Selecting Biomarkers for building optimal treatment selection rules using Kernel Machines [0.03%]
使用核机器选择生物标志物以建立最优的治疗选择规则
Sayan Dasgupta,Ying Huang
Sayan Dasgupta
Optimal biomarker combinations for treatment-selection can be derived by minimizing total burden to the population caused by the targeted disease and its treatment. However, when multiple biomarkers are present, including all in the model c...
A spatially varying distributed lag model with application to an air pollution and term low birth weight study [0.03%]
适用于空气污染和早产儿低出生体重研究的时变分布滞后模型
Joshua L Warren,Thomas J Luben,Howard H Chang
Joshua L Warren
Distributed lag models have been used to identify critical pregnancy periods of exposure (i.e. critical exposure windows) to air pollution in studies of pregnancy outcomes. However, much of the previous work in this area has ignored the pos...
Case-only trees and random forests for exploring genotype-specific treatment effects in randomized clinical trials with dichotomous endpoints [0.03%]
基于随机临床试验二分类终点的病例树和随机森林用以探索基因型特异性治疗效应
James Y Dai,Michael LeBlanc
James Y Dai
Discovering gene-treatment interactions in clinical trials is of rising interest in the era of precision medicine. Nonparametric statistical learning methods such as trees and random forests are useful tools for building prediction rules. I...
Partially latent class models for case-control studies of childhood pneumonia aetiology [0.03%]
病例对照研究中儿童肺炎病因的潜变量模型分析
Zhenke Wu,Maria Deloria-Knoll,Laura L Hammitt et al.
Zhenke Wu et al.
In population studies on the aetiology of disease, one goal is the estimation of the fraction of cases that are attributable to each of several causes. For example, pneumonia is a clinical diagnosis of lung infection that may be caused by v...
Ana-Maria Staicu,Md Nazmul Islam,Raluca Dumitru et al.
Ana-Maria Staicu et al.
The paper develops a parsimonious modelling framework to study the time-varying association between scalar outcomes and functional predictors observed at many instances, in longitudinal studies. The methods enable us to reconstruct the full...
Fast parameter inference in a biomechanical model of the left ventricle by using statistical emulation [0.03%]
基于统计相似性的快速左心室生物力学模型参数估算方法
Vinny Davies,Umberto Noè,Alan Lazarus et al.
Vinny Davies et al.
A central problem in biomechanical studies of personalized human left ventricular modelling is estimating the material properties and biophysical parameters from in vivo clinical measurements in a timeframe that is suitable for use within a...
Improving the identification of antigenic sites in the H1N1 influenza virus through accounting for the experimental structure in a sparse hierarchical Bayesian model [0.03%]
通过在稀疏分层贝叶斯模型中考虑实验结构来改进H1N1流感病毒抗原位点的识别方法
Vinny Davies,William T Harvey,Richard Reeve et al.
Vinny Davies et al.
Understanding how genetic changes allow emerging virus strains to escape the protection afforded by vaccination is vital for the maintenance of effective vaccines. We use structural and phylogenetic differences between pairs of virus strain...
Bayesian non-parametric survival regression for optimizing precision dosing of intravenous busulfan in allogeneic stem cell transplantation [0.03%]
用于同种异体干细胞移植的静脉_BUSULFAN_精确给药的非参数贝叶斯生存回归优化
Yanxun Xu,Peter F Thall,William Hua et al.
Yanxun Xu et al.
Allogeneic stem cell transplantation (allo-SCT) is now part of standard of care for acute leukemia (AL). To reduce toxicity of the pre-transplant conditioning regimen, intravenous busulfan is usually used as a preparative regimen for AL pat...
Landmark Linear Transformation Model for Dynamic Prediction with Application to a Longitudinal Cohort Study of Chronic Disease [0.03%]
慢性病纵向队列研究的动态预测的线性转化模型及其应用
Yayuan Zhu,Liang Li,Xuelin Huang
Yayuan Zhu
Dynamic prediction of the risk of a clinical event using longitudinally measured biomarkers or other prognostic information is important in clinical practice. We propose a new class of landmark survival models. The model takes the form of a...
Additive quantile regression for clustered data with an application to children's physical activity [0.03%]
具簇数据的加法分位数回归及其在儿童运动量分析中的应用
Marco Geraci
Marco Geraci
Additive models are flexible regression tools that handle linear as well as non-linear terms. The latter are typically modelled via smoothing splines. Additive mixed models extend additive models to include random terms when the data are sa...