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Semi-parametric Survival Analysis via Dirichlet Process Mixtures of the First Hitting Time Model

Race, Jonathan Andrew
http://rave.ohiolink.edu/etdc/view?acc_num=osu157357742741077

Abstract Details

2019, Doctor of Philosophy, Ohio State University, Biostatistics.
Time-to-event data often violate the proportional hazards assumption inherent in the popular Cox regression model. Such violations are especially common in the sphere of biological and medical data where latent heterogeneity due to unmeasured covariates or time varying effects are common. A variety of parametric survival models have been proposed which make more appropriate assumptions on the hazard function, at least for certain applications. One such model is derived from the First Hitting Time (FHT) paradigm which assumes that a subject's event time is determined by a latent stochastic process reaching a threshold value. Several random effects specifications of the FHT model have also been proposed which allow for better modeling of data with unmeasured covariates. While often appropriate, these methods often display limited flexibility due to their inability to model a wide range of heterogeneities. To address this issue, we propose two Bayesian models which loosen assumptions on the mixing distribution inherent in the random effects FHT models currently in use. The first proposed model is ideally suited for standard regression analyses. The second model is designed for use in clinical trials where survival is the outcome of interest. We demonstrate via simulation study that the proposed models greatly improve both survival and parameter estimation in the presence of latent heterogeneity. We also apply the proposed methodologies to data from a toxicology/carcinogenicity study which exhibits nonproportional hazards and contrast the results with competing methods.
Michael Pennell (Advisor)
149 p.
Biostatistics; Statistics
Survival analysis; threshold regression; Bayesian semi-parametrics; hierarchical modeling; Dirichlet process

Recommended Citations

Citations

  • Race, J. A. (2019). Semi-parametric Survival Analysis via Dirichlet Process Mixtures of the First Hitting Time Model [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu157357742741077

    APA Style (7th edition)

  • Race, Jonathan. Semi-parametric Survival Analysis via Dirichlet Process Mixtures of the First Hitting Time Model. 2019. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu157357742741077.

    MLA Style (8th edition)

  • Race, Jonathan. "Semi-parametric Survival Analysis via Dirichlet Process Mixtures of the First Hitting Time Model." Doctoral dissertation, Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu157357742741077

    Chicago Manual of Style (17th edition)

osu157357742741077
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© 2019, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.