CDISC

Figure 27.3 Once the data are transformed into CDISC standards and integrated with a drug safety analysis system, the data can be easily analyzed. In this figure we show a sample screen from WebSDM a drug safety analysis system being evaluated by the FDA. This screen allows the user to view different attributes of the variables in a user-specified data set. When a variable is selected, a graphical display of the data is produced on the right-hand side of the window. The user can then select to visualize a graphical display of the individual patient profiles under the variable in a different window.

Clinical Data Interchange Standards Consortium (CDISC)... 

The Clinical Data Interchange Standards Consortium (CDISC) is a non-profit group that defines clinical data standards for the pharmaceutical industry. CDISC has developed numerous data models that you should familiarize yourself with. Four of these models are of particular importance to you ... 

Analysis Dataset Models (ADaM). The CDISC ADaM team defines data set definition guidance for the analysis data structures. These data sets are designed for creating statistical summaries and analysis. 

Operational Data Model (ODM). The ODM is a powerful XML-based data model that allows for XMF-based transmission of any data involved in the conduct of clinical trials. SAS has provided support for importing and exporting ODM files via the CDISC procedure and the XML LIBNAME engine. 

Case Report Tabulation Data Definition Specification (Define.xml). Define.xml is the upcoming replacement for the data definition file (define.pdf) sent to the FDA with electronic submissions. Define.xml is based on the CDISC ODM model and is intended to provide a machine-readable version of define.pdf. Because define.xml is machine readable, the metadata about the submission data sets can be easily read by computer applications. This allows the FDA to work more easily with the data submitted to it. 

You will be exporting, importing, and creating data for these models, so it is important that you learn about them. The FDA has begun to formally endorse the use of these data models in their guidance. Eventually the FDA will probably require data to be formatted to the CDISC model standards for regulatory submissions. 

This guidance document governs how electronic files should be sent to the FDA. Currently, the FDA requests that electronic documents be submitted as Portable Document Format (PDF) files. The PDF page should be a standard 8.5" x 11" page with 1" margins and 12-point font. Data sets are currently to be sent to the FDA as SAS XPORT transport format files. In the future it is likely that data sets will be required to be sent as XMF files, probably formatted in the CDISC ODM. 

The Clinical Data Interchange Standards Consortium (CDISC) and its Submission Data Standards group have provided another way to broadly categorize clinical trial data. 

Concomitant or prior medications may be used in either safety or efficacy analyses. The presence of specific medications may be used as covariates for inferential analyses. Also, medications are often summarized to show that the therapies under study come from medically comparable populations. Medications may be used to determine protocol compliance and to help define a protocol-compliant study population. Concomitant medications may be examined to determine whether they interact with study therapy or whether they can explain the presence of certain adverse events. From a CDISC perspective, prior medications would be considered a finding while concomitant medications would be considered an intervention. 

Laboratory data are most often associated with safety analyses, but they may play a part in efficacy analyses as well, especially if the laboratory data are part of the clinical endpoint definition. From a CDISC perspective, laboratory data are a finding, as they are a planned assessment. 

In the end, because of the importance of the data, it is imperative that the entire adverse event form data are cleaned. Reconciling the adverse event data with other clinical data in the clinical data management system can be very difficult if the data management system does not provide variable keys for linking such data. Adverse event data fall into the safety area of statistical analyses and are considered an event from a CDISC perspective. 

The problem with endpoint data usually occurs when they need to be reconciled against data collected by the clinical endpoint committee (CEO, which we discuss next. The endpoint/event data are almost always used for efficacy analyses but may be used for safety analyses as well. From a CDISC perspective, the endpoint/assessment is often considered a finding, as it is a planned examination, but it could also be considered an unplanned event. 

The study termination form data may be used for efficacy or safety analysis purposes. With regard to safety, if patients discontinue a study medication earlier than patients on standard therapy or placebo, then that is important to know. For efficacy analyses, patients who withdraw due to a lack of efficacy or adverse event may be precluded from being considered a treatment responder or success. Also, often the study termination date is used as a censor date in time-to-event analyses for therapy efficacy. Study termination forms play a key role in patient disposition summaries found at the start of a clinical study report. From a CDISC perspective, the study termination form is a finding. 

