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

> **NIH NIH U01** · CEDARS-SINAI MEDICAL CENTER · 2020 · $666,397

## Abstract

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 predictiv...

## Key facts

- **NIH application ID:** 9995440
- **Project number:** 5U01CA232859-03
- **Recipient organization:** CEDARS-SINAI MEDICAL CENTER
- **Principal Investigator:** PATRICIA A. GANZ
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $666,397
- **Award type:** 5
- **Project period:** 2018-09-19 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9995440

## Citation

> US National Institutes of Health, RePORTER application 9995440, Advancing Analysis and Interpretation ofAdverse Events and PROs in Cancer Clinical Trials (5U01CA232859-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9995440. Licensed CC0.

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