Analyzing receiver operating characteristic curves with SAS

  • 134 Pages
  • 3.91 MB
  • 8653 Downloads
  • English
by
SAS Pub. , Cary, NC
SAS (Computer file), Receiver operating characteristec curves -- Computer programs, SAS (Computer program language), Data mining, Diagnosis -- Data processing, Medical statistics -- Computer pro
StatementMithat Gönen.
SeriesSAS Press series
ContributionsSAS Institute.
Classifications
LC ClassificationsQA279.2 .G66 2007
The Physical Object
Paginationx, 134 p. :
ID Numbers
Open LibraryOL23201330M
ISBN 101599942984
ISBN 139781599942988
LC Control Number2008299434
OCLC/WorldCa166390097

‎In this example-laden book, author Mithat Gonen illustrates the existing SAS procedures that can be tailored to produce ROC curves and expands upon further analyses using other SAS procedures and marcos.

Both parametric and nonparametric methods for analyzing ROC curves are covered in detail. Analyzing Receiver Operating Characteristic Curves with SAS - Ebook written by Mithat Gönen. Read this book using Google Play Books app on your PC, android, iOS devices. Download for offline reading, highlight, bookmark or take notes while you read Analyzing Receiver Operating Characteristic Curves with SAS.

In Analyzing Receiver Operating Characteristic Curves with SAS, author Mithat Gonen illustrates the many existing SAS procedures that can be tailored to produce ROC curves and expands upon further analyses using other SAS procedures and macros.

Both parametric and nonparametric methods for analyzing ROC curves are covered in detail. Both parametric and nonparametric methods for analyzing ROC curves are covered in detail. In this example-laden book, author Mithat Gönen illustrates the existing SAS procedures that can be.

As a diagnostic decision-making tool, receiver operating characteristic (ROC) curves provide a comprehensive and visually attractive way to summarize the accuracy of predictions. They are extensively used in medical diagnosis and increasingly in fields such as data mining, credit scoring, weather forecasting, and psychometry.

Analyzing Receiver Operating Characteristic Curves with SAS Reviews "Mithat Gönen has produced the best text to date regarding receiver operating characteristic (ROC) curves and their use in statistical analysis. He provides insight into how ROC curve analysis can be used to assess the accuracy of predictions and forecasts.

Analyzing Receiver Operating Characteristic Curves With SAS. Author: Mithat Gonen. Publisher: SAS Publishing. Introduction to the use of receiver operating characteristic (ROC) curves for studying the accuracy of predictive models.

Requires SAS/STAT. POSTED BY GLOBAL STATEMENTS BOOKS ON FEBRU   Topics covered include non-parametric methods, transformation models for continuous data, the binormal model for ordinal data and validation of multivariate prediction models. Censored data and data mining applications are also covered.

SAS code and macros are provided. Here is the SAS program. Here is the resulting ROC graph. Area under the curve is c = indicates good predictive power of the model.

Option ctable prints the classification tables for various cut-off points. Each row of this output is a classification table for the specified Prob Level, π 0.

Analyzing Receiver Operating Characteristic Curves With SAS. The American Statistician: Vol. 62, No. 4, pp. A receiver operating characteristic curve, or ROC curve, is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied.

The method was originally developed for operators of military radar receivers, which is why it is so named. We would like to show you a description here but the site won’t allow more.

Analyzing receiver operating characteristic curves with SAS ®.Mithat Gönen, SAS Institute Inc., Cary, NC, No. of pages: x+ Price: $ ISBN: OCLC Number: Description: x, pages: illustrations ; 28 cm. Contents: Introduction --Single binary predictor --Single continuous predictor --Comparison and covariate adjustment of ROC curves --Ordinal predictors --Lehmann family of ROC curves --ROC curves with censored data --Using the ROC curve to evaluate multivariable prediction models --ROC.

In Analyzing Receiver Operating Characteristic Curves with SAS, author Mithat Gönen illustrates the many existing SAS procedures that can be tailored to produce ROC curves and expands upon further analyses using other SAS procedures and macros. Both parametric and nonparametric methods for analyzing ROC curves are covered in detail.5/5(2).

Analyzing Receiver Operating Characteristic Curves with SAS, by M. Gönen Cary, NC: SAS Institute Inc.,ISBNx + pp., $ Sibabrata Banerjee Schering Plough Research Institute, Galloping Hill Road, Kenilworth, NJ. Download Analyzing Receiver Operating Characteristic Curves with SAS (Sas Press Series) pdf books They are used extensively in medical diagnosis and increasingly in fields such as data mining, credit scoring, weather forecasting, and psychometry.

