SAS & Clinical Data Repository Karthikeyan Chidambaram Cognizant Technology Solutions, Newbury Park, CA Clinical Data Repository (CDR) Drug development lifecycle consumes a lot of time, money and effort. With increasing competitions and stiff regulations set by regulatory bodies, the pharma companies are forced to have a better handle on the Clinical Trial Data Management, analytics and reporting space. The CDR is an integrated source of clinical data and metadata sourced from various data sources and departments. Effective Management of clinical trials through monitoring the quality and efficiency of the data collected allow medical review decisions based on safety and efficacy of investigational drugs and statistical analysis within and across trials, which are the main business drivers for implementation of CDR. Key issues faced by the industry today in the clinical trial and clinical safety space include: Non-uniform sets of data from EDC, CRO, Purchased Trial. (Patient Data, Metadata, Financial Data) Data not integrated between Clinical Trial & Clinical Safety Performance Metrics delay in getting Safety Signal Detection not effective on insufficient & poor quality of data. Double Data Entry Reporting is mostly manual, time consuming & costly. Manual reconciliation of data High down time & maintenance window. Key Features of CDR: Common Data Repository platform that suits all the industry interchange models - SDTM, ODM, ADAM SDTM standards and XML format ensures data transfer from multiple sources such as CROs, LAB vendors etc. Storage format that facilitates the usage of FDA tools that will be used to analyze the submitted data (compliance to JANUS). Processing only Incremental (Delta) data, Optimizing the network traffic. - 1 -
Clinical Data Repository Framework The key features required of the data warehouse architecture are: 1. Provision for a standard format for information collation and representation. A consistent definition and presentation of data can be achieved using a Central Metadata Repository supported by an Exchange Architecture. 2. The data coming from various internal and external source systems need to be verified before consolidation and aggregation. This can be achieved through stage transmitted data, i.e., where the data goes through an intermediate verification stage before consolidation. 3. There is a definite need for storing history data. This requirement will warrant the need for establishing a Data Warehouse that can store time-varied data. Time dimension would need to be implemented or a history needs to be maintained in the staging area. 4. There is a need for generating reports of an analytical nature. This will warrant the use of a best-of-breed OLAP tool running against a dimensionally modeled Data Warehouse. 5. There is a need to provide accelerated response times for the reports. Report using a dimensionally modeled system in which case the data access would be a simple query against the star schema can accelerate responses. 6. Services with reusable functional processes with its integrated subcomponents. - 2 -
Data Sources: The CDR system is capable of extracting data from both the structured and unstructured datasets. Structured data sources include the standard data elements such as CRO Data, EDC Data, Safety data, AERS data, Prescription Data, Patient Data, Purchased Trial data, Dictionary Data and Coding Systems. The unstructured datasets include the documents such as IVRS. The Source System Interface Architecture Component manages the extraction, verification and integration of changed data from the Source System into the Interface Design Framework and facilitates its transfer to the eclinical Data Warehouse Data Staging Subcomponent. Staging Layer: The staging layer is a temporary storage area where data extracted from the source systems is stored for further processing. The staging layer helps in overcoming any synchronization issues with data extraction from the source systems. Data in the staging layer is cleaned, integrated and transformed using ETL tool. Data which fails verification is moved to Suspended Processing and a corresponding quality assurance alert is forwarded to the Source System Custodian for resolution The data extraction, cleansing, transformation, and loading perform all of the conversions, summarization, key changes, structural changes and condensation needed to transform in a standard format that can be used by the decision support analytical tools. The key functionalities of this layer are: 1. Discard any unwanted data 2. Convert to common data names and definition 3. Calculate summaries, aggregation and derived data 4. Establish defaults for missing data 5. Accommodate source data definition changes The entire process of ETL follows a layered approach. In the layered approach, the ETL takes place in multiple layers depending on the types of transformation and processing to be carried out on the various data sources. The data transformation and processing rules, common to the various data sources are applied in a common ETL layer. Apart from the common layer, the rules specific to each of the data sources is carried out at source specific layers. DataWarehouse Layer: The data warehouse encompasses the integrated and verified data from the Data Staging Subcomponent for Enterprise-wide reporting. Data from the various Internal and External Sources - 3 -
Systems are consolidated into common data structures to facilitate plans for the consistent presentation and definition of data. This component also includes the process to support a primary data warehouse technique known as the Slowly Changing Dimension Policy. This technique provides a data warehouse with the flexibility of preserving the representation of data. The Data Warehouse Layer is based on a dimensional data model that functions as the central repository for informational data. Operational data from the source layer is cleansed, aggregated, summarized, organized and stored in this layer to make the entire environment manageable and accessible by end-user query and analysis tools. Data entering the data warehouse has an integrated structure and format. In addition, as the data warehouse contains historical data, it must be capable of holding and managing large volumes of data as well as different data structures. Reporting Layer: The CDR is capable of generating reports in any standard OLAP tool. The OLAP tools view information in the form of cubes, or multiple dimensions and allow the user to drill down to lower levels of detail and slice across different dimensions. They are easy to use for the clinical analysts or the epidemiologists as part of their research to answer questions for the decision making process. While there will be a whole lot of pre-defined reports that are available, the solution also gives the flexibility of highly customized reports. These reports help in visualization of the data in graphical manner that help in easy and quick decision-making. SAS Tools for Clinical Data Repository: The CDR framework can be effectively built using the SAS 9 BI Platform. SAS has a deep commitment to the development of data standards in life sciences. Anticipating the movement of the industry and its governing regulatory bodies toward standards, SAS has developed the technology available today to support the emerging CDISC data models. Globally recognized as the industry leader in analytics, the de facto standard for clinical data analysis, the FDA standard for electronic submissions and the choice of 100% percent of the Fortune 500 life sciences companies, SAS assures accurate, consistent and reliable analysis of pharmaceutical research data. - 4 -
