生存资料回归模型分析——基于MCMC过程构建生存资料Cox非比例风险回归模型

本文目的是介绍采用PHREG过程及MCMC过程且基于贝叶斯统计思想分别构建Cox非比例风险回归模型的相关内容及其SAS软件实现。在MCMC过程中,有两种构建模型的方法:一是对观测值进行转置之后,在MODEL语句中使用GENERAL函数;二是不对观测值进行转置,使用MCMC过程中的JOINTMODEL选项。两个过程所得计算结果基本一致。     

生存资料回归模型分析——生存资料Cox比例风险回归模型分析

本文目的是介绍生存资料Cox比例风险回归模型分析的概念、作用及使用SAS软件实现计算的方法。首先介绍相关基本概念,包括"Cox比例风险回归模型简介""模型假定及其检验""参数解释"和"参数估计与假设检验";然后通过一个实例并基于SAS软件演示如何实施生存资料Cox比例风险回归模型分析,内容包括"产生SAS数据集""绘制生存曲线图""判断PH假定是否成立"和"算出参数估计值与假设检验结果"。结果表明:当生存资料满足PH假定时,Cox比例风险回归模型可用于生存资料影响因素分析、校正混杂因素后的组间比较以及对每个个体进行预后指数和生存率的预测。     

生存资料回归模型分析--Cox比例风险假设的图形检验法

本文目的是介绍目前使用图形检验比例风险的常用方法。经典的Cox比例风险回归模型要求生存资料满足比例风险假设,而在临床资料中,这个假设往往并不成立。鉴于此,本文首先阐述了比例风险假设的概念;然后介绍了一些检验比例风险假设是否成立的常用图示方法,主要包括Kaplan-Meier生存曲线图、ln[-ln(St)]生存时间关系图、缩放Schoenfeld残差与时间的关系图、SAS软件PHREG过程中ACCESS语句的PH和RESAMPLE选项产生的模拟路径图;最后,基于SAS软件并通过实例演示上述方法的实现。     

运用Cox比例风险回归模型分析脑出血预后的影响因素

目的探讨运用Cox比例风险回归模型分析脑出血预后危险因素的作用。方法回顾性分析脑出血患者104例,其中死亡组24例,生存组80例,采用SPSS 18.0统计软件作Cox比例风险回归分析2组患者一般情况、刚入院时各项临床、实验室、影像学指标及并发症与脑出血预后的关系。结果多因素Cox比例风险回归分析显示,心脏病史、意识(GCS)、收缩压、舒张压、平均动脉压、血糖、甘油三酯和脑疝形成因素入选回归方程(P<0.05)。结论心脏病史、意识(GCS)、收缩压、舒张压、平均动脉压、血糖、甘油三酯和脑疝形成是影响脑出血患者生命预后的独立影响因素。     

生存资料回归模型分析--Cox比例风险假设的拟合优度法

本文目的是介绍一些检验比例风险假设的方法。图示法是通过绘图然后由人工进行判断是否符合比例风险假设,因而具有一定的主观性。在图示法的基础上,本文介绍了从客观角度检验比例风险假设的一些常用方法,主要包括两类:一类是基于残差的检验;另一类则是构建协变量与时间的交互项并对其进行检验的方法。首先阐述了上述方法的原理,然后基于SAS软件并通过一个实例介绍上述方法的实现。     

生存资料回归模型分析——生存资料及其统计分析方法概述

本文目的是全面介绍生存资料的特点及其常用统计分析方法。生存资料具有以下四个特点:①同时具有生存结局和生存时间;②生存时间可能含有删失数据或截尾数据;③生存时间的分布通常不服从正态分布,常呈指数分布、Weibull分布、对数正态分布;④影响生存时间的因素较复杂且不易控制。生存资料统计分析方法涉及统计描述、差异性分析和回归分析三大类,其中,统计描述主要有Kaplan-Meier(卡普兰-迈耶)估计法和Life table(寿命表)估计法;差异性分析主要有对数秩检验(log-rank test)和威尔考克森检验(Wilcoxon test);而回归分析主要有Cox比例和非比例风险回归模型、参数回归模型。在对生存资料进行统计分析时,需要合理选择统计分析方法,方可全面而又深入地揭示生存资料的内在变化规律。     

