Order by request
Ab einer bestimmten Bestellmenge stellen wir Ihnen ein individuelles Angebot zusammen.
Dieser Artikel wird Ihrem Warenkorb beigelegt, wird jedoch nicht in der Berechnung berücksichtigt
For many applications – particularly clinical trials – ignoring missing values is no option. No matter if the sample size is small or data is not missing completely at random (MCAR): This is where Solas comes into play. Use the large pool of different missing value imputation methods (single- and multiple imputations) to replace the empty cells in your data.
Solas offers five methods for multiple imputation and four different methods for single imputation. Just choose the right method for your data.
Solas stunning new graphs will support you in the visualisation of missing value patterns. It is even possible to evaluate the quality of the imputed data based on that graphs. The new data set can easily be exported to various formats, like SAS, SPSS, Stata, Minitab or R.
Arguments for Solas:
- Satisfies FDA requirements
- Without competition in the field of missing data imputation
SOLAS - for Missing Data Analysis and Multiple Imputation
What is SOLAS?
SOLAS is developed in close collaboration with Prof. Donald B. Rubin, the leading authority on Multiple Imputation.
SOLAS 5.0 for Missing Data Analysis offers principled approaches to missing data now has its own scripting language and features a choice of 9 imputation techniques, including 5 Multiple Imputation techniques based on the work of Prof. Donald B. Rubin. Data can be imported from a wide variety of file types including SAS (Unix/Windows), SPSS, Splus, Stata and many more. Once the data is imported, the missing data pattern can be displayed and a decision upon the most appropriate technique made. Once imputation is complete the imputed datasets can be analysed within SOLAS or exported to a variety of other packages in the correct format. It's that simple!
Solas is currently the only program that implements multiple imputation noniteratively and with substantial flexibility, even including ad-hoc methods, such as LOCF, as points of comparison for sensitivity analysis."
Prof. Donald B. Rubin, Harvard.
The incorrect analysis of datasets with incomplete data can lead to biased analysis and incorrect inference. SOLAS? 4.0 provides researchers with a range of imputation approaches in an easy to use, validated software package that includes principled, informed solutions to the problems presented by incomplete datasets.
|Operating System||Windows 95, or higher|
|Min. CPU||Pentium processor|
|Min. RAM||32 MB RAM|
|Disk Space||14 MB|
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.
- 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
- Choice of data imputation techniques
- Descriptive Statistics
- Frequency Tables
- Fully interfaced to the complete BMDP Statistical Software Program Library
- 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
- SAS (Unix/Windows)
- Excel, Lotus 1-2-3, dBase
- ASCII (optional delimiters)
- 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).
- 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
- Normal probability plots