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Bivariate Mixed Effects Model with Non-stationary Stochastic Processes for Prediction of Rapid Disease Progression: Empirical Performance and Construction

Wang, Ziyun
http://orcid.org/0000-0003-2505-8949
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1692299336219506

Abstract Details

2023, PhD, University of Cincinnati, Medicine: Biostatistics (Environmental Health).
In recent years, advancements in real-time prediction have been achieved by introducing a more flexible term representing a non-stationary stochastic process to replace the classic random slope in the linear mixed effects models. The resulting model has been used to form predictive probabilities for clinically relevant target functions involving rates of change in the mean response function for people with cystic fibrosis (CF). However, data patterns are changing over time, especially with the introduction of ivacaftor treatment in 2012 followed by other highly effective modulator therapies. In this dissertation, I focused on evaluating the epidemiologic impact of secular trends in CF care and treatments on acute lung decline prediction and characterized the changing patterns. Specifically, I evaluated the performance of predicting rapid lung decline events through a novel data-driven definition. There are often multiple, related, noisily-measured outcomes that are critical to monitoring and predicting disease progression of individuals over time. However, the current approach has been limited to a single outcome. Considering the case of two outcomes, I propose a bivariate mixed effects model utilizing integrated Brownian motion for each mean response function. Estimation of the proposed model was implemented through a combined approach of the Newton-Raphson and Fisher Scoring algorithm and profile likelihood. I also propose a bivariate target function that simultaneously predicts under the two-outcome scenario based on clinically meaningful thresholds of rates of change. This novel approach is applied to achieve real-time prediction of key changes in nutrition and lung function for children with CF who are followed in a national patient registry.
RhondaRhonda SzczesniakSzczesniak, Ph.D.Ph.D. (Committee Chair)
Marepalli Rao, Ph.D. (Committee Member)
Roman Jandarov, Ph.D. (Committee Member)
Richard Brokamp, Ph.D. (Committee Member)
Marepalli Rao, Ph.D. (Committee Member)
76 p.
Biostatistics
dynamic prediction algorithm; non-stationary stochastic process; predictive probability function; bivariate mixed effects model; cystic fibrosis; pulmonary exacerbation

Recommended Citations

Citations

  • Wang, Z. (2023). Bivariate Mixed Effects Model with Non-stationary Stochastic Processes for Prediction of Rapid Disease Progression: Empirical Performance and Construction [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1692299336219506

    APA Style (7th edition)

  • Wang, Ziyun. Bivariate Mixed Effects Model with Non-stationary Stochastic Processes for Prediction of Rapid Disease Progression: Empirical Performance and Construction. 2023. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1692299336219506.

    MLA Style (8th edition)

  • Wang, Ziyun. "Bivariate Mixed Effects Model with Non-stationary Stochastic Processes for Prediction of Rapid Disease Progression: Empirical Performance and Construction." Doctoral dissertation, University of Cincinnati, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1692299336219506

    Chicago Manual of Style (17th edition)

ucin1692299336219506
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© 2023, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.