SURVIVAL PREDICTION FOR BRAIN TUMOR PATIENTS USING GENE EXPRESSION DATA

Brain tumor is one of the most aggressive types of cancer in humans, with an estimated median survival time of 12 months and only 4% of the patients surviving more than 5 years after disease diagnosis. Until recently, brain tumor prognosis has been based only on clinical information such as tumor grade and patient age, but there are reports indicating that molecular profiling of gliomas can reveal subgroups of patients with distinct survival rates. We hypothesize that coupling molecular profiling of brain tumors with clinical information might improve predictions of patient survival time and, consequently, better guide future treatment decisions. In order to evaluate this hypothesis, the general goal of this research is to build models for survival prediction of glioma patients using DNA molecular profiles (U133 Affymetrix gene expression microarrays) along with clinical information. First, a predictive Random Forest model is built for binary outcomes (i.e. short vs. long-term survival) and a small subset of genes whose expression values can be used to predict survival time is selected. Following, a new statistical methodology is developed for predicting time-to-death outcomes using Bayesian ensemble trees. Due to a large heterogeneity observed within prognostic classes obtained by the Random Forest model, prediction can be improved by relating time-to-death with gene expression profile directly. We propose a Bayesian ensemble model for survival prediction which is appropriate for high-dimensional data such as gene expression data. Our approach is based on the ensemble "sum-of-trees" model which is flexible to incorporate additive and interaction effects between genes. We specify a fully Bayesian hierarchical approach and illustrate our methodology for the CPH, Weibull, and AFT survival models. We overcome the lack of conjugacy using a latent variable formulation to model the covariate effects which decreases computation time for model fitting. Also, our proposed models provides a model-free way to select important predictive prognostic markers based on controlling false discovery rates. We compare the performance of our methods with baseline reference survival methods and apply our methodology to an unpublished data set of brain tumor survival times and gene expression data, selecting genes potentially related to the development of the disease under study. A closing discussion compares results obtained by Random Forest and Bayesian ensemble methods under the biological/clinical perspectives and highlights the statistical advantages and disadvantages of the new methodology in the context of DNA microarray data analysis.

An examination of the rate of high school completion experienced by handicapped and nonhandicapped students in the state of Texas

This research examines the graduation rate experienced by students receiving public education services in the state of Texas. Special attention is paid to that subgroup of Texas students who meet Texas Education Agency criteria for handicapped status. The study is guided by two research questions: What are the high school completion rates experienced by handicapped and nonhandicapped students attending Texas public schools? and What are the predictors of graduation for handicapped and nonhandicapped students?^ In addition, the following hypotheses are explored. Hypothesis 1: Handicapped students attending a Texas public school will experience a lower rate of high school completion than their nonhandicapped counterparts. Hypothesis 2: Handicapped and nonhandicapped students attending school in a Texas public school with a budget above the median budget for Texas public schools will experience a higher rate of high school completion than similar students in Texas public schools with a budget below the median budget. Hypothesis 3: Handicapped and nonhandicapped students attending school in large Texas urban areas will experience a lower rate of high school completion than similar students in Texas public schools in rural areas. Hypothesis 4: Handicapped and nonhandicapped students attending a Texas public school in a county which rates above the state median for food stamps and AFDC recipients will experience a lower rate of high school completion than students living in counties below the median.^ The study will employ extant data from the records of the Texas Education Agency for the 1988-1989 and the 1989-1990 school years, from the Texas Department of Health for the years of 1989 and 1990, and from the 1980 Census.^ The study reveals that nonhandicapped students are graduating with a two year average rate of.906, while handicapped students following an Individualized Educational Program (IEP) achieve a two year average rate of.532, and handicapped students following the regular academic program present a two year average graduation rate of only.371. The presence of other handicapped students, and the school district's average expense per student are found to contribute significantly to the completion rates of handicapped students. Size groupings are used to elucidate the various impacts of these variables on different school districts and different student groups.^ Conclusions and implications are offered regarding the need to reach national consensus on the definition and computation of high school completion for both handicapped and nonhandicapped students, and the need for improved statewide tracking of handicapped completion rates. ^