Title page for etd-0704116-165025


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URN etd-0704116-165025
Author Chi-Chung Chen
Author's Email Address No Public.
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Department Applied Mathematics
Year 2015
Semester 2
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title Bayesian analysis for mixture nonlinear mixed-effects models with skewed random effects and errors with application to an ovarian cancer study
Date of Defense 2016-07-15
Page Count 40
Keyword
  • longitudinal data
  • skew-t distribution
  • nonlinear mixed-effects model
  • ovarian cancer
  • spline function
  • skew-normal distribution
  • time-varying coefficients
  • Bayesian hierarchical model
  • Cox proportional hazards model
  • tumor marker
  • Abstract It is common to analyze longitudinal data using nonlinear mixed-effects (NLME) model. And we often use NLME model with normality and homogeneity assumption. However, this assumption may be unrealistic in practice. Our aim is to model the longitu-
    dinal profiles of CA125, a tumor marker, in an ovarian cancer study. When fitting these profiles using NLME model, we observed that the distribution of the random effects and
    errors are skewed. Hence we propose an NLME model with skewed normal random effects and skewed-t errors. Moreover, we observed that errors and some of the random effects are heterogeneous due to early and late cancer stage. Therefore, we apply the Bayesian hierarchical framework using the heterogeneity and skewness information to construct our new NLME model. Most importantly, we hope that this model can be helpful for doctors during the clinical treatments.
       In the second part, we provide a more generalized Cox proportional hazard (Cox PH) model. The traditional Cox PH model has been used to identify the risk factors without considering time-varying effects. A generalized Cox PH model must satisfy the proportional hazard assumption, even though the risk factors are time-dependent. Wang (2015) has provided a more generalized Cox PH model by considering the risk factors which have time-varying effects and shared the R package. Here we extended the model even more. Some of the risk factors which are time-dependent can have time-varying effects simultaneously. We use spline function to approximate the time-varying coefficients and also provide an R function.
    Advisory Committee
  • Mong-Na Lo Huang - chair
  • Fu-Chuen Chang - co-chair
  • An-Jen Chiang - co-chair
  • Chung Chang - advisor
  • Files
  • etd-0704116-165025.pdf
  • Indicate in-campus at 99 year and off-campus access at 99 year.
    Date of Submission 2016-08-04

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