Online Program Home
My Program

Abstract Details

Activity Number: 296 - SPEED: Biometrics - Methods and Application, Part 1
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #304680 Presentation
Title: Item Response Theory Models for Survival Analysis and the Detection of Treatment Efficacy
Author(s): Charlie Iaconangelo*
Companies: Pharmerit International
Keywords: item response theory; survival analysis; randomized clinical trial; psychometrics; missing data ; quality of life

Randomized clinical trials frequently incorporate quality of life (QoL) measures in survival trials to evaluate whether patient QoL is maintained throughout the additional survival time. Patient QoL data is typically modeled using latent variable models from psychometrics, which treat responses to QoL items as observed manifestations of a latent construct. Limited research has been done on longitudinal item response theory (IRT) models in survival analysis context (i.e., when subject drop-out is assumed to occur under the MNAR mechanism). This research builds on existing methods referred to as IRT trees and/or partially-ordered sets. The work introduces a uniquely flexible IRT discrete hazard function, which allows for the simultaneous estimation of item response and MNAR drop-out. This approach reduces bias in the estimated parameters relating the latent variable to the treatment arm predictor, improving inference of separation of treatment arms on patient QoL in a survival analysis trial.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2019 program