PASS 2022 統計分析軟體
- PASS 2022 統計分析軟體
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類別統計分析軟體
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介紹PASS 軟體為超過 1100 個統計測試和置信區間場景提供樣本大小工具 -是任何其他樣本大小軟體的兩倍多。每個工具都經過了已發表的文章和/或文字的仔細驗證。 PASS 配備整合文件與PhD統計人員支援。 PASS 經過 20 多年的微調，現已成為臨床試驗、製藥和其他醫學研究的領先樣本量軟體選擇。它還已成為所有其他需要樣本量計算或評估的領域的支柱。
PASS 2022 Statistical Analysis Software
Vaccine Efficacy
Confidence Intervals for Vaccine Efficacy using a Cohort Design
Confidence Intervals for Vaccine Efficacy using an Unmatched Case-Control Design
Superiority by a Margin Tests for Vaccine Efficacy using the Ratio of Two Proportions
Non-Inferiority Tests for Vaccine Efficacy using the Ratio of Two
Proportions
Superiority by a Margin Tests for Vaccine Efficacy using the Ratio of Two Proportions in a Cluster-Randomized Design
Non-Inferiority Tests for Vaccine Efficacy using the Ratio of Two Proportions in a Cluster-Randomized Design
Superiority by a Margin Tests for Vaccine Efficacy using the Ratio of Two Poisson Rates
Non-Inferiority Tests for Vaccine Efficacy using the Ratio of Two Poisson Rates
Superiority by a Margin Tests for Vaccine Efficacy using the Ratio of Two Poisson Rates in a Cluster-Randomized Design
Non-Inferiority Tests for Vaccine Efficacy using the Ratio of Two Poisson Rates in a Cluster-Randomized Design
Superiority by a Margin Tests for Vaccine Efficacy using the Ratio of Two Negative Binomial Rates
Non-Inferiority Tests for Vaccine Efficacy using the Ratio of Two Negative Binomial Rates
Superiority by a Margin Tests for Vaccine Efficacy using the Hazard Ratio (Cox’s Proportional Hazards Model)
Non-Inferiority Tests for Vaccine Efficacy using the Hazard Ratio (Cox’s Proportional Hazards Model)
Tests for Vaccine Efficacy with Extremely Low Incidence
Superiority by a Margin Tests for Vaccine Efficacy with Extremely Low Incidence
Non-Inferiority Tests for Vaccine Efficacy with Extremely Low Incidence
Tests for Vaccine Efficacy with Composite Efficacy Measure (Ratio)
Tests for Vaccine Efficacy with Composite Efficacy Measure (Difference)
Group-Sequential Tests (with Efficacy & Futility Boundary Options)
For each of these group-sequential power and sample size procedures, there are corresponding group-sequential analysis and sample-size re-estimation procedures in NCSS 2021.
Group-Sequential Non-Inferiority Tests for One Mean with Known Variance (Simulation)
Group-Sequential Superiority by a Margin Tests for One Mean with Known Variance (Simulation)
Group-Sequential Non-Inferiority T-Tests for One Mean (Simulation)
Group-Sequential Superiority by a Margin T-Tests for One Mean (Simulation)
Group-Sequential Tests for One Proportion (Simulation)
Group-Sequential Non-Inferiority Tests for One Proportion (Simulation)
Group-Sequential Superiority by a Margin Tests for One Proportion (Simulation)
Ratio of Two Means
Tests for the Ratio of Two Means (Normal Data)
Non-Inferiority Tests for the Ratio of Two Means (Normal Data)
Superiority by a Margin Tests for the Ratio of Two Means (Normal Data)
Equivalence Tests for the Ratio of Two Means in a 2×2 Cross-Over Design (Normal Data)
Two Poisson Rates in a Cluster-Randomized Design
