NCSS_PASS2011是著名的样本量计算软件，其适用于大部分研究样本量计算的需要

PASS，即Power Analysis and Sample Size，主要用于样本量计算或者固定样本量的情况计算把握度（1-β），软件内容包括几乎所有科研模型，包括以下模型，该软件操作简单，适用于对SAS等编程软件不是很精通的人士，界面中还有辅助解释，例如进行临床试验时采用两组随机对照试验，进行非劣效分析，终点为生存率，那么就可以选择Logrank tests for non_inferiority，直接输入α，1-β，HR0危险比，h1标准组危险度，两组的比例，入组时间，总时间（入组+随诊时间），丢失率，后就可以得到所需样本量，并且结果中最有特色的是还给您提供相关的参考文献，写文章时当然可直接引用这个参考文献了：
Proportions
One Proportion
    Inequality Tests
    Non-Zero Null Tests
    Non-Inferiority Tests
    Equivalence Tests
    Confidence Intervals
    Single-Stage Phase II Clinical Trials
    Two-Stage Phase II Clinical Trials
    Three-Stage Phase II Clinical Trials
    Post-Marketing Surveillance
Two Correlated Proportions
    Inequality Tests (McNemar Test)
    Inequality Tests (Matched Case-Control Design)
    Non-Inferiority Tests
    Equivalence Tests
Two Independent Proportions
    Inequality Tests
    Inequality Tests (Repeated Measures Design)
    Non-Zero Null Tests
    Non-Inferiority Tests
    Equivalence Tests
    Confidence Intervals
    Group-Sequential Tests
    Group-Sequential Tests (Simulation)
    Non-Zero Null Group-Sequential Tests (Simulation)
    Group-Sequential Non-Inferiority Tests (Simulation)
    Inequality Tests (Stratified Design – Cochran-Mantel-Haenszel Test)
Two Independent Proportions in a Cluster-Randomized Design
    Inequality Tests
    Non-Zero Null Tests
    Non-Inferiority Tests
    Equivalence Tests
Many Proportions (Contingency Tables)
    Chi-Square Tests
    Two Ordered Categorical Variable Tests
    Cochran-Armitage Test for Trend in Proportions
ROC Curves
    Inequality Tests for One ROC Curve
    Inequality Tests for Two ROC Curves
Sensitivity and Specificity
    Sensitivity and Specificity Test for One Group
    Sensitivity Test for Two Groups
    Tests for Paired Sensitivities
Control Charts
    Control Charts for Process Means (Simulation)
    Control Charts for Process Variation (Simulation)
Means
One Mean
    Inequality Tests
    Inequality Tests (Exponential Data)
    Inequality Tests (Simulation)
    Inequality Tests (Poisson Data)
    Non-Zero Null Tests
    Non-Inferiority Tests
    Confidence Intervals
    Confidence Intervals with Tolerance Probability
Two Independent Means
    Inequality Tests using Differences (Two-Sample T-Test)
    Inequality Tests (Repeated Measures Design)
    Inequality Tests (Exponential Data)
    Inequality Tests (Poisson Data)
    Inequality Tests (Simulation)
    Inequality Tests using Ratios
    Non-Zero Null Tests using Differences
    Non-Inferiority Tests using Differences
    Non-Unity Null Tests using Ratios
    Non-Inferiority Tests using Ratios
    Equivalence Tests using Differences
    Equivalence Tests (Simulation)
    Equivalence Tests using Ratios
    Confidence Intervals for the Difference
    Confidence Intervals for the Difference with Tolerance Probability
    Group-Sequential Tests
    Group-Sequential Tests (Simulation)
    Group-Sequential Tests assuming Normality (Simulation)
    Group-Sequential Non-Inferiority Tests (Simulation)
    Inequality Tests (Cluster-Randomized Design)
Two Correlated (Paired) Means
    Inequality Tests
    Inequality Tests (Simulation)
    Equivalence Tests (Simulation)
    Confidence Intervals
    Confidence Intervals with Tolerance Probability
Two Independent Means in a 2×2 Cross-Over Design
    Inequality Tests using Differences
    Inequality Tests using Ratios
    Non-Zero Null Tests using Differences
    Non-Inferiority Tests using Differences
    Non-Unity Null Tests using Ratios
    Non-Inferiority Tests using Ratios
    Equivalence Tests using Differences
    Equivalence Tests using Ratios
Two Independent Means in a Higher-Order Cross-Over Design
    Non-Zero Null Tests using Differences
    Non-Inferiority Tests using Differences
    Non-Unity Null Tests using Ratios
    Non-Inferiority Tests using Ratios
    Equivalence Tests using Differences
    Equivalence Tests using Ratios
Many Means (ANOVA)
    One-Way Analysis of Variance
    Analysis of Covariance (ANCOVA)
    One-Way Analysis of Variance (Simulation)
    Fixed Effects Analysis of Variance
    Randomized Block Analysis of Variance
    Repeated Measures Analysis of Variance
Mixed Models
    Mixed Models
Multiple Comparisons
    Multiple Comparisons
    Pair-Wise Multiple Comparisons (Simulation)
    Multiple Comparisons of Treatments vs. a Control (Simulation)
    Multiple Contrasts (Simulation)
    Williams Test for the Minimum Effective Dose
Multivariate Means
    Hotelling's T2
    Multivariate Analysis of Variance (MANOVA)
Microarrays
    One-Sample or Paired T-Test
    Two-Sample T-Test
Standard Deviations
    Confidence Intervals for One Standard Deviation using Standard Deviation
    Confidence Intervals for One Standard Deviation with Tolerance Probability
    Confidence Intervals for One Standard Deviation using Relative Error
Variances
One Variance
    Inequality Tests for One Variance
    Confidence Intervals for One Variance using Variance
    Confidence Intervals for One Variance with Tolerance Probability
    Confidence Intervals for One Variance using Relative Error
Two Variances
    Inequality Tests for Two Variances
    Confidence Intervals for the Ratio of Two Variances using Variances
    Confidence Intervals for the Ratio of Two Variances using Relative Error
Normality Tests
    Normality Tests (Simulation)
Survival Analysis
    Logrank Tests (Freedman)
    Logrank Tests (Lachin and Foulkes)
    Logrank Tests for Non-Inferiority
    Group-Sequential Logrank Tests
    Group-Sequential Logrank Tests (Simulation)
    Logrank Tests (Lakatos)
Correlations
    Inequality Tests for One Correlation
    Confidence Intervals for One Correlation
    Inequality Tests for Two Correlations
    Inequality Tests for Intraclass Correlation
    Kappa Test for Agreement Between Two Raters
    Inequality Tests for One Coefficient Alpha
    Inequality Tests for Two Coefficient Alphas
Regression
    Cox Regression
    Linear Regression
    Confidence Intervals for Linear Regression Slope
    Logistic Regression
    Multiple Regression
    Poisson Regression

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