Analyse-it 6.15 方法評估分析軟體
- Analyse-it 6.15 方法評估分析軟體
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類別統計分析軟體
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介紹Analyse-it 是一個方法評估分析軟體，檢驗科或者那些需要檢驗、核實分析診斷數據方法的研究員使用。Analyse-it 於 1997 年發布，並迅速成為 Microsoft Excel 中用於統計分析的領先外掛程式。它為 Microsoft Excel 帶來了易於使用的統計軟體，並在軟體中引入了一些最新的創新統計分析功能。此後，Analyse-it 與世界上一些最大的公司合作，並擁有無可挑剔的聲譽。事實上，透過Analyse-it 的聲譽，CLSI 聯絡該公司開發 StatisPro，這是用於 CLSI 方法驗證指南的統計軟體。
Analyse-it 6.15 Methodology Evaluation Analysis Software
Probit regression
Fit Model now supports Probit regression. Probit regression is similar to logistic regression, as both use a link function to transform a linear model into a nonlinear relationship. A linear model uses the equation Y = α + β x, whereas both logit and probit equation use the form Y = f(α + β x). They only differ in the definition of the link function f(): the logit model uses the cumulative distribution function of the logistic distribution; the probit model uses the cumulative distribution function of the standard normal distribution. Both functions give a predicted probability, Y.
Health sciences, such as epidemiology, often use the logit model as the predictor coefficients are interpretable in terms of log odds-ratios. The probit model coefficients cannot be interpreted as easily but may produce a better fitting model in other scenarios. For example, in method validation, probit regression is used to model the hit rate of a molecular test. You can then use the model to establish a detection limit or determine diagnostic cut-off points from an underlying continuous response.
Transform
You can now apply a transformation to a variable during analysis. This feature is currently available on the Distribution and Fit Model (simple regressions) analysis, but it will be available in all analyses soon. To transform a variable, click the properties icon next to the variable selector drop-down, choose Transform, and then select the transformation function.
Diagnostic performance
A lot of customers buy Analyse-it for ROC analysis, as it has always lead the way in diagnostic test analysis (https://pubmed.ncbi.nlm.nih.gov/12600955/). We recently extended the Binary (Sensitivity/Specificity) test to allow testing equivalence and non-inferiority hypotheses tests. And, now you can calculate predictive values for different population prevalences – ideal for modelling the behavior of a test in different scenarios.
Method Comparison
Qualitative method comparison is now more prominent on the Method Comparison command menu, with clearer titles: Binary and Semi-Quantitative. We added Average agreement measures, which are useful when there is no reference/comparative method (for example when comparing laboratories or observers). There is also an excellent new plot for visualizing agreement between qualitative methods: the Bangdiwala agreement plot.
All the features
Built for CLSI protocols
The latest Clinical and Laboratory Standards Institute (CLSI) method validation protocols are recognized by the College of American Pathologists (CAP), The Joint Commission, and the US Food and Drug Administration (FDA).
Validate and verify measurement system performance characteristics
It’s essential to ensure the performance characteristics (precision, trueness, linearity, interferences, detection capability) of a measurement procedure meet the requirements for intended use. Manufacturers (IVD companies) must establish performance during product development to feedback into the development process, for FDA 510k submissions and product marketing, and to support customers in the field. Laboratories must verify they can achieve the manufacturer's claimed performance during implementation of a new measurement system, during regulatory inspections (under the CLIA ’88 act), and as part of proficiency testing (PT) schemes. Measurement systems analysis (MSA) lets you determine all these important performance characteristics in one analysis.
Examine diagnostic test performance to find the most effective
Rated best ROC curve software in Clinical Chemistry March 2003 vol. 49 no. 3 pg. 433-439, Analyse-it lets you establish and compare the ability of a diagnostic test to correctly diagnose patients. Explore how the test differentiates between positive and negative cases and explore optimum decision thresholds factoring in the costs of misdiagnosis.
