# PASS Upgrade Information

New in PASS 16

New in PASS 15

New in PASS 14

New in PASS 13

New in PASS 12

New in PASS 11

## What’s New in PASS 16?

How do I upgrade my PASS software?

We are pleased to announce the release of PASS 16. PASS 16 adds **55 new sample size procedures**, including new procedures for the odds ratio in logistic regression, generalized estimating equation (GEE) tests, repeated measures design tests, cross-over design proportions tests, tests for two Poisson rates in cross-over designs, ordinal data tests in cross-over designs, pairwise proportion differences in a Williams cross-over design, tests for comparing two or more time-averaged differences, multiple group slope tests, mixed models tests for two means/proportions/slopes in hierarchical designs, tests for multiple correlated proportions, and more.

**Installation Qualification (IQ)** and **Operational Qualification (OQ)** tools were added in PASS 16.

**Report section options** give the user flexibility to enhance output readability.

## New Procedures in PASS 16

### 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 (McNemar-Bowker Test of Symmetry)

- Tests for Multiple Correlated Proportions (McNemar-Bowker Test of Symmetry)

## Tools and Features added in PASS 16

- Installation Validation Tool for Installation Qualification (IQ)
- Procedure Validation Tool for Operational Qualification (OQ)
- Report Header Formatting and Colors

### Installation Validation Tool for Installation Qualification (IQ)

The Installation Validation Tool for Installation Qualification (IQ) may be used to validate that the software is installed completely and correctly. The tool can also be used to verify that no files have been changed since the software was installed.

The Installation Validation Tool compares all PASS installation files against factory requirements and reports if any required files or folders are missing or invalid. Summary and detailed validation reports are created and displayed in the Output Window. If all files and folders pass installation qualification, then you can be certain that the software is installed correctly.

### Procedure Validation Tool for Operational Qualification (OQ)

The Procedure Validation Tool for Operational Qualification (OQ) may be used to validate that the software is operating correctly. The tool verifies that one or more (up to all) procedures is/are functioning correctly by loading the procedure(s), executing calculations, and comparing the results to verified and expected outcomes. Summary and detailed validation reports are created and displayed in the Output Window. If all procedures pass operational qualification, then you can be certain that the software is functioning as expected.

Each procedure can also be validated individually by clicking Validate This Procedure in the Help Center or Help menu on any Procedure Window.

### Report Header Formatting and Colors

New system options give the user the ability to add (or remove) lines to the titles to improve separation of report sections. Title line color and text color for page headers, titles, and reports may also be specified individually.

## Compatibility of PASS 16

**PASS 16** is fully compatible with Windows 10, 8.1, 8, 7, and Vista SP2, on both 32-bit and 64-bit operating systems. Click here to view the complete system requirements.

## Prices

Upgrade from PASS 15:

$349 (academic/government)

$395 (commercial)

Upgrade from PASS 14:

$495 (academic/government)

$595 (commercial)

Upgrade from PASS 13*:

$649 (academic/government)

$749 (commercial)

*Older versions (e.g., PASS 12, PASS 11, PASS 2008, PASS 2005, PASS 2002, PASS 2000, and PASS 6.0) are not upgradeable. You’ll need to purchase a new license to get PASS 16.

Click here to view the complete price list.

## Ordering

To order your upgrade, order from our secure online store, email us at sales@ncss.com, call us at 1-800-898-6109 (US only) or (801) 546-0445, or fax us at (801) 546-3907.

## Documentation

PDF versions of the documentation are available directly from each procedure window. These may be displayed or printed. The documentation is also available in the help system or online by clicking here.

