156 research outputs found
Simultaneous Confidence Intervals Based on the Percentile Bootstrap Approach
This note concerns the construction of bootstrap simultaneous confidence intervals (SCI) for m parameters. Given B bootstrap samples, we suggest an algorithm with complexity of O(mB log(B)). We apply our algorithm to construct a confidence region for time dependent probabilities of progression in multiple sclerosis and for coefficients in a logistic regression analysis. Alternative normal based simultaneous confidence intervals are presented and compared to the bootstrap intervals
Estimating Time-to-Event From Longitudinal Categorical Data Using Random Effects Markov Models: Application to Multiple Sclerosis Progression
Designed Extension of Survival Studies: Application to Clinical Trials with Unrecognized Heterogeneity
It is well known that unrecognized heterogeneity among patients, such as is conferred by genetic subtype, can undermine the power of randomized trial, designed under the assumption of homogeneity, to detect a truly beneficial treatment. We consider the conditional power approach to allow for recovery of power under unexplained heterogeneity. While Proschan and Hunsberger (1995) confined the application of conditional power design to normally distributed observations, we consider more general and difficult settings in which the data are in the framework of continuous time and are subject to censoring. In particular, we derive a procedure appropriate for the analysis of the weighted log rank test under the assumption of a proportional hazards frailty model. The proposed method is illustrated through application to a brain tumor trial
A Pseudolikelihood Approach for Simultaneous Analysis of Array Comparative Genomic Hybridizations (aCGH)
DNA sequence copy number has been shown to be associated with cancer development and progression. Array-based Comparative Genomic Hybridization (aCGH) is a recent development that seeks to identify the copy number ratio at large numbers of markers across the genome. Due to experimental and biological variations across chromosomes and across hybridizations, current methods are limited to analyses of single chromosomes. We propose a more powerful approach that borrows strength across chromosomes and across hybridizations. We assume a Gaussian mixture model, with a hidden Markov dependence structure, and with random effects to allow for intertumoral variation, as well as intratumoral clonal variation. For ease of computation, we base estimation on a pseudolikelihood function. The method produces quantitative assessments of the likelihood of genetic alterations at each clone, along with a graphical display for simple visual interpretation. We assess the characteristics of the method through simulation studies and through analysis of a brain tumor aCGH data set. We show that the pseudolikelihood approach is superior to existing methods both in detecting small regions of copy number alteration and in accurately classifying regions of change when intratumoral clonal variation is present
A Pairwise Naïve Bayes Approach to Bayesian Classification
Despite the relatively high accuracy of the naïve Bayes (NB) classifier, there may be several instances where it is not optimal, i.e. does not have the same classification performance as the Bayes classifier utilizing the joint distribution of the examined attributes. However, the Bayes classifier can be computationally intractable due to its required knowledge of the joint distribution. Therefore, we introduce a “pairwise naïve” Bayes (PNB) classifier that incorporates all pairwise relationships among the examined attributes, but does not require specification of the joint distribution. In this paper, we first describe the necessary and sufficient conditions under which the PNB classifier is optimal. We then discuss sufficient conditions for which the PNB classifier, and not NB, is optimal for normal attributes. Through simulation and actual studies, we evaluate the performance of our proposed classifier relative to the Bayes and NB classifiers, along with the HNB, AODE, LBR and TAN classifiers, using normal density and empirical estimation methods. Our applications show that the PNB classifier using normal density estimation yields the highest accuracy for data sets containing continuous attributes. We conclude that it offers a useful compromise between the Bayes and NB classifiers
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Calcineurin activation causes retinal ganglion cell degeneration
Purpose: We previously reported that calcineurin, a Ca2+/calmodulin-dependent serine/threonine phosphatase, is activated and proposed that it participates in retinal ganglion cell (RGC) apoptosis in two rodent ocular hypertension models. In this study, we tested whether calcineurin activation by itself, even in the absence of ocular hypertension, is sufficient to cause RGC degeneration. Methods: We compared RGC and optic nerve morphology after adeno-associated virus serotype 2 (AAV2)–mediated transduction of RGCs with constitutively active calcineurin (CaNCA) or unactivated, wild-type calcineurin (CaNwt). Retinas and optic nerves were harvested 7–16 weeks after injection of the AAV into mouse vitreous. In flatmounted retinas, the transduced RGCs were identified with immunohistochemistry. The morphology of the RGCs was revealed by immunostaining for neurofilament SMI32 or by using GFP-M transgenic mice. A modified Sholl analysis was applied to analyze the RGC dendritic morphology. Optic nerve damage was assessed with optic nerve grading according to the Morrison standard. Results: CaNwt and CaNCA were highly expressed in the injected eyes. Compared to the CaNwt-expressing RGCs, the CaNCA-expressing RGCs had smaller somas, smaller dendritic field areas, shorter total dendrite lengths, and simpler dendritic branching patterns. At 16 weeks, the CaNCA-expressing eyes had greater optic nerve damage than the CaNwt-expressing eyes. Conclusions: Calcineurin activation is sufficient to cause RGC dendritic degeneration and optic nerve damage. These data support the hypothesis that calcineurin activation is an important mediator of RGC degeneration, and are consistent with the hypothesis that calcineurin activation may contribute to RGC neurodegeneration in glaucoma
DESIGNED EXTENSION OF SURVIVAL STUDIES: APPLICATION TO CLINICAL TRIALS WITH UNRECOGNIZED HETEROGENEITY
Abstract: It is well known that unrecognized heterogeneity among patients, such as is conferred by genetic subtype, can undermine the power of a randomized trial, designed under the assumption of homogeneity, to detect a truly beneficial treatment. We consider the conditional power approach to allow for recovery of power under unexplained heterogeneity. While Proschan and Hunsberger (1995) confined the application of conditional power design to normally distributed observations, we consider more general and difficult settings in which the data are in the framework of continuous time and are subject to censoring. In particular, we derive a procedure appropriate for the analysis of the weighted log rank test under the assumption of a proportional hazards frailty model. The proposed method is illustrated through application to a brain tumor trial
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