Wang, Zheng
(2020)
Longitudinal Multivariate Normative Comparisons and Accuracy Improvement Metrics for Competing Endpoints.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
Motivated by the Multicenter AIDS Cohort Study (MACS), we are interested in developing methods to address several challenges in analyzing the data. The objectives of the research include developing dementia classification procedures from longitudinal data to control family-wise error, modeling covariates effects on time to dementia, and evaluating the improvement of diagnostic accuracy when including a new biomarker in the model.
In order to properly define an event of interest, we adapt the cross-sectional multivariate normative comparison (MNC) method, which controls family-wise error by accounting for the inter-correlations among all covariates, to a longitudinal setting. Longitudinal MNC is constructed based on multivariate mixed effects models when hypothesis testing happens by the conclusion of study.
In practice, patients may require visit-by-visit classification. Prompt feedback can guide treatments for longer survival. We propose to modify longitudinal MNC statistics to build visit-by-visit test statistics. Then, based on predicted number of visits from survival model and Poisson regression, we can apply Bonferroni-type correction to control family-wise error for this prospective classification scenario.
Lastly, we examine a new biomarker's contribution to diagnostic accuracy for competing-risk outcomes. The net reclassification improvement (NRI) and the integrated discrimination improvement (IDI) were originally proposed to characterize accuracy improvement in predicting a binary outcome, when new biomarkers are added to regression models. These two indices have been extended from dichotomous outcomes to multi-categorical and survival outcomes. We extend the NRI and IDI to competing-risk outcomes, by adopting the definitions of the two indices for multi-category outcomes. The ``missing'' category due to independent censoring is handled through the inverse probability weighting.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
8 June 2020 |
| Date Type: |
Publication |
| Defense Date: |
26 March 2020 |
| Approval Date: |
8 June 2020 |
| Submission Date: |
26 March 2020 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
112 |
| 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: |
Competing risks; Cumulative incidence function; Family-wise error rate; Integrated discrimination improvement; Multivariate mixed-effect model; Net reclassification improvement |
| Date Deposited: |
08 Jun 2020 17:09 |
| Last Modified: |
08 Jun 2020 17:09 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/38397 |
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