DOI

10.17077/etd.00b01oyk

Document Type

Dissertation

Date of Degree

Spring 2016

Degree Name

PhD (Doctor of Philosophy)

Degree In

Biostatistics

First Advisor

Zhang, Ying

Second Advisor

Long, Jeffrey D.

First Committee Member

Cavanaugh, Joseph

Second Committee Member

Jones, Michael

Third Committee Member

Wang, Kai

Abstract

In the dissertation, a monotone spline-based nonparametric estimation method is proposed for analyzing longitudinal data with mixture distributions. The innovative and efficient algorithm combining the concept of projected Newton-Raphson algorithm with linear mixed model estimation method is developed to obtain the nonparametric estimation of monotone B-spline functions. This algorithm provides an efficient and flexible approach for modeling longitudinal data monotonically. An iterative 'one-step-forward' algorithm based on the K-means clustering is then proposed to classify mixture distributions of longitudinal data. This algorithm is computationally efficient, especially for data with a large number of underlying distributions. To quantify the disparity of underlying distributions of longitudinal data, we also propose an index measure on the basis of the aggregated areas under the curve (AAUC), which makes no distributional assumptions and fits the theme of nonparametric analysis.

An extensive simulation study is conducted to assess the empirical performance of our method under different AAUC values, covariance structures, and sample sizes. Finally, we apply the new approach in the PREDICT-HD study, a multi-site observational study of Huntington Disease (HD), to explore and assess clinical markers in motor and cognitive domains for the purpose of distinguishing participants at risk of HD from healthy subjects.

Keywords

B-spline, Longitudinal data, Mixture distributions, Monotone

Pages

ix, 112 pages

Bibliography

Includes bibliographical references (pages 109-112).

Copyright

Copyright © 2016 Wenjing Lu

Included in

Biostatistics Commons

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