Staging Layer: SAS Data Integration Studio (also know as SAS ETL Studio) is an ideal tool for the building the staging area for the CDR. SAS Data Integration Studio is a visual design tool for building, implementing and managing data integration processes regardless of data sources, applications or platforms. Powerful data transformations efficiently meet enterprise data integration requirements. The creation and management of data and metadata are improved with extensive impact analysis of potential changes made throughout the entire data integration process. SAS Data Integration Studio can completely integrate data from anywhere or any source, cleanse it, merge and/or transform it and place it into a target destination. Standardized metadata is shared across the entire SAS Enterprise Intelligence Platform, and multiple designers can capture, manage, organize and exploit data across the enterprise, quickly and repeatedly. Data Warehouse Layer: SAS SPD Server will be a ideal choice for housing the DW Data. The SAS Scalable Performance Data Server is optimized to deliver subsets of information that need to be harvested from large enterprise data mountains, quickly and upon demand. The capabilities of the SAS Scalable Performance Data Server ensure that business intelligence and analytics applications maintain consistent performance and that ETL processes do not exceed the time available as the amount of your enterprise data continues to grow. Reporting Layer: With the SAS 9 BI architecture, there are a band of tools from SAS for effective and efficient reporting. Some of the prominent tools and their utility are: SAS Web report studio: SAS Web Report Studio provides intuitive and efficient access to query and reporting capabilities on the Web. With SAS Web Report Studio, we can empower business users and decision makers across the enterprise with self-sufficient access to high quality data and the predictive power of SAS analytics, while minimizing administrative overhead, maximizing resources and freeing IT staff to focus on strategic projects. SAS OLAP Server: SAS OLAP Server is a multidimensional data store designed from the outset to provide quick access to pre-summarized data, generated from vast amounts of detailed data. - 5 -
Decision makers need fast access to accurate information. Instantaneous access to summarized data is expected so timely decisions can be based on knowledge instead of gut feelings. CDR Framework with SAS Components: SAS Add-in for Microsoft Office: The SAS Add-In for Microsoft Office enables business users to transparently leverage the power of SAS analytics, reporting and data access directly from Microsoft Office via integrated menus and toolbars. SAS Add-In for Microsoft Office revolutionizes the world of business intelligence. It provides easy access to SAS broad and deep set of analytic, reporting and data access functionality from within the Microsoft Office environment. SAS Information Map Studio: SAS Information Map Studio creates information maps business metadata that translates your warehouse and data structures into terms that business user can understand, enabling consistency and self-sufficiency in getting the information needed to drive decisions. SAS information maps provide a business metadata layer that enables your business users to ask questions and get answers themselves. This frees IT resources from onetime reporting requests and reduces the need to provide training in programming and database structures. Centralized Metadata Management: SAS Metadata Server provides an open, central repository for all metadata that is created and required by an organization to support its enterprise - 6 -
intelligence strategy. Unless organizations operate with a single source of integrated metadata (information about data sources, content, business rules and access authorizations), it is a struggle to deliver consistent information and intelligence. The SAS Metadata Server delivers the power to integrate, share, centrally manage and leverage metadata across entire organizations. Conclusion: Per Gartner By 2007, agility in data integration, management and access will be the single greatest source of competitive advantage for life sciences research organizations. Considering the current life sciences industry dynamics, it is critical for any organization to have a robust and scalable reporting architecture in the clinical space and CDR is an ideal solution for the same. With the use of SAS components, the organizations can develop a powerful CDR application based on a single platform SAS BI architecture. Some of the benefits of using SAS based solution include: SAS is widely used and accepted tool by most of the Life Sciences companies in the world The complete framework can be developed using just one single platform SAS 9 BI platform as opposed to using a suite of products from multiple vendors. Reduced deployment time Low maintenance overheads as only SAS needs to be maintained as opposed to overheads when considering disparate vendor tools REFERENCES Gartner Analysis, SAS Online Help, SAS product descriptions from www.sas.com ACKNOWLEDGMENTS The Author would like to thank his family, friends, peers and supervisors for their encouragement, support and suggestions. CONTACT INFORMATION Karthikeyan Chidambaram - Project Manager with the Life Sciences Practice, Cognizant Technology Solutions, is a SAS certified professional and has over 9 years of experience in SAS in a variety of roles including SAS Administration, Statistical Analysis and ETL programming. Author has worked with multiple Pharmaceutical / Biotechnology Companies as a consultant, - 7 -
providing consulting solutions in the SAS domain, right from Drug Discovery to Post-Marketing analysis. Author has presented at the PharmaSUG 2007, Denver CO, WUSS 12, Pasadena CA,PharmaSUG 2005, Phoenix, AZ and PharmaSUG 2006, Bonita Springs, FL. Contact the author at: Karthikeyan Chidambaram Cognizant Technology Solutions 1710, W Hillcrest Dr, 224 Newbury Park, CA - 91320 Karthikeyan.chidambaram@cognizant.com karthihere@hotmail.com SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. Indicates USA registration. Other brand and product names are trademarks of their respective companies. - 8 -