Analysis of Progression-Free Survival Data Using a Discrete Time Survival Model that Incorporates Measurements with and Without Diagnostic Error

• Activity Number: 314
• Type: Contributed
• Date/Time: Tuesday, August 2, 2011 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Sally Hunsberger*+ and Albert Paul and Lori Dodd
• Companies: National Cancer Institute and Eunice Kennedy Shriver National Institute of Child Health and Human Development and National Institute of Allergy and Infectious Diseases
• Keywords: Gaussian random effects ; Sensitivity ; Specificity ; Conditional Independence

Abstract

In cancer studies Progression Free Survival is an endpoint that is becoming very important in the development of new theraputic agents. Two methods of determining Progression are typically used: 1) the local radiologist evaluates scans and 2) scans are reviewed by and independent (central) reviewer. The second method is considered to be a gold standard (GS) but is expensive, time consuming and logistically difficult. The first method has measurement error associated with it but is less expensive and easier to obtain.

When PFS data using the test with measurement error are analyzed, inferences about covariate effects may be invalid. A sampling strategy is evaluated where data are collected on a subset of subjects using the GS test and on all subjects using the test that has error. The strategy is designed to maintain valid inferences while requiring the more expensive or difficult test on a small proportion of patients. We explore the effect of different diagnostic test properties on inference via simulation and use the methodology to analyze a renal cancer example.