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

Longitudinal Research02:20

Longitudinal Research

11.9K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
11.9K
Longitudinal Studies01:26

Longitudinal Studies

156
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
156
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

215
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
215
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Actuarial Approach01:20

Actuarial Approach

74
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
74
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

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

Riesz Representers for the Rest of Us.

Epidemiology (Cambridge, Mass.)·2026
Same author

Errata to: "lmtp: An R Package for Estimating the Causal Effects of Modified Treatment Policies".

Observational studies·2026
Same author

Beneficial effects of the rapid vs. standard procedure for injection naltrexone initiation operate through increased adjunctive medication use.

Drug and alcohol dependence·2026
Same author

µCT scanning effects on aDNA and a multi-step workflow for archaeological petrous portions.

PloS one·2026
Same author

Recanting Twins: Addressing Intermediate Confounding in Mediation Analysis.

Statistics in medicine·2026
Same author

Constructing targeted minimum loss/maximum likelihood estimators: a simple illustration to build intuition.

American journal of epidemiology·2025
Same journal

Correction to: Home dampness and molds and occurrence of respiratory tract infections in the first 27 years of life: the Espoo Cohort Study.

American journal of epidemiology·2026
Same journal

A SIMPLE AND POWERFUL TEST OF VACCINE WANING.

American journal of epidemiology·2026
Same journal

Association Between maternal body mass index, offspring growth and pubertal timing: results from a longitudinal birth cohort study.

American journal of epidemiology·2026
Same journal

Correction to: Developing a novel algorithm to identify incident and prevalent dementia in Medicare claims-the ARIC Study.

American journal of epidemiology·2026
Same journal

RE: advancing observational research on arts and health: theory-informed approaches using the RADIANCE framework.

American journal of epidemiology·2026
Same journal

Maternal Cesarean Section and Offspring ASD or ADHD Risk: A Nurses' Health Study II Analysis.

American journal of epidemiology·2026
查看所有相关文章

相关实验视频

Updated: Jun 23, 2025

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

10.7K

从纵向数据中学习最佳的动态处理方案.

Nicholas T Williams1, Katherine L Hoffman1, Iván Díaz2

  • 1Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY 10032, United States.

American journal of epidemiology
|June 16, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了最佳动态治疗规则 (ODTRs),以个性化医疗. 开发的ODTR用于阿片类药物使用障碍中的布普伦诺芬-纳洛剂量超过了标准的临床策略.

关键词:
有关因果推理的推理.两倍强大的方法.纵向研究是指纵向研究.最佳治疗规则的最佳治疗规则精准医学是一门精准医学.

更多相关视频

Pretargeted Radioimmunotherapy Based on the Inverse Electron Demand Diels-Alder Reaction
09:44

Pretargeted Radioimmunotherapy Based on the Inverse Electron Demand Diels-Alder Reaction

Published on: January 29, 2019

10.1K
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

14.4K

相关实验视频

Last Updated: Jun 23, 2025

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

10.7K
Pretargeted Radioimmunotherapy Based on the Inverse Electron Demand Diels-Alder Reaction
09:44

Pretargeted Radioimmunotherapy Based on the Inverse Electron Demand Diels-Alder Reaction

Published on: January 29, 2019

10.1K
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

14.4K

科学领域:

  • * 纵向数据分析数据分析
  • * 药理流行病学
  • * 生物统计学

背景情况:

  • *平均治疗效果 (ATE) 提供了人口层面的见解,而不是个人层面的治疗效果.
  • *最佳动态处理规则 (ODTR) 根据个体特征和随时间变化的情况量身定制治疗.
  • * 时间变化的治疗需要了解个人和随着时间的推移而发生的益处变化.

研究的目的:

  • * 为应用研究人员提供一个由纵向数据估计ODTR的教程.
  • * 开发和应用一种学习时间变化的ODTR的方法.
  • * 估计布普伦诺芬-纳洛剂量调整的ODTR,以尽量减少阿片类药物使用障碍的复发.

主要方法:

  • * 使用了条件平均治疗效应 (ATE) 的双倍强大的无偏转换.
  • * 采用了纵向观察和临床试验数据.
  • * 开发了一种学习时间变化的最佳动态治疗规则 (ODTR) 的方法.

主要成果:

  • *成功地学习了布普伦诺芬-纳洛剂量升级的时间变化的ODTR.
  • *与标准临床策略相比,估计的ODTR显示出更高的性能.
  • *强调了ODTRs在管理阿片类药物使用障碍的顺序决策中的有效性.

结论:

  • *最佳动态治疗规则 (ODTRs) 提供了个性化医疗在顺序决策中的强大方法.
  • * 拟议的方法有效地从纵向数据中估计时间变化的ODTR.
  • * ODTRs具有显著的潜力来改善患者的治疗结果,正如在阿片类药物使用障碍治疗的背景下所示.