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THEME DATA SHARING

WP CDISC

Presented by Mary Thiongo & Harry van Loen

4th ADMIT workshop, ITM Belgium, 30 NOV-4 DEC 2015

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Contents

CDISC

Introduction

SDTM

1. Introduction

2. Background

3. Objective

4. SDTM concept

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Mary Thiongo & Harry van Loen, 4th ADMIT Workshop, ITM BELGIUM, 30 NOV-4DEC2015

Contents

CDASH

1. Introduction

2. Value of CDASH

3. CDASH Model

4. CDASH Metadata Tables

5. CDASH Implementation

ADMIT – CDISC - considerations

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CDISC

CDISC

Clinical Data Interchange Standards Consortium

Introductional VIDEO

WEBSITE: http://www.cdisc.org

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SDTM

o 1. Introduction

Study Data Tabulation Model :

organize data

facilitate overall data management

improve regulatory review and approval

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SDTM

o 2. Background

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1999 2001 2002 2004

Submission Data Model (SDM) focus only ‘safety’

Submission Data Domain Model

Study Data submission Model v1.0 (SDTM) focus ‘all study’

2015

SDTM v 1.4

FDA recognizes SDTM

SDTM

o 3. Objective

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Data submission

SDTM datasets and

SDTM metadata

SDTM

o 4. SDTM Concept

4.1 SDTM documentation:

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Study Tabulation Data Model

SDTM Implementation Guide

Controlled Terminology Case Report Tabulation Data Definition Specification (‘define.’)

SDTM dataset(s)

SDTM metadata

SDTM

4.2 SDTM basics :

Observation(s) correspond to row in dataset

(~record (s))

Variable(s) correspond to column in dataset

Domain/Class group of related observations

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SDTM 10


Example data set (AE):

Domain: Adverse Events (AE) Variables Observation (s)

SDTM

4.2.1 Domains & Classes listed in SDTM IG doc v3.1.2 p. 13

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INTER VENTIONS CLASS

EVENTS CLASS

Adverse Events AE

Medical History MH

Demo graphics DM

FINDINGS CLASS

Vital Signs VS

Phyical Exam PE

Con Med CM

Substance use SU

GENERAL OBSERVATION CLASSES

SPECIAL PURPOSE DOMAINS

Trial Summary TS

TRIAL DESIGN DOMAINS

Trial Visits TV

Related Records RELREC

RELATIONSHIP

Supplemental qualifierss SUPP- -

Subject Visits SV

. . . . . . . . . . . . . . .

two character domain code: AE, DM…

SDTM


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EVENTS CLASS

Adverse Events AE

Medical History MH

Demo graphics DM

FINDINGS CLASS

Vital Signs VS

Phyical Exam PE

Con Med CM

Substance use SU



Trial Summary TS


Trial Visits TV


RELATIONSHIP


Subject Visits SV

. . . . . . . . . . . . . . .


SDTM


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Con Med CM

Substance use SU

. . .


1. Concomitant medication- CM Medication prior and during study

2. Substance Use- SU Substances (alcohol, coffee, smoking..) affecting efficay & safety

3. Exposure-EX Study treatment (e.g. placebo, investigational products

Following domains/SDTM datasets:

SDTM


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EVENTS CLASS

Adverse Events AE

Medical History MH

Demo graphics DM

FINDINGS CLASS

Vital Signs VS

Phyical Exam PE

Con Med CM

Substance use SU



Trial Summary TS


Trial Visits TV


RELATIONSHIP


Subject Visits SV

. . . . . . . . . . . . . . .


SDTM


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1. Adverse Events-AE Medical events during study

2. Disposition-DS Protocol milestones (e.g. randomization), completion status & reason discontinuation

3. Medical History-MH Medical events before and at start of study

EVENTS CLASS

Adverse Events AE

Medical History MH

. . .

