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Metadata�and�ADaM

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www.cytel.com ©2012�Cytel Statistical�Software�&�Services�Pvt.�Ltd.�

Disclaimer

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Any views or opinions presented in this presentation are solely those of the author and do not necessarily represent those of the company.


Agenda

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• Introduction�of�ADaMMetadata

• Clincial Trial�Process

• Examples�of�how�to�create�Metadata

• Goal�of�ADaMMetadata

• Pros�and�Cons

• Conclusion

• Questions�&�Answers


ADaM Metadata

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• Definition�– Information�of�ADaM(Analysis�Data�Model)�datasets

• Types– Analysis�Dataset�Metadata– Analysis�VariableMetadata�– Analysis�Parameter ValueͲLevel�Metadata– Analysis�ResultsMetadata


Normal Clinical Trial Process

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Protocol SAP Mock�Up�Tables

CRF

DMDB Raw TFLDerived


CDISC Clinical Trial Process

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Protocol SAP Mock�Up�Tables

eCRF

EDC SDTM

ADaMMetadata

TFLADaM

ADaM Related Process


ADaM Related Process

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Analysis�DatasetMetadata

TFLADaM

Analysis�VariableMetadata

Analysis�ParametervalueͲlevelMetadata

Analysis�ResultsMetadata

ADaM Metadata


Example 1 - Time to Event Mock Up table

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Table�14.2.1

Time to Death by TreatmentPARAM = ‘Days to Death’, TRTPAnalysis Population: Intent to Treat

ITTFL=‘Y’Drug 1 Drug 2 p-value

__________________________________________________________________________________________N xxx xxxMedian xx xx x.xxQ1, Q3 xx, xx xx, xx__________________________________________________________________________________________AVAL(Days to Event), CNSR(Censor information)

proc lifetest data=ADTTEOS;where PARAM=“Days to Death” and ITTFL=“Y”;time AVAL*CNSR(1);strata TRTP;

run;


Time to Event Analysis Dataset Metadata

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Dataset Name

Dataset Description

Dataset Location

Dataset Structure

Key Variables of Dataset

Class of Dataset

Documentation

ADTTEOS Overall Survival Analysis Dataset

adtteos.xpt One record per subject per parameter

STUDYID, USUBJID, PARAM

BDS adtteos.txt


Example 1 : Time to Event Analysis Variable Metadata including Analysis Parameter Value-Level Metadata (1)

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[CB1]Note to ADaM team: We have elected to use this format for the program names. According to the Study Data Specifications, since the programs created by S

Dataset Name

Parameter Identifier

Variable Name

Variable Label Variable Type

Display Format

Codelist / Controlled

Terms

Source / Derivation

ADTTEOS *ALL* STUDYID Study Identifier text $20 ADSL.STUDYID

ADTTEOS *ALL* USUBJID Unique Subject Identifier

text $20 ADSL.USUBJID

ADTTEOS *ALL* ITTFL Intent-To-Treat Population Flag

text $1 Y, null ADSL.ITTFL

ADTTEOS *ALL* TRTP Planned Treatment

text $40 Drug 1, Drug 2 ADSL.TRT01P

ADTTEOS *ALL* TRTPN Planned Treatment (N)

integer 1.0 1 = Drug 1, 2 = Drug 2

ADSL.TRT01PN

ADTTEOS PARAMCD PARAM Parameter text $50 Days to DeathADTTEOS *ALL* PARAMCD Parameter Code text $8 DEATH



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Dataset Name


Variable Name

Variable Label

Variable Type

Display Format

Codelist / Controlled Terms

Source / Derivation

ADTTEOS *ALL* AVAL Analysis Value float 8.2 ADT – STARTDT + 1

ADTTEOS *ALL* STARTDT Time to Event Origin Date for Subject

integer YYYYMMDD10.

ADSL.RANDDT

ADTTEOS *ALL* ADT Analysis Date integer YYYYMMDD10.

SAS Date of DS.DSDTC

ADTTEOS *ALL* CNSR Censor integer 1.0 0, 1 0 for DS.DSDECOD = ‘DEATH’, 1 for any other study completion

ADTTEOS *ALL* EVNTDESC Event or Censoring Description

text $40 DEATH, COMPLETED THE STUDY, LOST TOFOLLOW-UP, AE, PD

DS.DSDECOD


Example 1 : Time to Event ADaM Dataset

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USUBJID TRTP PARAM AVAL STARTDT ADT CNSR EVNTDESC001Ͳ01Ͳ001 Study Drug�1 Days�to�

Death157 2011Ͳ01Ͳ04 2011Ͳ06Ͳ10 1 COMPLETED

THE�STUDY

001Ͳ01Ͳ002 Study Drug�2 Days�to�Death

116 2011Ͳ02Ͳ01 2011Ͳ05Ͳ28 1 AE


88 2011Ͳ02Ͳ05 2011Ͳ05Ͳ04 0 DEATH


102 2011Ͳ03Ͳ20 2011Ͳ06Ͳ30 1 PD


Example 1 : Time to Event Analysis Results Metadata

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Metadata Field MetadataDISPLAY IDENTIFIER