Superiority by a Margin Tests for the Difference Between Two Poisson Rates in a Cluster-Randomized Design
Non-Inferiority Tests for the Difference Between Two Poisson Rates in a Cluster-Randomized Design
Superiority by a Margin Tests for the Ratio of Two Poisson Rates in a Cluster-Randomized Design
Non-Inferiority Tests for the Ratio of Two Poisson Rates in a Cluster-Randomized Design
Method Comparison Studies
Exact Method for Assessing Agreement in Method Comparison Studies
Confidence Intervals
Confidence Intervals for a Percentile of a Normal Distribution using Assurance Probability
Confidence Intervals for a Percentile of a Normal Distribution using Expected Width
Confidence Intervals for the Bland-Altman Range of Agreement using Assurance Probability
Confidence Intervals for the Bland-Altman Range of Agreement using Expected Width
Confidence Intervals for Regression-Based Reference Limits using Assurance Probability
Confidence Intervals for Regression-Based Reference Limits using Expected Relative Precision
Studentized Range
Studentized Range Test
Studentized Range Tests for Equivalence
Non-Zero Null Studentized Range Tests
Analysis of Variance
One-Way Analysis of Variance Contrasts Allowing Unequal Variances
One-Way Analysis of Variance Contrasts Assuming Equal Variances
One-Way Analysis of Variance Allowing Unequal Variances
One-Way Analysis of Variance Assuming Equal Variances (F-Tests)
One-Way Analysis of Variance F-Tests using Effect Size
Equivalence Tests for One-Way Analysis of Variance Assuming Equal Variances
Equivalence Tests for One-Way Analysis of Variance Allowing Unequal Variances
Non-Zero Null Tests for One-Way Analysis of Variance Assuming Equal Variances
2×2 Factorial Analysis of Variance Allowing Unequal Variances
Analysis of Covariance
Analysis of Covariance (ANCOVA)
Analysis of Covariance Contrasts
Two Ordered Categorical Variables
Tests for Two Ordered Categorical Variables (Non Proportional Odds, Wilcoxon-Mann-Whitney)
Logrank Tests
Logrank Tests (Freedman)
Weibull
One-Sample Tests of Weibull Hazard Rates
Confidence Interval for Weibull Shape Parameter
Fisher’s Exact Test
Fisher’s Exact Test
Intraclass Correlation
Confidence Intervals for Intraclass Correlation with Assurance Probability (Lower One-Sided)
Confidence Intervals for Intraclass Correlation with Assurance Probability (Two-Sided)
Two Proportions
Confidence Intervals for Odds Ratio of Two Proportions using an Unmatched Case-Control Design
Confidence Intervals for the Difference of Two Correlated Proportions
Phase II Selection Design
Randomized Phase II Selection Design for Binary Data (Simon)
Dose-Finding
Dose-Finding using the Bayesian Continual Reassessment Method (CRM)
Bayesian Approach
Tests of Two Means Assuming Equal Variances using a Bayesian Approach
Three-Arm Mean Ratio
Equivalence Tests for the Mean Ratio in a Three-Arm Trial (Normal Data)
Enhancements in PASS 2021
Report Auto-sizing
A program-wide auto-sizing of report columns was integrated, giving improved column spacing in reports.
Random Seed
For all procedures that utilize random number generation, a Random Seed option was added, to obtain output reproducibility.
