Classic Analysis

RequirementClassic Analysis
Section3.2.3
JIRA Task

EIR-37 - Getting issue details... STATUS

Introduction

The Epi Info™ 7 Classic Analysis (CA) module is used for the statistical analysis of data. Data to be analyzed may be collected using questionnaires created in Form Designer and populated using Enter or may be imported from external sources. It is the core analytical component of Epi Info™ 7 and has remained largely unchanged in form and function through many versions and implementations of the package. The user can execute commands from the interactive, menu-driven Command Explorer or the text-based Program Editor. Output is generated in HTML format and organized by a paging system that allows users to navigate forward and backward through the results using anchors embedded in the output of each command. The Explorer lists 46 commands organized into groups according to function: manipulation of datasets, individual variables, statistical analyses, defining new commands and generating output. A user can create a sequence of commands representing a complex workflow in the Program Editor, save it as a file and execute it in batch mode, independently of the Classic Analysis GUI.

 

User Interface

The user interface (UI) of the Classic Analysis module consists of the Command Explorer, which displays a "menu" of commands organized hierarchically by function; the Program Editor, which receives commands generated by the Command Explorer dialogs or commands entered directly by the user; and a panel that displays output from the commands as they are executed. Having the command browser (the Explorer) coupled to a command shell allows the user to choose from the functional repertoire of Classic Analysis and be prompted for parameters as the system records (and executes) the commands as they would appear in a program. The sequence of commands is, in fact, a program that can be modified and/or rerun interactively or in batch mode, independently of the UI.

Data Processing

In Classic Analysis, Data Processing encompasses the steps required to prepare data for analysis using the Epi Info™ 7 suite of statistical tools. Commands may be selected from the Command Explorer tree and configured using the command definition dialog, a graphical interface for selecting parameters and arguments. The process begins by loading project data into memory. The project's data tables can be analyzed directly, or joined or merged with tables from other projects (as well as the current project). Classic Analysis provides a number of tools to render the raw data suitable for statistical analysis. New variables may be defined and assigned values based on form variables directly or manipulated to rescale, recode or reformat the data to make them compatible with subsequent procedures or statistical tests. Simple arithmetic and logical operators are available for operations on numeric and Boolean variables.  More complex mathematical operators are implemented as functions, as are the tools for manipulating text, dates and other variable types.  Classic Analysis also supports an internal scripting language that can be typed into the Program Editor manually or generated using commands from the Command Explorer and configured with the corresponding dialog. Programs may be executed, all or in part, using the Program Editor or in batch mode, outside of the Epi Info™ application. Tables at the end of this section list and summarize the functions and operators available in Classic Analysis with links to their User Guide entries where more information can be found.

Statistical Analysis

Insights into the origins of a disease outbreak (or scientific studies, generally) typically emerge during the statistical analysis of the data.  The “Statistics” folder in the Command Explorer of Classic Analysis provides simple descriptive statistics and graphing tools.  More sophisticated inferential statistics, such as linear and logistics regression analysis, are available in “Advanced Statistics”, as well as tools especially useful in epidemiology, such as Kaplan-Meier Survival curves and Cox Proportional Hazards. Descriptive statistics under complex sampling models are also supported.