Software

Here is a catalogue of software available for biomarker evaluation. We welcome contributions in the form of documents describing what a program does and how to access it.

Stata Programs

• Commercial Package Stata 8, Stata Corp.
• Basic ROC Analysis, Gary Longton and Margaret Pepe
• Sample Size Calculations, Gary Longton and Margaret Pepe
• Evaluating Risk Prediction Markers, Gary Longton and Margaret Pepe
• Decision Curve Analysis, Andrew Vickers 

R/S-plus Programs

• Commercial Package S-Plus 7
• Time-dependent ROC Curves, Patrick Heagerty
• Basic ROC Analysis and Evaluation of Risk Prediction Markers, Aasthaa Bansal, Daryl Morris, and Margaret Pepe
• Multiple Gene Risk Prediction Performance (Windows binary package), Daryl Morris, Jessie Wen Gu Margaret Pepe
• Decision Curve Analysis, Andrew Vickers

SPSS

• Base SPSS (versions 9.0 and above)

SAS

• Base SAS 9.1
• User articles and documentation on available macros
  • Estimating the Area under a Receiver Operating Characteristic (ROC) Curve For Repeated Measures Design, Honghu Liu and Tongtong Wu
  • Statistical Methods in Diagnostic Medicine using SASŪ Software, Jay N. Mandrekar and Sumithra J. Mandrekar
    Includes comparing ROC curves when two tests are taken on the same measurements, ROC curves from a logistic regression, and power calculations for the AUC.
  • How to Display Correlated ROC Curves with the SAS System, Barbara Schneider
  • Selecting and Combining Biomarkers for Binary Outcomes, Ziding Feng
  • New capabilities for ROC curve plotting in SAS version 9.2, Dale McLerran (SAS datasets: compare markers, remission test, remission train)
• The Cleveland Clinic has SAS macros to calculate ROC sample sizes (1 reader, or 1-2 ROC curves), calculate ROC sample sizes with multiple readers, and plot ROC curves using SAS/Graph.

Fortran Programs

• ROCKIT
• The Cleveland Clinic has Fortran programs to
  • Estimate the sample size to compare two curves by estimating the pAUC or the sensitivity at a fixed false positive rate.
  • Make inferences on pAUCs through parametric methods
  • Make inferences on AUCs with clustered data, rating data, and continuous data, based on parametric and non-parametric data.
  • Analyze multi-reader and multi-modal ROC datasets.

MedCalc

• MedCalc is a commercial software package designed to analyze several different types of biomedical data. It includes an ROC component. MedCalc provides an online user's manual, with a chapter on their ROC features. A brief summary of MedCalc's capabilities can be found here.