Decimals Display
A system-wide improvement was implemented to better determine the number of decimals to display in each column.
Access Improvements
The Report Options dropdown was added to the Procedure Window toolbar. The menu and toolbar of the Output and Gallery windows were updated.
Window Loading Time
Optimization techniques were employed to improve the loading time of various windows.
Operational Qualification (Validation) Processing Time
The time to execute the operational qualification (all-procedure validation) was significantly reduced.
系統需求
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
RAM:
256 MB (512 MB recommended)
HD:
350 MB for NCSS (plus space for Microsoft .NET 4.6 if not already installed)
Processor:
450 MHz or faster processor
32-bit (x86) or 64-bit (x64) processor
Printer:
Any Windows-compatible inkjet or laser printer
PASS 2022 統計分析軟體
疫苗功效
使用隊列設計的疫苗效力的置信區間
使用無與倫比的病例對照設計的疫苗效力的置信區間
使用兩個比例的比例對疫苗效力進行邊際檢驗的優勢
使用兩個比例的比率進行疫苗效力的非劣效性檢驗
在集群隨機設計中使用兩個比例的比例對疫苗功效進行邊際檢驗的優勢
在整群隨機設計中使用兩個比例的比率進行疫苗效力的非劣效性檢驗
使用兩個泊松率之比對疫苗效力進行邊際檢驗的優勢
使用兩個泊松率的比率進行疫苗效力的非劣效性檢驗
在集群隨機設計中使用兩個泊松率的比率對疫苗功效進行邊際檢驗的優勢
在整群隨機設計中使用兩個泊松率的比率對疫苗效力進行非劣效性檢驗
使用兩個負二項式比率的比率對疫苗效力進行邊際檢驗的優勢
使用兩個負二項式比率的比率對疫苗效力進行非劣效性檢驗
使用危害比（Cox 比例危害模型）對疫苗功效進行邊際檢驗的優勢
使用危害比（Cox 的比例危害模型）進行疫苗效力的非劣效性測試
極低發生率的疫苗效力測試
具有極低發生率的疫苗功效邊際檢驗的優勢
具有極低發生率的疫苗效力的非劣效性測試
使用複合功效測量（比率）測試疫苗功效
使用複合功效測量（差異）測試疫苗功效
組序列測試（具有有效性和無用邊界選項）
對於這些組序列功效和样本大小程序中的每一個，在 NCSS 2021 中都有相應的組序列分析和样本大小重新估計程序。
具有已知方差的一個均值的組序貫非劣性檢驗（模擬）
具有已知方差的一個均值的邊際檢驗的組序貫優勢（模擬）
一個均值的組序非劣效 T 檢驗（模擬）
一個均值的邊際 T 檢驗的組序貫優勢（模擬）
一個比例的組序檢驗（模擬）
一個比例的組序非劣效性檢驗（模擬）
一個比例的邊際檢驗的組序列優勢（模擬）
兩均值比
兩個均值比的檢驗（正態數據）
兩個均值比的非劣效性檢驗（正態數據）
兩個均值比的邊際檢驗的優勢（正常數據）
2×2 交叉設計中兩個均值比的等效檢驗（正態數據）
集群隨機設計中的兩個泊松率
集群隨機設計中兩個泊松率之間差異的邊際檢驗的優勢
集群隨機設計中兩個泊松率之間差異的非劣效性檢驗
集群隨機設計中兩個泊松率之比的邊際檢驗的優勢
集群隨機設計中兩個泊松率之比的非劣效性檢驗
方法比較研究
在方法比較研究中評估一致性的確切方法
置信區間
使用保證概率的正態分佈百分位數的置信區間
使用預期寬度的正態分佈百分位數的置信區間
使用保證概率的 Bland-Altman 一致性範圍的置信區間
使用預期寬度的 Bland-Altman 一致性範圍的置信區間
使用保證概率的基於回歸的參考限值的置信區間
使用預期相對精度的基於回歸的參考限值的置信區間
學生化範圍
學生化範圍測試
學生化的等價範圍檢驗
非零零學生化範圍測試
方差分析
允許不等方差的方差對比的單向分析
假設方差相等的方差對比的單向分析
允許不等方差的方差的單向分析
假設方差相等的方差的單向分析（F 檢驗）
使用效應量對方差 F 檢驗進行單向分析
假設方差相等的方差單向分析的等價檢驗
允許不等方差的方差單向分析的等價檢驗
假設方差相等的方差單向分析的非零零檢驗
允許不等方差的方差的 2×2 因子分析
協方差分析
協方差分析 (ANCOVA)
協方差對比分析
兩個有序分類變量
兩個有序分類變量的檢驗（非比例優勢，Wilcoxon-Mann-Whitney）
對數秩檢驗
Logrank 測試（弗里德曼）
威布爾
威布爾危害率的單樣本檢驗
威布爾形狀參數的置信區間
費雪精確檢驗
費雪精確檢驗
類內相關
類內相關性與保證概率的置信區間（下側）
類內相關性與保證概率的置信區間（兩側）
兩個比例
使用不匹配案例控制設計的兩個比例優勢比的置信區間
兩個相關比例差異的置信區間
二期選型設計
二進制數據的隨機 II 期選擇設計 (Simon)
劑量探索
使用貝葉斯連續重新評估方法 (CRM) 進行劑量尋找
貝葉斯方法
使用貝葉斯方法假設方差相等的兩種方法的檢驗
三臂平均比率
三組試驗中平均比率的等效性檢驗（正常數據）
PASS 2021 中的增強功能
報告自動調整大小
集成了報告列的程序範圍自動調整大小，從而改進了報告中的列間距。
隨機種子
對於使用隨機數生成的所有程序，添加了隨機種子選項，以獲得輸出可重複性。
小數顯示
實施了系統範圍的改進，以更好地確定每列中顯示的小數位數。
訪問改進
報告選項下拉菜單已添加到程序窗口工具欄。“輸出”和“圖庫”窗口的菜單和工具欄已更新。
窗口加載時間
採用優化技術來改善各種窗口的加載時間。
操作資格（驗證）處理時間
執行操作確認（全程序驗證）的時間顯著減少。
AUXAL 4 結構分析軟體
縱向發展研究的全面資料分析，現在利用SSI的AUXAL程式在您桌面就可使用。 BTT模型或JPA2模型之結構分析 用於數據分析的NCSS軟件配有集成文檔，免費培訓視頻以及博士統計人員團隊提供的完整電話和電子郵件支持。 殘值或Kernel Smoothing的傅立葉變換之非結構分析
MLwiN 3.05 多層次模型軟體
MLwiN可以擬合從一般的多水平線形模型到極複雜的多水平生存分析模型、多水平時間序列模型等，幾乎已囊括多水平統計模型的全部領域，並且水平數不限！
SmartPLS 結構方程建模軟體
SmartPLS是一種具有圖形用戶界面的軟件，用於使用偏最小二乘路徑建模方法進行基於變異的結構方程式建模。用戶可以使用基本PLS-SEM，加權PLS-SEM，一致PLS-SEM和sumscores回歸算法來估計模型及其數據。該軟件可計算標準結果評估標準，並支持其他統計分析。