The randomization data are used in both efficacy and safety analyses, as they are typically the key stratification variable for the trial. The randomization data allow you to answer the question of whether patients who are getting the study therapy fare better than the alternative. CDISC places treatment assignment information in the special demographics domain. 

Because XML is an open standard, many industries are developing open standards for XML data exchange. CDISC is the organization leading XML data standardization for the clinical trial industry. 

PROC CDISC is a new SAS procedure that is available as a hotfix for SAS 8.2 and ships as part of SAS 9.1.3. PROC CDISC is a procedure that allows you to import (and export) XML files that are compliant with the CDISC ODM version 1.2 schema. Here is a two-observation sample demographics ODM file that you might want to import into SAS ... 

You can learn more about CDISC standards efforts at http //www.cdisc.org/standards/index.html... 

Program 3.12 Using PROC CDISC to Read ODM XML Data... 

Once the raw clinical data have been imported into SAS, the next step is to transform those raw data into more useful analysis-ready data. Raw data here mean data that have been imported without manipulation into SAS from another data source. That data source is likely to be a clinical data management system, but it could also be external laboratory data, IVRS data, data found in Microsoft Office files, or CDISC model data serving as the raw data. These raw data as they exist are often not ready for analysis. There may be additional variables that need to be defined, and the data may not be structured in a way that is required for a particular SAS analysis procedure. So once the raw data have been brought into SAS, they usually require some kind of transformation into analysis-ready files, which this chapter will discuss. 

Both approaches to study day calculation are used in the pharmaceutical industry, although the algorithm in Program 4.3 is probably used more often. The CDISC Submission Data Tabulation Model states that you should use the algorithm in... 

Program 4.3 for creating study day variables for the SDTM data sets. However, the General Considerations document from the CDISC Analysis Data Set Modeling Team states that you should use the algorithm in Program 4.2 for analysis data sets. Whether you are deriving data based on the CDISC models or not, you should calculate study day variables in a consistent fashion across a clinical trial or set of trials for an application. 

With the advent of the CDISC ODM model and the progression of the FDA s endorsement of the CDISC models, I believe that eventually all clinical trial data will likely be submitted to the FDA in ODM or a similar XML format. The XML-based ODM is already gaining acceptance within the pharmaceutical industry as a means of transferring clinical trial data. SAS provides two ways to produce ODM data files using either PROC CDISC or the XML LIBNAME engine. 

To export this file to ODM using PROC CDISC, you run the following SAS program. It is annotated for further discussion. 

Program 8.3 Using PROC CDISC to Create an ODM XML File... 

SPECIFY PROC CDISC PARAMETERS IN DATA STEPS, data odm ... 

O PROC CDISC requires a number of parameters that specify the clinical trial metadata not typically found in your SAS data sets. These parameters can be specified within PROC CDISC or in separate data sets that can be passed to the procedure. This example chooses the latter method. 

You need to define the key set variables for PROC CDISC to export your data to XML. ODM specifications have a maximum length of 100 characters for these fields, so here I have set the length of the key set fields to 100. It is important to understand what these key set fields are in order to set them properly. In brief, a STUDYEVENT is essentially a visit, and many FORMs can be attributed to a STUDYEVENT. A FORM is equivalent to a CRF page. An ITEMGROUP is a group of variables that make up a discrete piece or all of a FORM. The REPEATKEY fields indicate whether there are multiple observations within a STUDYEVENT, FORM, or ITEMGROUP. For the DM file all of the... 

Complete SAS documentation for PROC CDISC can be found at http //support.sas.com/md/base/topics/sxle82/TW8774.pdf. In the future, it is expected that PROC CDISC will be able to export multiple SAS data sets to a single ODM XML file. As the CDISC models and PROC CDISC rapidly evolve, continue to watch PROC CDISC as a tool for CDISC model conversion. 

The resulting ODM XML file produced by the XML LIBNAME engine is very similar to the one produced by PROC CDISC. However, PROC CDISC allows you to specify and pass more of the metadata information along to the XML file. If you need to create customized XML files for a sponsor that do not match the ODM specification, you can use the SAS XML Mapper and the XML LIBNAME engine to write your own custom XML files. 

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