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相关概念视频

Sample Size Calculation01:19

Sample Size Calculation

6.2K
Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
6.2K
Margin of Error01:27

Margin of Error

6.8K
The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
6.8K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.7K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
8.7K
Contaminants and Errors01:16

Contaminants and Errors

330
Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
330
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

10.1K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
10.1K
Prediction Intervals01:03

Prediction Intervals

3.1K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.1K

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相关实验视频

Updated: Jan 9, 2026

An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

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个体风险预测的有效样本大小:量化机器学习模型中的不确定性.

Doranne Thomassen1, Toby Hackmann1, Jelle Goeman1

  • 1Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands.

The Lancet. Digital health
|November 29, 2025
PubMed
概括
此摘要是机器生成的。

临床预测模型可能对个别患者有不同的不确定性,影响公平性. 我们开发了一种方法来估计有效的样本大小,即使在大型数据集中也显示出显著的预测不确定性,这对于有效地传达风险至关重要.

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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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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Establishing a Competing Risk Regression Nomogram Model for Survival Data

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相关实验视频

Last Updated: Jan 9, 2026

An R-Based Landscape Validation of a Competing Risk 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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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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科学领域:

  • 临床预测建模临床预测建模
  • 机器学习在医疗保健中的应用
  • 统计不确定性量化和量化

背景情况:

  • 临床预测模型的标准性能指标不能充分捕捉个体预测的不确定性.
  • 这种缺乏不确定性评估引发了对公平性的担忧,因为模型对某些患者可能比其他患者更确定.
  • 实际样本大小已被提议作为衡量样本不确定性的指标.

研究的目的:

  • 开发和说明一种计算方法,用于在各种预测模型中估计有效的样本大小.
  • 评估有效样本大小在理解大型临床数据集中的个体预测不确定性的有用性.
  • 探索有效样本大小对沟通风险预测不确定性的影响.

主要方法:

  • 开发了一种计算方法来估计各种预测模型的有效样本大小,包括后勤回归,弹性网,XGBoost,神经网络和随机森林.
  • 该方法应用于包括23,034个人的临床数据集.
  • 为了评估不同模型类型的有效样本大小估计的准确性,进行了模拟.

主要成果:

  • 开发的方法准确地估计了物流回归和弹性网模型的有效样本大小,XGBoost,神经网络和随机森林的微小偏差.
  • 尽管模型的整体性能指标相似,但有效样本大小和患者特定风险预测的实质性差异被观察到.
  • 个人预测的不确定性被发现是显著的,即使模型被训练在大样本大小.

结论:

  • 临床模型中的个体预测不确定性可以很大,无论数据集的大小如何.
  • 有效的样本大小是量化和传达与个人风险预测相关的不确定性的一种有价值的措施.
  • 这种方法有望提高基于机器学习的预测模型在临床实践中的透明度和公平性.