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相关概念视频

Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

10.2K
A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
10.2K
Confidence Intervals01:21

Confidence Intervals

10.9K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
10.9K
Confidence Coefficient01:24

Confidence Coefficient

10.8K
The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
10.8K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

11.9K
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...
11.9K
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

9.0K
A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
9.0K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

8.8K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
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相关实验视频

Updated: Mar 3, 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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对错误指定的Cox模型的概率置信区间

Yongwu Shao1, Xu Guo1

  • 1Gilead Sciences, Foster City, California, USA.

Statistics in medicine
|March 1, 2026
PubMed
概括
此摘要是机器生成的。

对于Cox模型,强大的沃尔德置信区间 (CI) 在罕见事件中可能不可靠. 这项研究引入了一种新的强大的概率置信区间,以提高这种场景的准确性.

关键词:
非对称的理论理论.概率比率测试的可能性比率测试.模型错误的规格错误坚固性 坚固性 坚固性幸存率数据 幸存率数据

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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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相关实验视频

Last Updated: Mar 3, 2026

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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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科学领域:

  • 生物统计学 生物统计学
  • 生存分析的分析.
  • 统计建模 统计建模

背景情况:

  • 强大的沃尔德置信区间 (CI) 广泛用于考克斯模型,特别是在模型错误规格或应用权重的情况下.
  • 然而,Wald CI的表现很差,很少发生事件,在罕见事件研究或高效治疗中很常见,导致反直观的结果.
  • 考克斯模型的现有概率CI缺少一个强大的版本,标准软件可能会在要求强度时也错误地提供常规版本.

研究的目的:

  • 为考克斯模型开发和评估一个强大的概率置信区间 (CI).
  • 解决强大的沃尔德CI的局限性以及标准统计软件中缺乏强大的概率CI的问题.
  • 为生存数据提供更准确,更可靠的CI,特别是在处理罕见事件或小样本大小时.

主要方法:

  • 证明了考克斯模型的概率比率测试统计数据在错误规范下趋于加权的奇平方分布.
  • 通过逆转这个强大的概率比测试,推导出强大的概率CI.
  • 通过模拟研究和现实世界的数据评估了拟议的CI的表现.

主要成果:

  • 建议的强大的概率置信区间与Wald CI相比显示出更好的表现,特别是在事件较少的场景中.
  • 模拟研究证实了新信贷机构在匹配名义覆盖概率方面的卓越准确性.
  • 该方法成功地应用于来自HIV预防试验的真实数据,显示了其实际实用性.

结论:

  • 开发的强大的概率CI为考克斯模型提供了比强大的沃尔德CI更可靠的替代方案,特别是在具有挑战性的数据情况下.
  • 这项工作填补了统计方法学的关键缺口,为考克斯模型提供了强大的概率CI.
  • 一个伴随的R包"CoxLikelihood"可用,以促进研究人员应用这些新方法.