Presentation on CDISC- SDTM guidelines.

CDISC, SDTM guidelines.

Genelife Clinical Research Pvt. Ltd. By,

Ankita Vaidya,Biostatistician.

Definitions

• CDISC : Clinical Data Interchange Standard Consortium.

• SDTM : Standard Data Tabulation Model.

CDISC- Introduction

• CDISC is a global, open, multi-disciplinary, non-profit organization that has established standards to support the acquisition, exchange, submission and archive of clinical research data and metadata.• The CDISC mission is to develop and support global, platform-independent data

standards that enable information system interoperability to improve medical research and related areas of health-care. • CDISC standards are vendor-neutral, platform-independent and freely available via the

CDISC website.• Leads the development of standards that improve efficiency while supporting the

scientific nature of clinical research.

SDTM- Introduction

• SDTM defines a standard structure for study data tabulations (datasets) that are to be submitted as part of a product application to a regulatory authority such as the Drug Controller General Of India (DCGI) or United States Food and Drug Administration (USFDA)

• SDTM provides the general framework for describing the organization of information collected during human and animal studies and submitted to regulatory authorities.

Overview of CDISC standards

ProtocolCRF Operational database

PRM CDASH ODM

Tabulation datasetsAnalysis datasetsResults

SDTMADaM

Concepts and Terms

• SDTM model is built around the concept of observations, which consist of discrete pieces of Information collected during a study.

• Observations are normally corresponds to a row in a dataset. A collection of observations on a particular topic is considered as domain.

• Each observation can be described by a series of named variable. Variable can be classified according to its role

• A role describe the type of Information conveyed by the variable.• SDTMIG describes the general conceptual model for preparing clinical study

data that is submitted to regulatory authorities.

• SDTM Variable Classification:1. Identifier : These are the variable which identifies the study, subject

involved, domain & sequence number.2. Topic :This specifies the focus of the observations.3. Timing : Variables which describe the timing of an observation.4. Qualifier : Contains additional text, values or results which helps

describes the observation.5. Rule : This explains the calculation involved to derived date i.e.

times or visits.

Classification of SDTM Variables.

• Qualifier is further categorized as :1. Grouping Qualifier : are used to group together a collection of observations

within same domain. Ex. LABCAT2. Result Qualifier :describe the specific results associated with the topic

variable. They answer the question raised by the topic variable. Results qualifiers are ORRES, STRESC & STRESN.

3. Synonym Qualifiers : specify an alternative name for a particular variable in an observation. E.g. MODIFY & DECOD, which are equivalent terms for a TRT or term topic variable.

4. Record Qualifier : define additional attributes of the observation record as a whole for e.g. SAE variables in the AE domain. AGE,SEX in DM domain.

5. Variable Qualifier : are used to further modify or describe a specific variable within an observation. E.g. DOSU which is variable qualifier of Dose.

Classification of qualifier variables

• “Suppose subject 01001 had mild nausea starting on study day 6” is an observation belonging to the Adverse Events domain in clinical trial.• The Topic Variable value is the term of adverse event, “Nausea”• The Identifier Variable is the subject identifier, “01001”• The Timing variable is the study day of the start of the event, which

captures the information, “Starting on study day 6”.• The Record Qualifier is severity, the value for which is “Mild”.

Example

• Observations about study subjects are normally collected for all subjects in a series of domain.• Each domain dataset is distinguished by a unique two character code.• Which is stored in the SDTM variable named DOMAIN, is used in four

ways : as the dataset name, the value of the domain variable in that dataset, as a prefix for most variables in dataset, as a value in RDOMAIN.• Each dataset is described by metadata. The metadata are defined in data

definition document named “define”.

Domain

• The variable name (limited to 8 character).• A descriptive variable label, limited up to 40 characters, which should be

unique for each variable in the dataset.• The data type i.e. character or numeric.• The set of controlled terminology for the value or the presentation format

of the variable.• The origin or source of each variable.• The role of the variable (Identifier, Topic, Timing, Qualifiers).• Comments or other relevant information about the data.

Submission metadata model uses seven distinct metadata attributes.

• Datasets containing observations are classified into 3 classes :• Intervention: captures investigational, therapeutic treatments that are

administered to the subject (with some actual or expected physiological effect) either as specified by the study protocol, coincident with the study assessment period.(e.g. Concomitant medications), or other substances self administer by the subject (e.g. alcohol, tobacco).• Events: Captures planned protocol milestones such as randomization &

study completion & occurrences or incidents independent of planned study evaluations occurring during a clinical trial.(e.g. AE, MH).• Findings: Captures the observations resulting from planned evaluations

to address specific test such as laboratory tests, ECG testing.

