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建模研究间异质性以提高基因特征选择与临床预测的可重复性AI 解读

In the genomic era, the identification of gene signatures associated with disease is of significant interest. Such signatures are often used to predict clinical outcomes in new patients and aid clinical decision-making. However, recent studies have shown that gene signatures are often not replicable. This occurrence has practical implications regarding the generalizability and clinical applicability of such signatures. To improve replicability, we introduce a novel approach…

黎曼对称空间上的回归模型AI 解读

Summary The paper develops a general regression framework for the analysis of manifold-valued response in a Riemannian symmetric space (RSS) and its association with multiple covariates of interest, such as age or gender, in Euclidean space. Such RSS-valued data arise frequently in medical imaging, surface modelling and computer vision, among many other fields. We develop an intrinsic regression model solely based on an intrinsic conditional moment assumption, avoiding…

协变量缺失的 Cox 回归模型诊断度量AI 解读

-values of the goodness of fit statistics. Simulation studies are conducted to evaluate our methods, and a real data set is analyzed to illustrate their use.

研究内样本协方差矩阵部分缺失时的多元元回归贝叶斯推断AI 解读

Multivariate meta-regression models are commonly used in settings where the response variable is naturally multidimensional. Such settings are common in cardiovascular and diabetes studies where the goal is to study cholesterol levels once a certain medication is given. In this setting, the natural multivariate endpoint is low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), and triglycerides (TG) (LDL-C, HDL-C, TG). In this article, we…

SPReM：高维线性回归的稀疏投影回归模型AI 解读

The aim of this article is to develop a sparse projection regression modeling (SPReM) framework to perform multivariate regression modeling with a large number of responses and a multivariate covariate of interest. We propose two novel heritability ratios to simultaneously perform dimension reduction, response selection, estimation, and testing, while explicitly accounting for correlations among multivariate responses. Our SPReM is devised to specifically address the low…

神经影像表型与遗传标记的贝叶斯广义低秩回归AI 解读

We propose a Bayesian generalized low rank regression model (GLRR) for the analysis of both high-dimensional responses and covariates. This development is motivated by performing searches for associations between genetic variants and brain imaging phenotypes. GLRR integrates a low rank matrix to approximate the high-dimensional regression coefficient matrix of GLRR and a dynamic factor model to model the high-dimensional covariance matrix of brain imaging phenotypes. Local…

DAE-seq 数据分析：变量选择与观测依赖建模AI 解读

In DAE (DNA After Enrichment)-seq experiments, genomic regions related with certain biological processes are enriched/isolated by an assay and are then sequenced on a high-throughput sequencing platform to determine their genomic positions. Statistical analysis of DAE-seq data aims to detect genomic regions with significant aggregations of isolated DNA fragments ("enriched regions") versus all the other regions ("background"). However, many confounding factors may influence…

皮层下结构中轴表示的内蕴回归模型AI 解读

The aim of this paper is to develop a semiparametric model for describing the variability of the medial representation of subcortical structures, which belongs to a Riemannian manifold, and establishing its association with covariates of interest, such as diagnostic status, age and gender. We develop a two-stage estimation procedure to calculate the parameter estimates. The first stage is to calculate an intrinsic least squares estimator of the parameter vector using the…

神经影像数据的多尺度自适应回归模型AI 解读

Summary Neuroimaging studies aim to analyse imaging data with complex spatial patterns in a large number of locations (called voxels) on a two-dimensional surface or in a three-dimensional volume. Conventional analyses of imaging data include two sequential steps: spatially smoothing imaging data and then independently fitting a statistical model at each voxel. However, conventional analyses suffer from the same amount of smoothing throughout the whole image, the arbitrary…

正定矩阵的内蕴回归模型及其扩散张量成像应用AI 解读

The aim of this paper is to develop an intrinsic regression model for the analysis of positive-definite matrices as responses in a Riemannian manifold and their association with a set of covariates, such as age and gender, in a Euclidean space. The primary motivation and application of the proposed methodology is in medical imaging. Because the set of positive-definite matrices do not form a vector space, directly applying classical multivariate regression may be inadequate…

识别磁共振图像噪声来源的回归模型AI 解读

Stochastic noise, susceptibility artifacts, magnetic field and radiofrequency inhomogeneities, and other noise components in magnetic resonance images (MRIs) can introduce serious bias into any measurements made with those images. We formally introduce three regression models including a Rician regression model and two associated normal models to characterize stochastic noise in various magnetic resonance imaging modalities, including diffusion-weighted imaging (DWI) and…

利用 EM 算法构造缺失数据问题的模型选择准则AI 解读

We consider novel methods for the computation of model selection criteria in missing-data problems based on the output of the EM algorithm. The methodology is very general and can be applied to numerous situations involving incomplete data within an EM framework, from covariates missing at random in arbitrary regression models to nonignorably missing longitudinal responses and/or covariates. Toward this goal, we develop a class of information criteria for missing-data…

