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相关概念视频

Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

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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...
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Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

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A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
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Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

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In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
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Prediction Intervals01:03

Prediction Intervals

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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. 
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End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
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Relative Risk01:12

Relative Risk

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Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
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相关实验视频

Updated: Jun 26, 2025

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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多状态风险预测模型的校准图.

Alexander Pate1, Matthew Sperrin1,2, Richard D Riley3

  • 1Centre for Health Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.

Statistics in medicine
|May 9, 2024
PubMed
概括

本研究介绍了用于风险预测的多状态模型中的校准评估方法. 伪值和二元物流回归与反向概率的审查权重 (BLR-IPCW) 方法提供可靠的校准曲线,即使在审查.

关键词:
校准校准的时间临床预测 临床预测模型验证模型验证多州模式的模型.风险预测风险预测

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Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
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Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

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相关实验视频

Last Updated: Jun 26, 2025

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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
  • 流行病学 流行病学
  • 医疗信息学 医疗信息学

背景情况:

  • 缺乏已建立的方法来评估校准在多状态风险预测模型.
  • 在存在审查的情况下,需要可靠的校准评估.

研究的目的:

  • 介绍和评估用于生成校准图表的技术,用于多状态模型过渡概率.
  • 在随机和独立的审查下评估这些技术的性能.

主要方法:

  • 使用伪值 (Aalen-Johansen估计器),二进制逻辑回归与反向概率审查权重 (BLR-IPCW) 和多项式逻辑回归与反向概率审查权重 (MLR-IPCW).
  • 模拟数据具有不同的审查级别,以评估校准曲线估计.
  • 将方法应用于现实世界队列,以预测多种慢性疾病.

主要成果:

  • 伪值,BLR-IPCW和MLR-IPCW方法在随机审查下产生了公正的校准曲线估计.
  • 这些方法证明了对独立审查的稳定性,在高密度预测区域的偏差最小.
  • 通过校准分散图,MLR-IPCW提供了额外的洞察力.

结论:

  • 推伪值或BLR-IPCW用于校准曲线和MLR-IPCW用于分散图.
  • 开发的方法被集成到"calibmsm"R包中.
  • 这些工具提高了多状态模型在风险预测中的可靠性.