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

Cluster Sampling Method01:20

Cluster Sampling Method

11.9K
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...
11.9K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

181
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...
181
Stratified Sampling Method01:16

Stratified Sampling Method

12.0K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures 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 stratified sample, divide the population into groups called strata and then take a...
12.0K
Sampling Plans01:23

Sampling Plans

181
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...
181
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

136
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,...
136
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

126
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.
126

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

Beta spending function based on conditional power in group sequential design.

Biometrical journal. Biometrische Zeitschrift·2024
Same author

The heterogeneity effect of surveillance intervals on progression free survival.

Journal of applied statistics·2024
Same author

Risk difference, relative risk, and odds ratio for non-inferiority clinical trials with risk rate endpoint.

Journal of biopharmaceutical statistics·2022
Same author

Midline signaling regulates kidney positioning but not nephrogenesis through Shh.

Developmental biology·2010
Same author

Y chromosomal STR polymorphism in northern Chinese populations.

Biological research·2010
Same author

Construction of NF-κB-targeting RNAi adenovirus vector and the effect of NF-κB pathway on proliferation and apoptosis of vascular endothelial cells.

Molecular biology reports·2010

相关实验视频

Updated: Jun 29, 2025

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

10.2K

用于分层集群随机化试验与生存终点的样本大小估计.

Senmiao Ni1, Zihang Zhong1, Yang Zhao1

  • 1Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.

Statistical methods in medical research
|March 29, 2024
PubMed
概括

这项研究引入了一个新的样本大小公式,用于分层集群随机化试验与生存终点. 这个公式准确地估计了样本大小,考虑了关键的设计因素,以提高临床研究中的统计能力.

关键词:
分层集群随机化试验分层集群随机化试验样本大小估计的估计.生存终点的终点是生存.不同的集群大小不同.

更多相关视频

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

261
A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
00:04

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition

Published on: September 20, 2019

10.7K

相关实验视频

Last Updated: Jun 29, 2025

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

10.2K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

261
A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
00:04

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition

Published on: September 20, 2019

10.7K

科学领域:

  • 生物统计学 生物统计学
  • 临床试验 临床试验
  • 流行病学 流行病学

背景情况:

  • 集群随机化试验在小组级干预研究中至关重要,特别是在药物开发中.
  • 与传统设计相比,分层集群随机化增强了对预后因素和集群大小可变性的控制.
  • 不准确的样本大小估计可能是由于忽视分层和集群大小变化造成的,导致研究不足.

研究的目的:

  • 为分层集群随机化试验与生存终点开发明确的样本大小公式.
  • 为样本大小估计提供综合解决方案,解决群集大小变化,基线危险异质性和集群内相关性.
  • 为研究人员提供一个实用的工具,以确保复杂的试验设计中的足够的统计能力.

主要方法:

  • 开发一种封闭形式的样本大小公式,利用分层集群日志等级统计.
  • 整合诸如集群大小变化,基线危险异质性和集群内相关系数等因素.
  • 通过各种参数配置的模拟研究进行验证.

主要成果:

  • 拟议的公式准确地估计了分层集群随机化试验的样本大小,具有生存终点.
  • 模拟研究证实了该公式在各种条件下实现所需统计功率的能力.
  • 该方法用冠状动脉心脏病患者的现实世界试验来说明.

结论:

  • 开发的样本大小公式是设计具有生存终点的强有力的分层集群随机化试验的宝贵工具.
  • 准确的样本大小计算对于此类试验的有效性和功率至关重要.
  • 这项研究解决了复杂的临床试验设计样本大小方法的差距.