4_SDTM_AMuraro

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CDISC Italian User Group

16 November 2007

Study Data Tabulation

Model (SDTM)

Annamaria MuraroHelsinn Healthcare


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Regulatory Background

1999, FDA guidance Providing Regulatory Submissions inElectronic Format NDAs

SAS XPT datasets

Pdf documents: Define.pdf (data contents), blankcrf.pdf (annotatedCRF)

Case Report Tabulation (Patient Profiles) as pdf files

October 2005: FDA issued the guidance Providing Regulatorysubmissions in Electronic format using the eCTD specifications

September 2006: FDA published in the Federal Register a notice ofwithdrawal of electronic submission guidance for eNDA.

This notice designates eCTD as preferred format for electronic

submissions and notes that beginning Jan 2008 any electronicsubmission must be eCTD


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What is SDTM: Study Data Tabulation Model

This model describes the

contents and structure ofdata collected during a

clinical trial

The purpose is to provide

regulatory authority

reviewers (FDA) a clear

description of the

structure, attributes and

contents of each dataset

and variables submitted

as part of a product

application

Animals

(SEND) Humans

(SDTMIG)

SDTM


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Implementation Guidelineshttp://www.cdisc.org/standards/index.html

http://www.fda.gov/oc/datacouncil/cdisc.html

Study Data Tabulation Model, Version 1.1, April 2005

which represents the underlying conceptual model behind thesubmission data standards (SDS)

SDTM Implementation Guide, Version 3.1.1, August 2005

Detailed Domains description, Variables to be included and theirattributes, assumptions and convention, Examples

Version 3.1.2 posted for Comment July 2007

New standard domains

More details

Controlled Terminology: SDTM Package-1 & Lab Test ControlledTerminology, Package 2A, Package 2B (under development)

SEND, Standard for Exchange of Nonclinical Data ImplementationGuide for Animal Toxicology Studies, Version 2.3
http://www.cdisc.org/standards/index.htmlhttp://www.fda.gov/oc/datacouncil/cdisc.htmlhttp://www.fda.gov/oc/datacouncil/cdisc.htmlhttp://www.cdisc.org/standards/index.html


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Fundamentals of SDTM - Domains

Domain:collection of observations with common topic

Case Report Form SDTM domain Dataset A domain may collect data from more than one CRF form

Generally each domain is represented by a dataset

Each domain has a unique two-character domain name (e.g., AE,CM, VS)

Variables in domain begin with the domain prefix: (e.g., VSTESTCD)

Domain structure: vertical

Two categories of domains: CDISC StandardDomains (spelled out in detail in the Implementation Guide).

Custom Domains Based on one of the General Observation Classes (findings, events, interventions)

Basic variables are outlined in the SDTM

Rules for creating these are in Section 2 of the IG


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SDTM Basics - Structures Based Upon General Observation Classes

Interventions:

Investigational treatments, therapeutic treatments, and proceduresadministered to or taken by the subject

One record per constant dosing/treatment interval

Examples: study medications(EX), concomitant medications(CM)

Events: Occurrences or incidents independent of planned study evaluations

occurring during the trial or prior to the trial One record per event

Examples: medical history(MH), adverse events(AE)

Findings: Observations resulting from planned evaluations

One record per finding result or measurement

Examples: lab data(LB), vital signs(VS)

Each of these has defined structures and variables.

No new variables may be added to these domain classes


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SDTM Basics - Special-Purpose Datasets

Not Classified as Interventions, Events, or Findings They Have Special Rules

Demographics (DM)

Comments (CO): free text comments

Trial domains: to describe the design of a trial

RELREC dataset: represent the relationship between

datasets and records

SUPPQUAL: used for data items not included in the

SDTM standard


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SDTM Standard Domains

Interventions Events

ConMeds(CM)

Exposure(EX)

Adverse

Event (AE)

MedHist(MH)

Disposition(DS)

Findings

ECG(EG)

PhysExam(PE)

Labs(LB)

Vitals(VS)

Demog(DM)

Other

SubjChar(SC)

Subst Use(SU)

Incl/Excl(IE)

Relationships

Supp Qualifiers

Trial DesignQuestionnaire

(QS)

Comments(CO)

Deviations

(DV)

Concentrations (PC)

Drug

Accountability (DA)Microbiology (MB)

PK Param (PP)Draft 3.1.2


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Fundamentals of SDTM - Variables

CDISC categorizes variables as being Required: variables need to be in the domains

Their values cannot be null

Expected: variables need to be in the domain Some values may be null

Permissible: variables included in the domain as needed

CDISC categorizes variables into five roles Identifier: identify the study, subject of the observation, the sequence

number

Topic: specify the focus of the observation (such as the name of the labtest)

Timing: describe the timing of the observation (Visit, Start/End date,Days, Time Points, Duration)

Qualifier: additional text or numeric values Rule: express an algorithm or method to define start, end or looping

conditions in the Trial Design model


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Fundamentals of SDTM - Variables

Variables Attributes Variable Name: limited to 8-chars Variable Label:


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Example: DMRequiredExpected

Permissible

CRF

Derived

Sponsor Defined

External Lab Details

YYYY-MM-DDThh:mm:ss


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Example: DM

Reference standards


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Example: DS


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Example: DS

DISPOSITION EVENT

PROTOCOL MILESTONE

OTHER EVENT


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Example (LB)

ORIGINAL

RESULT


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Example (LB)

STANDARD

FORMAT,

char

STANDARD

FORMAT,

num


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Trial Design Model

Defines a standard structure for representing theplanned sequence of events and the treatment plan forthe trial

Trial Arms (Planned): described each planned arm in the trial(ordered sequence of elements)

Trial Element (Planned): describes the Element, rules forstarting/ending the element

Trial Visits (Planned): describes the planned order and numberof visits in the study

IGSection

7.1,

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7


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Trial Design Model

Subject Element (Actual), Subject Visit (Actual)

Subject Element and Subject Visit are special-purpose domain in the draft version SDTMIG 3.1.2

Trial Inclusion/Exclusion Criteria: lookup table: one record foreach inclusion/exclusion criterion. Not subject oriented

Trial Summary: descriptive attributes of trial like trialphase, title, objective etc.)


