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相关概念视频

Randomized Experiments01:13

Randomized Experiments

7.0K
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...
7.0K
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

104
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
104
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

133
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,...
133
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
Blinding01:11

Blinding

2.5K
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.
2.5K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

44
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...
44

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相关实验视频

Updated: Jul 11, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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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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一个可解释的基于机器学习的现象映射策略,用于随机对照试验中的自适应预测丰富.

Evangelos K Oikonomou1, Phyllis M Thangaraj1, Deepak L Bhatt2

  • 1Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.

medRxiv : the preprint server for health sciences
|November 14, 2023
PubMed
概括

机器学习通过预测患者的益处来优化随机对照试验 (RCT). 这种使用计算现象图的自适应策略可以减少试验规模,同时保持治疗效果的准确性.

关键词:
适应性试验是指适应性试验.临床试验是指临床试验中的临床试验.机器学习是机器学习.预测丰富的预测丰富

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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科学领域:

  • 临床试验 临床试验
  • 生物统计学 生物统计学
  • 机器学习 机器学习

背景情况:

  • 随机对照试验 (RCT) 对于以证据为基础的医学至关重要,但成本昂贵且耗时.
  • 在RCT中优化患者入学率对于效率和及时结果至关重要.

研究的目的:

  • 提出和评估机器学习 (ML) 策略,用于RCT中的自适应预测丰富.
  • 为了优化RCT招生使用计算试验现象图.

主要方法:

  • 模拟组对两个心血管结局RCT (IRIS和SPRINT) 的顺序分析.
  • 在中间分析期间构建动态表型表示以推断响应概况.
  • 根据预测的好处,有条件的潜在候选人入学概率.

主要成果:

  • 在ML策略中确定了动态的表型特征,可以预测跨临时分析的个性化心血管益处.
  • 预计试验规模减少:14.8% (IRIS) 和17.6% (SPRINT) 在十个模拟中.
  • 在两个模拟试验中保留了原始平均治疗效果.

结论:

  • 使用ML现象图的自适应预测丰富可以显著提高RCT招生效率.
  • 这种方法有可能减少试验规模和资源需求.
  • 该策略保持了治疗效果估计的完整性和准确性.