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• PASS（Power Analysis and Sample Size）是用于效能分析和樣本量估計的統計軟件包，是市場研究中非常好的效能檢驗的軟件。它能對數十種統計學檢驗條件下的檢驗效能和樣本含量進行估計，主要包括區間估計、均數比較、率的比較、相關與回歸分析和病例隨訪資料分析等情形。該軟件界面友好，功能齊全，操作簡便。用戶不需要精通統計學知識，只要確定醫學研究設計方案，并提供相關信息，就可通過簡單的菜單操作，估計出檢驗效能和樣本含量。
• PASS特點
• 一個或兩個均值檢驗
• PASS包含60多種用于樣本量估計的工具和一個、兩個、或同時兩個不同均值的效能檢驗比對，包括t檢驗、等價性檢驗、非劣效性檢驗、交叉檢驗、無參數檢驗、仿真檢驗等等。每一個過程的使用都很簡單，并且經過了精密的準確性驗證。
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• 多均值檢驗
• PASS包含幾種用于樣本量估計的工具和三個或更多不同均值的效能檢驗比對。包括ANOVA、混合模型、多重對比、多變量方差分析和重復測量等等。每一個過程的使用都很簡單，并且經過了精密的準確性驗證。
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• 相關性檢驗
• PASS包含幾種用于樣本量估計的工具和相關性效能檢驗，包括單相關性和雙相關性檢驗、單相關性的置信區間、組內相關性檢驗。PASS還可以計算樣本量和效能，用于檢驗系數的透明度，檢驗兩個評價指標間一致性的kappa值和線性一致性相關系數。每一個過程的使用都很簡單，并且經過了精密的準確性驗證。
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• 正態性檢驗
• PASS包含對8種不同正態性檢驗方法的樣本量計算和效能檢驗。使用過程很簡單，并且都經過了準確性驗證。
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• 方差和標準差
• PASS包含了多種對方差和標準差的樣本量計算和效能檢驗方法，包括單一方差和兩個方差的檢驗、單方差的置信區間檢驗、兩個方差比值的置信區間檢驗、標準差的置信區間檢驗。每一個過程的使用都很簡單，并且經過了精密的準確性驗證。
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• 回歸檢驗
• PASS包含了幾種用于回歸分析的樣本量計算和效能檢驗方法，包括線性回歸、線性回歸斜率的置信區間、多重回歸、多因素回歸、泊松回歸和邏輯回歸。每一個過程的使用都很簡單，并且經過了精密的準確性驗證。
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• 一比重檢驗
• PASS包含了20多種用于一比重的樣本量計算和效能檢驗工具，包括z檢驗、等價性檢驗、非劣效性檢驗、置信區間檢驗和條件效能檢驗等等。每一個過程的使用都很簡單，并且經過了精密的準確性驗證。
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• 二比重檢驗
• PASS包含了50多種用于二比重的樣本量計算和效能檢驗工具，包括z檢驗、等價性檢驗、非劣效性檢驗、置信區間檢驗、相關比例檢驗、隨機聚類檢驗和條件效能檢驗等等。每一個過程的使用都很簡單，并且經過了精密的準確性驗證。
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• 卡方和其它比重檢驗
• PASS包含幾種用于多比重的樣本量計算和效能檢驗工具，包括卡方檢驗、 Cochran-Armitage、二序分類變量檢驗、靈敏性和特效性檢驗等等。每一個過程的使用都很簡單，并且經過了精密的準確性驗證。
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• 殘存檢驗
• PASS包含了25種用于殘存方法的樣本量計算和效能檢驗工具，包括時序檢驗、非劣效性檢驗、組連續性檢驗、條件效能檢驗等等。每一個過程的使用都很簡單，并且經過了精密的準確性驗證。
• PASS 2019的系統要求
• 要運行PASS 2019，您的計算機至少必須符合以下標準：
• 處理器：
• 450 MHz或更快的處理器
• 32位（x86）或64位（x64）處理器
• 內存：
• 256MB（推薦512MB）
• 操作系統：
• Windows 10或更高版本
• Windows 8.1、8
• Windows 7的Windows Vista Service Pack 2或更高版本
• Windows Server 2016或更高版本
• Windows Server 2012 R2
• Windows Server 2012
• Windows Server 2008 SP2 / R2
• 特權：
• 僅在安裝期間需要管理權限
• 硬盤空間：
• PASS 300 MB（如果尚未安裝，則加上Microsoft .NET 4.6的空間）
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• 英文介紹
• PASS software is an easy-to-use research tool for determining the number of subjects that should be used in a study. As the leader in sample size technology, PASS performs power analysis and calculates sample sizes for over 200 statistical tests and confidence intervals. With more sample size options than any other package, ASS is the best rsearch planning tool on the market.