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SDTM adoption by FDA

July 2004: FDA has endorsed the CDISC standards

October 2005, FDA Guidance for ICH eCommon TechnicalDocument (eCTD) is updated. CDISC SDTM format isrecommended for clinical data and define.xml for metadata


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Dec 2006: Proposed Rule

The Food and Drug Administration isproposing to amend the regulations

governing the format in which clinical

study data and bioequivalence data arerequired to be submitted for new drug

applications (NDAs), biological license

applications (BLAs), and abbreviated

new drug applications (ANDAs). The

proposal would revise our regulations

to require that data submitted for

NDAs, BLAs, and ANDAs, and their

supplements and amendments be

provided in an electronic format that

FDA can process, review, and archive.

The proposal would also require the

use of standardized data structure,

terminology, and code sets contained in

current FDA guidance (the Study DataTabulation Model (SDTM) developed

by the Clinical Data Interchange

Standards Consortium) to allow for

more efficient and comprehensive data

review.

Federal Register / Volume 71, No. 237 /Monday, December 11, 2006Federal Register / Volume 71, No. 237 /Monday, December 11, 2006

The CDISC SDTM is still the preferreddata specification. It will become requireddata specification when the proposed ruleis approved.


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Data standards allow FDA to develop standard tools for

review (http://www.fda.gov/oc/datacouncil/meetings/oliva.pdf)

WebSDM was developed under aCooperative Research andDevelopment Agreement(CRADA) between the FDA andLincoln Technologies with the goal

of providing a user-friendlyenvironment for browsing andreviewing CDISC SDTM-compliantclinical trial. This product has beenin use at FDA since 2004

For each domain, users canaccess variable summarystatistics, data visualization toolsand drill down into individualsubject data and patient profiles

FDA standard tools
http://www.fda.gov/oc/datacouncil/meetings/oliva.pdfhttp://www.fda.gov/oc/datacouncil/meetings/oliva.pdf


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SDTM Adoption by FDA

SDTM / ADaM Pilot projects

Pilot regulatory submission using SDTM/ADaM (datafrom Eli Lilly company, lesson learned reported atCDISC Interchange 2006)

Integrated Safety Data Pilot is now underway

CDISC SDTM Training at FDA: In preparation for theincreasing volume in SDTM submissions to the FDA, CDISC and FDA have joinedforces to ensure that FDA personnel, "speak SDTM". On 9 July 2007, there was an

open SDTM training session for FDA at the White Oak facility in

Maryland. Approximately 50 people from FDA attended with representation from

statistical, data management, and programming functions (CDISC newsletter,

Sept 2007)


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Implementation: Helsinn experience

Integrated approach to standardization

Team work and training Standards applied to any study, any phase

Mapping to CDISC standards CDISC general assumptions 100% implemented Include any Required and Expected variables Select Permissible variables based on data we collect Some deviations from CDISC acceptable

Need to develop code lists (lab parameter, pk parameters, etc.), CDISCcontrolled terminology not available yet

Clear understanding of the SDTMIG Are custom domains needed? Where to collect cancer history? How to map data

collected by patient diary?

Coding for CM, intermediate levels were needed

Trial domains mapping for cycle based studies, cross-over studies

Interacting with CROs and providers Provide CROs with a clear understanding of CDISC mapping requirements Central Lab, Bioanalytical provider: collect data CDISC compliant from the

source

Integration of old clinical data


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Should SDTM be used for submissionsto the FDA only?

Benefit of SDTM as internal data standard No need to convert data for esubmission Reduce data transformation efforts

Efficient (and coste effective) data exchange with providers (CROs,Central Lab, etc)

Easier interaction with CROs, in-licensing made easier, mergers lesspainful

To maximize clarity in the process using a data standard whicheveryone will be familiar: integrated process from CRF to submission

To use standards with large consensus with pharma industry instead ofmaintain its own standard

But dont expect it to be easy!

Expect resistance e.g. whats wrong with my existing standard, wedont have time/resource

Have answers ready for questions such as How long will it take?How much resource will be required? What is the cost of doing this?What are the cost savings if we implement this?


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Questions


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Representing Relationship Back-up

Relating groups of records in a domain for a subject Use the variable --GRPID

Relating non-standard variables to a parent domain SDTM does not allow the addition of new variables

Use the SUPPQUAL dataset to capture non-standard variablesand their association to parent records

Suppl variables may be collected by separete SUPP dataset(convention SUPP where is the two letter domain code(example SUPPLB, SUPPDM)

Relating independent records in separate domains Use the RELREC dataset

Relating datasets Use the RELREC dataset

IG,par.8.1,

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Creating a New Domain: BACK-UP slide

Ensure that there is a definite need to create a new

domain

Assign a 2-letter domain code

Choose the general observation class (Interventions,Events or Findings) that best fits the data

Add variables as follow: Identifier Variables (studyid,domain, usubjid, --seq), Timing Variables, Topic &Qualifiers

No new sponsor-defined variables can be added toSDTM

Follow SDTM conventions for variable order,name/label/type

IG,par.2.6

,

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