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SAS Global Forum 2008
Pharma, Life Sciences and Healthcare
Paper 207-2008
Practical Methods for Creating
CDISC SDTM Domain Data Sets from Existing Data
Robert W. Graebner, Quintiles, Inc., Overland Park, KS
Creating CDISC SDTM domain data sets from existing clinical trial data can be a challenging task, particularly if the
database was not designed with the SDTM standards in mind. A key step in the process involves determining which
of the STDM domain datasets need to be produced for submission and then determining what conversion process
will be necessary to produce them from the existing data. Adequate planning and documentation of the conversion
process is an essential first step before programming begins. The basic component of the planning phase involves
metadata mapping – determining how each of the variables in the existing data will relate to the variables contained
in the SDTM domains to be produced. The documentation of the conversion process should be recorded in a format
that facilitates efficient access by those involved in the planning, programming and validation phases of the
conversion. Tools suited to the task of complex data mapping and data manipulation can significantly reduce cost
and improve quality. This paper presents an example of a simple metadata mapping tool developed using SAS,
Microsoft Excel and Visual Basic. The examples in this paper are based on the CDISC SDTM version 1.1, the SDTM
Implementation Guide version 3.1.1 and SAS version 9.1.3.
In order to increase the efficiency of the drug development process, the Clinical Data Interchange Standards
Consortium (CDISC) has developed a series of clinical study data standards to facilitate efficient transfer, access
and review of clinical trial data. These standards include the Operational Data Model (ODM), the Study Data
Tabulation Model (SDTM) and the Analysis Data Model (ADaM). This paper presents basic strategies and practical
methods for creating SDTM domain data sets from clinical data management (CDM) system files. Before initiating
the data mapping and conversion process it is crucial to have a basic understanding of the SDTM specifications.
CDISC provides implementation guides for all of the CDISC data standards on their Website ( The
SDTM Implementation Guide (SDTMIG) is an essential tool for anyone involved with the metadata mapping or
programming associated with the creation of SDTM data sets. The SDTM Implementation Guide contains the
specifications and metadata for all of the SDTM data domains and guidance for producing SDTM domain files. The
SDTM is an evolving standard and it is important to ensure that everyone involved in the conversion process is
adhering to the same version of the SDTM. It is also important to understand the difference in the version numbers
for the SDTM standard and the associated implementation guide. The most recent versions in production are SDTM
1.1 and SDTMIG 3.1.1, which were released in 2005.
The purpose of creating CDISC SDTM domain data sets is to provide Case Report Tabulation (CRT) data to a
regulatory agency, such as the FDA, in a standardized format that is compatible with available software tools that
allow efficient access and correct interpretation of the data submitted. The SDTMIG provides documentation on
metadata for the domain data sets that includes the file name, variable names, types, labels, formats, roles and
controlled terminology. While most of the SDTM domain data sets have a normalized (vertical) structure, they were
not designed for use in a clinical data management (CDM) system. It is highly desirable to incorporate CDISC
standards to the extent practical when designing CDM data structures. Proper adherence to the standards can
greatly reduce the effort necessary for data mapping. Important standards to adhere to are domain name, variable
name, variable type and format. Matching the SDTM variable labels is not important. The SDTM standard labels are
available in the standard metadata and the labels are not used for match merging in the mapping process. While the
SDTM documentation does not specify variable lengths, it is highly desirable to maintain consistency in length
among variables with the same name across domains and between studies.
While the SDTM data sets do contain some derived variables, they are not designed for use as analysis data sets.
Adherence to the ―one proc away‖-philosophy for analysis files dictates the addition of additional derived variables
and conversion to a horizontal structure. The SDTM data sets can however, be used in the creation of analysis files.
The creation of standardized STDM data sets will aid in the creation of analysis files for each individual study, and
the future task of integrating data from multiple studies will be accomplished with greater efficiency and quality. The
ability to submit SDTM data sets in place of listings or patient profiles, resulting in additional cost reductions.