  
• PASS Upgrade Information
• Updated and/or Improved Procedures in PASS 2019
• Conditional Power
• Conditional Power of Logrank Tests
• Conditional Power of Tests for the Difference Between Two Proportions
• Conditional Power of Tests for One Proportion
• Conditional Power of Tests for Two Means in a 2×2 Cross-Over Design
• Conditional Power of Paired T-Tests
• Conditional Power of Two-Sample T-Tests
• Conditional Power of One-Sample T-Tests
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• Survival
• Tests for the Difference of Two Hazard Rates Assuming an Exponential Model
• Tests for Two Survival Curves Using Cox's Proportional Hazards Model
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• Non-Inferiority Logrank Tests
• Non-Inferiority Tests for Two Survival Curves Using Cox's Proportional Hazards Model
• Non-Inferiority Tests for the Difference of Two Hazard Rates Assuming an Exponential Model
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• Superiority by a Margin Tests for Two Survival Curves Using Cox's Proportional Hazards Model
• Superiority by a Margin Tests for the Difference of Two Hazard Rates Assuming an Exponential Model
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• Equivalence Tests for Two Survival Curves Using Cox's Proportional Hazards Model
• Equivalence Tests for the Difference of Two Hazard Rates Assuming an Exponential Model
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• Proportions
• 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
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• Non-Inferiority Tests for the Difference Between Two Correlated Proportions
• Non-Inferiority Tests for the Ratio of Two Correlated Proportiona
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• 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
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• 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
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• 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
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• Equivalence Tests for the Difference Between Two Correlated Proportions
• Equivalence Tets for the Ratio of Two Correlated Proportions
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• Non-Zero Null Tests for the Difference Between Two Proportions
• Non-Unity Null Tests for the Ratio of Two Proportions
• Non-Unity Null Tests for the Odds Ratio of Two Proportions
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• Non-Zero Null Tests for the Difference of Two Proportions in a Cluster-Randomized Design
• Non-Unity Null Tests for the Ratio of Two Proportions in a Cluster-Randomized Design
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• Tests for Two Proportions in a Stratified Design （Cochran-Mantel-Haenszel Tests）
• Tests for Two Proportions in a Cluster-Randomized Design
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• Means
• One-Sample T-Tests for Superiority by a Margin
• One-Sample T-Tests for Non-Inferiority
• One-Sample T-Tests for Equivalence
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• Paired T-Tests for Equivalence
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• Two-Sample T-Tests Assuming Equal Variance
• Two-Sample T-Tests Allowing Unequal Variance
• Two-Sample T-Tests for Equivalence Assuming Equal Variance
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• Tests for the Ratio of Two Means
• Non-Inferiority Tests for the Ratio of Two Means
• Superiority by a Margin Tests for the Ratio of Two Means
• Equivalence Tests for the Ratio Two Means
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• Tests for the Difference Between Two Means in a 2×2 Cross-Over Design
• Tests for the Ratio of Two Means in a 2×2 Cross-Over Design
• Non-Inferiority Tests for the Difference Between Two Means in a 2×2 Cross-Over Design
• Non-Inferiority Tests for the Ratio of Two Means in a 2×2 Cross-Over Design
• Superiority by a Margin Tests for the Difference of Two Means in a 2×2 Cross-Over Design
• Superiority by a Margin Tests for the Ratio of Two Means in a 2×2 Cross-Over Design
• Equivalence Tests for the Difference Between Two Means in a 2×2 Cross-Over Design
• Equivalence Tests for the Ratio of Two Means in a 2×2 Cross-Over Design
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• Tests for Two Means in a Cluster-Randomized Design
• Non-Inferiority Tests for Two Means in a Cluster-Randomized Design
• Superiority by a Margin Tests for Two Means in a Cluster-Randomized Design
• Equivalence Tests for Two Means in a Cluster-Randomized Design
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• Hotelling's One-Sample T2
• Hotelling's Two-Sample T2
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• Multiple Testing for One Mean（One-Sample or Paired Data）
• Multiple Testing for Two Means
• Linear Regression Slope
• Confidence Intervals for Linear Regression Slope
• Conefficient Alpha
• Tests for One Coefficient Alpha
• Tests for Two Coeffcient Alphas
• Variances
• Tests for One Variance
• Compatibility of PASS 2019
• PASS 2019 is fully compatible with Windows 10, 8.1, 8, 7, and Vista SP2, on both 32-bit and 64-bit operating systems.