MedCalc

 MedCalc is a statistical software designed specifically for biomedical research. It provides a range of tools for data analysis, including ROC curve analysis, survival analysis, Bland-Altman plots, regression analysis, and more. It is widely used by researchers, clinicians, and statisticians in the medical field.

Known for its intuitive interface and powerful analytical tools, MedCalc simplifies complex statistical analyses, making it accessible even to users with limited statistical expertise. The software excels in diagnostic test evaluations, including ROC curve analysis, sensitivity-specificity assessments, and method comparison studies like Bland-Altman plots. Additionally, it supports a wide range of statistical tests, regression models, survival analyses, and data visualization options tailored for medical research. While it lacks the open-source flexibility of R or Python, MedCalc stands out for its precision, ease of use, and focus on biomedical applications, making it a preferred choice for clinical studies, laboratory research, and evidence-based medicine. Whether analyzing laboratory results, conducting diagnostic accuracy studies, or preparing publication-ready graphs, MedCalc provides a reliable and efficient solution for researchers in the Medical Field.

 

User-friendly statistical software

 

  1. MedCalc is a statistical software package for biomedical research.
  2. Statistics include more than 220 statistical tests, procedures and graphs.
  3. ROC curve analysis, method comparison and quality control tools.

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 MedCalc 
  • Description
  • Features
  • Benefits
  • USED CASES OF MedCalc

Easy to learn, fast and reliable

Data management

  • Integrated spreadsheet with 1048576 rows and 16384 columns.
  • Correct handling of missing data.
  • Outliers can easily be excluded.
  • Built-in WYSIWYG text editor.
  • Imports Excel, Excel 2007, SPSS, DBase and Lotus files, and files in SYLK, DIF or plain text format.
  • Easy selection of subgroups for statistical analysis.


MedCalc Documentation

  • Comprehensive context-sensitive help.
  • Complete HTML manual on MedCalc web site.
  • Context help in dialog boxes.
  • Paperback manual.


Statistical features

See list of statistical procedures.


ROC curve analysis

MedCalc is the reference software for ROC curve analysis. Our ROC curve analysis module includes:

Go to the ROC curve analysis section of the MedCalc manual for more information on ROC curve analysis in MedCalc.


Graphs


Accessibility


System requirements

  • PC with Windows Vista, Windows 7, 8, 8.1, 10 or 11; or Windows Server 2008 or more recent (all 32-bit and 64-bit versions supported).
  • On Windows versions prior to Windows 8, support for Excel *.xlsx files additionally requires Microsoft .NET Framework 4.0 (a free download from Microsoft).
  • 2048 Mb of memory.
  • 60 Mb free space on the hard disk.
  • To run MedCalc on a Mac, a Windows emulator is necessary such as Parallels or use Bootcamp.
  1. User-Friendly Interface – Designed for medical researchers, it simplifies complex statistical calculations.
  2. Comprehensive Statistical Tools – Offers a variety of statistical tests and methods tailored for medical research.
  3. Graphical Representation – Provides high-quality graphs and plots for data visualization.
  4. Handles Large Datasets – Efficiently processes large amounts of data commonly found in biomedical research.
  5. Specialized Medical Research Features – Includes ROC curve analysis, survival analysis, and epidemiological statistics.
  6. Automated Reporting – Generates detailed reports, making it easier for researchers to interpret and present their findings.
  7. Data Import & Export – Supports various file formats, including Excel, CSV, and SPSS.

1. ROC Curve Analysis – Used in diagnostic testing to evaluate the performance of medical tests.

2. Survival Analysis – Helps in understanding patient survival rates and treatment efficacy.

3. Bland-Altman Analysis – Compares two measurement methods to assess agreement.

4. Regression Analysis – Determines relationships between variables in medical research.

5. Meta-Analysis – Combines results from multiple studies to derive overall conclusions.

6. Epidemiological Studies – Used for risk factor analysis, prevalence studies, and disease association.

7. Clinical Trials – Assists in analyzing treatment effects and patient responses.

MedCalc is particularly beneficial for medical professionals and researchers who need a robust yet easy-to-use tool for statistical analysis in health sciences.

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