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The degree to which you can define a standard process for converting clinical study data to SDTM domains depends
on the environment in which you are working. In an ideal situation, the CDM data structures would be designed to be
as compatible as possible with the SDTM specifications. An SDTM annotated CRF is a valuable tool to aid in the
mapping process. Creating a standard metadata library would allow you to maximize the consistency within and
between studies. This level of consistency would allow you to develop a library of standard annotated CRF pages
and a library of SAS macros for creating SDTM domain files with a minimum amount of metadata mapping and
additional programming at the study level. This level of standardization would also reduce the cost of consolidating
data for integrated studies. In such an environment a very detailed and specific SDTM conversion process can be
In many current situations, existing data does not contain this level of standardization or compatibility with the SDTM
standards. In such cases the conversion process must be very flexible and it can only be defined in general terms.
Even though the process must be designed with considerable flexibility to accommodate different CDM data
structures, it is still important to have a process in place to serve as a general frame work to promote consistency in
SDTM domain creation, promote the use of standard terms to enhance communication, and provide guidance to
those new to SDTM. Establishing a process will also facilitate the use of standard tools for metadata mapping and
documentation, SDTM file creation and SDTM file validation. The focus of this paper is on this second situation,
where significant metadata mapping and programming will be necessary.
If a standard process for SDTM conversion does not currently exist, it is important to define one, at least in general
terms, prior to starting the conversion. The process definition is a large-scale map that defines the major steps
necessary to create the desired SDTM domains from the existing data. Once the major steps are defined, the
components of each step can be determined. This will allow you to define dependencies between tasks, determine
where there are possibilities for performing steps in parallel, and define the types of tools that will be necessary. The
steps listed below outline a basic process for SDTM conversion. Starting with the end in mind, the goal is defined,
the current situation is assessed, and a path is defined between the two.
Determine which SDTM domains will be created
Determine the extent of SDTM compliance in the existing data
Implement automaic direct mapping where possible
Map remaining source data sets to SDTM domains
Map variables in source data sets to SDTM domain variables
Determine if SUPPQUAL domain or custom domains will be required
Generate SAS programs to perform the data conversion
Validate the SDTM data sets
It is important to adequately document the general process and the specific steps requires for a particular study. This
includes revising the documentation if it becomes necessary to modify the process. The documentation will play a
critical role in validating the process and will be very useful as a guide during future SDTM conversion projects.
A basic understanding of the SDTM domains, their structure and their interrelations is vital to determining which
domains you need to create and in assessing the level to which your existing data is compliant. The SDTM consists
of a set of clinical data file specifications and underlying guidelines. These different file structures are reffered to as
domains. Each domain is designed to contain a particular type of data associated with clinical trials, such as
demographics, vital signs or adverse events. In the current specification, each of these domains will be contained in
a separate XPORT data file, based on the SAS version 5 data set file format, which is in the public domain. Future
versions will support the use of XML files.
The CDISC SDTM Implementation Guide provides specifications for 30 domains and new domains are being
developed. It is important to check the CDISK website for the latest updates before you beging a new conversion
project. The SDTM domains are divided into six classes. The 21 clinical data domains are contained in three of these
classes: Interventions, Events and Findings. The trial design class contains seven domains and the special-purpose
class contains two domains (Demographics and Comments). The trial design domains provide the reviewer with
information on the criteria, structure and scheduled events of a clinical trail. By placing key trial design information in
a concise and standard data structure, the reviewer can have ready access to details of the trial design that allow
them to view the clinical data in the proper context. The focus of this paper is on creating clinical data domains from
CDM system data files. A list of the SDTM clinical data domains is given below in Figure 1. Only the domains that
are pertanent to a particular study need to be created. The only required domain is demographics. Demographics
also differs from the other domains in the fact that it has a horizontal structure, with a single row per subject.
