相关实验视频
Updated: Jul 11, 2025
05:37
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
2.1K
用层次数据评估风险预测:对AUC的依赖性调整的信心区间
Camden Bay1, Robert J Glynn1, Johanna M Seddon2
1Harvard Medical School, Brigham and Women's Hospital, Boston, MA, 02115, USA.
Stats
|November 3, 2023
概括
本研究将分析接收器操作特征曲线 (AUC) 下面区域的方法扩展到三级层次数据. 新方法可以在复杂的集群数据结构中对AUC进行可靠的统计推断.
科学领域:
- 生物统计学 生物统计学
- 统计建模 统计建模
- 机器学习评估 机器学习评估
背景情况:
- 真正ROC曲线下的面积 (AUC) 是二进制结果歧视的一个关键指标.
- 标准AUC推断假定独立的结果,限制其使用与层次数据.
- 现有的方法处理的是两层层次的层次数据,而不是更复杂的结构.
研究的目的:
- 将AUC推断方法扩展到三级层次数据.
- 开发一种强大的方法来处理AUC分析中的绑定预测得分.
- 在复杂的集群数据中提供可靠的统计推理方法.
主要方法:
- 开发了通用估计方程 (GEE) 模型的扩展,采用探针转换得分.
- 结合了一种方法来解释绑定的预测得分.
- 模拟了三级集群数据,集群大小不同,数据绑定.
主要成果:
- 拟议的方法证明了AUC (0.943-0.958) 的准确95%置信区间覆盖率.
- 理论和经验AUC差异的比率接近1 (0.920-1.013).
- 性能超过了对等级数据的现有AUC推断方法.
结论:
- 新的方法允许在三级层次的等级设置中进行有效的AUC推断.
- 该方法对绑定预测得分和可变集群大小具有稳定性.
- 这项工作促进了复杂数据结构中预测模型的统计评估.
相关概念视频
01:15Receiver Operating Characteristic Plot
240
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
240
01:19Survival Tree
88
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
88
01:03Prediction Intervals
2.3K
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.
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.
2.3K
01:21Confidence Intervals
6.3K
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...
A...
6.3K
01:19Interpretation of Confidence Intervals
5.8K
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...
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...
5.8K
01:11Hazard Rate
115
The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
115