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相关概念视频

Regression Toward the Mean01:52

Regression Toward the Mean

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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...
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Randomized Experiments01:13

Randomized Experiments

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

Comparing the Survival Analysis of Two or More Groups

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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...
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Kaplan-Meier Approach01:24

Kaplan-Meier Approach

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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,...
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Blinding01:11

Blinding

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Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control trial or RCT. It reduces the bias that affects the results. In an RCT, blinding is used in the form of a placebo. A placebo effect occurs when untreated subjects falsely believe they have received the treatment and report improved symptoms. A placebo or a dummy treatment is administered to subjects to negate the bias caused by such an effect.
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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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...
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相关实验视频

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A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
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在随机试验中,在半监督设置下对最佳治疗方案的平滑估计.

Xiaoqi Jiao1, Mengjiao Peng1, Yong Zhou1

  • 1Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, Academy of Statistics and Interdisciplinary Sciences, East China Normal University, Shanghai, China.

Biometrical journal. Biometrische Zeitschrift
|November 23, 2024
PubMed
概括

这项研究引入了一种新的半监督框架,以优化使用未标记数据的治疗方案. 该方法提高了准确性,并减少了个性化医学的计算负载.

关键词:
个性化治疗规则是个性化治疗的规则.最佳的治疗方案是最佳的治疗方案.随机化试验是一种随机化试验.半监督学习 半监督学习平的技术 平的技术

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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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科学领域:

  • 生物统计学 生物统计学
  • 医疗保健中的机器学习
  • 个性化医疗是个性化的医疗.

背景情况:

  • 最佳的治疗方案对于个性化医疗至关重要,但通常依赖于有限的标记数据.
  • 现有的治疗方案的半监督方法面临着模型假设和高计算成本的挑战.
  • 在医疗保健中大量存在的未标记数据,为改善治疗方案估计提供了有价值的信息.

研究的目的:

  • 为估计最佳治疗方案提出一个无模型的半监督框架.
  • 利用大量未标记的数据来提高治疗方案的估计.
  • 解决现有方法在模型假设和计算负担方面的局限性.

主要方法:

  • 使用单一指数模型进行尺寸缩小.
  • 在未标记的数据中归算缺失的结果,通过内核回归.
  • 开发含有标记和未标记数据的半监督值函数.
  • 通过最大化半监督值函数来推导最佳处理方案.

主要成果:

  • 拟议的框架证明了估计器的一致性和非对称的正常性.
  • 引入了一个扰乱重抽样程序来估计非对称差异.
  • 模拟证实了将未标记的数据纳入最佳治疗方案估计的好处.

结论:

  • 新的半监督框架有效地利用未标记的数据进行最佳治疗方案估计.
  • 该方法为现有方法提供了一个计算效率高且无模型的替代方案.
  • 该方法适用于随机试验和观察性研究.