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There are two other special purpose relationship data sets, the Supplemental Qualifiers (SUPPQUAL) data set and
the Relate Records (RELREC) data set. SUPPQUAL is a highly normalized data set that allows you to store virtually
any type of information related to one of the domain data sets.The initial specification for SUPPQUAL indicates that
a single file should be used for all domains. The current trend, and possibly the requirement for the next version of
SDTM, is to use a separate file for each domain named SUPP--, where the hyphens are replaced with the two-letter
designation for each domain.
In general, the use of SUPPQUAL should be minimized. Its purpose is to provide a means of adding variables which
are critical to a study, but which are not included in the specifications of the pertanent domain and are not suitable as
an additional identifier, topic or timimg variable. If the number of additional variables is large or if they are not
pertanent to an existing domain, then the creation of a custom domain shuld be consided. Before considering the
creation of a custom domain, you should review the latest information on the CDISC Web site, it is possible that a
new domain has been defined that will suite your needs. Guidelines for creating custom domains are included in the
SDTM Implementation Guide. Information on RELREC is provided in the section below on key variables and relating
Special Purpose
Trial Design
Relationship Data Sets
Concomitant Medications
Substance Use
Adverse Events
Protocol Deviations
Medical History
Drug Accountability
Inclusion / Exclusion Criteria Exceptions
Laboratory Results
Microbiology Specimens
Microbiology Susceptibility
Pharmacokinetic Concentrations
Pharmacokinetic Parameters
Physical Exam
Subject Characteristics
Vital Signs
Trial Elements
Trial Arms
Trial Visits
Subject Elements
Subject Visits
Trial Inclusion/Exclusion Criteria
Trial Summary
Supplemental Qualifiers
Relate Records
Figure 1. CDISC SDTM Domains
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Each of the SDTM domains has a collection of variables associated with it. There are five roles that a variable can
have: Identifier, Topic, Timing, Qualifier, and for trial design domains, Rule. Using lab data as an example, the
subject ID, domain ID and sequence (e.g. visit) are identifiers. The name of the lab parameter is the topic, the date
and time of sample collection are timing variables, the result is a result qualifier and the variable containing the units
is a variable qualifier. The SDTM guidelines contain a section on the fundamentals of the SDTM that cover this topic
in detail. The SDTM fundamentals are important to understand before you begin the process of metadata mapping,
particularly if you need to create custom domains.
Variables that are common across domains include the basic identifiers study ID (STUDYID), a two-character
domain ID (DOMAIN) and unique subject ID (USUBJID). In studies with multiple sites that are allowed to assign their
own subject identifiers, the site ID and the subject ID must be combined to form USUBJID. All other variable names
are generally formed by prefixing a standard variable name fragment with the two-character domain ID.
It is also important to understand which variables should be included in each domain to which you will be mapping
study metadata. The SDTM specifications do not require all of the variables associated with a domain to be included
in a submission. The SDTM is a standard designed to accommodate the wide range of trials that are conducted in
the Pharmaceutical and Biotechnology industries, and some variable may not be necessary for a particular trial. Your
metadata mapping will not necessarily include all of the variables associated with the domains you are creating nor
will it necessarily include all of the variables contained in the CDM database. Any questions regarding which
variables to submit should be addressed with your reviewer. In regard to complying with the SDTM standards, the
implementation guide specifies each variable as being included in one of three categories: Required, Expected, and
Permitted. An explanation of each is given below.
These variables are necessary for the proper functioning of standard software tools used by
reviewers. They must be included in the data set structure and should not have a missing value for
any observation.
These variables form the fundamental core of information within a domain. They must be included
in the data set structure; however it is permissible to have missing values.
These variables are not a required part of the domain and they should not be included in the data
set structure if the information they were designed to contain was not collected.
The implementation guide provides information on the expected structure of each domain data set. For each
variable, a name, label and type are provided. The length of the variables is not specified. The file structure is
designed to comply with the XPORT file format, which is based on the SAS version 5 data set specifications.
Variable names have a maximum length of 8, labels a maximum length of 40 and character variables a maximum
length of 200. These restrictions may change in the future as the use of XML becomes standard.
