它对不同的 总体而言，我们表明，与传统的毒性相比，TI能更好地识别与治疗相关的毒性的预测因素 总分，如最高分和平均分。在这个研究项目中，我们计划(1)应用我们的 使用PRO数据评估内分泌治疗毒性的方法 使用血浆样本检测CYP2D6的NSABP P-1临床试验中的基因型和三苯氧胺停用 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 对ePRO数据的评估检验了ePRO每周测量相对于数据收集的附加值 以临床医生的CTCAE评估为基准的每个周期的ePROS，(3)进一步评估和传播 乳腺癌症状资源管理器可视化在线工具通过更新内容、功能、技术 通过返回到原来的焦点小组并招募更多的焦点小组来进一步定性 评估，(4)继续我们对绝经后妇女症状轨迹的分析 与个体轨迹中的成员相关的患者宿主因素以及这些因素如何影响治疗 (5)继续开展开发和建立动态风险预测的工作 使用纵向PROS、临床医生评估的CTCAE和 NSABP B-35第三阶段临床试验的基线临床和人口学数据。我们将测试附加值 包括纵向临床医生评估的CTCAE数据，以及预测中的纵向PRO 动态模型的性能，评估和比较CTCAE在 总结了我们的新型TI相对于平均档次和最高档次，并构建了基于 关于这些闪亮应用程序中的新预测模型，以帮助医疗保健专业人员做出决策。

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.