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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

14
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...
14
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

26
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
26
Regression Toward the Mean01:52

Regression Toward the Mean

6.2K
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...
6.2K
Multiple Regression01:25

Multiple Regression

2.9K
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...
2.9K
Randomized Experiments01:13

Randomized Experiments

6.6K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
6.6K
Weighted Mean00:57

Weighted Mean

4.8K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
4.8K

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

Handling Missing Data in Participants with Baseline but No Post-Baseline Data.

Pharmaceutical statistics·2026
Same author

Comparative effectiveness of tirzepatide and semaglutide for obesity management in US clinical practice: a 6-month retrospective cohort study.

Journal of endocrinological investigation·2026
Same author

Overview and Practical Recommendations on Using Shapley Values for Identifying Predictive Biomarkers via CATE Modeling.

Statistics in medicine·2026
Same author

Next-Generation Sequencing-Based Testing Among Patients With Advanced or Metastatic Nonsquamous Non-Small Cell Lung Cancer in the United States: Predictive Modeling Using Machine Learning Methods.

JMIR cancer·2025
Same author

Improving randomized controlled trial analysis via data-adaptive borrowing.

Biometrika·2025
Same author

Identifying individuals at risk for weight gain using machine learning in electronic medical records from the United States.

Diabetes, obesity & metabolism·2025
Same journal

Correction.

Journal of biopharmaceutical statistics·2026
Same journal

Leveraging external controls in clinical trials: estimands, estimation, assumptions.

Journal of biopharmaceutical statistics·2026
Same journal

Special issue of nonclinical statistics in regulatory applications guest editors' notes.

Journal of biopharmaceutical statistics·2026
Same journal

Comparison of flexible parametric modeling and nonparametric methods to estimate restricted mean survival time: A simulation study.

Journal of biopharmaceutical statistics·2026
Same journal

Simulated treatment comparisons with jackknife pseudo values for estimating population-adjusted marginal treatment effects.

Journal of biopharmaceutical statistics·2026
Same journal

Sample sizes for randomized controlled trials utilizing Bayesian response adaptive randomization for continuous outcomes.

Journal of biopharmaceutical statistics·2026
查看所有相关文章

相关实验视频

Updated: May 10, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.1K

估计平均治疗效果的双重机器学习方法:一项比较研究

Xiaoqing Tan1, Shu Yang2, Wenyu Ye3

  • 1Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA.

Journal of biopharmaceutical statistics
|April 22, 2025
PubMed
概括
此摘要是机器生成的。

双重可靠的方法通过结合治疗和结果模型来改善比较有效性研究. 将机器学习与这些估计器相结合,就像目标最大概率一样,为准确的治疗效果估计提供了最佳性能.

关键词:
增强的反向概率加权.超级学习者 超级学习者双得分匹配对应的比赛.对待方法的倾向性方法的惩罚性分线,对待方法的比较.

更多相关视频

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

14.3K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K

相关实验视频

Last Updated: May 10, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.1K
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

14.3K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K

科学领域:

  • 流行病学 流行病学
  • 生物统计学 生物统计学
  • 医疗保健服务研究 医疗服务研究

背景情况:

  • 观察性队列研究对于比较有效性研究 (CER) 评估治疗安全至关重要.
  • 双强 (DR) 方法通过整合治疗和结果模型来提高平均治疗效果 (ATE) 估计.
  • 现有的DR方法使用各种策略,如匹配,加权和回归,如果任何模型都被正确指定,则提供稳定性.

研究的目的:

  • 调查各种DR估计器之间的性能差异.
  • 探索机器学习 (ML) 与DR方法的协同作用,称为双机器学习 (DML) 估计器.
  • 为在CER中应用DR估计器提供实用指南.

主要方法:

  • 使用多种治疗和结果建模策略,对流行的DR方法进行比较分析.
  • 广泛的模拟用于在不同条件下评估估计器性能.
  • 将方法应用于真实世界的数据集进行验证.

主要成果:

  • DML估计器,特别是那些结合目标最大概率估计 (TMLE) 的ML的估计器,表现出优越的整体性能.
  • 该研究确定了基于模型规格的不同DR方法的具体优点和缺点.
  • 性能因治疗和结果模型的复杂性而异.

结论:

  • 将机器学习与两倍强大的方法相结合,可以显著提高平均治疗效果估计的准确性和精度.
  • 与机器学习相结合的有针对性的最大概率估计显示出在观察性研究中对强大的因果推理有希望的结果.
  • 这些发现为寻求在比较有效性研究中优化因果推理的研究人员提供了宝贵的见解.