To accommodate character variables longer than 200, the first 200 characters should be stored in the domain
variable and the remaining text should be stored in the SUPPQUAL domain. For the sake of readability, the text from
the source variable should be split between words, into substrings of length 200 or less. The first substring is stored
in the appropriate variable in the parent domain. Each of the remaining substrings should then be stored in the
variable QVAL in an observation within SUPPQUAL. In SUPPQUAL, the variable QLABEL should contain the same
label as the domain variable and the variable QNAM should contain the name of the variable in the parent domain
with a sequential integer from 1 to 9 appended. If the name of the parent domain variable has a length of 8 then the
sequential number replaces the last character of the name. The variable IDVAR and IDVARVAL are used to relate
the records in SUPPQUAL back to the appropriate record in the parent domain.
In addition, some variables require the specification of a controlled terminology or format. In such cases, the
implementation guide specifies whether the controlled terminology is provided by an external source (e.g. MedDRA)
or by the investigator. It is generally recommended that the text used in defining controlled terminology be placed in
all uppercase. Exceptions to this rule are controlled terminology from external sources or designations such as units,
which employ a generally accepted use of mixed case text. When defining controlled terminology, it is important to
prevent ambiguity.
Before beginning the task of developing programs to create SDTM domain data sets from your existing data, it is
important to have a ―road map‖ to design and document the process. As with planning any journey, the first step is to
specify your current location and the location of your destination. By comparing alternate routes before starting the
actual trip, you can avoid getting lost or needing to back track.
The first step in the mapping process involves the comparison of the study metadata with the SDTM domain
metadata. If the CDM metadata is compliant to a significant extent with the SDTM metadata, it is possible to use
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automated mapping as a first pass. If CDISC standard data set and variable names were properly used in the CDM
data sets, it is possible to use a DATA step merge or SQL join to combine rows of study metadata with matching
rows of SDTM metadata based on variable name, type and format. Note that the SDTM standards do not specify
variable length. They do provide the standard variable label, so it is important to make sure you are keeping the
SDTM label rather than your CDM data label. Automatic mapping can potentially results in a significant reduction in
cost, however it is important to check the validity of the mappings. This process only serves as a first pass of
metadata mapping, in most cases some manual mapping will be necessary. If the CDM metadata is not compliant
with the SDTM or worse yet, if SDTM specifications were improperly used, then auto mapping should be avoided.
The next step involves manually mapping the study data sets to the domain data sets and then mapping each
individual variable to the appropriate domain. Depending on how the CDM data sets are structured, you may map
each CDM file to a single domain, split its variables among multiple domains, or combine variables from multiple
CDM files into a single domain. There are several possible types of variable mappings. In some cases it may be
necessary to use more that one method in order to create the desired SDTM variable from the existing data. A list of
basic variable mappings is given below.
a CDM variable is copied directly to a domain variable without any changes other than assigning
the CDISC standard label
only the variable name and label may change but the contents remain the same
mapping reported values to standard units or standard terminology
the actual value being represented does not change, only the format in which is stored changes,
such as converting a SAS date to an ISO8601 format character string
directly combining two or more CDM variables to form a single SDTM variable
a CDM variable is divided into two or more SDTM variables
creating a domain variable based on a computation, algorithm, series of logic rules or decoding
using one or more CDM variables
While any mapping that involves changing or combining CDM variables to form a domain variable could be referred
to as a derivation, further categorizing the type of mapping facilitates assigning a standard process (e.g. a SAS
macro or block of SAS source code) to perform the mapping operation.
Effective manual mapping requires a method of managing and accessing the metadata for both your existing data
and the SDTM domains. If your study data resides in SAS data sets, and you define a SAS library for their location,
SAS will automatically provide a view to an internal table that contains the structure information for all data sets in
any defined library. This metadata can be easily accessed by either specifying SASHELP.VCOLUMN as an input
data set in a DATA step, or by selecting rows and columns from the table DICTIONARY.COLUMNS using PROC
SQL. This file contains the library name, data set name, variable name, type, length, label, format and more for every
variable in every data set in every currently defined library. The amount of information in this view can be
overwhelming and it is usually necessary to use a where clause to obtain only the specific information needed. The
fact that it contains metadata for all currently accessible data sets facilitates easy metadata comparisons across
data sets or across studies, such as determining which variables have identical or similar names.
