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分量分别删失的复合终点半参数回归分析AI 解读

Composite endpoints with censored data are commonly used as study outcomes in clinical trials. For example, progression-free survival is a widely used composite endpoint, with disease progression and death as the two components. Progression-free survival time is often defined as the time from randomization to the earlier occurrence of disease progression or death from any cause. The censoring times of the two components could be different for patients not experiencing the…

存在依赖性终止时复发事件数据的现有分析方法AI 解读

There has been a recent surge of interest in modeling and methods for analyzing recurrent events data with risk of termination dependent on the history of the recurrent events. To aid the future users in understanding the implications of modeling assumptions and modeling properties, we review the state of the art statistical methods and present novel theoretical properties, identifiability results and practical consequences of key modeling assumptions of several fully…

协变量随机缺失回归模型的筛极大似然估计AI 解读

Missing covariates are common in regression problems. We propose a new semiparametric method based on a fully nonparametric distribution for the missing covariates that are assumed to be missing at random. The method of sieve maximum likelihood estimation is used to obtain the estimators of the regression coefficients. These estimators are shown to be consistent and asymptotically normal with their asymptotic covariance matrix that achieves the semiparametric efficiency…

扩散加权磁共振成像中扩散张量的统计分析AI 解读

Diffusion tensor imaging has been widely used to reconstruct the structure and orientation of fibers in biological tissues, particularly in the white matter of the brain, because it can track the effective diffusion of water along those fibers. The raw diffusion-weighted images from which diffusion tensors are estimated, however, inherently contain noise. Noise in the images produces uncertainty in the estimation of the tensors (which are 3 × 3 positive-definite matrices)…

用于发现基因调控网络的回归混合模型变量选择AI 解读

The profusion of genomic data through genome sequencing and gene expression microarray technology has facilitated statistical research in determining gene interactions regulating a biological process. Current methods generally consist of a two-stage procedure: clustering gene expression measurements and searching for regulatory “switches,” typically short, conserved sequence patterns (motifs) in the DNA sequence adjacent to the genes. This process often leads to misleading…

营养流行病学中的结构化测量误差AI 解读

Preterm birth, defined as delivery before 37 completed weeks' gestation, is a leading cause of infant morbidity and mortality. Identifying factors related to preterm delivery is an important goal of public health professionals who wish to identify etiologic pathways to target for prevention. Validation studies are often conducted in nutritional epidemiology in order to study measurement error in instruments that are generally less invasive or less expensive than "gold…

含治愈比例的生存数据半参数变换模型AI 解读

We propose a class of transformation models for survival data with a cure fraction. The class of transformation models is motivated by biological considerations and includes both the proportional hazards and the proportional odds cure models as two special cases. An efficient recursive algorithm is proposed to calculate the maximum likelihood estimators (MLEs). Furthermore, the MLEs for the regression coefficients are shown to be consistent and asymptotically normal, and…

贝叶斯模型平均及其在饮用水砷暴露基准剂量估计中的应用AI 解读

An important component of quantitative risk assessment involves characterizing the dose-response relationship between an environmental exposure and adverse health outcome and then computing a benchmark dose, or the exposure level that yields a suitably low risk. This task is often complicated by model choice considerations, because risk estimates depend on the model parameters. We propose using Bayesian methods to address the problem of model selection and derive a…

一类变换风险率模型的统计推断AI 解读

A new class of transformed hazard rate models is considered that contains both the multiplicative hazards model and the additive hazards model as special cases. The sieve maximum likelihood estimators are derived for the model parameters, and the estimators for the regression coefficients are shown to be consistent and asymptotically normal with variance achieving the semiparametric efficiency bound. Simulation studies are conducted to examine the small-sample properties of…

广义线性模型的缺失数据方法AI 解读

Missing data is a major issue in many applied problems, especially in the biomedical sciences. We review four common approaches for inference in generalized linear models (GLMs) with missing covariate data: maximum likelihood (ML), multiple imputation (MI), fully Bayesian (FB), and weighted estimating equations (WEEs). There is considerable interest in how these four methodologies are related, the properties of each approach, the advantages and disadvantages of each…

随机缺失协变量回归模型的后验正常化与极大似然估计存在性AI 解读

Characterizing model identifiability in the presence of missing covariate data is a very important issue in missing data problems. In this article, we characterize the propriety of the posterior distribution of the regression coefficients for some general classes of regression models, including the class of generalized linear models (GLM's) and parametric survival models with right-censored data. Toward this goal, we derive some very general and easy-to-check conditions for…

DNA 微阵列基因表达数据的贝叶斯模型AI 解读

Two of the critical issues that arise when examining DNA microarray data are (1) determination of which genes best discriminate among the different types of tissue, and (2) characterization of expression patterns in tumor tissues. For (1), there are many genes that characterize DNA expression, and it is of critical importance to try and identify a small set of genes that best discriminate between normal and tumor tissues. For (2), it is critical to be able to characterize…

Cox 比例风险模型中缺失协变量的似然方法AI 解读

Problems associated with missing covariate data are well known but often ignored. We present a method for estimating the parameters in the Cox proportional hazards model when the missing data are missing at random (MAR) and censoring is noninformative. Due to the computational burden of this method, we introduce an approximation that allows us to use a weighted expectation-maximization (EM) algorithm to estimate the parameters more easily. When the missing covariates are…