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CDISC SDTM Transformation mit Hilfe von

SAS®

Thomas Wandt

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Introduction Communication in Clinical Trials

SDTM transformation with SAS®

Conclusion and Outlook

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Introduction Communication in Clinical Trials

SDTM transformation with SAS®

Conclusion and Outlook

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Introduction

Communication in clinical trials

Clinical data

management: A

big amount of

data has to be

transferred in

clinical study

environment

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Introduction

Communication in clinical trials

Current state: Transfer of

clinical data in a clinical

study

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CDISC

Overview

• Clinical Data Interchange Standards Consortium

• Founded in 1997 in Austin Texas

• global, multidisciplinary, non profit organization

• About 200 corpoarte sponsors

• Main goal: describing a standard for data

communication and data management in clinical

trials

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Introduction

StandardsBSI British Standard : “standards

help to make life simpler and to

increase the reliability and the

effectiveness […]”

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Introduction

Standards

• Some advantages of standards:

– Increased speed and accuracy

– Faster trial set-up

– Fewer errors in completion, editing & coding

– Reduced rework and training

– Increased efficiency and cost effectiveness

– Standard SAS ® programs

• Disadvantages:

– Implementation

– Touching a running system

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CDISC models

Overview

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CDISC models

SDTMDemographics DM

Comments CO

Concomitant Medications CM

Exposure EX

Substance Use SU

Adverse Events AE

Disposition DS

Medical History MH

Protocol Deviations DV

Drug Accountability DA

ECG Tests EGInclusion/ Exclusion

Exceptions IE

Laboratory Tests LB

Microbiology Specimens MB

Questionnaires QS

Microbiology Susceptibility MS

Physical Examinations PEPharmacokinetics

Concentrations PC

Subject Characteristics SC

Pharmacokinetics Parameters PP

Vital Signs VS

Trial Elements TE

Trial Arms TA

Trial Visits TV

Subject Elements SE

SDTM

- Study Data Tabulation Model

- model is accredited to be submitted to

FDA

- model for representing clinical study data

- 25 domains

- interventions, events and findings

- bases on the Implementation Guide (see

next pages)

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CDISC models

SDTM –Implementation Guide

SDTM - IG:

- rules

- assumptions

- implementation examples

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Introduction

SDTM transformation with SAS®

Conclusion and Outlook

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SDTM transformation with SAS®

Overview

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SDTM transformation with SAS®

Overview

Transformation Tool vs SAS

- SAS ist in vielen Betrieben schon vorhanden und muss nicht

angeschafft werden.

- ‘A transformation software safes only 15% of time, most of

manpower is needed to match the variables and domains’

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SDTM transformation with SAS®

OverviewThe transformation process must arrange the following points to create SDTM format:

• Renaming of variable names

• Reorganization of variables to SDTM domains

• Dataset names must be matched to SDTM standards

• The descriptive labels of SAS® datasets must be modified to conform to SDTM

standards

• Each variable within a SAS® dataset has a unique name

• Each variable has an associated label that describes the variable in more detail

• A variable’s type are SDTM Conform (numeric or character)

• A character variable can vary in length from 1 to 200 characters

• Combine values of multiple source variables into one destination variable

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SDTM transformation with SAS®

Overview

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SDTM transformation with SAS®

Domain Dictionary

• Contains

information about

used domains

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SDTM transformation with SAS®

Overview

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SDTM transformation with SAS®

attrib_XX macros

• Creates empty

datasets

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SDTM transformation with SAS®

Filling empty datasets

• Filling step

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SDTM transformation with SAS®

Overview

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SDTM transformation with SAS®

Validation process - Proc CDISC

Proc CDISC validation: ‘content checking against the domain

provided by the SDTM’

• supports 15 of 23 standard domains

• verifies that all required variables are present in the data set

• reports all variables in the data set that are not defined in the

domain

• reports warnings for all expected domain variables that are not

in the data set

• notes all permitted domain variables that are not in the data set

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SDTM transformation with SAS®

Validation process - Proc CDISC

The result of Proc CDISC:’

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SDTM transformation with SAS®

Proc CDISC: error examples

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SDTM transformation with SAS®

Overview

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SDTM transformation with SAS®

Validation process

plausibility checks

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Introduction

CDISC models

SDTM transformation with SAS®

Conclusion and Outlook

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Conclusion and outlook

Conclusion

• SAS kann ein Transformationstool einfach ersetzen

• Die Transformationsarbeit ist nicht zu umgehen

• “Currently, the FDA does not make a clear statement regarding an introduction of the

CDISC standards”

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Conclusion and outlook

Literature

• http://www.lexjansen.com

• I:\bio\projects\LR-QB_Biostatistics\CDISC

• Thomas Wandt (2008): A CDISC Strategy for Roche Diagnostics Biostatistics -

Alternatives and Implementation Case Study

• Müller, Nadja (2007): Diploma Thesis – Reorganization and Conversation of Clinical

Trial Data of diverse Formats into CDISC compliant Data Repository Reorganization

and Conversion of Clinical Trial Data of diverse Formats into a CDISC compliant Data

Repository

• Questions ?

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Dankeschön!