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Related Concept Videos

Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

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...
Region of Convergence of Laplace Tarnsform01:20

Region of Convergence of Laplace Tarnsform

The Region of Convergence (ROC) is a fundamental concept in signal processing and system analysis, particularly associated with the Laplace transform. The ROC represents an area in the complex plane where the Laplace transform of a given signal converges, determining the transform's applicability and utility.
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Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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Calibration Curves: Correlation Coefficient01:10

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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 other increases, and...
Residual Plots01:07

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A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...

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Related Experiment Video

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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

Comparing ROC curves derived from regression models.

Venkatraman E Seshan1, Mithat Gönen, Colin B Begg

  • 1Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA.

Statistics in Medicine
|October 5, 2012
PubMed
Summary
This summary is machine-generated.

Testing the incremental value of predictive markers using receiver operating characteristic (ROC) curve areas can yield biased results. Alternative methods like Wald or likelihood ratio tests are preferred for accurate marker evaluation.

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Area of Science:

  • Biostatistics
  • Medical Informatics
  • Epidemiology

Background:

  • Assessing predictive marker incremental value often involves comparing receiver operating characteristic (ROC) curves.
  • Empirical observations suggest ROC area tests yield non-significant results when Wald tests are significant.
  • Prior simulations indicated ROC area tests have conservative size and low power.

Purpose of the Study:

  • To demonstrate bias in test statistics and variance when using nested regression model predictions.
  • To investigate the underlying reasons for these biases in ROC area testing.
  • To recommend preferred methods for evaluating new marker contributions.

Main Methods:

  • Analysis of test statistics and estimated variance in predictive modeling.
  • Examination of data inputs derived from nested regression models.
  • Comparison of ROC area tests with Wald and likelihood ratio tests.

Main Results:

  • Both the test statistic and its estimated variance are seriously biased when using predictions from nested regression models.
  • The widely used ROC area test demonstrates a conservative test size and low statistical power.
  • Resampling can create a bias-free reference distribution, but it is complex.

Conclusions:

  • The standard ROC area test for incremental marker value is unreliable due to significant bias.
  • Wald or likelihood ratio tests are the preferred and more robust methods for assessing new marker contributions.
  • Understanding these biases is crucial for accurate predictive model development and marker selection.