二项回归模型中 Jeffreys 先验的性质与计算实现AI 解读

We study several theoretical properties of Jeffreys's prior for binomial regression models. We show that Jeffreys's prior is symmetric and unimodal for a class of binomial regression models. We characterize the tail behavior of Jeffreys's prior by comparing it with the multivariate t and normal distributions under the commonly used logistic, probit, and complementary log-log regression models. We also show that the prior and posterior normalizing constants under Jeffreys's…

局部影响分析中的扰动选择与影响度量AI 解读

Cook’s [J. Roy. Statist. Soc. Ser. B 48 (1986) 133–169] local influence approach based on normal curvature is an important diagnostic tool for assessing local influence of minor perturbations to a statistical model. However, no rigorous approach has been developed to address two fundamental issues: the selection of an appropriate perturbation and the development of influence measures for objective functions at a point with a nonzero first derivative. The aim of this paper is…

Cox 回归模型的后验适定性与计算：缺失协变量应用AI 解读

In this paper, we carry out an in-depth theoretical investigation of Bayesian inference for the Cox regression model. We establish necessary and sufficient conditions for posterior propriety of the regression coefficient, β, in Cox's partial likelihood, which can be obtained as the limiting marginal posterior distribution of β through the specification of a gamma process prior for the cumulative baseline hazard and a uniform improper prior for β. We also examine necessary…

癌症预防研究中的生存数据模型AI 解读

Summary We propose a new parametric survival model for cancer prevention studies. The formulation of the model is in the spirit of stochastic modelling of the occurrences of tumours through two stages: initiation of an undetected tumour and promotion of the tumour to a detectable cancer. Several novel properties of the model proposed are derived. In addition, we examine the relationship of our model with the existing lagged regression model of Zucker and Lakatos. Also, we…

具有类似最大似然性质的缺失协变量加权估计方程AI 解读

In regression analysis, missing covariate data occurs often. A recent approach to analyzing such data is weighted estimating equations. With weighted estimating equations, the contribution to the estimating equation from a complete observation is weighted by the inverse probability of being observed. In this article we propose a weighted estimating equation that is almost identical to the maximum likelihood estimating equations. As such, we propose an EM-type algorithm to…

具有类似最大似然性质的缺失协变量加权估计方程AI 解读

In regression analysis, missing covariate data occurs often. A recent approach to analyzing such data is weighted estimating equations. With weighted estimating equations, the contribution to the estimating equation from a complete observation is weighted by the inverse probability of being observed. In this article we propose a weighted estimating equation that is almost identical to the maximum likelihood estimating equations. As such, we propose an EM-type algorithm to…

含治愈比例生存数据的新贝叶斯模型AI 解读

We consider Bayesian methods for right-censored survival data for populations with a surviving (cure) fraction. We propose a model that is quite different from the standard mixture model for cure rates. We provide a natural motivation and interpretation of the model and derive several novel properties of it. First, we show that the model has a proportional hazards structure, with the covariates depending naturally on the cure rate. Second, we derive several properties of the…

Logistic 回归模型的先验 elicitation、变量选择与贝叶斯计算AI 解读

Summary Bayesian selection of variables is often difficult to carry out because of the challenge in specifying prior distributions for the regression parameters for all possible models, specifying a prior distribution on the model space and computations. We address these three issues for the logistic regression model. For the first, we propose an informative prior distribution for variable selection. Several theoretical and computational properties of the prior are derived…

缺失机制不可忽略时广义线性模型中的协变量缺失AI 解读

Summary We propose a method for estimating parameters in generalized linear models with missing covariates and a non-ignorable missing data mechanism. We use a multinomial model for the missing data indicators and propose a joint distribution for them which can be written as a sequence of one-dimensional conditional distributions, with each one-dimensional conditional distribution consisting of a logistic regression. We allow the covariates to be either categorical or…

利用历史对照调整二元数据趋势检验中的协变量AI 解读

Historical data often play an important role in helping interpret the results of a current study. This article is motivated primarily by one specific application: the analysis of data from rodent carcinogenicity studies. By proposing a suitable informative prior distribution on the relationship between control outcome data and covariates, we derive modified trend test statistics that incorporate historical control information to adjust for covariate effects. Frequentist and…

预测式模型选择AI 解读

SUMMARY We consider the problem of selecting one model from a large class of plausible models. A predictive Bayesian viewpoint is advocated to avoid the specification of prior probabilities for the candidate models and the detailed interpretation of the parameters in each model. Using criteria derived from a certain predictive density and a prior specification that emphasizes the observables, we implement the proposed methodology for three common problems arising in normal…

设计实验分析的预测方法AI 解读

Viewing the analysis of designed experiments as a model selection problem, we introduce the use of a predictive Bayesian criterion in this context based on the predictive density of a replicate experiment (PDRE). A calibration of the criterion is provided to assist in the model choice. The relationships of the proposed criterion to other prevalent criteria, such as AIC, BIC, and Mallows's C p , are given. An information theoretic criterion based on the PDRE's of two…