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

456
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...
456
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

176
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
176
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

289
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...
289
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

309
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
309
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

568
The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of...
568
Multiple Regression01:25

Multiple Regression

3.2K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.2K

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

Powering RCTs for Marginal Effects With GLMs Using Prognostic Score Adjustment.

Statistics in medicine·2026
Same author

"Within-Trial" Prognostic Score Adjustment Is Targeted Maximum Likelihood Estimation.

Pharmaceutical statistics·2026
Same author

Effect of Adherence to Oral Semaglutide on Glycemic Control in People With Type 2 Diabetes Treated With Metformin: Protocol for an Open-Label Clinical Trial.

JMIR research protocols·2025
Same author

Late infections after high-dose therapy and autologous stem cell transplantation for lymphoma: A Danish population-based study.

British journal of haematology·2025
Same author

Growth Response to Weekly Somapacitan Therapy in Children With GH Deficiency Is Related to GH Thresholds in GH Stimulation Testing.

Journal of the Endocrine Society·2025
Same author

Composite likelihood inference for space-time point processes.

Biometrics·2025
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: Sep 13, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.6K

关于使用线性模型的预测得分调整来提高RCT功率的教程

Emilie Højbjerre-Frandsen1,2, Mathias Lerbech Jeppesen1, Rasmus Kuhr Jensen1

  • 1Biostatistics, Novo Nordisk A/S, Søborg, Denmark.

Pharmaceutical statistics
|July 31, 2025
PubMed
概括
此摘要是机器生成的。

利用历史数据与线性预后得分调整可以提高临床试验的功率. 这种方法改善了治疗效果的估计,并保持了I型错误控制,优于传统技术.

关键词:
有关因果推理的推理.糖尿病 糖尿病患者 糖尿病患者历史数据 历史数据预测得分 预测得分随机化试验是一种随机化试验.

更多相关视频

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

2.2K
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

10.3K

相关实验视频

Last Updated: Sep 13, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.6K
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

2.2K
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

10.3K

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
  • 制药指标 (Pharmacometrics) 是一个指标.

背景情况:

  • 在临床试验中利用历史数据是一个长期以来的研究领域.
  • 最近的进展侧重于对预后得分的线性调整,以提高统计能力.
  • 非对称和有限样本最佳性结果支持这些先进的估计技术.

研究的目的:

  • 审查和提供使用临床试验中的预后分数进行线性调整的指南.
  • 为了评估这种方法的性能与标准方法相比,如倾向性得分匹配和ANCOVA.
  • 在现实世界的临床试验环境中展示实际应用和好处.

主要方法:

  • 在随机对照试验 (RCT) 中对随机对照试验 (RCT) 中平均治疗效果的插件和线性估计器的审查.
  • 开发用于历史数据策划和预测得分构建的指导方针.
  • 模拟研究比较线性调整与RCT (PSM-RCT) 和ANCOVA的倾向性得分匹配.
  • 在2型糖尿病的IIIb期临床试验中的案例研究应用.

主要成果:

  • 对预后得分的线性调整可以避免偏见的治疗效果估计,并控制I型错误,与PSM-RCT不同.
  • 该方法证明了对假设偏差和预后模型性能问题的稳定性.
  • 一个案例研究证实了2型糖尿病试验中潜在功率的增加.

结论:

  • 对预后得分进行线性调整是提高临床试验功率的有效方法.
  • 这种方法比传统方法具有优势,特别是在保持估计有效性和错误控制方面.
  • 提供了实施建议,并考虑了诸如子组分析等局限性问题.