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相关概念视频

5-Number Summary01:04

5-Number Summary

4.5K
In a dataset, the 5-number summary includes the minimum data value, the data value of the first quartile, the median data value or data value of the second quartile, the data value of the third quartile, and the maximum data value. These 5 data values can be visualized as a box and whisker plot.
In a box plot, the minimum and maximum data values represent the lower and upper whiskers in the graph, and the median is designated as the center of the box in the chart. The first quartile and third...
4.5K
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.0K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
4.0K
Decision Making: P-value Method01:09

Decision Making: P-value Method

5.4K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
5.4K
Survival Curves01:18

Survival Curves

166
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
166
Finding Critical Values for Chi-Square01:18

Finding Critical Values for Chi-Square

3.0K
Consider a curve representing sample data drawn randomly from a normally distributed population. One must construct confidence intervals to estimate or to test a claim regarding the population standard deviation. For example, a 95% confidence interval covers 95% of the area under the curve, and the remaining 5% is equally distributed on either side of the curve. To achieve such confidence intervals, one must determine the critical values. The critical values are simply the values separating the...
3.0K
Contingency Table01:29

Contingency Table

2.5K
A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
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相关实验视频

Updated: Jul 9, 2025

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
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Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

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基于总结数据的决策曲线分析.

Iztok Hozo1, Gordon Guyatt2, Benjamin Djulbegovic3

  • 1Department of Mathematics, Indiana University Northwest, Gary, Indiana, USA.

Journal of evaluation in clinical practice
|December 4, 2023
PubMed
概括
此摘要是机器生成的。

现在可以使用汇总数据进行决策曲线分析 (DCA),消除对个体患者数据 (IPD) 的需求. 这一进步有助于更广泛地整合精准医学和个性化决策的预测模型.

关键词:
决策曲线分析的方法医疗决策中的医疗决策精准医学是一门精准医学.预测建模预测建模统计模拟的统计模拟.

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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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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

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相关实验视频

Last Updated: Jul 9, 2025

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

15.4K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

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科学领域:

  • 生物统计学 生物统计学
  • 临床决策 临床决策
  • 精准医学是一门精准的医学.

背景情况:

  • 精准医学需要将预测模型与决策曲线分析 (DCA) 等决策分析框架相结合.
  • 目前的DCA应用程序需要个体患者数据 (IPD),通常无法访问.
  • 开发用于DCA总数据的方法可以增强精准医学的采用.

研究的目的:

  • 提出一个统计框架,使得DCA仅使用从预测模型概率的总数据 (平均值和标准偏差) 来实现.
  • 通过模拟和现实世界数据集,通过对比传统的IPD-based DCA来验证这种聚合数据方法.

主要方法:

  • 用预测模型概率的平均值和标准偏差开发了DCA总量数据的统计框架.
  • 进行了广泛的模拟,以比较基于聚合物的DCA与基于IPD的DCA.
  • 将框架应用于使用IPD的四种不同的预测模型,这些模型来自对他类药物,临终关怀转诊,血栓预防和鼻腔阻塞综合征预防的研究.

主要成果:

  • 当预测模型被精确校准时,模拟显示了总和IPD DCA之间的微不足道差异.
  • 对于充足的动力模型,DCA的总数据与IPD衍生的DCA结果非常接近.
  • 由于固有的不稳定性,在样本规模较小的模型中,观察到总和基于IPD的DCA之间存在较大的差异.

结论:

  • 从充足的动力和校准模型中使用总结统计数据 (平均值和SD) 的DCA与基于IPD的DCA非常接近.
  • 使用聚合数据显著扩大了DCA的适用性,克服了IPD可访问性限制.
  • 这种方法促进了预测和决策建模的整合,以推进个性化患者护理.