Compare methods and evaluate the impact of making changes
When introducing a new measurement procedure you want to see how it stacks-up against your existing procedure or evaluate its performance against the gold-standard. Bland-Altman lets you see the agreement between methods and what effect the differences between methods might have on clinical interpretation. More advanced procedures like Deming regression and Passing-Bablok tell you the bias between methods, how medical decision points may be affected, and let you test if bias meets performance requirements.
Establish reference intervals to make clinical diagnoses
Reference intervals are essential for clinicians to interpret results and make a diagnosis. As a laboratory it's your job to provide normal reference ranges they can rely on. With the widest range of methods available in any software package, the ability to partition the intervals by factors such as sex, age, ethnicity, Analyse-it makes it easy to establish reference ranges or transfer them to a new measurement procedure.
Bring processes under statistical control
Gain insight and improve process performance with Shewhart variable and attribute, CUSUM, and moving average control charts (EWMA & UWMA). Apply WECO, Nelson and Montgomery rules to help identify possible out of control situations. Use stratification to gain further insight into problems and spot trends and patterns. And when you've implemented improvements, or made other changes, phases let you track performance before and after so you can ensure improvements have been made and are sustained.
Ensure products are meeting end-user specifications
Determine process capability indices for process performance to ensure you deliver products that meet your customers’ requirements. A happy customer means fewer rejected goods and service complaints, improving your business and lowering costs.
Identify improvements that will reap the most rewards
Pareto analysis helps you quickly identify commonly occurring defects so you can focus your efforts making improvements that will reap the most rewards. Stratification lets you break-down defects so you can identify contributing factors, such as an operator that is influencing defect rates, or look at defects before and after process improvements to ensure the changes are reducing defects.
Integrated into Microsoft Excel, so it's easy to use...
That's right. Analyse-it integrates into Microsoft Excel 2007, 2010, 2013, 2016, 2019 and Office 365 for Microsoft Windows. There's virtually no learning curve, and the intuitive user interface and logical task-based workflow makes sense to those of us that aren’t programmers or full-time statisticians.
