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It has been well known that ignoring measurement error may result in substantially biased estimates in many contexts including linear and nonlinear regressions. For survival data with measurement error in covariates there has been extensive discussion in the literature with the focus being on the Cox proportional hazards models. However, the impact of(More)
Epigenetic changes are involved in learning and memory, and histone deacetylase (HDAC) inhibitors are considered potential therapeutic agents for Alzheimer's disease (AD). We previously reported that (-)-epigallocatechin-3-gallate (EGCG) acts as an HDAC inhibitor. Here, we demonstrate that EGCG reduced β-amyloid (Aβ) accumulation in vitro and rescued(More)
Parkinson's disease (PD) is a neurodegenerative disorder that affects fitness to drive. Research that has examined clinical predictors of fitness to drive in PD, using the on-road assessment as the gold standard, has generally used a dichotomous pass/fail decision. However, on-road assessments may also result in one of two additional outcomes (pass with(More)
This article presents methodology for multivariate proportional hazards (PH) regression models. The methods employ flexible piecewise constant or spline specifications for baseline hazard functions in either marginal or conditional PH models, along with assumptions about the association among lifetimes. Because the models are parametric, ordinary maximum(More)
In this paper we study U-statistics with side information incorporated using the method of empirical likelihood. Some basic properties of the proposed statistics are investigated. We find that by implementing the side information properly, the proposed U-statistics can have smaller asymptotic variance than the existing U-statistics in the literature. The(More)
MOTIVATION A primary objective of microarray studies is to determine genes which are differentially expressed under various conditions. Parametric tests, such as two-sample t-tests, may be used to identify differentially expressed genes, but they require some assumptions that are not realistic for many practical problems. Non-parametric tests, such as(More)
Patients with high-grade gliomas usually have heterogeneous response to surgery and chemoirradiation. The objectives of this study were (1) to evaluate serial changes in tumor volume and perfusion imaging parameters and (2) to determine the value of these data in predicting overall survival (OS). Twenty-nine patients with World Health Organization grades(More)
Clustering is a major tool for microarray gene expression data analysis. The existing clustering methods fall mainly into two categories: parametric and nonparametric. The parametric methods generally assume a mixture of parametric subdistributions. When the mixture distribution approximately fits the true data generating mechanism, the parametric methods(More)
Bayesian hierarchical models that characterize the distributions of (transformed) gene profiles have been proven very useful and flexible in selecting differentially expressed genes across different types of tissue samples (e.g. Lo and Gottardo, 2007). However, the marginal mean and variance of these models are assumed to be the same for different gene(More)