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Concordance Measures for Variable Screening and Model Evaluation with Competing Risks Data

Qu, Yang (2023) Concordance Measures for Variable Screening and Model Evaluation with Competing Risks Data. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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We focus on analysis of time-to-event data with competing risks. In the first project, we make additional assumption of natural ordered event status, and propose a time-dependent model-free variable screening method for high-dimensional data that evaluate the discrimination ability of a biomarker to distinguish multiple event status simultaneously. The proposed method utilizes the Volume under the ROC surface (VUS), which measures the concordance between values of biomarkers and event status at certain time points. We show that the VUS possesses the sure screening property, i.e., true important covariates can be retained with probability tending to one. Simulations and data analysis show that VUS appears to be a viable screening metric, and is robust to data contamination.
In the second project, we provide a systematic examination of model evaluation metrics that evaluate the discrimination ability of prognostic models. Most of the existing metrics focus on how a particular cause of event can be discriminated from the healthy control by the prognostic models when competing events exist, and one metric, the polytomous discrimination index (PDI), additionally provides an overall evaluation of diagnostic accuracy of a group of models for predicting all competing events. A systematic comparison of PDI with other existing methods is missing. We thus fill this gap and illustrate the performance of different model evaluation metrics under various scenarios via simulation studies and data analyses. Several natural extensions of concordance index are also considered, and their performance of model evaluation is also assessed. An R package is developed to provide model evaluation and model comparisons based on existing methods and extended concordance indices.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Qu, YangYAQ4@pitt.eduYAQ4
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairCheng,
Committee MemberIyengar,
Committee MemberChen,
Committee MemberDing,
Date: 26 January 2023
Date Type: Publication
Defense Date: 19 September 2022
Approval Date: 26 January 2023
Submission Date: 13 October 2022
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 74
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Statistics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Model-free screening, sure screening property, concordance index, discrimination capacity
Date Deposited: 26 Jan 2023 14:24
Last Modified: 26 Jan 2024 06:15


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