CDISC Standards Certification
CDISC Certification is a benchmark of excellence for demonstrating expertise in SDTM, a required standard for data submission to US FDA and Japan PMDA.
Organizations
- Assess Potential Hires
- Validate Experienced Candidates
- Expedite Recruitment Efforts
- Provide Clients with Proven Expertise
- Reduce Training Time and Expenses
Individuals
- Validate Deep SDTM Knowledge and Skillset
- Demonstrate Professional Credibility
- Advance Your Career
- Increase Earning Potential
- Streamline Your Job Search
Test Delivery
Take the test at an approved test center or from the convenience of your home or office.
Showcase Certification with a Digital Badge
CDISC will provide successful candidates with an exclusive digital badge to display in email signatures, social media profiles and CVs.
CDISC Standards Certification Exam Scope
Certification Requirements
To obtain the CDISC Tabulate Certification you must meet the following requirements:
- Register for the certification and pay the corresponding fee
- Pass the CDISC Tabulate Certification Exam
To extend your certification you must retake and pass the exam in the six-month period prior to your certification expiration date.
Exam Composition
Passing the CDISC Tabulate Certification Exam demonstrates that you have attained knowledge and the competency necessary to utilize the SDTM and the SDTMIG.
The CDISC Tabulate Certification Exam consists of 125 questions. The average exam duration is 3.5 - 4 hours.
Exam Scope
| Exam Domains and Corresponding Weight |
Knowledge and Skill Statements |
|---|---|
| General Concepts, Terms and Assumptions, and Conformant Dataset Structure (30%) | Knowledge of the five major SDTM variable role types: identifier, topic, timing, qualifier, and rule |
| Knowledge of variables and their usage restrictions (e.g., variables not for use in human clinical trials, domain-specific prohibitions of variables, domain-specific variables) | |
| Knowledge of the multiple uses of the two-character domain code | |
| Knowledge of dataset naming conventions | |
| Skill to correctly use the EPOCH variable | |
| Skill in ordering variables correctly for the general observation classes (e.g., identifier variables, then topic variable, then qualifier variables, then timing variables) | |
| Knowledge of the three values of the Core column: required, expected, and permissible | |
| Knowledge of the rules for splitting domains into separate datasets | |
| Knowledge of the use of metadata to describe the origin of variables or records in Define-XML | |
| Knowledge of how to determine the natural keys for a dataset in which not all records have the same combination of variables defining a unique natural key | |
| Skill to appropriately handle multiple values for a qualifier variable (e.g., race multiple) for result qualifiers and non-result qualifiers | |
| Skill to appropriately handle multiple values for topic variables for events and interventions | |
| Knowledge of the hierarchy of grouping variables and the scope of each grouping variable (e.g., for the subject, across subjects, and within subjects) | |
| Skill to correctly apply formats for date/time variables, including the correct precision | |
| Skill to correctly apply formats for duration and elapsed time variables | |
| Knowledge of the algorithm for “study day” variables | |
| Skill to appropriately use relative timing variables | |
| Skill to appropriately use time point variables | |
| Skill to appropriately use disease milestone timing variables | |
| Skill to correctly represent original and standardized results | |
| Skill to correctly represent options for indicating the fact that a test or set of tests was not done | |
| Skill to correctly represent lengthy text strings | |
| Knowledge of the of the Supplemental Qualifiers Name Codes appendix | |
| Skill to correctly represent pre-specified interventions | |
| Skill to correctly represent baseline values | |
| Skill to identify the natural key structure of each dataset | |
| Knowledge of the difference between a natural key and a surrogate key when identifying records and the use of the surrogate key --SEQ in SDTM | |
| Knowledge of conformance with SDTM Domain Models, including metadata structure, domain names, variable names, data types, formatting, appropriate identifier and timing variables, and rules in assumptions and CDISC Notes |
| General Observation Classes (12%) | Knowledge of the three general observation classes: interventions, events, and findings observation classes |
| Skill to accurately apply the SDTM requirements for the topic and qualifier variables in interventions observation class |
|
| Knowledge of the standard domains of the interventions observation class (e.g., Procedure Agents, Concomitant/Prior Medications) | |
| Skill to accurately apply the SDTM requirements for the topic and qualifier variables in the events observation class | |
| Knowledge of the standard domains of the events observation class (e.g. Adverse Events,Clinical Events) | |
| Skill to accurately apply the SDTM requirements for the topic and qualifier variables in the findings observation class | |
| Knowledge that the findings observation class captures the observations resulting from evaluations to address specific tests or questions | |
| Knowledge of the standard domains of the findings observation class (e.g.Tumor/Lesion Results, Vital Signs) | |
| Knowledge of the required identifier variables (e.g., STUDYID, DOMAIN, and --SEQ), other identifier variables available in the SDTM, and their appropriate use in the three general observation classes | |
