Bring repeatability and automation to the data integration process with SAS® Clinical Data Integration

WHITE PAPER Unlock the Value of Clinical Data Bring repeatability and automation to the data integration process with SAS® Clinical Data Integration UnloCk the ValUe of CliniCal Data Table of Contents Executive Summary .........................................................................................1 Introduction .....................................................................................................2 Improve Costs, Quality and Efficiency with Standards and Automation .......3 Driving Competitive Advantage for CROs and Biopharmaceuticals ..............4 CROs ............................................................................................................4 Pharmaceutical and Biotechnology Companies ..........................................5 Automating Data Integration and Standardization ........................................6 The Future of Standards ..................................................................................8 Closing Thoughts .............................................................................................8 About SAS ........................................................................................................9 A Closer Look .................................................................................................10 i UnloCk the ValUe of CliniCal Data Executive Summary The life sciences industry is under pressure to accelerate time to market for new compounds – at lower cost. A key to compressing the cycle is to streamline the data management tasks that consume as much as 65 percent of the clinical trial process, by some estimates. Traditionally, the process of managing clinical trials data has been cumbersome and resource-intensive. The data needed for analysis of drug safety and efficacy resides in multiple, disparate systems. Time-consuming and expensive manual coding is required to integrate all that data into analysis-ready form. Further data manipulations are required to prepare regulatory submissions. Every clinical trial brings a new data integration and transformation project, requiring just as much programming effort as the previous ones. There is much room for improvement in the traditional modus operandi by taking advantage of industry standards and automated data integration processes. Contract research organizations (CROs) can reduce the cost of their services while simultaneously growing revenues through new service offerings such as legacy data migration projects and Clinical Data Interchange Standards Consortium (CDISC) standards consulting. By efficiently standardizing legacy clinical data, pharmaceutical and biotechnology companies can unlock the scientific and business value hidden in their clinical repositories. In addition, these companies can leverage their standardized clinical data for more efficient and effective cross-study data analysis, review and utilization. With SAS Clinical Data Integration, clinical research professionals can improve efficiency, quality and speed in collecting, managing, analyzing, reporting and assessing data from clinical trials. Prebuilt models automatically transform legacy data to CDISC standards. Research analysts can readily customize the models using visual interfaces, create new ones and reuse the work of others. Embedded data quality routines ensure standard, trusted clinical data for analysis within and across clinical trials. Consistent, validated data structures enable timely, seamless flow of information among all the parties involved in a clinical trial. Industry analysts have stated that automated data integration and validation can trim 30 to 50 percent from the clinical trial cycle. Field experience with the SAS solution has shown that even greater savings are possible. This white paper makes a case for implementing data standards and applying automated processes for managing data throughout the clinical trials process, from study design to regulatory submissions. 1 UnloCk the ValUe of CliniCal Data Introduction Does the experience of this life sciences executive match the current reality in your organization? “We also had a number of disparate systems holding various pieces of information and they didn’t talk to each other, and we were actually … incapable of … having the information at our fingertips …” The workflow of clinical trials depends on multiple, complex data interactions that may occur across multiple organizations. Information collected from study subjects is deposited into a clinical repository, painstakingly transformed to suit study purposes, analyzed to determine product efficacy and safety, and then used to prepare the regulatory submission. By some estimates, as much as 65 percent of the time it takes to conduct a clinical trial is spent on data interchange among collaborating entities. The process entails a series of exchanges between sponsors and investigators, between sponsors and CROs, between CROs and labs, between sponsors and regulators, and so on. The growing volume and complexity of these data interactions is seen as a primary cause for escalating costs in drug development. The ideal of converging on a set of data standards such as those from CDISC to streamline collaboration is gaining momentum – but facing three key obstacles as well: 1. Collaborating organizations rarely have compatible systems. ■ Clinical study data is typically In many cases, the parties involved in the process of bringing a drug to market scattered among multiple clinical do not share a common corporate data network, electronic data capture tool, clinical data repository, statistical computing environment or clinical reporting systems and stored in multiple system. Key clinical information is scattered across disparate IT systems or formats across multiple operating platforms that use different data definitions and formats. This inconsistent data environments and organizations. must be aggregated and transformed before it can be used for meaningful analysis. 2. Data transformation processes tend to be highly manual. In many cases, data preparation is treated as a unique project for each clinical study. Time-intensive manual coding and processes are required to prepare data for analysis. These processes are run protocol by protocol, often edited and updated to reflect the unique nature of the study, and then executed many times as data is updated over the course of the trial. And don’t forget the dramatic schedule impact of last-minute changes identified by the project team. 2 UnloCk the ValUe of CliniCal Data This highly manual approach is not repeatable across studies, nor does this process scale without adding expensive headcount. The company misses ■ Inefficiencies in the collection opportunities to reuse processes, code and information. As a result, more and preparation of clinical data programmers are needed to support additional studies. And still there is the risk for analysis can slow the pace of of data corruption from coding errors and inconsistencies. drug development and dramatically increase the cost of bringing a 3. Standards are not necessarily seen as a top priority. new drug to market. For one, they are not required – yet. The US Food and Drug Administration (FDA) has recommended the submission of clinical data in CDISC SDTM (Study Data Tabulation Model) format, but has not mandated the use of standards. Within the life sciences industry, conversations about standards are generally moving from a discussion of “what and why” to “how and when.” But, when faced with competing priorities, is the implementation of standards truly a top priority within most life sciences organizations? Some may argue that – with limited programmer resources – high-priority drug development projects must take precedence over some possible future requirement. Some organizations would assert that they can perform business activities faster using their own unique processes and data formats. Internally, that may be true, but since so much clinical trial activity is collaborative outside the organization, unique processes and data formats hinder the overall flow of the process. Improve Costs, Quality and Efficiency with Standards and Automation Agreement on data standards enables seamless information flow through the entire pre-clinical and clinical research process, from protocol design to various sources for data collection, data management, analysis and reporting, regulatory submission and electronic data archive.