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

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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.