Integrating Proteomic Analysis and Machine Learning to Predict Prostate Cancer Aggressiveness [0.03%]
整合蛋白质组学分析和机器学习以预测前列腺癌的侵袭性
Sheila M Valle Cortés,Jaileene Pérez Morales,Mariely Nieves Plaza et al.
Sheila M Valle Cortés et al.
Prostate cancer (PCa) poses a significant challenge because of the difficulty in identifying aggressive tumors, leading to overtreatment and missed personalized therapies. Although only 8% of cases progress beyond the prostate, the accurate...
Doubly Robust Estimation and Semiparametric Efficiency in Generalized Partially Linear Models with Missing Outcomes [0.03%]
带有缺失结果的广义半参数线性模型中的双重稳健估计和拟似然效率
Lu Wang,Zhongzhe Ouyang,Xihong Lin
Lu Wang
We investigate a semiparametric generalized partially linear regression model that accommodates missing outcomes, with some covariates modeled parametrically and others nonparametrically. We propose a class of augmented inverse probability ...
Exact Inference for Random Effects Meta-Analyses for Small, Sparse Data [0.03%]
小数据稀疏数据的随机效应Meta分析的精确推理
Jessica Gronsbell,Zachary R McCaw,Timothy Regis et al.
Jessica Gronsbell et al.
Meta-analysis aggregates information across related studies to provide more reliable statistical inference and has been a vital tool for assessing the safety and efficacy of many high-profile pharmaceutical products. A key challenge in cond...
Assessing Spillover Effects of Medications for Opioid Use Disorder on HIV Risk Behaviors among a Network of People Who Inject Drugs [0.03%]
阿片类药物使用障碍治疗的溢出效应及其对注射吸毒人群艾滋病风险行为的影响评估
Joseph Puleo,Ashley Buchanan,Natallia Katenka et al.
Joseph Puleo et al.
People who inject drugs (PWID) have an increased risk of HIV infection partly due to injection behaviors often related to opioid use. Medications for opioid use disorder (MOUD) have been shown to reduce HIV infection risk, possibly by reduc...
Bidirectional f-Divergence-Based Deep Generative Method for Imputing Missing Values in Time-Series Data [0.03%]
基于双向f散度的深度生成方法在时间序列数据中填补缺失值
Wen-Shan Liu,Tong Si,Aldas Kriauciunas et al.
Wen-Shan Liu et al.
Imputing missing values in high-dimensional time-series data remains a significant challenge in statistics and machine learning. Although various methods have been proposed in recent years, many struggle with limitations and reduced accurac...
Bayesian Mediation Analysis with an Application to Explore Racial Disparities in the Diagnostic Age of Breast Cancer [0.03%]
用于探讨乳腺癌诊断年龄的种族差异的一种中介分析方法
Wentao Cao,Joseph Hagan,Qingzhao Yu
Wentao Cao
A mediation effect refers to the effect transmitted by a mediator intervening in the relationship between an exposure variable and a response variable. Mediation analysis is widely used to identify significant mediators and to make inferenc...
Investigating Risk Factors for Racial Disparity in E-Cigarette Use with PATH Study [0.03%]
利用PATH研究调查种族差异的电子烟使用风险因素
Amy Liu,Kennedy Dorsey,Almetra Granger et al.
Amy Liu et al.
Background: Previous research has identified differences in e-cigarette use and socioeconomic factors between different racial groups However, there is little research examining specific risk factors contributing to the r...
Multivariate Time Series Change-Point Detection with a Novel Pearson-like Scaled Bregman Divergence [0.03%]
一种新的 pearson 类似比例 bregman 熵用于多元时间序列数据异常点检测
Tong Si,Yunge Wang,Lingling Zhang et al.
Tong Si et al.
Change-point detection is a challenging problem that has a number of applications across various real-world domains. The primary objective of CPD is to identify specific time points where the underlying system undergoes transitions between ...
A Comparison of Existing Bootstrap Algorithms for Multi-Stage Sampling Designs [0.03%]
多重抽样设计中自举算法的比较研究
Sixia Chen,David Haziza,Zeinab Mashreghi
Sixia Chen
Multi-stage sampling designs are often used in household surveys because a sampling frame of elements may not be available or for cost considerations when data collection involves face-to-face interviews. In this context, variance estimatio...
Evaluation of Risk Prediction with Hierarchical Data: Dependency Adjusted Confidence Intervals for the AUC [0.03%]
分级数据风险预测的评估：AUC的依赖性校正置信区间
Camden Bay,Robert J Glynn,Johanna M Seddon et al.
Camden Bay et al.
The area under the true ROC curve (AUC) is routinely used to determine how strongly a given model discriminates between the levels of a binary outcome. Standard inference with the AUC requires that outcomes be independent of each other. To ...