Every domain contains a required set of variables that form a unique key for that record. These include STUDYID,
DOMAIN and USUBJID. DOMAIN contains the two-character domain name and is hard-coded into each record.
USUBJID is a unique subject identifier within a study. Therefore, if multiple sites are used and subject numbers
overlap between sites, then USUBJID must combine the initial site and subject numbers. An additional required key
variable is –SEQ, where the two hyphens represent the domain name. When a subject has more than one record in
a domain, then –SEQ is used to form a unique key. An additional, sponsor-defined key is –SPID. This variable is
typically used for external identifiers, such as a sample number assigned by a lab.
The SDTM design provides several ways to relate records within and between domains. Records within a domain
can be related by assigning them the same value for –GRPID. The RELREC data set can be used to relate multiple
records in multiple domains. Each record in RELREC with the same value of RELID defines a relation. Each record
also contains the key variables necessary to point to a record or group of records in a domain.
The use of software tools is essential to the efficient creation of SDTM data sets. The process of mapping study data
to the SDTM domains can be complex. The large number of variables involved and the many different
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transformations required make mapping without a tool tedious and error prone. When decisions are made regarding
process steps, it is important that the process be documented for consistency and repeatability. Direct electronic
access to metadata for both the study data and the SDTM domains facilitates an efficient mapping process.
Automation of basic processes can save significant amounts of time. Metadata about the mapping process can be
used to generate documentation of the process and to generate the SAS source code to perform the derivation of
domain data sets. Once the domain data sets have been produced, software tools documenting the metadata
mapping can improve the efficiency of validating the domain data sets and producing the define.xml file.
The use of a metadata mapping tool can also be extended to the creation of ADaM analysis data sets from the
SDTM data sets. A typical ADaM data set is created by merging data from two or more SDTM data sets,
restructuring the data to a form convenient for analysis and creating derived variables. The use of a metadata
mapping tool for creating ADaM data sets will provide similar advantages to those for producing SDTM data sets.
Including metadata on both transformations in one system will provide complete documentation of the creation of the
analysis data sets. The process by which each variable was created can be traced back to the original source. This
approach will also simplify maintaining consistency between the SDTM and ADaM data sets. The CDISC
specifications state that any variables copied from an SDTM domain into an ADaM data set must retain all of the
attributes found in the SDTM domain. By storing the metadata for SDTM and ADaM in the same system and form it
is easy to ensure that this condition will be met.
The SAS Metadata Server and the SAS Data Integration Studio provide a very powerful environment for mapping
study data and producing domain data sets. This environment provides direct access to study metadata and CDISC
SDTM domain metadata. The visual interface allows you to define data transformation and mapping steps using
icons that represent predefined process steps. The system is extensible, allowing you to add new capabilities and
the sequence of steps used in your process is stored in metadata.
It is possible to create your own simple, but effective tools to aid in the metadata mapping process. Leveraging the
power of SAS and Microsoft Excel together allows you to create a practical metadata mapping tool with relatively
little programming. The combination of SAS and Excel allows you to combine a user interface with the familiarity of
an Excel workbook with the power of SAS to access and manipulate data in a variety of forms. Important skills
needed to develop such a tool includes a solid understanding of SAS DATA step programming, basic SAS Macro
programming skills, and a working knowledge of Visual Basic and the Excel object model.
A key reason for the power of pairing SAS with Excel is the flexibility SAS provides for exchanging data with Excel.
