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

Sampling Plans01:23

Sampling Plans

180
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
180
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

36
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
36
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

119
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,...
119
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

172
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...
172
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

120
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.
120
Cluster Sampling Method01:20

Cluster Sampling Method

11.8K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.8K

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

Beyond Surgical Margins: Fully Mature Tertiary Lymphoid Structures (fmTLSs) Are Predictive Biomarkers for Local Recurrence in Primary Soft-Tissue Sarcomas.

Cancers·2026
Same author

Empirical Comparison of Win Ratio and Joint Frailty Models for Recurrent Event Endpoints With Applications in Oncology and Cardiology.

Statistics in medicine·2026
Same author

Epidemiology and Treatment Patterns of Stage III Resectable Melanoma Treated with Adjuvant Therapy: A Real-World Study Using the SNDS Database in France.

Dermatology and therapy·2026
Same author

Metastatic Ewing Sarcoma, Patterns of Care and Outcomes of Patients in a Real-Life National Setting Over a Decade.

Cancer medicine·2026
Same author

Mediation Analysis With Bayesian Nonlinear Joint Models: Evaluation of the Treatment Causal Pathways Between Tumor Growth Kinetics and Overall Survival.

Statistics in medicine·2026
Same author

The DeepJoint Algorithm: An Innovative Approach for Studying the Longitudinal Evolution of Quantitative Mammographic Density and Its Association With Screen-Detected Breast Cancer Risk.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Impact of Information Leakage in Platform Trials With Survival Endpoints on Type I Error Control.

Pharmaceutical statistics·2026
Same journal

Harmonic Fowlkes-Mallows Index for Medical Diagnostics Tests and Optimal Cut-Off Point Selection of Binary Diseases.

Pharmaceutical statistics·2026
Same journal

Early Phase Dose-Finding Designs for CAR-T Cell Therapies.

Pharmaceutical statistics·2026
Same journal

Optimizing Randomization Ratios in Clinical Trials With Survival Endpoints.

Pharmaceutical statistics·2026
Same journal

CUI-MET: A Clinical Utility Index Based Analysis and Decision Framework for Dose Optimization in Multiple-Dose, Multiple-Outcome Randomized Trials.

Pharmaceutical statistics·2026
Same journal

Will the Pharmaceutical Industry Need Statisticians in an AI World?

Pharmaceutical statistics·2026
查看所有相关文章

相关实验视频

Updated: Jun 21, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.5K

使用部分聚类脆弱模型进行样本大小估计,用于多种治疗的生物标志物策略设计.

Derek Dinart1,2, Virginie Rondeau1,3, Carine Bellera1,2

  • 1Bordeaux Population Health Research Center, Epicene Team, U1219, University of Bordeaux, Inserm, Bordeaux, France.

Pharmaceutical statistics
|July 17, 2024
PubMed
概括
此摘要是机器生成的。

生物标志物策略设计 (BSD) 通过比较治疗策略来优化个性化医疗. 使用部分聚类脆弱模型 (PCFM) 的新模拟方法有助于估计这些试验的样本大小.

关键词:
生物标志物策略策略脆弱模型的脆弱性模型不同质性的异质性随机的,随机的,随机的,随机的,随机的样本的大小 样本大小

更多相关视频

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K
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

10.1K

相关实验视频

Last Updated: Jun 21, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.5K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K
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

10.1K

科学领域:

  • 生物统计学 生物统计学
  • 临床试验设计 临床试验设计
  • 个性化医疗是个性化的医疗.

背景情况:

  • 生物标志物引导治疗正在推动医学研究.
  • 优化生物标志物使用需要创新的研究设计.
  • 生物标志物策略设计 (BSD) 专注于治疗策略,而不仅仅是分子.

研究的目的:

  • 提出一种模拟方法,用于在生物标志物策略设计 (BSD) 中估计样本大小,使用多种向治疗.
  • 评估影响BSD样本大小的因素.
  • 为传统的样本大小计算方法提供替代方案.

主要方法:

  • 开发了一种基于部分聚类脆弱模型 (PCFM) 的模拟方法.
  • 用弗雷德林公式的扩展来估计样本大小.
  • 该方法应用于BSD,使用多种向治疗.

主要成果:

  • 拟议的PCFM模拟方法提供了BSD.的样本大小估计.
  • 影响样本大小的关键因素包括治疗效果异质性,生物标志物阴性患者的比例和随机化比率.
  • PCFM适用于BSD.中的数据结构.

结论:

  • 基于PCFM的模拟方法是生物标志物策略设计中样本大小计算的可行方法.
  • 该方法为复杂的试验设计提供了传统统计方法的替代方案.
  • 准确的样本大小估计对于生物标志物引导的治疗策略的成功至关重要.