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Solas - Studentenversion kaufen
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inkl. 19 % USt

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In vielen Anwendungen - besonders bei der Auswertung von klinischen Studien - ist es keine Option einzelner Beobachtungen mit fehlenden Daten einfach zu ignorieren. Sei es, dass die Datenmenge insgesamt klein ist oder, dass die Daten nicht völlig zufällig fehlen (MCAR). Hier kommt Solas for Missing Data ins Spiel. Schöpfen Sie aus einem umfangreichen Pool von Ersetzungsverfahren für fehlende Werte (Single & Multiple Imputation) um Missing Values zu ersetzen.

SOLAS bietet fünf verschiedene multiple Imputationsmethoden und vier Imputationstechniken für single Imputation. Wählen Sie die entsprechende Methode für das Dataset.

Die atemberaubenden neuen Graphiken im SOLAS helfen Ihnen, Ihre fehlenden Daten auf beispiellose Weise zu visualisieren. Mit Hilfe dieser Graphiken lässt sich der Einfluss der Ersatzdaten beurteilen. Der vervollständigte Datensatz lässt sich aus Solas heraus in das Dateiformat Ihrer Wahl (SAS, SPSS, Stata, Minitab, R, ...) exportieren.

Argumente für Solas:

  • Kompatibilität mit Anforderungen der FDA
  • Konkurrenzlos im Bereich der Missing Data Imputation
  Windows®
Andere Voraussetzungen  
Betriebssystem Windows 95, oder höher
Minimum CPU Pentium processor
Min. RAM 32 MB RAM
Festplattenplatz 14 MB freier Speicherplatz auf der Festplatte

Missing Data Pattern

The Missing Data Pattern in SOLAS 4.0 provides a clear overview of the quantity, positioning, and types of missing values in your dataset. By right clicking on any cell in the matrix, you can identify the variable and observation details. This feature allows you to study the missing data patterns and helps you to choose the most appropriate imputation techniques.
Furthermore, you may also now use the Missing Value Pattern to view the monotone and non-monotone missing values in your dataset.

Multiple Imputation

  • Based on techniques developed by Rubin et al.
  • Choice of Model-based or Propensity Score-based approaches.
  • Applicable to longitudinal/repeated measures, and single observation survey type data.
  • Control over the regression model used for imputing each variable.
  • Automatically combines results of requested analyses on Multiple imputed datasheets.
  • Applies principled approaches to dealing with monotone missing data,and non-monotone missing data, avoiding iteration.
  • Predictive Model -based Multiple Imputation
    • Fully configurable ordinary least squares multiple regression algorithm.
    • Imputed values are based on predictive information contained in covariates.
    • Preserves correlations between variables.
  • Propensity Score-based Multiple Imputation
    • Fully configurable logistic regression algorithm.
    • Uses information contained in a set of covariates to predict missingness in the variable to be imputed.
    • Avails of additional variables to preserve relationships between variables.
  • Predictive Mean Matching Method
  • Propensity Score Based Multiple Imputation(Non Parametric Approach Based on Propensity Scores and the Approximate Bayesian Bootstrap)
  • Propensity Score/Predictive Mean Matching/Mahalanobis Distance Combination Method
  • Mahalanobis Distance Matching Method

Statistical Features

  • Choice of data imputation techniques
  • Descriptive Statistics
  • t-Test
  • ANOVA
  • Frequency Tables
  • Regression
  • Fully interfaced to the complete BMDP Statistical Software Program Library

Data Management

  • Script language available to facilitate imputation set-up and simulation runs.
  • Spreadsheet-like data entry
  • Easy specification of variable attributes:
    Type, role, grouping, cutpoints, etc.
  • Windows data editing features such as:
    Cut, Copy, Paste, Undo, Select/Unselect variables
  • Easy specification of variable transformation

Data Import/Export

  • SAS (Unix/Windows)
  • SPSS
  • S-Plus
  • SYSTAT
  • Stata
  • BMDP
  • Excel, Lotus 1-2-3, dBase
  • ASCII (optional delimiters)

Single Imputation

  • Standard range of traditional imputation techniques, useful for sensitivity analysis.
  • Hot Decking
    • Imputed values are selected from responders that are similar with respect to a set of auxiliary variables.
  • Predicted Mean Imputation using Regression
    • Imputed values are predicted using an ordinary least squares multiple regression algorithm.
  • Last Value Carried Forward
    • Imputed values are based on previously observed value.
  • Group Means
    • Imputed values are set to the variable's group mean (or mode in the case of categorical data).

Graphical Capabilities

  • Unique Missing Data Pattern
  • New Pre Imputation Marginplots
  • Post Imputation Scatterplots
  • Customizable plotting facility
  • Plots integrated within all analyses
  • Wide variety of charts and plots including
    • Bar charts
    • Mean comparison charts
    • Box plots
    • Scatterplots
    • Normal probability plots
    • Histograms