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Joint modeling of time-to-event data and multiple ratings of a discrete diagnostic test without a gold standard

Won, Seung Hyun (2014) Joint modeling of time-to-event data and multiple ratings of a discrete diagnostic test without a gold standard. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Histologic tumor grade is a strong predictor of risk of recurrence in breast cancer. Nevertheless, tumor grade readings by pathologists are susceptible to intra- and inter-observer variability due to its subject nature. Because of this limitation, histologic tumor grade is not included in the breast cancer stating system. Latent class models have been considered for analysis of such discrete diagnostic tests with regarding the underlying truth as a latent variable. However, the model parameters in latent class models are only locally identifiable, that is, any permutation on the categories of the underlying truth can lead to the same likelihood value.

In many clinical practices, the underlying truth is known associated with the risk of a certain event in a trend. Here, we proposed a joint model with a Cox proportional hazards model for time-to-event data where the underlying truth is a latent predictor and a latent class model for multiple ratings of a discrete diagnostic test without a gold standard. With the known association between the underlying truth and the risk of an event in a trend, the proposed joint model not only fully identifies all model parameters but also provides valid assessment of the association between the diagnostic test result and the risk of an event.

The modified EM algorithm was used for estimation with employing the survey-weighted Cox model in the M-step. To test whether the known trend imposed on model parameters can be assumed, we applied the Union-Intersection principle for the proposed joint model. The proposed method is illustrated in the analysis of data from the National Surgical Adjuvant Breast and Bowel Project (NSABP) B-14 sub-study and through simulation studies.

The proposed method is relevant to public health fields, such as chronic diseases and psychiatry, where some components of the initial diagnostics are subjective but have important implications in patient management. Application of our method leads to accurate assessment on the association between the diagnostic tests and the clinical outcomes and subsequently significant improvement in decision-making on treatment or patient management.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Won, Seung Hyunsew53@pitt.eduSEW53
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairTang , Gonggot1@pitt.eduGOT1
Committee MemberCheng, Yuyucheng@pitt.eduYUCHENG
Committee MemberJeong, Jong-Hyeonjjeong@pitt.eduJJEONG
Committee MemberLi, Ruosharul12@pitt.eduRUL12
Date: 29 September 2014
Date Type: Publication
Defense Date: 20 August 2014
Approval Date: 29 September 2014
Submission Date: 25 August 2014
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 71
Institution: University of Pittsburgh
Schools and Programs: School of Public Health > Biostatistics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Discrete diagnostic test, Misclassification, Latent class model, EM algorithm, Survey-weighted Cox model, Order restricted hypothesis testing, Union-Intersection principle
Date Deposited: 29 Sep 2014 21:16
Last Modified: 15 Nov 2016 14:23


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