The SAS Excel libname engine allows you to read and write from Excel worksheets as though they were a SAS data
set. The IMPORT and EXPORT procedures allow you exchange data for an entire data set as a stand-alone process
or from within a SAS program. Dynamic data exchange (DDE) allows you to define a DDE triplet that defines a range
of cells in Excel to be treated as a flat file in SAS. The SAS Add-In for Microsoft Office allows you to use SAS as a
powerful data access, manipulation and analysis back end for Excel applications. SAS also provides the XML
libname engine to facilitate reading and writing XML files. In version 9, SAS added ODM native mode support
(xmltype = CDISCODM) to the XML engine. The SAS CDISC procedure currently provides read and write capability
for ODM, and content and structure validation for SDTM.
The example presented here is a simple tool developed using Microsoft Excel, Visual Basic and SAS. The SDTM
metadata mapping tool allows users to manage and document the mapping of study data to SDTM domains and it
can produce text files containing SAS source code to be used as a starting point for programs to generate SDTM
domain data sets from the study data sets. The tool consists of an Excel workbook with three main worksheets: an
SDTM domain metadata dictionary, a study metadata dictionary with CDM data set specifications imported from the
SAS view SASHELP.VCOLUMN, and a SDTM mapping sheet containing variable mapping and derivation
An advantage of using Excel is that there is a great deal of functionality available without any programming. One
example of this is the Excel auto filter. When an auto filter is set for a column, a selection button appears in the label
cell. Clicking on it displays a pick list containing all of the unique items in that column. If an item is selected, the
sheet will then only display rows that contain that value in that column. This feature makes it easy to view subsets of
the metadata. For example, you can view all of the variables in a particular data set or domain, or you can view all of
the occurrences of a given variable name across all domains. The sheet containing the SDTM metadata dictionary is
shown in Figure 2, the study metadata sheet is shown in Figure 3.
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Figure 2. SDTM Metadata dictionary with auto filter selection list
Figure 3. Study Metadata Sheet
The user interface includes a new main item called SDTM Mapper that is temporarily added to the Excel main menu
just before the Help menu item, or within the Add-Ins tab if you are using Excel 2007. Current active submenu items
include Map Study Variables and Generate SAS Code for Domain. The functionality behind these menu options is
provided by a series of Visual Basic modules containing subroutines and functions stored within the workbook.
Mapping study variables involves selecting the row corresponding to a given SDTM domain variable in the
SDTM_MAPPING sheet, then selecting the desired study variable from a pick list that uses the study metadata
dictionary as its row source. Once a variable is selected, the metadata for that variable is added to the same row in
the appropriate columns of the SDTM_MAPPING sheet. The names of the study data metadata columns all begin
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with ‗s_‘ to differentiate them from the columns containing metadata for the STDM domain. If additional study
variables are required to derive an SDTM domain variable, they can be added to the s_addvars column. Blocks of
executable SAS source code or a SAS macro call can be entered into the SAS_code column. The SAS code is
included the SAS program text files that are generated by the mapping tool and it also provides documentation on
how the variable was created. If only basic instructions or pseudo code are available, they can be entered as a SAS
comment statement. A valuable addition to this sheet would be a column to containing the derivation or imputation
description or algorithm. This would ensure that the method used to create a variable can be easily understood by
those who do not program and the contents could serve as a source for ComputationMethod items in the define.xml
file. The mapping sheet with the variable selection user form is shown in Figures 4 and 5.
