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相关概念视频

Randomized Experiments01:13

Randomized Experiments

6.7K
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...
6.7K
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
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.5K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.5K
Blinding01:11

Blinding

2.4K
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.
2.4K
Censoring Survival Data01:09

Censoring Survival Data

63
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
63
Group Design02:01

Group Design

8.9K
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
8.9K

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相关实验视频

Updated: Jun 6, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

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在使用机器学习的随机对照试验中检测不规则.

Walter Nelson1,2, Jeremy Petch1,3,4,5, Jonathan Ranisau1

  • 1Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, ON, Canada.

Clinical trials (London, England)
|November 26, 2024
PubMed
概括
此摘要是机器生成的。

机器学习算法可以在临床试验中像人类一样早检测数据不规则,提高效率. 这种自动化方法有助于在大型多中心试验中进行中央统计监测.

关键词:
中央统计监测中心人工智能的人工智能是人工智能.数据质量数据质量数据质量机器学习是机器学习.异常标志的检测异常标志的检测质量保证 质量保证 质量保证随机对照试验是随机对照试验.

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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

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相关实验视频

Last Updated: Jun 6, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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

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科学领域:

  • 临床试验数据管理
  • 生物统计学 生物统计学
  • 机器学习在医疗保健中的应用

背景情况:

  • 临床试验产生可能含有不规则的数据,需要手动,资源密集的中央统计监测.
  • 机器学习 (ML) 显示了在多中心试验中自动检测中心级异常的潜力.

研究的目的:

  • 描述历史临床试验中的形式级数据不规则.
  • 评估基于ML的异常值检测算法的识别这些不规则的能力.

主要方法:

  • 将初步试验快照与最终数据库进行比较,以确定人类确定的违规行为.
  • 使用一致性,正预测值和灵敏度评估ML算法性能.

主要成果:

  • 分析了77,001名参与者的7项试验,确定了24,850种形式的不规则.
  • 拟议的ML算法实现了0.74的中位对应度,超过了以前的ML方法 (中位AUC0.73).

结论:

  • 机器学习算法可以在没有干预的情况下,比人类更早或更早地检测出形式级数据不规则.
  • 这种ML方法可以补充现有的中央统计监测,可能提高大规模试验数据验证的效率.