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Abstract Details

Activity Number: 663
Type: Contributed
Date/Time: Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
Sponsor: Biopharmaceutical Section
Abstract - #306793
Title: The Effect of Skip Patterns on the Validity and Reliability of Selected Items from the Patient-Reported Outcomes Version of the Common Terminology Criteria for Adverse Events
Author(s): Tito R Mendoza*+ and Amylou C Dueck and Sandra A. Mitchell and Bryce B Reeve and Yuelin Li and Thomas M Atkinson and Antonia V Bennett and Steven B Clauser and Ethan Basch
Companies: MD Anderson Cancer Center and Mayo Clinic and National Cancer Institute and The University of North Carolina at Chapel Hill and Memorial Sloan-Kettering Cancer Center and Memorial Sloan-Kettering Cancer Center and Memorial Sloan-Kettering Cancer Center and National Cancer Institute and Memorial Sloan-Kettering Cancer Center
Address: , Houston, TX, 77030,
Keywords: patient-reported outcomes ; symptoms ; cancer clinical trials ; psychometrics ; intraclass correlations
Abstract:

Symptom adverse events in cancer clinical trials are reported by clinicians using the National Cancer Institute's (NCI) Common Terminology Criteria for Adverse Events (CTCAE). NCI contracted to create a patient-reported outcomes companion tool (PRO-CTCAE) comprising of 124 items reflecting up to 5 attributes for 78 symptoms. The validation study for the PRO-CTCAE has revealed numerous challenges in establishing item validity/reliability. The PRO-CTCAE items were designed with deliberate skips via a web-based interface to minimize patient burden. However, if skipped items are coded as zero, the effects of these skip patterns on the validity and reliability of patients' symptom ratings is largely unknown. In this paper, we compute intraclass correlations for reliability and perform t-tests for known-group validity on items with and without skip patterns. Differences are evident in the ICCs, t-tests and effect sizes. Results are compared and implications for validating future PROs with skip patterns are discussed. Future directions are discussed, particularly in treating skipped items as missing data rather than zeros as a workaround in a Bayesian Item Response Model.


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