## Procedures added in the PASS 15 Upgrade from PASS 14

### Logistic and Conditional Logistic Regression

- Logistic Regression with One Binary Covariate using the Wald Test
- Logistic Regression with Two Binary Covariates using the Wald Test
- Logistic Regression with Two Binary Covariates and an Interaction using the Wald Test
- Confidence Intervals for the Odds Ratio in a Logistic Regression with Two Binary Covariates
- Confidence Intervals for the Interaction Odds Ratio in a Logistic Regression with Two Binary Covariates
- Tests for the Odds Ratio in a Matched Case-Control Design with a Binary Covariate using Conditional Logistic Regression
- Tests for the Odds Ratio in a Matched Case-Control Design with a Quantitative X using Conditional Logistic Regression

### Stepped-Wedge Cluster-Randomized Designs

- Tests for Two Proportions in a Stepped-Wedge Cluster-Randomized Design
- Tests for Two Means in a Stepped-Wedge Cluster-Randomized Design
- Tests for Two Poisson Rates in a Stepped-Wedge Cluster-Randomized Design

### Tolerance Intervals

- Tolerance Intervals for Normal Data
- Tolerance Intervals for Any Data (Nonparametric)
- Tolerance Intervals for Exponential Data
- Tolerance Intervals for Gamma Data

### Proportions

- Group-Sequential Tests for One Proportion in a Fleming Design
- Multiple Comparisons of Proportions vs. Control
- Tests for One Proportion to Demonstrate Conformance with a Reliability Standard
- Tests for One Proportion to Demonstrate Conformance with a Reliability Standard with Fixed Adverse Events

### Poisson and Negative Binomial Rates

- Non-Inferiority Tests for the Ratio of Two Poisson Rates
- Non-Inferiority Tests for the Ratio of Two Negative Binomial Rates
- Superiority by a Margin Tests for the Ratio of Two Poisson Rates
- Superiority by a Margin Tests for the Ratio of Two Negative Binomial Rates
- Equivalence Tests for the Ratio of Two Poisson Rates
- Equivalence Tests for the Ratio of Two Negative Binomial Rates

### Effect Size

- Two-Sample T-Tests using Effect Size
- Tests for One Mean using Effect Size
- Tests for Paired Means using Effect Size
- Tests for Two Proportions using Effect Size
- Tests for One Proportion using Effect Size
- One-Way Analysis of Variance F-Tests using Effect Size
- Factorial Analysis of Variance using Effect Size
- Multiple Regression using Effect Size

### Survival

- Two-Group Survival Comparison Tests (Simulation)

### Sensitivity and Specificity

- Confidence Intervals for One-Sample Sensitivity
- Confidence Intervals for One-Sample Specificity
- Confidence Intervals for One-Sample Sensitivity and Specificity

### Matched-Pair Difference in a Cluster-Randomized Design

- Tests for the Matched-Pair Difference of Two Event Rates in a Cluster-Randomized Design
- Tests for the Matched-Pair Difference of Two Proportions in a Cluster-Randomized Design
- Tests for the Matched-Pair Difference of Two Means in a Cluster-Randomized Design

### Percentiles

- Confidence Intervals for a Percentile of a Normal Distribution

## Features added in PASS 15

- Sample Sizes Adjusted for Drop-Out
- Report of Procedure Input Settings
- Autosave Procedure Settings

### Sample Sizes Adjusted for Drop-Out

The option for a Dropout-Inflated Sample Size report has been added to most procedures. The user specifies a drop-out (lost, not enrolled, etc.) percentage rate, and the report gives the total number of individuals needed such that power requirements will be met after drop-outs. That is, the report gives the drop-out inflated enrollment sample size.

### Report of Procedure Input Settings

An option was added to every procedure to allow the user to show all the procedure input settings as a report of the output.

### Autosave Procedure Settings

In PASS 15, the procedure settings are saved for every run of a procedure. This allows the user to go back and load the procedure settings of any PASS 15 run. Each settings file is given a unique date-time stamp to distinguish each run. The autosaved settings file name is also given in the Procedure Input Settings report.

## Updated and/or Improved Procedures in PASS 15

### Combined Procedures

In previous versions of PASS, some procedures differed only by the input parameter used. In PASS 15, several of these groups of procedures were combined into single procedures.

