Functional Multivariable Logistic Regression With an Application to HIV Viral Suppression Prediction [0.03%]
功能多重逻辑回归在HIV病毒抑制预测中的应用研究
Siyuan Guo,Jiajia Zhang,Yichao Wu et al.
Siyuan Guo et al.
Motivated by improving the prediction of the human immunodeficiency virus (HIV) suppression status using electronic health records (EHR) data, we propose a functional multivariable logistic regression model, which accounts for the longitudi...
Ningning Xu,Aldo Solari,Jelle J Goeman
Ningning Xu
Closed testing has recently been shown to be optimal for simultaneous true discovery proportion control. It is, however, challenging to construct true discovery guarantee procedures in such a way that it focuses power on some feature sets c...
Simultaneous Inference of Multiple Binary Endpoints in Biomedical Research: Small Sample Properties of Multiple Marginal Models and a Resampling Approach [0.03%]
医学研究中多个二元终点的同步推断：边际模型的小样本性质及一种重抽样方法
Sören Budig,Klaus Jung,Mario Hasler et al.
Sören Budig et al.
In biomedical research, the simultaneous inference of multiple binary endpoints may be of interest. In such cases, an appropriate multiplicity adjustment is required that controls the family-wise error rate, which represents the probability...
Penalized Regression Methods With Modified Cross-Validation and Bootstrap Tuning Produce Better Prediction Models [0.03%]
改进交叉验证和自助法调优的惩罚回归方法能更好地预测模型
Menelaos Pavlou,Rumana Z Omar,Gareth Ambler
Menelaos Pavlou
Risk prediction models fitted using maximum likelihood estimation (MLE) are often overfitted resulting in predictions that are too extreme and a calibration slope (CS) less than 1. Penalized methods, such as Ridge and Lasso, have been sugge...
Causal inference in the absence of positivity: The role of overlap weights [0.03%]
缺乏可实施性的因果推断：重叠权重的作用
Roland A Matsouaka,Yunji Zhou
Roland A Matsouaka
How to analyze data when there is violation of the positivity assumption? Several possible solutions exist in the literature. In this paper, we consider propensity score (PS) methods that are commonly used in observational studies to assess...
A Bayesian hierarchical hidden Markov model for clustering and gene selection: Application to kidney cancer gene expression data [0.03%]
一个用于聚类和基因选择的贝叶斯分层隐马尔可夫模型及其在肾癌基因表达数据分析中的应用
Thierry Chekouo,Himadri Mukherjee
Thierry Chekouo
We introduce a Bayesian approach for biclustering that accounts for the prior functional dependence between genes using hidden Markov models (HMMs). We utilize biological knowledge gathered from gene ontologies and the hidden Markov structu...
Adaptive predictor-set linear model: An imputation-free method for linear regression prediction on data sets with missing values [0.03%]
自适应预测集线性模型：数据缺失情况下的免插补线性回归预测方法
Benjamin Planterose Jiménez,Manfred Kayser,Athina Vidaki et al.
Benjamin Planterose Jiménez et al.
Linear regression (LR) is vastly used in data analysis for continuous outcomes in biomedicine and epidemiology. Despite its popularity, LR is incompatible with missing data, which frequently occur in health sciences. For parameter estimatio...
Valid instrumental variable selection method using negative control outcomes and constructing efficient estimator [0.03%]
基于负面结果的变量选择方法及有效估计量的构造
Shunichiro Orihara,Atsushi Goto,Masataka Taguri
Shunichiro Orihara
In observational studies, instrumental variable (IV) methods are commonly applied when there are unmeasured covariates. In Mendelian randomization, constructing an allele score using many single nucleotide polymorphisms is often implemented...
A nonparametric proportional risk model to assess a treatment effect in time-to-event data [0.03%]
一种非参数风险比例模型用于评估时间事件数据中的治疗效果
Lucia Ameis,Oliver Kuss,Annika Hoyer et al.
Lucia Ameis et al.
Time-to-event analysis often relies on prior parametric assumptions, or, if a semiparametric approach is chosen, Cox's model. This is inherently tied to the assumption of proportional hazards, with the analysis potentially invalidated if th...
Predicting class switch recombination in B-cells from antibody repertoire data [0.03%]
基于抗体谱数据预测B细胞类别转换重组
Lutecia Servius,Davide Pigoli,Joseph Ng et al.
Lutecia Servius et al.
Statistical and machine learning methods have proved useful in many areas of immunology. In this paper, we address for the first time the problem of predicting the occurrence of class switch recombination (CSR) in B-cells, a problem of inte...