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PASS | 分析及样本量计算软件

新版本PASS 2019已发布!

PASS(Power Analysis and Sample Size)是用于效能分析和样本量估计的统计软件包,是市场研究中非常好的效能检验的软件。它能对数十种统计学检验条件下的检验效能和样本含量进行估计,主要包括区间估计、均数比较、率的比较、相关与回归分析和病例随访资料分析等情形。该软件界面友好,功能齐全,操作简便。用户不需要精通统计学知识,只要确定医学研究设计方案,并提供相关信息,就可通过简单的菜单操作,估计出检验效能和样本含量。

PASS特点

一个或两个均值检验

PASS包含60多种用于样本量估计的工具和一个、两个、或同时两个不同均值的效能检验比对,包括t检验、等价性检验、非劣效性检验、交叉检验、无参数检验、仿真检验等等。每一个过程的使用都很简单,并且经过了精密的准确性验证。


多均值检验

PASS包含几种用于样本量估计的工具和三个或更多不同均值的效能检验比对。包括ANOVA、混合模型、多重对比、多变量方差分析和重复测量等等。每一个过程的使用都很简单,并且经过了精密的准确性验证。


相关性检验

PASS包含几种用于样本量估计的工具和相关性效能检验,包括单相关性和双相关性检验、单相关性的置信区间、组内相关性检验。PASS还可以计算样本量和效能,用于检验系数的透明度,检验两个评价指标间一致性的kappa值和线性一致性相关系数。每一个过程的使用都很简单,并且经过了精密的准确性验证。


正态性检验

PASS包含对8种不同正态性检验方法的样本量计算和效能检验。使用过程很简单,并且都经过了准确性验证。


方差和标准差

PASS包含了多种对方差和标准差的样本量计算和效能检验方法,包括单一方差和两个方差的检验、单方差的置信区间检验、两个方差比值的置信区间检验、标准差的置信区间检验。每一个过程的使用都很简单,并且经过了精密的准确性验证。


回归检验

PASS包含了几种用于回归分析的样本量计算和效能检验方法,包括线性回归、线性回归斜率的置信区间、多重回归、多因素回归、泊松回归和逻辑回归。每一个过程的使用都很简单,并且经过了精密的准确性验证。


一比重检验

PASS包含了20多种用于一比重的样本量计算和效能检验工具,包括z检验、等价性检验、非劣效性检验、置信区间检验和条件效能检验等等。每一个过程的使用都很简单,并且经过了精密的准确性验证。


二比重检验

PASS包含了50多种用于二比重的样本量计算和效能检验工具,包括z检验、等价性检验、非劣效性检验、置信区间检验、相关比例检验、随机聚类检验和条件效能检验等等。每一个过程的使用都很简单,并且经过了精密的准确性验证。


卡方和其它比重检验

PASS包含几种用于多比重的样本量计算和效能检验工具,包括卡方检验、 Cochran-Armitage、二序分类变量检验、灵敏性和特效性检验等等。每一个过程的使用都很简单,并且经过了精密的准确性验证。


残存检验

PASS包含了25种用于残存方法的样本量计算和效能检验工具,包括时序检验、非劣效性检验、组连续性检验、条件效能检验等等。每一个过程的使用都很简单,并且经过了精密的准确性验证。

 

PASS 2019的系统要求

要运行PASS 2019,您的计算机至少必须符合以下标准:

处理器:

450 MHz或更快的处理器

32位(x86)或64位(x64)处理器

内存:

256MB(推荐512MB)

操作系统:

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

特权:

仅在安装期间需要管理权限

硬盘空间:

PASS 300 MB(如果尚未安装,则加上Microsoft .NET 4.6的空间)




英文介绍

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



Survival

Tests for the Difference of Two Hazard Rates Assuming an Exponential Model

Tests for Two Survival Curves Using Cox's Proportional Hazards Model

-

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

-

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


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

-

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 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

-

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

-

Tests for Two Proportions in a Stratified Design (Cochran-Mantel-Haenszel Tests)

Tests for Two Proportions in a Cluster-Randomized Design


Means

One-Sample T-Tests for Superiority by a Margin

One-Sample T-Tests for Non-Inferiority

One-Sample T-Tests for Equivalence

-

Paired T-Tests for Equivalence

-

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

-

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

-

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.



 

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