- Logrank Tests
- Group-Sequential Logrank Tests (Simulation)
- Two-Sample Z-Tests Assuming Equal Variance
- Two-Sample Z-Tests Allowing Unequal Variance
- Two-Sample T-Tests Assuming Equal Variance
- Two-Sample T-Tests Allowing Unequal Variance

### Repeated Measures

- Repeated Measures Analysis

### Regression

- Multiple Regression

## Procedures added in the PASS 14 Upgrade from PASS 13

PASS 14 added over 25 new PASS sample size software procedures, including 13 means procedures, 3 rates and counts procedures, 3 survival analysis procedures, 5 regression procedures, and 2 acceptance sampling procedures.

### Means

- Equivalence Tests for the Difference Between Two Paired Means
- Non-Inferiority Tests for Two Means in a Cluster-Randomized Design
- Equivalence Tests for Two Means in a Cluster-Randomized Design
- Superiority by a Margin Tests for Two Means in a Cluster-Randomized Design
- Tests for the Difference of Two Means in a Higher-Order Cross-Over Design
- Tests for the Ratio of Two Means in a Higher-Order Cross-Over Design
- Tests for Fold Change of Two Means
- MxM Cross-Over Designs
- M-Period Cross-Over Designs using Contrasts
- One-Way Repeated Measures
- One-Way Repeated Measures Contrasts
- One-Way Analysis of Variance Contrasts
- Confidence Intervals for One-Way Repeated Measures Contrasts

### Rates and Counts

- Tests for the Difference Between Two Poisson Rates
- Tests for the Difference Between Two Poisson Rates in a Cluster-Randomized Design
- Tests for the Ratio of Two Negative Binomial Rates

### Survival

- Logrank Tests in a Cluster-Randomized Design
- One-Sample Logrank Tests
- One-Sample Cure Model Tests

### Regression

- Reference Intervals for Clinical and Lab Medicine
- Tests for the Difference Between Two Linear Regression Slopes
- Tests for the Difference Between Two Linear Regression Intercepts
- Mendelian Randomization with a Binary Outcome
- Mendelian Randomization with a Continuous Outcome

### Acceptance Sampling

- Acceptance Sampling for Attributes
- Operating Characteristic Curves for Acceptance Sampling for Attributes

## Procedures Updated in the PASS 14 Upgrade from PASS 13

Over 45 procedures were updated and/or improved as well.

### Means

- Tests for Two Means using Ratios
- Tests for Two Means in a Cluster-Randomized Design
- Non-Inferiority Tests for the Difference of Two Means in a Higher-Order Cross-Over Design
- Non-Inferiority Tests for the Ratio of Two Means in a Higher-Order Cross-Over Design
- Equivalence Tests for the Difference of Two Means in a Higher-Order Cross-Over Design
- Equivalence Tests for the Ratio of Two Means in a Higher-Order Cross-Over Design
- Superiority by a Margin Tests for the Difference of Two Means in a Higher-Order Cross-Over Design
- Superiority by a Margin Tests for the Ratio of Two Means in a Higher-Order Cross-Over Design
- One-Way Analysis of Variance F-Tests

