Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Cancer Survival Analysis01:21

Cancer Survival Analysis

633
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
633
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

541
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
541
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

885
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
885
Biostatistics: Overview01:20

Biostatistics: Overview

705
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
705
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

995
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
995
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

1.4K
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
1.4K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Dyslexia, School-Connectedness, Depression, and Anxiety During the Transition From Primary to Secondary School.

Dyslexia (Chichester, England)·2026
Same author

Geographic and Social Equity in Population-Wide Genomic Screening.

JAMA network open·2026
Same author

Sex differences in stress responding in a randomized, placebo-controlled clinical trial of progesterone in cannabis use disorder.

Psychopharmacology·2026
Same author

Meta-analysis of survival by phased-variant ctDNA and PET response in large B-cell lymphoma.

Blood advances·2026
Same author

Phase I study of oral azacitidine plus salvage chemotherapy in relapsed/refractory diffuse large B-cell lymphoma.

Annals of hematology·2026
Same author

Reply.

Gastroenterology·2026

相关实验视频

Updated: Jan 10, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

475

GRASS-NB:对空间负二项式数据的组结构变量选择与癌症注册表和空间奥米克的应用.

Chloe Mattila1, Brian Neelon1, Kalyani Sonawane1

  • 1Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA.

bioRxiv : the preprint server for biology
|November 24, 2025
PubMed
概括

本研究引入了贝叶斯负二项式回归模型,用于分析复杂的空间计数数据. 它通过结合一个新的组结构的先验来增强特征选择,改进了关键风险因素和生物标志物的识别.

关键词:
一个层次的收缩.马之前的马之前的负二项式分布的负二项式分布.空间计数数据空间计数数据在之前的尖峰和板块之前.

更多相关视频

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.1K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.7K

相关实验视频

Last Updated: Jan 10, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

475
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.1K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.7K

科学领域:

  • 生物统计学 生物统计学
  • 空间流行病学 空间流行病学
  • 计算生物学 计算生物学

背景情况:

  • 在流行病学和空间奥米克学中,分析具有空间结构的,过度分散的计数数据与许多预测器是常见的.
  • 有效的特征选择对于识别这些数据集中的风险因素或生物标志物至关重要.
  • 现有的贝叶斯负二项回归模型具有可变选择先验,往往缺乏空间考虑和组结构.

研究的目的:

  • 为空间自相关计数数据提出一个灵活的贝叶斯负二项式回归模型.
  • 通过结合尖和板和连续马方法来进行增强的特征选择,引入一种新的组结构预先.
  • 评估模型的性能,包括特异性,精度和计算效率,特别是在高维设置中.

主要方法:

  • 开发了一个贝叶斯负二项式回归框架,包含空间自相关性.
  • 引入了混合组结构的先前组合尖和板和连续形收缩.
  • 使用模拟评估模型性能,包括"大p,小n"场景,并将其应用于现实世界的数据集.

主要成果:

  • 拟议的模型有效地处理数量数据中的空间自相关性和高维预测器.
  • 新型组结构的先验在特征选择方面表现强.
  • 该模型成功应用于识别癌症风险因素,并预测空间奥米克数据中的基因表达.

结论:

  • 开发的贝叶斯负二项回归模型与新型组结构前期提供了一个灵活而强大的工具来分析复杂的空间计数数据.
  • 这种方法增强了从人口流行病学到空间经济学等领域影响力预测因素的识别.
  • 一个R包可用,以促进这种方法的应用.