| Knowledge of the availability of timing variables in the SDTM and the appropriate use of timing variables in the three general observation classes |
| Domain-Specific Knowledge (10%) | Knowledge that the Pharmacokinetic Parameters (PP) domain represents results of analyses |
| Knowledge that pharmacodynamic data would not be represented in a custom domain of that name but in appropriate standard findings domains (e.g., response of blood pressure to dosing belongs in the Vital Signs (VS) domain) |
|
| Knowledge that biomarker data would not be represented in a custom domain of that name but in appropriate standard findings domains | |
| Knowledge of the difference between representing dosing data in the Exposure as Collected (EC) domain versus Exposure (EX) domain | |
| Knowledge that the units for Exposure (EX) domain are the protocol-specified units | |
| Knowledge of the types of data that do and do not belong in Medical History (MH) domain (e.g., prior surgeries belong in the Procedures (PR) domain, not MH) | |
| Skill to correctly construct the Disposition (DS) domain, including the correct use of DSCAT | |
| Skill to appropriately use the DSTERM and DSDECOD variables |
| Findings About, Custom Domains, Associated Persons, and Study References (10%) | |
| Knowledge of the requirements for the use of the --OBJ variable with Findings About events or interventions | |
| Knowledge of when to use Findings About | |
| Knowledge of the different references that would be needed when creating a new domain | |
| Skill to create a custom domain | |
| Skill to recognize data that are about an associated person rather than a study subject | |
| Knowledge of considerations for creating an Associated Person (AP) dataset (e.g., family medical history, caregiver questionnaires) | |
| Knowledge of the situations for which additional identifiers may be needed (e.g., identifiers for devices, identifiers for non-host organisms) | |
| Knowledge of the Device Identifiers (DI) dataset used to establish identifiers for devices that are used to populate the variable SPDEVID | |
| Knowledge of the Non-host Organism Identifiers (OI) dataset used to establish identifiers for organisms used to populate the variable NHOID |
| Special Purpose Domains (14%) | |
| Knowledge of the appropriate use of special purpose domain datasets (e.g., Demographics [DM], Comments [CO], Subject Elements [SE]) | |
| Knowledge that the Demographics (DM) domain is required and which of its variables are required or expected | |
| Skill to accurately create a Demographics (DM) dataset | |
| Knowledge of the interaction between the Demographics domain (DM) variables and the Trial Arms (TA) dataset variables | |
| Skill to accurately apply the SDTM requirements for representing comments in the Comments (CO) domain | |
| Knowledge of the different ways of relating data in the Comments (CO) domain to data in other subject domains | |
| Skill to accurately apply the SDTM requirements to create a Subject Elements (SE) dataset | |
| Skill to accurately apply the SDTM requirements to create a Subject Visits (SV) dataset | |
| Skill to accurately use the appropriate timing variables to represent planned and unplanned visits | |
| Skill to accurately apply the SDTM requirements for representing the timing of disease milestones (defined by Trial Disease Milestones (TM) for each subject) |
| Controlled Terminology (8%) | |
| Knowledge of the appropriate use of code lists, Controlled Terminology, and code list subsets (e.g., NY, STENRF) | |
| Knowledge of CDISC Controlled Terminology Codetable mapping files | |
| Knowledge of references to code lists (e.g., implementation guides) | |
| Knowledge of the appropriate use of the new term request process | |
| Knowledge of when sponsor code lists may be needed | |
| Knowledge that the SDTM Controlled Terminology standards are versioned independently from the SDTM and SDTMIG | |
| Knowledge of the existence of global supplemental conformance guides | |
| Knowledge of the relationship between SDTM/SDTMIG and other CDISC standards and publications | |
| Knowledge of the relationship between SDTM/SDTMIG and other dictionaries such as MedDRA and WHODrug |
| The Trial Design Model (10%) | |
| Skill to create Trial Elements (TE) dataset for a straightforward randomized clinical trial | |
| Knowledge of trial design concept definitions (e.g., epoch, arm, element) | |
| Skill to create a Trial Arms (TA) dataset for a straightforward randomized clinical trial | |
| Skill to create a Trial Visits (TV) dataset for a straightforward randomized clinical trial | |
| Skill to create a Trial Inclusion/Exclusion Criteria (TI) dataset | |
| Skill to create a Trial Summary (TS) dataset | |
| Knowledge of considerations for creating Trial Disease Milestones (TM) dataset (e.g., protocol-defined events of special interest) |
| Relationships Among Datasets and Record (6%) | |
| Skill to appropriately use the --GRPID variable to describe the relationship between a group of records for a given subject within the same dataset | |
| Skill to create the RELREC special purpose dataset to describe relationships between records for a subject | |
| Skill to create the RELREC special purpose dataset to describe relationships between datasets | |
| Knowledge that RELREC should not contain assumed relationships | |
| Knowledge of when it is appropriate to represent non-standard variables and their association to parent records as Supplemental Qualifiers (SUPP--) for SDTMIG-defined use cases |