Outcome-Dependent Sampling Design and Inference for Cox's Proportional Hazards Model [0.03%]
基于结果的抽样设计及Cox比例风险模型的推断方法
Jichang Yu,Yanyan Liu,Jianwen Cai et al.
Jichang Yu et al.
We propose a cost-effective outcome-dependent sampling design for the failure time data and develop an efficient inference procedure for data collected with this design. To account for the biased sampling scheme, we derive estimators from a...
Fang-Shu Ou,Donglin Zeng,Jianwen Cai
Fang-Shu Ou
Current status data arise frequently in demography, epidemiology, and econometrics where the exact failure time cannot be determined but is only known to have occurred before or after a known observation time. We propose a quantile regressi...
J McGinniss,O Harel
J McGinniss
Missing values present challenges in the analysis of data across many areas of research. Handling incomplete data incorrectly can lead to bias, over-confident intervals, and inaccurate inferences. One principled method of handling incomplet...
Rigorous Error Control Methods for Estimating Means of Bounded Random Variables [0.03%]
有界随机变量均值估计的严格误差控制方法
Zhengjia Chen,Xinjia Chen
Zhengjia Chen
In this article, we propose rigorous sample size methods for estimating the means of random variables, which require no information of the underlying distributions except that the random variables are known to be bounded in a certain interv...
Uniformly Most Powerful Tests for Simultaneously Detecting a Treatment Effect in the Overall Population and at Least One Subpopulation [0.03%]
同时检测总体和至少一个子群体中治疗效果的最强一致检验方法
Michael Rosenblum
Michael Rosenblum
We take the perspective of a researcher planning a randomized trial of a new treatment, where it is suspected that certain subpopulations may benefit more than others. These subpopulations could be defined by a risk factor or biomarker meas...
Exploring dependence between categorical variables: Benefits and limitations of using variable selection within Bayesian clustering in relation to log-linear modelling with interaction terms [0.03%]
探索类别变量之间的依赖关系：在贝叶斯聚类中使用变量选择与通过交互作用项进行对数线性建模的关系及其优缺点
Michail Papathomas,Sylvia Richardson
Michail Papathomas
This manuscript is concerned with relating two approaches that can be used to explore complex dependence structures between categorical variables, namely Bayesian partitioning of the covariate space incorporating a variable selection proced...
A Two-step Estimation Approach for Logistic Varying Coefficient Modeling of Longitudinal Data [0.03%]
纵向数据逻辑变系数模型的两步估计方法
Jun Dong,Jason P Estes,Gang Li et al.
Jun Dong et al.
Varying coefficient models are useful for modeling longitudinal data and have been extensively studied in the past decade. Motivated by commonly encountered dichotomous outcomes in medical and health cohort studies, we propose a two-step me...
A general approach to categorizing a continuous scale according to an ordinal outcome [0.03%]
一种根据序数结果对连续量表进行分类的通用方法
Limin Peng,Amita Manatunga,Ming Wang et al.
Limin Peng et al.
In practice, disease outcomes are often measured in a continuous scale, and classification of subjects into meaningful disease categories is of substantive interest. To address this problem, we propose a general analytic framework for deter...
Jeankyung Kim,Hyune-Ju Kim
Jeankyung Kim
The Schwarz criterion or Bayes Information Criterion (BIC) is often used to select a model dimension, and some variations of the BIC have been proposed in the context of change-point problems. In this paper, we consider a segmented line reg...
A Bayesian Nonparametric Approach for the Analysis of Multiple Categorical Item Responses [0.03%]
分析多个分类项目反应的贝叶斯非参数方法
Andrew Waters,Kassandra Fronczyk,Michele Guindani et al.
Andrew Waters et al.
We develop a modeling framework for joint factor and cluster analysis of datasets where multiple categorical response items are collected on a heterogeneous population of individuals. We introduce a latent factor multinomial probit model an...