Fall 2018
All colloquia will be held at 4pm in AUST 108, unless otherwise noted. Coffee will be served at 3:30pm in room 326.
Information about past colloquia is available here.
Date |
Speaker |
Title |
Location |
| Friday, August 31 | Abdus Sattar, Case Western Reserve University | Modeling of High-Dimensional Clinical Longitudinal Oxygenation Data from Retinopathy of Prematurity | 11 am in AUST 108
Coffee at 10:30 in AUST 326 |
Colloquium is organized by Professor Xiaojing Wang.
Abdus Sattar, Case Western Reserve University
Modeling of High-Dimensional Clinical Longitudinal Oxygenation Data from Retinopathy of Prematurity
August 31, 2018
Many remarkable advances have been made in the non-parametric and semiparametric methods for high-dimensional longitudinal data. However, there is a lack of a method for addressing missing data in these important methods. Motivated by oxygenation of retinopathy of prematurity (ROP) study, we developed a penalized spline mixed effects model for a high-dimensional nonlinear longitudinal continuous response variable using the Bayesian approach. The ROP study is complicated by the fact that there are non-ignorable missing response values. To address the non-ignorable missing data in the Bayesian penalized spline model, we applied a selection model. Properties of the estimators are studied using Markov Chain Monte Carlo (MCMC) simulation. In the simulation study, data were generated with three different percentages of non-ignorable missing values, and three different sample sizes. Parameters were estimated under various scenarios. The proposed new approach did better compare to the semiparametric mixed effects model with non-ignorable missing values under missing at random (MAR) assumption in terms of bias and percent bias in all scenarios of non-ignorable missing longitudinal data. We performed sensitivity analysis for the hyper-prior distribution choices for the variance parameters of spline coefficients on the proposed joint model. The results indicated that half-t distribution with three different degrees of freedom did not influence to the posterior distribution. However, inverse-gamma distribution as a hyper-prior density influenced to the posterior distribution. We applied our novel method to the sample entropy data in ROP study for handling nonlinearity and the non-ignorable missing response variable. We also analyzed the sample entropy data under missing at random.