生存资料回归模型分析——生存资料参数回归模型分析SAS实现

本文目的是介绍生存资料参数回归模型的SAS实现,包括创建SAS数据集、依据图示法选择模型、拟合参数模型和似然比检验。利用SAS中的LIFEREG过程绘制生存函数关于生存时间的关系图,拟合对应的参数分布回归模型,通过拟合优度检验选择最优的参数回归模型,最后对相关结果进行解释。     

生存资料回归模型分析——生存资料参数回归模型分析基础

本文目的是介绍生存资料参数回归模型有关的基础知识。首先,介绍了构建三个常见的生存资料参数回归模型的基本原理,包括指数分布回归模型、Weibull分布回归模型和Log-logistic分布回归模型;其次,介绍了基于图示法判断生存时间服从何种概率分布的方法;最后,介绍了基于最大似然估计法求解参数回归模型中的参数和两个参数回归模型拟合优度的比较。得到如下结论:①当资料中的生存时间服从特定概率分布时,应选用相应的参数回归模型;②图示法可用于粗略判断生存时间服从何种概率分布;③似然比检验可用于包含不同参数数目的两个参数回归模型之间拟合优度的比较。     

髓母细胞瘤预后影响因素的Cox模型分析

目的分析影响髓母细胞瘤预后的相关因素。方法应用Cox回归模型,对60例髓母细胞瘤患者的年龄、性别、病程、肿瘤体积、切除范围、放疗、uPA、uPAR、KDR9项指标进行预后单因素及多因素分析。结果Cox回归分析显示反应肿瘤生物学行为的uPA、uPAR和KDR与预后存在高度负相关关系。结论uPA、uPAR和KDR是决定髓母细胞瘤预后的最重要危险因素。     

脑动静脉畸形出血危险因素的Cox回归分析

目的：探讨与脑动静脉畸形（ＡＶＭ）出血相关的临床及血管影像学特征，以期对脑ＡＶＭ的出血倾向作出评价和预测。方法：应用Ｃｏｘ回归模型，对５９例脑ＡＶＭ进行出血危险因素的单因素及多因素分析。结果：５９例脑ＡＶＭ中，观察期内出血４８例（占８１％），年平均出血率为２％；病灶大小、供应动脉支数以及引流静脉支数是决定脑ＡＶＭ出血倾向最重要的危险因素；小型ＡＶＭ、多支动脉供应的ＡＶＭ以及仅有单支静脉引流的ＡＶＭ最易破裂出血。结论：建议脑血管造影时着重对此三项指标进行描述；对于出血风险较大的脑ＡＶＭ应及早治疗。     

An efficient sampling algorithm with adaptations for Bayesian variable selection

In Bayesian variable selection, indicator model selection (IMS) is a class of well-known sampling algorithms, which has been used in various models. The IMS is a class of methods that uses pseudo-priors and it contains specific methods such as Gibbs variable selection (GVS) and Kuo and Mallick’s (KM) method. However, the efficiency of the IMS strongly depends on the parameters of a proposal distribution and the pseudo-priors. Specifically, the GVS determines their parameters based on a pilot run for a full model and the KM method sets their parameters as those of priors, which often leads to slow mixings of them. In this paper, we propose an algorithm that adapts the parameters of the IMS during running. The parameters obtained on the fly provide an appropriate proposal distribution and pseudo-priors, which improve the mixing of the algorithm. We also prove the convergence theorem of the proposed algorithm, and confirm that the algorithm is more efficient than the conventional algorithms by experiments of the Bayesian variable selection.     