### Rates and Counts

- Tests for One Poisson Rate
- Tests for the Ratio of Two Poisson Rates

### Proportions

- Tests for One Proportion
- Non-Inferiority Tests for One Proportion
- Equivalence Tests for One Proportion
- Superiority by a Margin Tests for One Proportion
- Tests for Two Proportions
- Tests for Two Proportions in a Repeated Measures Design
- Non-Inferiority Tests for the Difference Between Two Proportions
- Non-Inferiority Tests for the Ratio of Two Proportions
- Non-Inferiority Tests for the Odds Ratio of Two Proportions
- Equivalence Tests for the Difference Between Two Proportions
- Equivalence Tests for the Ratio of Two Proportions
- Equivalence Tests for the Odds Ratio of Two Proportions
- Superiority by a Margin Tests for the Difference Between Two Proportions
- Superiority by a Margin Tests for the Ratio of Two Proportions
- Superiority by a Margin Tests for the Odds Ratio of Two Proportions
- Confidence Intervals for the Difference Between Two Proportions
- Confidence Intervals for the Ratio of Two Proportions
- Confidence Intervals for the Odds Ratio of Two Proportions
- Tests for Two Correlated Proportions (McNemar Test)
- Non-Inferiority Tests for the Difference Between Two Correlated Proportions
- Non-Inferiority Tests for the Ratio of Two Correlated Proportions
- Equivalence Tests for the Difference Between Two Correlated Proportions
- Equivalence Tests for the Ratio of Two Correlated Proportions
- Tests for Two Proportions in a Cluster-Randomized Design
- Non-Inferiority Tests for the Difference of Two Proportions in a Cluster-Randomized Design
- Non-Inferiority Tests for the Ratio of Two Proportions in a Cluster-Randomized Design
- Equivalence Tests for the Difference of Two Proportions in a Cluster-Randomized Design
- Equivalence Tests for the Ratio of Two Proportions in a Cluster-Randomized Design
- Superiority by a Margin Tests for the Difference of Two Proportions in a Cluster-Randomized Design
- Superiority by a Margin Tests for the Ratio of Two Proportions in a Cluster-Randomized Design
- Group-Sequential Tests for Two Proportions (Simulation)
- Group-Sequential Non-Inferiority Tests for the Difference of Two Proportions (Simulation)
- Group-Sequential Non-Inferiority Tests for the Ratio of Two Proportions (Simulation)
- Group-Sequential Non-Inferiority Tests for the Odds Ratio of Two Proportions (Simulation)
- Group-Sequential Superiority by a Margin Tests for the Difference of Two Proportions (Simulation)
- Group-Sequential Superiority by a Margin Tests for the Ratio of Two Proportions (Simulation)
- Group-Sequential Superiority by a Margin Tests for the Odds Ratio of Two Proportions (Simulation)

## Features added in the PASS 13 Upgrade from PASS 12

- 3D Power and Sample Size Plots
- Multiple Value Selection Improvements
- Help Center and Video Help Expansion
- Two-Sample Sample Size Group Allocation Enhancements

### 3D Power and Sample Size Plots

### Multiple Value Selection Improvements

### Help Center and Video Help Expansion

## Procedures added in the PASS 13 Upgrade from PASS 12

PASS 13 added over 25 new power and sample size procedures, including one-way tests (3), variance tests (5), correlation tests (5), correlation confidence intervals (4), exponential distribution parameter confidence intervals (4), quality control (2), Coefficient (Cronbach’s) Alpha confidence interval (1), Kappa confidence interval (1), area under an ROC curve confidence interval (1), Michaelis-Menten parameters confidence intervals (1), and tests for two means in a multicenter randomized design (1).

### One-way Tests of Mean or Center

- Kruskal-Wallis Tests (Simulation)
- Terry-Hoeffding Normal-Scores Tests of Means (Simulation)
- Van der Waerden Normal Quantiles Tests of Means (Simulation)

### Variance Tests

- Bartlett Test of Variances (Simulation)
- Levene Test of Variances (Simulation)
- Brown-Forsythe Test of Variances (Simulation)
- Conover Test of Variances (Simulation)
- Power Comparison of Tests of Variances (Simulation)

### Correlation Tests

- Pearson’s Correlation Tests (Simulation)
- Spearman’s Rank Correlation Tests (Simulation)
- Kendall’s Tau-b Correlation Tests (Simulation)
- Point Biserial Correlation Tests
- Power Comparison of Correlation Tests (Simulation)

