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PASS 16 versions & prices
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NCSS PASS is one of the leading software tools for the design of medical trials and pharmaceutical or medical research in general. PASS provides the right methods for the power analysis of over 680 different statistical tests, confidence intervals and research scenarios.

Fast import of historical data – even in foreign data formats – and the simple interface help you focusing on the real problem: determining just the right sample size for your study.

Getting the right sample size involves just three steps:

  1. Choose the right study design from the navigator
  2. Enter the required parameters (typically: noise/standard deviation, relevant effect)
  3. Interpretation of results (Power, sample size)

That’s how easy NCSS PASS makes power calculation. All results will be visualized by high-level scientific graphs. All graphs are highly customizable and will add an additional level of clarity to your reports.

NCSS PASS offers:

  • Calculation of sample size and power
  • Validated procedures
  • Easy to learn, easy to use
  • Professional graphics
  • Helpful documentation as part of the output
  • Easy export to all commonly used text editors

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PASS - Power Analysis & Sample Size Software

PASS has been fine-tuned for over 20 years, and has become the leading sample size software choice for clinical trial, pharmaceutical, and other medical research. It has also become a mainstay in all other fields where sample size calculation or evaluation is needed. PASS software performs power analysis and calculates sample sizes for over 680 statistical tests.

PASS Overview

PASS is a standalone system
Although it is integrated with NCSS, you do not have to own NCSS to run it. You can use it with any statistical software you want.

PASS is accurate
It has been extensively verified using books and reference articles. Proof of the accuracy of each procedure is included in the extensive documentation.

PASS comes with complete help system documentation
That contains tutorials, examples, annotated output, references, formulas, validation, and complete instructions on each procedure. All procedures are validated with published articles or books.

Choose PASS. It's more comprehensive, easier-to-use, accurate, and less expensive than any other sample size program on the market.


Sample Size in PASS
In PASS, you can estimate the sample size for a statistical test or confidence interval in a few short steps. If you need guidance during any of the steps, PASS has excellent documentation.

Obtaining a Sample Size in PASS

Choosing a Procedure in PASS

Choosing A Procedure
Finding the sample size procedure is easy using the drop-down menu, the procedure search, or the category tree.


Enter The Values
The sample size procedure tools are easy to use and have built-in help messages for every option.

Sample Size tools in PASS

Ready to Use Output
When a PASS procedure is run the results are displayed in the output window. The sample size or power curves can be clicked to be shown in a separate window for viewing or saving. Multiple output runs can be sent to the output gallery for saving or for comparing sample size analyses.

Further Information

Trial version of the software PASS

The Producer (NCSS) provides a free trial version of the software. The trial version is 7-day's long usable without any restrictions in it's features and/or functions. The license key for the trialversion can be upgraded to a full version, after purchasing an appropriate license. So there is no struggle with reinstallation or re-registrations of licenses.

You can access the trial version on the website of the producer, just click on the following link: http://www.ncss.com/download/pass/free-trial/

System Requirements for the Software PASS

In order to run PASS 16, your computer must meet the following minimum standards:

  • Processor:
    • 450 MHz or faster processor
    • 32-bit (x86) or 64-bit (x64) processor
  • RAM:
    • 256 MB (512 MB recommended)
  • Operating Systems:
    • Windows 10 or later
    • Windows 8.1
    • Windows 8
    • Windows 7
    • Windows Vista with Service Pack 2 or higher
    • Windows Server 2016 or later
    • Windows Server 2012 R2
    • Windows Server 2012
    • Windows Server 2008 SP2/R2
  • Privileges:
    • Administrative rights required during installation only
  • Third Party Software:
    • Microsoft .NET 4.6 (Comes pre-installed with Windows 10 or later and Windows Server
      2016 or later. Installation required on Windows 8.1 or earlier and Windows Server
      2012 R2 or earlier. For systems where .NET 4.6 installation is required, a .NET 4.6
      download helper will start automatically when you run the PASS setup file.
      )
    • Microsoft Windows Installer 3.1 or higher
    • Adobe Reader® 7 or higher (required for the Help System only)
  • Hard Disk Space:
    • 220 MB for PASS (plus space for Microsoft .NET 4.6 if not already installed)
  • Printer:
    • Any Windows-compatible inkjet or laser printer
    •  

PASS 16 on a Mac

A Windows emulator (such as Parallels) is required to run PASS 16 on a Mac.

 

 

New Features in PASS 16

PASS 16 adds over 55 new sample size procedures.

New Procedures

 

Logistic Regression

  • Tests for the Odds Ratio in Logistic Regression with One Normal X (Wald Test)
  • Tests for the Odds Ratio in Logistic Regression with One Normal X and Other Xs (Wald Test)
  • Tests for the Odds Ratio in Logistic Regression with One Binary X and Other Xs (Wald Test)

 

Repeated Measures Slopes (GEE)

  • GEE Tests for the Slope of Two Groups in a Repeated Measures Design (Continuous Outcome)
  • GEE Tests for the Slope of Two Groups in a Repeated Measures Design (Binary Outcome)
  • GEE Tests for the Slope of Two Groups in a Repeated Measures Design (Count Outcome)
  • GEE Tests for the Slope of Multiple Groups in a Repeated Measures Design (Continuous Outcome)
  • GEE Tests for the Slope of Multiple Groups in a Repeated Measures Design (Count Outcome)

 

Repeated Measures Time-Averaged Differences (GEE)