4. Protocol Deviations-DV Protocol deviations during study 5. Clinical Events-CE Medical events during study but not classified as AEs e.g. efficacy endpoints like malaria in study on malaria


SDTM


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EVENTS CLASS

Adverse Events AE

Medical History MH

Demo graphics DM

FINDINGS CLASS

Vital Signs VS

Phyical Exam PE

Con Med CM

Substance use SU



Trial Summary TS


Trial Visits TV


RELATIONSHIP


Subject Visits SV

. . . . . . . . . . . . . . .


SDTM


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1. ECG –EG ECG data

2. Inclusion/Exclusion criteria not met-IE criteria that are not met

3. LAB Test Results –LB Lab data of study (incl. central LAB or CRO) 4. Physical Examination-PE General physical exam data 5. Questionnaire-QS Questionnaire, diary data

FINDINGS CLASS

Vital Signs VS

Phyical Exam PE

. . .


SDTM


(cont.)

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6. Subject Characteristics–SC Subject related data not in other domains e.g. subject intials, eye colour,…

7. Vital Signs -VS Blood Pressure, Height, Weight, Pulse,Body temperature & BMI

8. Drugs Accountability –DA Accountability of study drug : receipt, dispensing, return, packaging 9. Microbiology Specimen-MB Microorganisms species data, growth status 10. Microbiology Susceptability-MS Any findings not in MB

FINDINGS CLASS

Vital Signs VS

Phyical Exam PE

. . .


SDTM


(cont.)

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11. Pharmacokinetic Concentrations –PK Tissue related data (serum, blood) – concentrations of study drug & metabolites

12. Pharmacokinetic Parameters –PP Parameters on the time~concentration curve

FINDINGS CLASS

Vital Signs VS

Phyical Exam PE

. . .


13. Findings About –FA Findings related to Events and Interventions not represented within Events/Interventions (e.g. AE severity changing over time)

SDTM


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EVENTS CLASS

Adverse Events AE

Medical History MH

Demo graphics DM

FINDINGS CLASS

Vital Signs VS

Phyical Exam PE

Con Med CM

Substance use SU



Trial Summary TS


Trial Visits TV


RELATIONSHIP


Subject Visits SV

. . . . . . . . . . . . . . .


SDTM


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1. Demographics–DM Date of birth, Age, Race, Ethnic origin & values of ARM (study treatment)

2. Comments - CO To collect free text from other domains or CRF comments pages

3. Subject Elements–SE actual timing of the study elements (screening, randomization, Follow up...)

4. Subject Visits -SV actual timing of the subject visits


Demo graphics DM


Subject Visits SV

. . .

SDTM


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EVENTS CLASS

Adverse Events AE

Medical History MH

Demo graphics DM

FINDINGS CLASS

Vital Signs VS

Phyical Exam PE

Con Med CM

Substance use SU



Trial Summary TS


Trial Visits TV


RELATIONSHIP


Subject Visits SV

. . . . . . . . . . . . . . .


SDTM


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1. Trial Arms–TA Planning of the Arms (study treatment)

2. Trial Elements - TE Planning of the study elements (screening, randomization, Follow up...)

3. Trial Visits–TV The planned visits

4. Trial Inclusion/Exclusion criteria - TI in/exclusion criteria for the trial


Trial Summary TS


Trial Visits TV

. . . 5. Trial Summary- TS basic trial information, such as trial phase, protocol title, design objectives

SDTM


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EVENTS CLASS

Adverse Events AE

Medical History MH

Demo graphics DM

FINDINGS CLASS

Vital Signs VS

Phyical Exam PE

Con Med CM

Substance use SU



Trial Summary TS


Trial Visits TV


RELATIONSHIP


Subject Visits SV

. . . . . . . . . . . . . . .