### Correlation Confidence Intervals

- Confidence Intervals for Spearman’s Rank Correlation
- Confidence Intervals for Kendall’s Tau-b Correlation
- Confidence Intervals for Point Biserial Correlation
- Confidence Intervals for Intraclass Correlation

### Exponential Distribution Parameter Confidence Intervals

- Confidence Intervals for the Exponential Lifetime Mean
- Confidence Intervals for an Exponential Lifetime Percentile
- Confidence Intervals for Exponential Reliability
- Confidence Intervals for the Exponential Hazard Rate

### Quality Control

- Confidence Intervals for Cp
- Confidence Intervals for Cpk

### Other

- Tests for Two Means in a Multicenter Randomized Design
- Confidence Intervals for Michaelis-Menten Parameters
- Confidence Intervals for the Area Under an ROC Curve
- Confidence Intervals for Kappa
- Confidence Intervals for Coefficient Alpha

## Procedures added in the PASS 12 Upgrade from PASS 11

PASS 12 added over 15 new power and sample size procedures, including z tests (3), conditional power (6), repeated measures (2), non-inferiority logrank (2), equivalence logrank (2), Lin’s concordance coefficient (1), probit analysis (1), and competing risks (1).

### Conditional Power

- Conditional Power of One-Sample T-Tests
- Conditional Power of Two-Sample T-Tests
- Conditional Power of Paired T-Tests
- Conditional Power of 2×2 Cross-Over Designs
- Conditional Power of Logrank Tests
- Conditional Power of One-Proportions Tests
- Conditional Power of Two-Proportions Tests

### Z-Test

- Two-Sample Z-Test Assuming Equal Variances
- Two-Sample Z-Test Allowing Unequal Variances

### Survival Analysis

- Test for Two Survival Curves using Cox Regression
- Non-Inferiority Test for Two Survival Curves using Cox Regression
- Equivalence Test for Two Survival Curves using Cox Regression
- Superiority Test for Two Survival Curves using Cox Regression
- Test for Difference of Two Hazard Rates Assuming an Exponential Model
- Non-Inferiority Test for Difference of Two Hazard Rates Assuming an Exponential Model
- Equivalence Test for Difference of Two Hazard Rates Assuming an Exponential Model
- Superiority Test for Comparing Hazard Rates assuming Exponential Data
- Logrank Test Accounting for Competing Risks

### Other

- Lin’s Concordance Correlation Coefficient
- Probit Analysis
- Test for Comparing Mean Change Score in Pre-Post Design
- Confidence Interval for a Proportion from a Finite Population

### Revised and Simplified

- Repeated Measures Analysis
- MANOVA
- Two-Sample T-Test Assuming Equal Variances
- Two-Sample T-Test with Unequal Variances
- Mann-Whitney-Wilcoxon Test

## Procedures added in the PASS 11 Upgrade from PASS 2008

PASS 11 added 17 new power and sample size procedures and features to PASS, including procedures for analysis of covariance, group-sequential testing, sensitivity and specificity, Poisson means testing, tests for two ordered categorical variables, Williams test for the minimum effective dose, Control Charts, an enhanced user interface, increased computation speed, and an improved graphics system.

## New Procedures in the PASS 11 Upgrade from PASS 2008

- Analysis of Covariance (ANCOVA)
- Group-Sequential Tests for Two Means using Simulation
- Group-Sequential Non-Inferiority Tests for Two Means using Simulation
- Group-Sequential Tests for Two Proportions using Simulation
- Group-Sequential Non-Inferiority Tests for Two Proportions using Simulation
- Group-Sequential Logrank Tests using Simulation
- Sensitivity and Specificity Tests for One Group
- Tests for Independent Sensitivities of Two Groups
- Tests for Paired Sensitivities
- Tests for One Poisson Mean
- Tests for Two Poisson Means
- Tests for Two Ordered Categorical Variables
- Williams Test for the Minimum Effective Dose
- Control Charts for Process Means
- Control Charts for Process Variation