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Activity Number: 483
Type: Contributed
Date/Time: Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #308465
Title: Joint Modeling of Time-to-Event Data and Multiple Ratings of a Discrete Diagnostic Test Without Gold Standard
Author(s): Seunghyun Won*+ and Gong Tang and Ruosha Li
Companies: University of Pittsburgh and University of Pittsburgh and University of Pittsburgh
Keywords: Discrete diagnostic test ; Misclassification ; EM algorithm ; Joint modeling ; Latent class model ; Survey-weighted Cox model
Abstract:

Histologic tumor grade is a strong predictor of risk of recurrence in breast cancer. However, tumor grade readings by pathologists are susceptible to intra- and inter-observer variability due to its subjective nature. For this limitation, tumor grade is not included in the breast cancer staging system. Latent class models are considered for analysis of such discrete diagnostic tests with the underlying truth as a latent variable. However, the model parameters are only locally identifiable that any permutation on the categories of the truth also leads to the same likelihood function. In many circumstances, the underlying truth is known associated with risk of certain event in a trend. Here we propose a joint model with a Cox proportional hazard model for the time-to-event data where the underlying truth is a latent predictor. The joint model not only fully identifies all model parameters but also provide valid assessment of the association between the diagnostic test and the risk of event. The proposed method is illustrated in the analysis of data from a breast cancer clinical trial and simulation studies.


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