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Completed NON-SBIR/STTR RPGS NIH (US)

Analysis of ECOG-ACRIN adverse event data to optimize strategies for the longitudinal assessment of tolerability in the context of evolving cancer treatment paradigms (EVOLV)

$3.5M USD

Funder NATIONAL CANCER INSTITUTE
Recipient Organization Dana-Farber Cancer Inst
Country United States
Start Date Jul 10, 2023
End Date Aug 31, 2024
Duration 418 days
Number of Grantees 2
Roles Principal Investigator; Co-Investigator
Data Source NIH (US)
Grant ID 10884567
Grant Description

Project Summary EVOLV proposes to deliver sophisticated and standardized methods for assessing, monitoring, analyzing, and reporting adverse events (AEs) experienced by individuals undergoing cancer treatment. These methods will harness the potential of the patient-reported outcomes version of the NCI Common Terminology Criteria for

Adverse Events (PRO-CTCAETM) to provide previously unavailable patient perspectives on the tolerability of treatments (including targeted agents, immunotherapies, and other evolving treatments for which the type, severity, timing, and trajectory of adverse events is less known). Such information will help providers better

identify and support patients at risk for treatment discontinuation, dose reductions, and treatment delays. Specifically, this study aims to: 1) perform longitudinal analyses of CTCAE and PRO-CTCAE data from trials conducted within the ECOG-ACRIN Cancer Research Group, using traditional and innovative strategies to

examine AE trajectories and to produce a new reporting standard that reflects severity and fluctuations over time; 2) examine PRO-CTCAE and CTCAE predictors of treatment adherence and discontinuation; and 3) validate the broader predictive value of GP5, a single item from the Functional Assessment of Cancer Therapy-

General (FACT-G) shown to predict early treatment discontinuation among women with breast cancer taking aromatase inhibitors. The study will also explore two novel measurement models for PRO-CTCAE scores and CTCAE grades: a phenotypic model including co-occurrence of symptoms and a cumulative burden index

(CBI) for characterizing the quantity of burden accumulated by patients over time. Analyses will include demographic factors and insurance status to identify potential disparities.

All Grantees

Dana-Farber Cancer Inst

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