颅脑损伤院内获得性肺炎预测:一项基于机器学习的PCA-Logistic回归分析模型

目的 探讨PCA-Logistic回归分析模型在颅脑损伤病人院内获得性肺炎（HAP）预测建模中的应用效果。方法 收集2011年12月至2017年11月开颅手术治疗的108例颅脑损伤的相关临床数据,建立PCA-Logistic回归分析模型,利用受试者工作特征（ROC）曲线评估模型预测效果。结果 PCA-Logistic回归分析模型发现影响病人HAP发生的重要临床指标,经ROC曲线评估PCA-Logistic回归分析模型预测HAP结局具有较高的预测效能,灵敏度为83.9%,特异度为94.8%,曲线下面积为0.949。结论 PCA-Logistic回归分析模型可以有效的挖掘颅脑损伤后的临床变量,可建立HAP的预测模型,不规范的肠外营养营养支持可能是影响HAP发生的重要临床因素。     

Study of Haralick’s and GLCM texture analysis on 3D medical images

Purpose of the study: Medical field has highly evolved with advancements in the technologies which prove to be beneficial for radiologists and patients for better diagnosis. The era of medical science provides best healthcare solutions with the help of medical images. Till now, 2D MRIs played a prominent role in early detection of disease but with latest technologies taking over the charge, 3D MRIs are highly effective and great in demand nowadays. With the aid of advanced techniques such as edge detection, segmentation and texture analysis on these images, the disease detection may become much easier.

Materials and Methods: Texture of any image is recognized by distribution of gray levels in the neighborhood. The Texture Analysis plays an important role in study of medical images. It identifies the prominent features of an image and highlights the same using different feature extraction technique. In this paper, 3D MRI of human brain is considered and texture analysis based on Haralick's and GLCM texture features is performed. Haralick's feature explains the image intensities of each pixel and their relationship with neighborhood pixels. The entire data set consists of 40 brain tumor patients, out of which a sample has been depicted.

Results: The analysis of different features such as Contrast, Correlation, Energy, Homogeneity and Entropy is carried out. Conclusion: Further, the study highlights about the highly useful features for early detection of brain tumor disease.     

Adaptive smoothing based on Gaussian processes regression increases the sensitivity and specificity of fMRI data

Temporal and spatial filtering of fMRI data is often used to improve statistical power. However, conventional methods, such as smoothing with fixed‐width Gaussian filters, remove fine‐scale structure in the data, necessitating a tradeoff between sensitivity and specificity. Specifically, smoothing may increase sensitivity (reduce noise and increase statistical power) but at the cost loss of specificity in that fine‐scale structure in neural activity patterns is lost. Here, we propose an alternative smoothing method based on Gaussian processes (GP) regression for single subjects fMRI experiments. This method adapts the level of smoothing on a voxel by voxel basis according to the characteristics of the local neural activity patterns. GP‐based fMRI analysis has been heretofore impractical owing to computational demands. Here, we demonstrate a new implementation of GP that makes it possible to handle the massive data dimensionality of the typical fMRI experiment. We demonstrate how GP can be used as a drop‐in replacement to conventional preprocessing steps for temporal and spatial smoothing in a standard fMRI pipeline. We present simulated and experimental results that show the increased sensitivity and specificity compared to conventional smoothing strategies. Hum Brain Mapp 38:1438–1459, 2017. © 2016 Wiley Periodicals, Inc.     

A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging data

IntroductionIt is challenging at baseline to predict when and which individuals who meet criteria for mild cognitive impairment (MCI) will ultimately progress to Alzheimer's disease (AD) dementia.MethodsA deep learning method is developed and validated based on magnetic resonance imaging scans of 2146 subjects (803 for training and 1343 for validation) to predict MCI subjects' progression to AD dementia in a time-to-event analysis setting.ResultsThe deep-learning time-to-event model predicted individual subjects' progression to AD dementia with a concordance index of 0.762 on 439 Alzheimer's Disease Neuroimaging Initiative testing MCI subjects with follow-up duration from 6 to 78 months (quartiles: [24, 42, 54]) and a concordance index of 0.781 on 40 Australian Imaging Biomarkers and Lifestyle Study of Aging testing MCI subjects with follow-up duration from 18 to 54 months (quartiles: [18, 36, 54]). The predicted progression risk also clustered individual subjects into subgroups with significant differences in their progression time to AD dementia (P < .0002). Improved performance for predicting progression to AD dementia (concordance index = 0.864) was obtained when the deep learning–based progression risk was combined with baseline clinical measures.DiscussionOur method provides a cost effective and accurate means for prognosis and potentially to facilitate enrollment in clinical trials with individuals likely to progress within a specific temporal period.