SDTM


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1. Relating datasets -RELREC To identify relationships between datasets

2. Supplemental Qualifiers – SUPP- - To collect all non-standard data from the other domains



RELATIONSHIP


SDTM 26


4.2.2 Variable(s) listed in SDTM doc v1.4 p. 9-35

SDTM

Variable(s) classification by role

3 core categories: presence can be null

Required: always no

Expected: always yes

Permissible: if collected yes

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Identifier

- Study STUDYID - Subject USUBJID - Domain DOMAIN - Sequence - -SEQ

Topic Qualifier Timing

To identify the observation

To specify the focus

e.g. - Labtest name LBTEST - AE term AETERM - Drug name CMTRT

To specify results or traits

To specify the timing

e.g. - Lab result LBORRES - AE severity AESEV - Drug dose CMDOSE

eg. - Lab date LBDTC - AE start AESTDTC - Drug end CMENDTC

(Note: 5th variable ‘Rule’ referring to algoritm/method to define start/end and looping conditions, only in Trial Design model)

SDTM 28


Example data set (AE):

Identifiers Topic Qualifier

Variable

SDTM 29


Variable(s) list metadata per domain

(1)Variable (2)Label (3)Type (4)Controlled terms/

(5)Origin (6)Role (7)Comment

STUDYID Study identifier

Char CRF page Identifier

DOMAIN Domain Abbreviation (=AE)

Char AE CRF page

Identifier

USUBJID Unique Subject number

Char

CRF page

Identifier

AESEQ Sequence number

Num Derived Identifier

AETERM Reported term f or AE

Char CRF page

Topic

AEDECOD Char

Derived Qualifier

(1) Field name (code)

(2) Description variable

(3)Variable type

(4)Controlled teminology if applicable

(5)Origin

(6)Which role in dataset: identifier, topic, timing , qualifier

(7)Comment: other relevant information

SDTM

4. 2.3 Some rules/assumptions

o Submit only domain datasets that were collected

o Per domain:

select required Identifier variables (e.g. STUDYID, DOMAIN, USUBJID, --SEQ)

include Topic variable

include relevant Qualifier variables

include applicable Timing variable

set order of variables as in SDTM (tables 2.2.1;2.2.2;2.2 3)

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SDTM

4. 2.3 Some rules/assumptions (cont.)

o Apply two character domain code to the variables

e.g. - -TRT AETRT; MHTRT

o Variable : max 8 characters/Label: max 40 characters

o Any non-standard (SDTM) variables SUPPQUAL domain/dataset

o Text in uppercase ; exceptions e.g. MedDRA, units mg/dL…)

o Date/Time = ISO 8601 YYYY-MM-DDThh:mm:ss

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Clinical Data Acquisition Standards Harmonization (CDASH)

1.0 Introduction

Its a standard way to organize and format the way you collect clinical trial data.

Follow CDISC defined standards development process

Developed to support the streamlining of processes within medical research.

CDASH Standard Version 1.1 Specifications includes 16 domains that are all therapeutic area

agnostic

Download guide at: http://www.cdisc.org/cdash

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http://www.cdisc.org/cdash

CDASH Published Documents 33


Why CDASH? 34

2.0 Value of CDASH

Streamline the processes You don’t reinvent the wheel every time when

conducting a study:

reuse of CRFs,

edit checks and output programs

Reuse of analysis programs i.e. do files.

Makes data more usable Data aggregation

Data mining

Data usability


Why CDASH? 35

Sharing research data with your research partners made easy

Integrates the SDTM requirements into CRF

Provides traceability and more transparency to regulators and other s who review your data.


Data collection without standard 36


Data Without Standards…

ID GENDER

A1 Male

A2 Male

A3 Female

A4 Female

A5 Male

SUBJID SEX

0001 M

0002 F

0003 F

0004 M

0005 F

USUBID SEX

00011 0

00012 1

00013 1

00014 0

00015 1

PTID GENDER

0001 1

0002 1

0003 2

0004 2

0005 1

Study #2 – dmg.xpt

Study #3 – axd222.xpt

Study #4 – dmgph.xpt

Name for Subject ID is never the

same

Name for demography

dataset is variable???

