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

Censoring Survival Data01:09

Censoring Survival Data

529
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
529
Prediction Intervals01:03

Prediction Intervals

3.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.3K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.0K
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...
1.0K
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

577
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
577
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

456
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
456
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

398
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
398

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 authorSame journal

A New Estimation Algorithm for Destructive Cure Model: Illustration with Exponentially Weighted Poisson Competing Risks.

Communications in statistics: Simulation and computation·2026
Same author

A Support vector machine-based mixture cure model for mixed case interval censored data.

Statistics and computing·2026
Same author

A PINN-driven game-theoretic framework in limited data photoacoustic tomography.

Inverse problems·2025
Same author

Machine Learning Approach for Analyzing Mixed Case Interval Censored Data with a Cured Subgroup.

Advances in statistical analysis : AStA : a journal of the German Statistical Society·2025
Same author

A Neural Network Integrated Accelerated Failure Time-Based Mixture Cure Model.

Statistics and computing·2025
Same author

A New Cure Rate Model with Discrete and Multiple Exposures.

Communications in statistics: Simulation and computation·2025
Same journal

Simulating survival data with predefined censoring rates under a mixture of non-informative right censoring schemes.

Communications in statistics: Simulation and computation·2026
Same journal

Sampling Spiked Wishart Eigenvalues.

Communications in statistics: Simulation and computation·2025
Same journal

BayCAR: A Bayesian based Covariate-Adaptive Randomization method for multi-arm trials.

Communications in statistics: Simulation and computation·2025
Same journal

Bayesian variable selection for logistic regression with a differentially misclassified binary covariate.

Communications in statistics: Simulation and computation·2025
Same journal

Statistical methods for assessing treatment effects on ordinal outcomes using observational data.

Communications in statistics: Simulation and computation·2025
查看所有相关文章

相关实验视频

Updated: Jan 17, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.5K

对于半参数转换治疗模型的基于概率的推理,使用间隔审查数据.

Suvra Pal1,2, Sandip Barui3

  • 1Department of Mathematics, University of Texas at Arlington, 411 S Nedderman Drive, Arlington, TX, 76019, USA.

Communications in statistics: Simulation and computation
|September 18, 2025
PubMed
概括
此摘要是机器生成的。

盒子-考克斯转换治愈模型 (BCTM) 有效地将生存数据与间隔审查数据的治愈分数模拟为模型. 预期最大化算法增强了参数估计,以提高生存分析的准确性.

关键词:
盒子-Cox转换的变化在EM算法中,EM算法一块一块的线性近似.同时最大化的最大化.戒烟的方法是戒烟.统一的治疗模式 统一的治疗模式

更多相关视频

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.0K
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

10.8K

相关实验视频

Last Updated: Jan 17, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.5K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.0K
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

10.8K

科学领域:

  • 生物统计学 生物统计学
  • 生存分析的分析.
  • 统计建模 统计建模

背景情况:

  • 使用治愈分数的生存数据需要专门的建模技术.
  • 现有的模型,如混合和推广的时间治愈模型都有局限性.
  • 盒子-考克斯转换疗法模型 (BCTM) 提供了一个统一的方法.

研究的目的:

  • 为了数值地研究BCTM对间隔审查数据的统计特性.
  • 开发和评估一个预期最大化 (EM) 算法用于BCTM中的参数估计.
  • 在各种条件下评估模型的性能和估计准确性.

主要方法:

  • 将Box-Cox转换疗法模型 (BCTM) 应用于间隔审查的生存数据.
  • 模拟时间到事件数据使用比例危险结构与非参数基线危险.
  • 开发一个预期最大化 (EM) 算法,用于对模型参数的最大概率估计,包括Box-Cox转换参数 (α).

主要成果:

  • 开发的EM算法有效地同时估计BCTM参数,与传统的概率分析方法不同.
  • 模拟研究证明了BCTM和EM估计方法在各种参数设置中的稳定性和准确性.
  • 该模型和方法在应用到吸烟戒烟研究中的真实数据时表现良好.

结论:

  • 该BCTM是一个多功能和有效的工具,用于建模生存数据与治愈分数,特别是对于间隔审查的数据.
  • 拟议的EM算法为BCTM框架内的参数估计提供了强大而准确的方法.
  • 这些发现支持BCTM和EM算法的在生物统计学研究和应用中的实际实用性.