In Analyzing Receiver Operating Characteristic Curves with SAS, author Mithat Gonen illustrates the. Genre/Form: Electronic books: Additional Physical Format: Print version: Gonen, Mithat. Analyzing receiver operating characteristic curves with SAS.

A SAS user sincehe is the author of numerous professional papers and a member of the American Statistical Association and the International Biometric Society.

Description Analyzing receiver operating characteristic curves with SAS FB2

Analyzing Receiver Operating Characteristic Curves with SAS with books developed and written by SAS experts. Richann Watson Richann Watson is an independent statistical.

Receiver operating characteristic (ROC) curves are used to evaluate and compare the performance of diagnostic tests; they can also be used to evaluate model fit. An ROC curve is just a plot of the proportion of true positives (events predicted to be events) versus the proportion of false positives (nonevents predicted to be events).

ROC Curves with Censored Data Introduction Lung Cancer Example ROC Curves with Censored Data Concordance Probability with Censored Data Concordance Probability and the Cox Model - Selection from Analyzing Receiver Operating Characteristic Curves with SAS [Book].

I learned tons from this book. Analyzing Receiver Operating Characteristic Curves with SAS by Mithat Gonen. I use to think that Receiver Operating Characteristic (ROC) Curves were restricted to logistic regression. Also, I have never dug deeper than thinking if the area under the curve was somewhat close to 1, my model was good.

For each ROC model, the model fitting details in Outputs OutputOutputand Output can be suppressed with the ROCOPTIONS(NODETAILS) option; however, the convergence status is always displayed.

The ROC curves for the three models are displayed in Outputs OutputOutputand Output Note that the labels on the ROC curve are produced by specifying the ID.

Find helpful customer reviews and review ratings for Analyzing Receiver Operating Characteristic Curves With SAS (Sas Press Series) at Read honest and.

Generating Receiver Operating Characteristic (ROC) curve using SAS Macros, continued 2 NPV is the probability of a patient won’t have the disease given the test result is negative NPV = TN / (FN + TN) = D/(C+D) PPV is the probability of a patient will have the disease given the test result is positive PPV = TP / (TP + FP) = A/(A+B).

Receiver Operating Characteristic Curves In a sample of n individuals, suppose n 1 individuals are observed to have a certain condition or event.

Let this group be denoted by C 1, and let the group of the remaining n 2 =n-n 1 individuals who do not have the condition be denoted by C factors are identified for the sample, and a logistic regression model is fitted to the data. An incredibly useful tool in evaluating and comparing predictive models is the ROC curve.

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Its name is indeed strange. ROC stands for receiver operating characteristic. Its origin is from sonar back in the s; ROCs were used to measure how well a sonar signal (e.g., from a submarine) could be detected from noise (a school of fish).

In its current usage, ROC curves are a nice way to see how. After searching, I found that chapter " Selecting an Optimal Threshold" of book "Analyzing Receiver Operating Characteristic Curves with SAS" on Google Book has some detailed explanation on selecting optimal threshold.

But the ROC Curve itself is just a graph. I would highly recommend the book "Analyzing Receiver Operating Characteristic Curves with SAS: by Gonen.

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It provides a gentle introduction to many ROC Curve concepts including bias, and applying curves to ordinal and survival data. Gönen M. Analyzing Receiver Operating Characteristic Curves with SAS.

Cary: North Carolina: SAS Publishing. As with most SAS-specific books, this is a very practical guide. It has a fair amount of theory/ background but this is not its primary goal or strength. Re: Comparing Receiver Operating Characteristic Curves using SAS?

Posted ( views) | In reply to Gayatriv As was suggested in your tracking entry on this question, you should use the NOFIT option in the MODEL statement to remove the second AUC estimate that is from the MODEL statement.The Receiver Operating Characteristic (ROC) analysis curve is mainly used for diagnostic studies in Clinical Chemistry, Pharmacology, and Physiology.

It has been widely accepted as the standard method used for describing and comparing the accuracy of diagnostic tests. Please refer to the Origin help file for details on how to use the ROC curve.Analyzing Receiver Operating Characteristic Curves with SAS As a diagnostic decision-making tool, receiver operating characteristic (ROC) curves provide a comprehensive and visually attractive way to summarize the accuracy of predictions.