Figure 4. Metadata mapping sheet showing the study variable pick list
Figure 5. Metadata mapping sheet showing additional variables and SAS code
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To generate a text file containing SAS source code, the user selects Generate SAS Code for Domain from the SDTM
Mapper menu and then selects the desired domain. A Visual Basic module utilizes the metadata in the
SDTM_MAPPING sheet to generate a text file with SAS source code that includes:
A program header comment block which indicates the name of the SDTM domain is produced and the
names of the source data sets
RETAIN and KEEP statements containing all of the selected variable names
A LABEL statement containing the name and standard CDISC label for all selected variables
A DATA step to create the domain data set with a SET statement if it is created from a single source data
set or a MERGE statement if it is created from two or more data sets
All blocks of SAS source code from the relevant rows of the SDTM_MAPPER sheet
Because the metadata is used to generate the SAS source code you will end up with code that includes all of the
necessary variables, with correct names, labels and types. While the text file is not meant to be a read-to-run
program, it helps increase efficiency and consistency by eliminating most of the tedious tasks associated with
developing conversion programs and allows the programmer to focus on the challenging issues of data mapping and
A simple application like this can be useful in situations where timelines are tight and do not afford the opportunity to
develop a full-scale application. It is designed as a flexible, ―in the trenches‖ tool. In addition to filling immediate
project needs, such an application can serve as a prototype for testing new ideas and as a focal point while defining
and refining the user requirements for a more robust, enterprise-level application. When using an Excel application
of this type, it is important to limit the extent to which users and modify the functionality. The most critical safeguard
is to password protect the Visual Basic source code modules so that only those with sufficient skill and adequate
knowledge of the application can modify them.
The SAS CDISC procedure is a very valuable tool for validating SDTM domain data sets is. With SAS version 9.1.3,
Proc CDISC can be used to validate domain data sets. Future version will provide additional functionality. For
validating STDM domain data sets, I developed a SAS macro that utilizes PROC CDISC. The macro has three
- The two-letter of the SDTM domain to validate
SUPPQUAL - If this parameter is not missing, the SUPPQUAL data set is validated
- If this parameter is not missing, the comments (CO) data set is validated
Only the domain name is required. The category parameter of PROC CDISC is automatically set by the macro. If the
SUPPQUAL parameter is not missing, then the rows in SUPPQUAL that pertain to the specified domain are test
merged with the domain data set. An error statement is generated in the SAS log for any SUPPQUAL rows that do
not have a match in the domain data set. The same process is done with the comments data set if the COMM
parameter is not missing. Any findings from PROC CDISC are also included in the log. This can include:
An ERROR for any required variable that is not found or has a missing value, or any expected variable that is
not found
A WARNING for any expected variable that has a missing value
A NOTE for any permissible variable that is not found
Note that unless you have the Beta patch for PROC CDISC, the SDTM 3.1.1 ISO8601 format is not supported and
dates with missing components will generate an error in the log.
The CDISC standard uses the nonproprietary ISO8601 format to represent date and time values. This standard
expresses dates and times with character strings in a format that can readily be understood by humans and
interpreted by software. A full representation of a date and time value would be of the form YYYY-MMDDThh:mm:ss. Years are represented using four digits, the remaining date and time components are all two digits
with leading zeros if necessary. The date components are separated by a hyphen and the time components are
separated by a colon. For values containing date and time, an upper case letter ‗T‘ is used to separate the date and
time. There are no spaces between components and delimiters. The ISO8601 standard allows for the use of either
the basic format, without delimiters, or the extended format described above. The SAS XML libname engine provides
both basic and extended formats and informats. The CDISC specification requires the use of the extended format
with delimiters.
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Partial dates and times can be stored in this format, however the ISO8601 standard of handling partial dates was
modified. In the original standard, the representation would start with the largest scale component (e.g. year) and
continue until a missing component occurred. The representation would end at that point, resulting in a reduced
precision representation. For example, if a date was recorded with a year and day, but missing month, it would only
be stored in ISO8601 format as a year. With the new standard, hyphens could be inserted for the two missing month
digits, resulting in a missing component representation. The SDTM 3.1 standard utilizes the reduced precision
method, the 3.1.1 standard uses the missing component standard. The current version of SAS 9 was developed
based on the SDTM 3.1 implementation guide however there are updates available to comply with the SDTM 3.1.1
implementation of the ISO8601 date and time formats. The examples below show the full representation of 10:30 AM
on March 3, 2008, and the partial representations if the day was missing.