Analyse-it 系統需求
Microsoft Windows Vista, 7, 8, 10, Server 2003, 2008, 2012, & 2016 or later
RAM:
2GB RAM minimum recommended
HD:
80MB disk space
OTHER:
Microsoft Excel 2007, 2010, 2013, 2016, 2019 and Office 365 (桌面安裝版本)
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Analyse-it 6.15 方法評估分析軟體
概率回歸
擬合模型現在支持 Probit 回歸。Probit 回歸類似於邏輯回歸，因為兩者都使用鏈接函數將線性模型轉換為非線性關係。線性模型使用方程 Y = α + β x，而 logit 和 probit 方程都使用 Y = f (α + β x) 形式。它們的區別只是鏈接函數f ()的定義不同：logit模型使用的是logistic分佈的累積分佈函數；probit 模型使用標準正態分佈的累積分佈函數。這兩個函數都給出了預測概率 Y。
健康科學，例如流行病學，經常使用 logit 模型，因為預測係數可以根據對數優勢比進行解釋。概率模型係數不能簡單地解釋，但在其他情況下可能會產生更好的擬合模型。例如，在方法驗證中，概率回歸用於模擬分子測試的命中率。然後，您可以使用該模型建立檢測限或根據潛在的連續響應確定診斷截止點。
轉換
您現在可以在分析期間對變量應用轉換。此功能目前可用於分佈和擬合模型（簡單回歸）分析，但很快將可用於所有分析。要轉換變量，請單擊變量選擇器下拉列表旁邊的屬性圖標，選擇轉換，然後選擇轉換函數。
診斷性能
許多客戶購買 Analyse-it 進行 ROC 分析，因為它在診斷測試分析方面一直處於領先地位 ( https://pubmed.ncbi.nlm.nih.gov/12600955/ )。我們最近擴展了二元（敏感性/特異性）檢驗，以允許檢驗等價性和非劣效性假設檢驗。而且，現在您可以計算不同人群流行率的預測值——非常適合對不同場景中的測試行為進行建模。
方法比較
定性方法比較現在在方法比較命令菜單上更加突出，標題更清晰：二元和半定量。我們添加了平均一致性度量，這在沒有參考/比較方法時很有用（例如，在比較實驗室或觀察者時）。還有一個很好的新圖用於可視化定性方法之間的一致性：Bangdiwala 一致性圖。
所有功能
為 CLSI 協議而構建
最新的臨床和實驗室標準協會 (CLSI)方法驗證協議得到美國病理學家學會 ( CAP)、聯合委員會和美國食品和藥物管理局 (FDA) 的認可
驗證和驗證測量系統性能特徵
確保測量程序的性能特徵（精度、正確度、線性、干擾、檢測能力）滿足預期用途的要求至關重要。製造商（IVD 公司）必須在產品開發期間建立績效以反饋到開發過程、FDA 510k 提交和產品營銷，並支持該領域的客戶。實驗室必須在實施新測量系統期間、在監管檢查期間（根據 CLIA '88 法案）以及作為能力驗證 (PT) 計劃的一部分，驗證他們是否能夠實現製造商聲稱的性能。測量系統分析 (MSA) 可讓您在一次分析中確定所有這些重要的性能特徵。
檢查診斷測試性能以找到最有效的
在臨床化學 2003 年 3 月卷中被評為最佳 ROC 曲線軟件。49號 3 頁。433-439，Analyse-it 可讓您建立和比較診斷測試正確診斷患者的能力。探索測試如何區分陽性和陰性病例，並探索考慮誤診成本的最佳決策閾值。
比較方法並評估更改的影響
在引入新的測量程序時，您希望查看它與現有程序的對比情況，或根據黃金標準評估其性能。Bland-Altman讓您看到方法之間的一致性以及方法之間的差異可能對臨床解釋產生什麼影響。Deming 回歸和 Passing-Bablok 等更高級的程序會告訴您方法之間的偏差、醫療決策點可能受到的影響，並讓您測試偏差是否滿足性能要求。
建立參考區間進行臨床診斷
參考區間對於臨床醫生解釋結果和做出診斷至關重要。作為實驗室，您的工作是提供他們可以信賴的正常參考範圍。借助任何軟件包中可用的最廣泛的方法，能夠按性別、年齡、種族、分析等因素劃分區間，從而輕鬆建立參考範圍或將它們轉移到新的測量程序。
將過程置於統計控制之下
使用 Shewhart 變量和屬性、CUSUM 和移動平均控製圖（EWMA 和 UWMA）獲得洞察力並提高過程性能。應用 WECO、納爾遜和蒙哥馬利規則來幫助識別可能的失控情況。使用分層進一步了解問題並發現趨勢和模式。當您實施改進或進行其他更改時，階段可讓您跟踪前後的性能，從而確保已進行改進並持續改進。
確保產品符合最終用戶的規格
確定過程性能的過程能力指數，以確保您提供滿足客戶要求的產品。滿意的客戶意味著更少的拒絕商品和服務投訴，從而改善您的業務並降低成本。
確定將獲得最大回報的改進
帕累托分析可幫助您快速識別常見缺陷，以便您可以集中精力進行改進，從而獲得最大的回報。分層可讓您分解缺陷，以便識別影響因素，例如影響缺陷率的操作員，或查看流程改進前後的缺陷以確保更改減少缺陷。
整合到 Microsoft Excel 中，因此易於使用...
這是正確的。Analyse-it 集成到 Microsoft Excel 2007、2010、2013、2016、2019 和 Office 365 for Microsoft Windows。幾乎沒有學習曲線，直觀的用戶界面和基於任務的邏輯工作流對我們這些不是程序員或全職統計員的人來說很有意義。
您的所有數據和結果都保存在 Excel 工作簿中，便於與同事協作和共享，這意味著沒有鎖定的文件格式。
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