Observation Class

Special purpose datasets represents additional important information.• Demographics is the parent domain for all other observations for subjects

& should be identified with the domain code “DM”.• Comments are collected during the conduct of study. These are normally

supplied by Principal Investigator. Collected comments should be submitted in a single comments domain “CO”.• The Subjects Elements Table describes the actual order of the element that

were traversed by the subject with the start date/time & end date/time for each element.• The Subjects Visits Table describes the actual start date/time &end

date/time for each visit of each individual subject.

Special Purpose Domain

• The design of a clinical trial is a plan for what assessment will be done to the subjects & what data will be collected during the trial.• These datasets fall under this model :• Trial Arms (TA)• Trial Element (TE)• Trial Visits (TV) VISITNUM VISIT VISITDY• Trial Inclusion/Exclusion Criteria (TI)• Trial Summary Information (TS)

Trial Design Model.

• Trial Arm has the structure of one record per planned element per arm.• Arms describes sequence of elements in each Epoch for each arm &

thus describes the complete sequence of elements in each arm.

Here the no. of Arms is two.

Trial Arms (TA)

Screen Run-in

Drug A

Drug B

• ARMCD & ARM• ARM Code it’s a required variable & its value must be in accordance

with value of ARMCD in Trial Arm (TA).• ARMCD defines the coded value & the structure of the drug

assignment.• For any patient which is screen failure then ARMCD is assigned as

“SCRNFAIL” & arm value is “Screen Failure”.

Continued…

• Planned Order of Elements with Arm:• This represents planned order of an element in an Arm.• Its value will not be populated for the element that are not planned for the

Arm for which subject was assigned. Thus for any value of ETCD as “UNPLAN”, TAETORD should be blank.• ETCD :is the code value assigned to the variable element.• In any case if value of element entered is entirely different from the

planned element as specified in TE then this value must be replace with text value “UNPLAN”.

TAETORD

• This consist the description of the element, which usually indicates the treatment assigned during an element.• It involves administering a planned intervention, which may be

treatment or no treatment, during a period of time.• Elements for which the planned intervention is “no treatment” would

include elements for screening, wash-out, follow-up.

Trial Element (TE)

• This is the expected variable. This describes the outcome of a branch decision point in the trial design. Branch decision point take place between epochs & is associated with the element. “TABRANCH”

• TRANSITION Rule : this is the expected variable. If the design allow any subject to move to other element than the next one in the sequence, then that condition which allows the subject to do so, populates the value for variable “TATRANS.”

Branch & Transition

• TESTRL (Start): Elements that involves the treatment is usually the start of treatment.• TEENRL (End): This rule describes how any subject moved out or

completed the element it was assigned to. End rule must be describe referring to the Trial Element.• TEDUR (Trial Duration) : must be populated for every record where

TEENRL is populated. Must be presented in ISO 8601 format.• E.g. P2W is equivalent to TEENRL of 2 weeks after start of element.

Rule for start & end of element

• VISITNUM : Numerical sequence provided to the visit according to protocol.• VIST : Textual presentation of the numeric visit.• E.g. VISITNUM is 1 then VISIT is assigned as SCREENING.• VISITDY :Visit planned by study protocol to follow.• E.g. planned study day structure for any study is 1,2,3,4 which

explains treatment period start from the visit 1 and goes till 4.• TVSTRL : Trial Visit Start Rule describing when the visit starts.• TVENRL : Trial Visit End Rule describing the end of the visit.

Trial Visit

• As per CDISC guidelines all timing variable must be presented in ISO 8601 format.• ISO 8601 Format: as per FDA guidelines all the dates must follow the

following format.• YYYY-MM-DDThh:mm:ss

Date & Time Variable

• A relationship between group of records for a given subjects within the same datasets.• A relationship between independent records for a subjects, such as a

concomitant medications taken to treatment an adverse event.• A dependent relationship between a comment in the comments domain &

parent record in other datasets. Such as a comment recorded in association with an adverse event.• Related subjects datasets some studies include subjects who are related

to each other & in some cases it is important to record those relationships.

Related Record Datasets (RELREC)

Conclusions.

• Since becoming the recommended standard for the submission of clinical trial data to DCGI, FDA in marketing applications, the SDTM has begun to be used by sponsors for their upstream processing to support clinical study report.• As data is increasingly delivered to the FDA in CDISC standards, the

FDA will be able to more efficiently review individual submissions.

Thank You

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