  • GEE Tests for the TAD of Two Groups in a Repeated Measures Design (Continuous Outcome)
  • GEE Tests for the TAD of Two Groups in a Repeated Measures Design (Binary Outcome)
  • GEE Tests for the TAD of Two Groups in a Repeated Measures Design (Count Outcome)
  • GEE Tests for the TAD of Multiple Groups in a Repeated Measures Design (Continuous Outcome)
  • GEE Tests for the TAD of Multiple Groups in a Repeated Measures Design (Binary Outcome)
  • GEE Tests for the TAD of Multiple Groups in a Repeated Measures Design (Count Outcome)

 

Hierarchical Design Comparisons using Mixed Models

  • Mixed Models Tests for Two Means in a 2-Level Hierarchical Design (Level-2 Randomization)
  • Mixed Models Tests for Two Means in a 2-Level Hierarchical Design (Level-1 Randomization)
  • Mixed Models Tests for Two Proportions in a 2-Level Hierarchical Design (Level-2 Randomization)
  • Mixed Models Tests for Two Proportions in a 2-Level Hierarchical Design (Level-1 Randomization)
  • Mixed Models Tests for the Slope Difference in a 2-Level Hierarchical Design with Fixed Slopes
  • Mixed Models Tests for the Slope Difference in a 2-Level Hierarchical Design with Random Slopes
  • Mixed Models Tests for Two Means in a 3-Level Hierarchical Design (Level-3 Randomization)
  • Mixed Models Tests for Two Means in a 3-Level Hierarchical Design (Level-2 Randomization)
  • Mixed Models Tests for Two Means in a 3-Level Hierarchical Design (Level-1 Randomization)
  • Mixed Models Tests for Two Proportions in a 3-Level Hierarchical Design (Level-3 Randomization)
  • Mixed Models Tests for Two Proportions in a 3-Level Hierarchical Design (Level-2 Randomization)
  • Mixed Models Tests for Two Proportions in a 3-Level Hierarchical Design (Level-1 Randomization)
  • Mixed Models Tests for the Slope Diff. in a 3-Level Hier. Design with Fixed Slopes (Level-2 Rand.)
  • Mixed Models Tests for the Slope Diff. in a 3-Level Hier. Design with Random Slopes (Level-2 Rand.)
  • Mixed Models Tests for the Slope Diff. in a 3-Level Hier. Design with Fixed Slopes (Level-3 Rand.)
  • Mixed Models Tests for the Slope Diff. in a 3-Level Hier. Design with Random Slopes (Level-3 Rand.)

 

2×2 Cross-Over Design – Odds Ratio

  • Tests for the Odds Ratio of Two Proportions in a 2×2 Cross-Over Design
  • Non-Inferiority Tests for the Odds Ratio of Two Proportions in a 2×2 Cross-Over Design
  • Superiority by a Margin Tests for the Odds Ratio of Two Proportions in a 2×2 Cross-Over Design
  • Equivalence Tests for the Odds Ratio of Two Proportions in a 2×2 Cross-Over Design

 

2×2 Cross-Over Design – Proportion Difference

  • Tests for the Difference of Two Proportions in a 2×2 Cross-Over Design
  • Non-Inferiority Tests for the Difference of Two Proportions in a 2×2 Cross-Over Design
  • Superiority by a Margin Tests for the Difference of Two Proportions in a 2×2 Cross-Over Design
  • Equivalence Tests for the Difference of Two Proportions in a 2×2 Cross-Over Design

 

2×2 Cross-Over Design – Ratio of Poisson Rates

  • Tests for the Ratio of Two Poisson Rates in a 2×2 Cross-Over Design
  • Non-Inferiority Tests for the Ratio of Two Poisson Rates in a 2×2 Cross-Over Design
  • Superiority by a Margin Tests for the Ratio of Two Poisson Rates in a 2×2 Cross-Over Design
  • Equivalence Tests for the Ratio of Two Poisson Rates in a 2×2 Cross-Over Design

 

2×2 Cross-Over Design – Generalized Odds Ratio for Ordinal Data

  • Tests for the Generalized Odds Ratio for Ordinal Data in a 2×2 Cross-Over Design
  • Non-Inferiority Tests for the Generalized Odds Ratio for Ordinal Data in a 2×2 Cross-Over Design
  • Superiority by a Margin Tests for the Gen. Odds Ratio for Ordinal Data in a 2×2 Cross-Over Design
  • Equivalence Tests for the Generalized Odds Ratio for Ordinal Data in a 2×2 Cross-Over Design

Williams Cross-Over Design – Pairwise Proportion Differences

  • Tests for Pairwise Proportion Differences in a Williams Cross-Over Design
  • Non-Inferiority Tests for Pairwise Proportion Differences in a Williams Cross-Over Design
  • Superiority by a Margin Tests for Pairwise Proportion Differences in a Williams Cross-Over Design
  • Equivalence Tests for Pairwise Proportion Differences in a Williams Cross-Over Design

Williams Cross-Over Design – Pairwise Mean Differences

  • Tests for Pairwise Mean Differences in a Williams Cross-Over Design
  • Non-Inferiority Tests for Pairwise Mean Differences in a Williams Cross-Over Design
  • Superiority by a Margin Tests for Pairwise Mean Differences in a Williams Cross-Over Design
  • Equivalence Tests for Pairwise Mean Differences in a Williams Cross-Over Design

Multiple Correlated Proportions (Stuart-Maxwell Test)

  • Tests for Multiple Correlated Proportions (Stuart-Maxwell Test)