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OSCAR - Observational Source Characteristics Analysis Report (OSCAR) Design Specification and Feasibility Assessment

In order to interpret the results of any analysis on a data source, the characteristics of the data source be clearly understood. The Observational Source Characteristics Analysis Report (OSCAR) and Source Code for CDM v2.0 (and for CDM v4.0) provides a systematic approach for summarizing all observational healthcare data within the OMOP common data model. The procedure creates structured output of descriptive statistics for all relevant tables within the model to facilitate rapid summary and interpretation of the potential merits of a particular data source for addressing active surveillance needs.

Observational Source Characteristics Analysis Report (OSCAR) and Source Code:
If you have implemented CDM v4.0, use OSCAR for CDM v4.0 otherwise, use OSCAR for CDM v2.0.

OSCAR has many uses, including:

  • Automatic summarization of available data from a given source
  • Providing context for interpreting and analyzing findings of drug safety studies
  • Facilitating comparisons between data sources
  • Enabling comparison of overall database to specific subpopulations of interest
  • Supporting validation of transformation from raw data to OMOP common data model

OSCAR provides descriptive statistics that summarizes the entire database as a means to benchmark all studies. The diagram below outlines how we envision OSCAR fitting into the workflow for validating the transformation from raw data to the OMOP common data model.

OSCAR is a SAS© program that OMOP will provide to participating organizations to execute within their IT data environment. The only prerequisite for OSCAR is that the program must be applied to a data source that conforms to the OMOP common data model, including all necessary tables and fields, and the organization must have SAS 9.1 to execute the program. OSCAR creates summary results datasets in a structured format. These datasets contain descriptive statistics for all the various data elements with the common data model, but do not contain any person-level data. Organizations within the OMOP Data Community will share these aggregate summary results by loading them into the OMOP Research Lab, where comparative analyses across the different sources will be conducted.