A Multiple Imputation Approach to Distinguish Curative From Life-Prolonging Effects in the Presence of Missing Covariates [0.03%]
一种在协变量缺失时区分治愈效果与延寿效果的多重插补方法
Marta Cipriani,Marta Fiocco,Marco Alfò et al.
Marta Cipriani et al.
Medical advances have increased cancer survival rates and the possibility of finding a cure. Hence, it is crucial to evaluate the impact of treatments both in terms of cure and prolongation of survival. To achieve this, we may use a Cox pro...
Tests for Categorical Data Beyond Pearson: A Distance Covariance and Energy Distance Approach [0.03%]
超越皮尔逊检验的定性数据分析：距离协方差和能量度量方法
Fernando Castro-Prado,Wenceslao González-Manteiga,Javier Costas et al.
Fernando Castro-Prado et al.
Categorical variables are of uttermost importance in biomedical research. When two of them are considered, it is often the case that one wants to test whether or not they are statistically dependent. We show weaknesses of classical methods-...
Alessandra Ragni,Torben Martinussen,Thomas Scheike
Alessandra Ragni
In clinical trials with recurrent events, such as repeated hospitalizations terminating with death, it is important to consider the patient events overall history for a thorough assessment of treatment effects. The occurrence of fewer event...
Two-Stage Multiple Test Procedures Controlling False Discovery Rate With Auxiliary Variable and Their Application to Set4 [Formula: see text] Mutant Data [0.03%]
带有辅助变量控制错误发现率的两阶段多重检验及其在SETDB1突变数据中的应用
Seohwa Hwang,Mark Louie Ramos,DoHwan Park et al.
Seohwa Hwang et al.
In this paper, we present novel methodologies that incorporate auxiliary variables for multiple hypotheses testing related to the main point of interest while effectively controlling the false discovery rate. When dealing with multiple test...
The Univariate Distribution of Hierarchical Composite Endpoints and the Condorcet Non-transitivity Paradox [0.03%]
层次复合终点的一元分布和康德罗杰非传递性悖论
Samvel B Gasparyan,Gary G Koch,Edgar Brunner
Samvel B Gasparyan
Hierarchical Composite Endpoints (HCEs), as analyzed with Generalized Pairwise Comparisons (GPC) statistics, are general methods of constructing endpoints in clinical trials across various therapeutic areas to establish the efficacy of nove...
HPV-Adjusted Feature Screening With FDR Control in Head and Neck Cancer [0.03%]
用于头颈癌的FDR调整的人乳头瘤病毒特征筛选算法
Atika Farzana Urmi,Chenlu Ke,Dipankar Bandyopadhyay
Atika Farzana Urmi
Human papillomavirus (HPV) is a well-established prognostic factor in head and neck (HN) cancer, with HPV-positive patients exhibiting markedly better survival outcomes compared to their HPV-negative counterparts. While advances in (cancer)...
Yanfei He,Jianhong Shi,Weixing Song
Yanfei He
In this paper, we present a Monte Carlo method for estimating a nonlinear function of the mean of a multivariate normal distribution. Building on this method, we propose a parametric estimation procedure for unimodal regression models, assu...
Optimal Designs in Open-Cohort Longitudinal Cluster Randomized Trials With a Continuous Outcome [0.03%]
开放式队列纵向集群随机试验的连续结局的最优设计方法研究
Jingxia Liu,Fan Li,Xuping Luo et al.
Jingxia Liu et al.
Although sample size calculation for open-cohort longitudinal cluster randomized trials (LCRTs) under a fixed design framework was developed by Kasza et al., unifying the closed-cohort and repeated cross-sectional sampling provided in Hoope...
A Novel Secondary-Outcome Approach to Estimating Primary Causal Effects With Unmeasured Confounders [0.03%]
具有未测量混淆因素的主要因果效应的新型次要结果估计方法
Desu Kong,Minghao Chen,Yingchun Zhou
Desu Kong
Unmeasured confounding remains a fundamental challenge permeating contemporary causal inference, substantially impeding the valid estimation of treatment effects. A rigorous identification strategy is presented for estimating average causal...
The DeepJoint Algorithm: An Innovative Approach for Studying the Longitudinal Evolution of Quantitative Mammographic Density and Its Association With Screen-Detected Breast Cancer Risk [0.03%]
一种创新算法：纵向分析定量乳腺密度及其与乳腺癌筛查检出风险相关性的新方法
Manel Rakez,Julien Guillaumin,Aurelien Chick et al.
Manel Rakez et al.
High mammographic density is a well-known risk factor for breast cancer and reduces the sensitivity of mammography-based screening. While automated machine and deep learning-based methods provide more consistent and precise measurements com...