Detailed Statistical Features List

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.

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).

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

Graphical Capabilities

• Missing Data Pattern
• 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

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

On-Line Help Features

• Three Kinds of help
  • Procedural
  • Statistical usage
  • Statistical definition