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5 Questions to Ask Before Converting Legacy Data to CDISC Standards

Posted by Brook White on Mon, Jul 23, 2012 @ 01:03 PM
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CDISC standards conversionWith a growing focus on CDISC standards from FDA, more companies are considering converting legacy data. Here are five questions you should consider:


1. How long will it take and how much will it cost? Any legacy conversion project will require a substantial investment of effort by experienced personnel. The time and cost required for the conversion will be dependent on several factors, including number of studies, complexity of study designs, therapeutic area, number of unique data domains, similarity of input data to SDTM structures, and quality of input data and documentation. If possible, get started as soon as possible after the end of Phase II meeting with FDA.


2. What problem are you trying to solve with the legacy conversion? Clearly understanding the goals for your conversion will help you weigh potential benefits and costs. Some common reasons for undertaking a conversion of legacy data to CDSIC standards include:

  • FDA asks for SDTM and/or ADaM formatted datasets.
  • Data across studies lack uniformity and would lead to a challenging database for a reviewer at FDA to understand and use.
  • Data across studies needs to be standardized for archival or data mining purposes.
  • Interaction between multiple stakeholders requires a data exchange standard.

 

3. Have you had a discussion with FDA about whether the work is necessary?In many cases, the answer will be yes. It is still in your best interests to ask and confirm. Some cases where legacy conversion may not be necessary include:

  • Phase I studies or non-pivotal phase II studies.
  • Your company has an internal standard which has been implemented uniformly across all studies in a submission. The database and documentation are already high quality.
  • A legacy conversion will negatively impact traceability.

 

4. What will be the impact to the traceability of analysis data that was previously created or submitted? You need to understand this upfront to make an informed decision about moving forward with a legacy conversion. If you undertake a legacy conversion, this is critical input to your planning. Some traceability issues include:

  • If clinical data is converted to SDTM, there is no longer traceability from the clinical data to analysis data.
  • If analysis data is converted to ADaM: 
    (1) If original source data is not used, traceability is lost. 
    (2) Even if the original source data is used as input, if analysis data is converted to ADaM, the displays and analysis were not produced from the converted analysis datasets. Results will have to be re-generated to ensure that they match the original results (in the CSR).

 

5. Do you have all the necessary documentation for all studies that are a component of the legacy conversion?Missing documentation could present a substantial hurdle to the documentation process. Documentation you will need includes:

  • Annotated CRFs
  • Protocols
  • Data management plans
  • Input clinical datasets
  • Format libraries
  • Additional documentation about the clinical database
 
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10 Tips for Choosing a Contract Research Organization for CDISC Work

Posted by Brook White on Thu, Jun 14, 2012 @ 09:53 AM
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contract research organization (CRO) for CDISC workSelecting the right contract research organization (CRO) to handle your CDISC work can be a daunting task.  As you evaluate potential partners, it’s critical that you consider factors beyond whether or not they have a CDISC certification. This list provides 10 criteria that can help you make an informed decision about which CRO is right for your CDISC project or program.  A successful contract research organization should:

  1. Have experience mapping and programming clinical data to SDTM for individual studies, for an entire submission, and for legacy conversions.
  2. Demonstrate an understanding of the validation requirements for SDTM data and the define files and should have validation tools endorsed by FDA.
  3. Be able to explain how metadata requirements drive SDTM programming efforts.
  4. Have tools that create efficiencies across multiple studies.
  5. Express familiarity with XML and related technologies.
  6. Have tools to produce a CDISC compliant define.xml file as well as other CDISC deliverables.
  7. Understand how to integrate SDTM and analysis work to meet your deadlines.
  8. Have completed multiple submissions to FDA.
  9. Have a technical acceptance rate of more than 90%.
  10. Demonstrate an understanding of how CDISC models fit into the life cycle of a drug development project and how CDISC standards fit into a regulatory submissions strategy.

If you are already using a contract research organization for CDISC services, what criteria did you consider?

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The Pros and Cons of Converting to the CDISC SDTM Standard

Posted by Brook White on Mon, May 21, 2012 @ 10:51 AM
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weighing pros and cons of converting to CDISC SDTM standardThere is a lot of buzz in the industry about the CDISC SDTM standard.  Before you make a decision to convert your existing data to SDTM, you should consider both the advantages and disadvantages of doing so.  Here are a few of the things you should keep in mind:

Advantages:
  • More efficient and timely review. The FDA review process may be more timely and efficient if the FDA reviewer is able to utilize standard tools and checks.
  • FDA encourages the use of SDTM.  Currently, the FDA is encouraging sponsors to submit clinical data in SDTM format.
  • Uniformity across studies.  SDTM is a standard that the FDA, the industry, and several of our clients are embracing because it provides a uniform standard from study to study to ease data exchange internally and across vendors. Additionally, since SDTM specifies standard variable names, controlled terminology values, and data set structures, others (from a biostatistician to an FDA reviewer) benefit if data sets are in SDTM format. They know what to expect and gain efficiencies from the familiarity that comes with working with data in SDTM format.

Disadvantages:
  • Additional documentation is necessary.  When submitting in SDTM format, additional documentation, such as file definition in XML format and readme documentation, is required to support SDTM data sets.
  • CDISC SDTM standard is still evolving.  The SDTM standard is still evolving. Consider how future releases of the guidance coincide with your program time line and how they could affect your submission deliverable.
  • Additional effort, time, and cost.  Data may need to be significantly restructured to fit into SDTM format. Converting to the CDISC SDTM standard requires additional effort, time, and cost.