Full Datetime Representation:
Reduced Precision Representation (SDTM 3.1)
Missing Component Representation (SDTM 3.1.1)
There are many features in SAS that facilitate reading and writing dates and times in the ISO8601 format. SAS
provides a wide range of ISO8601 date and time formats and informats with the XML libname engine. When working
with SAS data sets there are several informats that can be used to read ISO8601 text strings in as a SAS date. This
might be necessary if the ISO8601 formats were used in creating the source data sets and you need to perform
computations or comparisons of dates to create your SDTM domains. Partial dates or times will result in a missing
value for the SAS date or time variable. The applicable SAS informats are listed below.
Reading ISO8601
SAS provides many functions that are useful in creating dates and times in the ISO8601 format. The individual date
and time components can be extracted, formatted and combined with the appropriate delimiter characters to form
the equivalent ISO8601 representation.
FDA guidance for electronic submissions specifies that all electronic submissions include a Data Definition
Document that describes the structure and content of the data included in the submission. In 1999 the FDA
standardized on the use of SAS version 5 XPORT (.XPT) files for study data, and Portable Document Format (.PDF)
files for metadata. In 2003 the FDA expanded the list of acceptable file types to include Extensible Markup Language
(.XML) files. By transitioning from the use of define.pdf to define.xml, the metadata for the submission will be in a
machine-readable form that can be used by standard data review tools. Placing both study data and metadata in a
standard XML schema will facilitate validation and transfer into a data warehouse. The schema for the SDTM
define.xml is based on an extension of the CDSIC ODM, which is a specification of a standard XML schema
designed to facilitate efficient and robust storage and interchange of clinical trial data and associated metadata.
Details on define.xml are published in the Case Report Tabulation Data Definition Specification (CRT-DDS)
document available at the CDISC website listed at the beginning of this paper. CDISC also provides standard style
sheets that can be used to render the define.xml file into a readable form. The United States Food and Drug
Administration (FDA) also provides guidance on preparing files for electronic submission.
The creation of the define.xml file must conform to the CDISC standards. The XML must be well-formed, standard
XML without any proprietary XML tags, such as you can find in an Excel file saved as XML. The XML specification
does not define a single file structure definition as is common with proprietary file formats such as SAS data sets or
Excel spreadsheets. The ‗X‘ stands for extensible. Within the XML specification, matching tags are used to delimit
items. In XML however, it is possible to define new tags to meet specific needs. It is essential that the tags used
conform to the CDISC ODM standard.
The define.xml file is comprised of several sections. The file header contains information that identifies the file as
XML and specifies the XML version used. The file also contains SDTM study-level metadata. The table of contents
section contains domain-level metadata including the data set name for each domain, a description, structure
description (e.g. one record per subject per event), the purpose, a list of the variables that form the key and a link to
the actual data set. Another section is the Data Definition Table (DDT) that contains the variable-level metadata.
Validation of the define.xml must be done on several levels including checks for conformance with the define.xml
specification, checks for internal integrity between elements and checks for external integrity with other files
referenced in define.xml such as domain data files and an annotated CRF in PDF format.
SAS Global Forum 2008
Pharma, Life Sciences and Healthcare
The mapping of existing study data to CDISC SDTM domain data sets can be a daunting task. Developing an
adequate understanding of the SDTM standard is an important first step. Proper planning and the use of metadata
mapping tools can increase both the efficiency of the process and the quality of the resulting data sets. The use of
standard processes and tools will increase the return on your development investment if they are flexible enough to
be used on future conversion projects. If you are allowed to submit SDTM domain data sets in lieu of study report
listings, patient profiles or monitoring board report listings, the cost of creating the STDM domain data sets can be
offset. The ability of reviewers to readily access tabulation data can potentially eliminate some of the costs
associated with ad-hoc requests. Having you study data in a standardized format can facilitate significant gains in
efficiencies when creating analysis file data sets or when combining data from different trials for an integrated study.
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Other brand and product names are registered trademarks or trademarks of their respective
Robert Graebner
Quintiles, Inc.
P.O. Box 9708
Overland Park, KS
[email protected]
[email protected]
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