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多个时变关联群组的建模：动态层次贝叶斯方法及其在健康与退休研究中的应用AI 解读

As the population of the older individuals continues to grow, it is important to study the relationship among the variables measuring financial health and physical health of the older individuals to better understand the demand for healthcare, and health insurance. We propose a semiparametric approach to jointly model these variables. We use data from the Health and Retirement Study which includes a set of correlated longitudinal variables measuring financial and physical…

对 PENCOMP 的讨论AI 解读

We would like to congratulate Zhou, Elliott, and Little (henceforth referred to as ZEL) on an interesting article in an important area of research. ZEL propose a new approach for causal inference u...

非可忽略单调缺失纵向数据的灵活贝叶斯方法及精神分裂症试验应用AI 解读

We develop a Bayesian nonparametric model for a longitudinal response in the presence of nonignorable missing data. Our general approach is to first specify a working model that flexibly models the missingness and full outcome processes jointly. We specify a Dirichlet process mixture of missing at random (MAR) models as a prior on the joint distribution of the working model. This aspect of the model governs the fit of the observed data by modeling the observed data…

竞争传染病原体的贝叶斯建模：实验室结果不完整的流感研究AI 解读

In seasonal influenza epidemics, pathogens such as respiratory syncytial virus (RSV) often co-circulate with influenza and cause influenza-like illness (ILI) in human hosts. However, it is often impractical to test for each potential pathogen or to collect specimens for each observed ILI episode, making inference about influenza transmission difficult. In the setting of infectious diseases, missing outcomes impose a particular challenge because of the dependence among…

疾病进展或死亡导致信息性缺失时的治疗因果效应AI 解读

In longitudinal clinical trials, when outcome variables at later time points are only defined for patients who survive to those times, the evaluation of the causal effect of treatment is complicated. In this paper, we describe an approach that can be used to obtain the causal effect of three treatment arms with ordinal outcomes in the presence of death using a principal stratification approach. We introduce a set of flexible assumptions to identify the causal effect and…

纵向二元与连续过程关联的联合模型及戒烟试验应用AI 解读

Joint models for the association of a longitudinal binary and a longitudinal continuous process are proposed for situations in which their association is of direct interest. The models are parameterized such that the dependence between the two processes is characterized by unconstrained regression coefficients. Bayesian variable selection techniques are used to parsimoniously model these coefficients. A Markov chain Monte Carlo (MCMC) sampling algorithm is developed for…

谱密度的收缩估计量AI 解读

We propose a shrinkage estimator for spectral densities based on a multilevel normal hierarchical model. The first level captures the sampling variability via a likelihood constructed using the asymptotic properties of the periodogram. At the second level, the spectral density is shrunk towards a parametric time series model. To avoid selecting a particular parametric model for the second level, a third level is added which induces an estimator that averages over a class of…

协方差矩阵的非共轭贝叶斯估计及其在层级模型中的应用AI 解读

The problem of estimating a covariance matrix in small samples has been considered by several authors following early work by Stein. This problem can be especially important in hierarchical models where the standard errors of fixed and random effects depend on estimation of the covariance matrix of the distribution of the random effects. We propose a set of hierarchical priors (HPs) for the covariance matrix that produce posterior shrinkage toward a specified structure—here…

医疗服务利用差异分析中的层级广义线性模型AI 解读

In recent years many studies have reported large differences in the use of medical treatments and procedures across geographic regions, hospitals, and individual health care providers. Beyond reporting on the extent of observed variations, these studies examine the role of contributing factors including patient, regional, and provider characteristics. In addition, they may assess the relation between health care processes and outcomes, such as patient mortality, morbidity,…