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University Day
UD01:
Improving medical research and related healthcare through
standardization
Jennie Mc Guirk October 2013
Drug Development Life Cycle
Inception
1 IN 5 REACH
MARKET
$800-900
MILLION
8-12 YEARS
Market
Drug Development and Clinical Trials
‒the
is drug
the
effect
valuable?
Phase
Phase
IVPreclinical
II‒ ‒Phase
post
does
the
drug
follow-up,
have
a for
therapeutic
any
long term
effect?
effects?
the
drug
have
merit?
Phase
I marketing
‒ III
Is- Does
safe
humans?
Clinical Trials Process Flow
Presentation &
Publication of results
Protocol Approval
Data filed & Registration
Obtained / Rejected
Investigator Selection
Statistical analysis
Patient Recruitment &
Participation
Data Collected
& Reviewed
Why do we need to standardize?
1 IN 5 REACH
MARKET
TIME
8-12 YEARS
$800-900
MILLION
T
S
O
C
How are we standardizing??
CDISC Est 1997
To improve and standardize the way clinical data is
acquired and exchanged
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 healthcare.
CDISC Goals and Strategies
Goals
Strategies
CDISC Models and Initiatives
Provide End to End standardisation
The Protocol
•  Protocol -> Key document; Plan or blueprint of the trial;
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Describes trial objectives, methods, analysis, organization
PRG : Standardize the structure & content of the protocol
Reduced protocol development and review time
Increased end-user awareness / understanding
Can consume the information electronically & leverage the
information downstream
The Database
•  Database -> subject (case) database, referred to as the
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Electronic Case Report Form (eCRF)
CDASH – standardizes data collection
Reduced database development and review time
Increased efficiencies for data cleaning
Increased end-user familiarity / reduce system training time
Data extracted from the database is a consistent structure
The Analysis
•  Analysis -> Data Extracted from the Database (SAS datasets)
•  SDTM – data model used to represent the data collected (also
referred to as the Case Report Tabulation (CRT))
•  ADAM – data model used to represent the analysis data
•  Increased efficiencies for data analysis
•  Increased end-user familiarity / reduces review time
•  Enable standard programming
The Interchange
•  Interchange -> Provision of data to the regulatory body
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(e.g. FDA) for review/approval
CRT-DDS - standard way of packaging the data; standard
way of defining the data (SDTM + ADAM)
One mechanism of data exchange
Increased end-user familiarity
Increased efficiencies for data and analysis review
CDISC Models and Initiatives
eCRF page example
STUDNUM
STUDY
PROT
STUDYID
CDISC SDTM
Study Data Tabulation Model
SDTM
Implementation
Guide
SDTM Framework
1. Where should the
data go?
2. What type of
information should
it contain?
3. What is the minimum
information needed?
SDTM Framework:
General Observation Class
Interventions
1. Where should the
data go?
Events
Findings
Treatments that are
administered to the
subject
Planned protocol
milestones,
occurrences,
conditions, or incidents
Observations resulting
from planned
evaluations
Conmeds (CM)
Adverse Events (AE)
Vital Signs (VS)
Exposure (EX)
Disposition Events
(DS)
Physical Exam (PE)
SDTM Fundamentals:
Other Data Classes
Special
Purpose
Demographics
(DM)
Relationship
Related Records
(RELREC)
Comments (CO)
Subject Visits
Subject Elements
(SV and SE)
Trial Design
Trial Summary
(TS)
Trial Inclusion (TI)
Supplemental
Qualifiers
(SUPP--)
Trial Visits (TV)
SDTM Framework:
Variable Roles
Identifier
Variables
used to
identify the
record
Study Identifier
(STUDYID)
Subject Identifier
(USUBJID)
Sequence
Identifier (--SEQ)
Topic
Timing
Qualifier
Specifies the
focus oftype
the
What
observation
2.
of
information should
it contain?
Timing of the
observation
Values that
describe the
results or
traits of the
observation
Lab Test Name
(LBTEST)
Lab Assessment
Date (LBDTC)
Lab Test Result
(LBORRES)
Adverse Event
Term (AETERM)
Adverse Event
Start Date
(AESTDTC)
Adverse Event
Severity
(AESEV)
Reported Drug
Name (CMTRT)
Exposure End
date (EXENDTC)
Conmed Dose
(CMDOSE)
SDTM Framework:
Core Variables
3. What is the minimum
Required information
Expected
needed?
Permissible
Basic to the
identification of
a data record
Establish the
observation
context
Present if
collected or
derived
Must always be
present
Must always be
present
Must be present
if collected
Cannot be null
Can be null
Can be null or
excluded
SDTM Framework:
Variable
Core
DataFor
Class
a more detailedRole
introduction to SDTM
visit
Variables
the PhUSE website…
General Observation
Identifier
http://www.phusewiki.org/docs/2012/PAPERS/
Required
IS/IS04.pdf
Special Purpose
Topic
Expected
and/or
attend
my
presentation
today
at
4
p.m !!
Relationship
Timing
Trail Design
Qualifier
Permissible
SDTM Implementation
Creating a Mapping
1.
Determine
the Data
Class
2. Identify
Required
Variables
3. Identify
Expected
Variables
4. Identify
applicable
Permissible
Variables
5. Identify
Relationship
Variables
SDTM Implementation
Example
LBCAT
Findings
LBORRES
LBORRESU
STUDYID
DOMAIN = ‘LB
USUBJID
LBSEQ
= Derived
LBSPID
LBNAM
Derived
LBADD in SUPPLB
LBTESTCD
LBTEST
LBCLSIG = N in SUPPLB
LBCLSIG = Y in SUPPLB
4.2.
Identify
Permissible
Variables
1.
Identify
Determine
Required
Data
Class
Identify
ExpectedVariables
Variables
5.3.Identify
Relationship
Variables
VISITNUM
LBDTC
CDISC – Summary
Benefits
Challenges
•  improve data exchange
•  lack of understanding of the
standards
•  improve study efficiency
•  improve data quality
•  reduce burden of
regulatory submissions
•  cost of implementing
•  existing standard do not
cover all types of data
•  improve speed of
approval
•  lack of FDA or other
regulatory authority
regulation
•  reduce cost of data
transfer
•  standards change/evolve
over time
•  reduce ost of the drug
development life cycle
•  concern about the longevity
of the CDISC standards
Some References and Terminology Index
http://www.cdisc.org/
http://www.phuse.eu/
http://www.cdisc.org/stuff/contentmgr/files/0/
fdf8540f5324c81f48d3630923b95fd6/misc/
cdisc_journal_friggle_etal_p2.pdf
Questions