Is Sex Male or Female,

M or F, 1 or 2?

Gender or Sex, what will

this study use?

Adapted from slide courtesy of Armando Oliva, M.D. and Amy Malla, FDA

Study #1 – demo.xpt

Data collection with CDASH (paper) 38

The standard CDASH CRF allow efficient data acquisition and efficient data monitoring at the sites.

Leads to more standard SDTM datasets.


CDASH MODEL 39

Harmonize process across

standards

Ensure Harmonization

with other CDISC

standards

Uses an SDTM concept (domain

variable) if available and

possible.

When data collected can

be placed directly into

SDTM variable

Standardize a CDASH variable if there is not an

appropriate SDTM one

CDASH variable is

used that can map to SDTM

Use existing controlled

terminology

That supports:

• Data analysis • Traceability • Data mining

3.0 CDASH Model


CDASH Metadata Tables 40

CDASH standards are presented in metadata tables

• Common header fields • Domains in alphabetical order • Eight columns in each table

4.0 . CDASH Metadata Tables


CDASH Metadata Tables

Full question text for the

data collection

field.

Short prompt for

the data collection

field; could be used as

the CRF label

SDTMIG Variable

Name OR

CDASH Variable

Name

BRIDG

Mapping Defines the purpose of

Data collection

field

Contains standard

Instructions for the

clinical site on how to enter data

on the CRF. Includes

controlled terminolog

y

Information/rationale and

instructions on how to

implement the CRF data

collection fields

Contains CDASH core designations

for basic data

collection fields


Conformance to CDASH 42

Following :

Highly recommended(H

R),

Recommended/conditional (R/C)

Optional(O)

Using Question

test or prompt

Using controlled

terminology

Following Best practice

recommendation in the

specifications

Conformance to CDASH

4.1 . Conformance


CDASH Domain Names 43

4.2 . Domain Names


Implementing CDASH in your data stream

44

Creating new variables

Creating new domain

Custom domains are created when they are no published domains

The rules for creating custom domains are described in STDMIG

5.0. CDASH Implementation


CDASH Variables 45

Created to address the specific needs of data collection and management.

Not possible to collect data using same data type, controlled

terminology or format with SDTM.

More than one variable is needed for data collection & data management, but will be mapped into a single SDTM variable.

When a single variable can map into more than one place in SDTM.


CDASH Variables 46

Process of creating new variables

Confirm

• Might be in SDTM and not in CDASH

Use existing names

• Look in SDTM • Domains • QNAMs • TESTCD

Create your Own

• Use CDASH/SDTM variable naming fragments to create with similar conventions


Activity-Creating variables 47

Using the SDTMIG V 3.1 or higher & CDASH standard guides ver. 1.1, to complete the activity


Process of creating custom domain 48

• From SDTM Confirm there is no published

domain

• Must be based on a single class:

• Finding, Intervention, Events Determine the one General

Observation class for your custom domain

• Reserved domain codes • Use X,Y.Z 1. X=Interventions 2. Y=Events 3. Z=Finds

Create an appropriate 2 character domain code

• Adding the domain code to the generic variable

from: class, common identifier and timing fields Create the variables

Create a standard mapping into SDTM from your output


Activity- Creating custom domains 49

Using the SDTMIG V 3.1 or higher as a reference, to complete the activity


CDISC

ADMIT – CDISC - considerations

oADMIT will focus on CDASH

oExamining possible cooperation with CDISC

oExploring the 2 Days CDISC course “End-to-End” (which has academics as target audience)

Online courses are available

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References 51

Http://www.cdisc.org/standard/cdash/downloads/CDASH_STD_1_1_2011_01_18.pdf

CDISC SDTM Implementation Guide version 3.2, November 2013


http://www.cdisc.org/standard/cdash/downloads/CDASH_STD_1_1_2011_01_18.pdf

http://www.cdisc.org/standard/cdash/downloads/CDASH_STD_1_1_2011_01_18.pdf