Table 14.2.1

DISPLAY NAME Time to Death by Treatment, Analysis Population: Intent to TreatRESULT IDENTIFIER

Days to Death

PARAM Days to DeathPARAMCD DEATHANALYSIS VARIABLE

AVAL

REASON Primary efficacy analysisDATASET ADTTEOSSELECTION CRITERIA

ITTFL=“Y” and PARAM = ‘Days to Death’

DOCUMENTATION See SAP Section XX for details. Program: t-14-012-001-death.txtPROGRAMMING STATEMENTS

proc lifetest data= ADTTEOS;where ITTFL=‘Y’ and PARAM = “Days to Death”;time AVAL*CNSR(1);strata TRTP;

run;


Example 2 : Time to Event Mock Up table

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Table�14.2.2

Time to Progression Free Survival : Cox Proportional Hazard ModelPARAM = ‘Days to Progression Free Survival’, TRTP

Analysis Population: Intent to Treat ITTFL=‘Y’

Drug 1 Drug 2 p-value__________________________________________________________________________________________N xxx xxxMedian xx xx x.xxQ1, Q3 xx, xx xx, xx__________________________________________________________________________________________AVAL(Days to Event), CNSR(Censor information)

proc phreg data=ADTTEOS;where PARAM=“Days to Progression Free Survival” and ITTFL=“Y”;model AVAL*CNSR(1) = TRTP AGE SEX ;

run;



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Dataset Name


Variable Name

Variable Label Variable Type

Display Format


Source / Derivation

ADTTEOS *ALL* STUDYID Study Identifier text $20 ADSL.STUDYIDADTTEOS *ALL* USUBJID Unique Subject

Identifiertext $20 ADSL.USUBJID

ADTTEOS *ALL* AGE Age integer 3.0 ADSL.AGEADTTEOS *ALL* SEX Sex text $1 ADSL.SEXADTTEOS *ALL* ITTFL Intent-To-Treat

Population Flagtext $1 Y, null ADSL.ITTFL

ADTTEOS *ALL* TRTP Planned Treatment

text $40 Drug 1, Drug 2 ADSL.TRT01P

ADTTEOS *ALL* TRTPN Planned Treatment (N)

integer 1.0 1 = Drug 1, 2 = Drug 2

ADSL.TRT01PN

ADTTEOS PARAMCD PARAM Parameter text $50 Days to DeathDays to Progression Free Survival

ADTTEOS *ALL* PARAMCD Parameter Code text $8 DEATHPFS



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Dataset Name


Variable Name

Variable Label

Variable Type

Display Format


Source / Derivation

ADTTEOS *ALL* AVAL Analysis Value float 8.2 ADT – STARTDT + 1

ADTTEOS *ALL* STARTDT Time to Event Origin Date for Subject

integer YYYYMMDD10.

ADSL.RANDDT

ADTTEOS *ALL* ADT Analysis Date integer YYYYMMDD10.

SAS Date of DS.DSDTC

ADTTEOS DEATH CNSR Censor integer 1.0 0, 1 0 for DS.DSDECOD = ‘DEATH’, 1 for any other study completion

ADTTEOS PFS CNSR Censor integer 1.0 0, 1 0 for DS.DSDECOD in(‘DEATH’ , ‘PD’), 1 for any other study completion

ADTTEOS *ALL* EVNTDESC Event or Censoring Description

text $40 DEATH, COMPLETED THE STUDY, LOST TOFOLLOW-UP, AE, PD

DS.DSDECOD


Example 2 : Time to Event ADaM Dataset

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USUBJID

AGE SEX TRTP PARAM AVAL

STARTDT

ADT CNSR EVNTDESC

001Ͳ01Ͳ001

43 M StudyDrug�1

Days�to�Death 157 2011Ͳ01Ͳ04

2011Ͳ06Ͳ10

1 COMPLETEDTHE�STUDY

001Ͳ01Ͳ002

57 F StudyDrug�2


2011Ͳ05Ͳ28

1 AE

001Ͳ01Ͳ003

71 M StudyDrug�2


2011Ͳ05Ͳ04

0 DEATH

001Ͳ01Ͳ004

55 F StudyDrug�1


2011Ͳ06Ͳ30

1 PD

001Ͳ01Ͳ001

43 M StudyDrug�1

Days to�Progression�Free�Survival

157 2011Ͳ01Ͳ04

2011Ͳ06Ͳ10

1 COMPLETEDTHE�STUDY

001Ͳ01Ͳ002

57 F StudyDrug�2


116 2011Ͳ02Ͳ01

2011Ͳ05Ͳ28

1 AE

001Ͳ01Ͳ003

71 M StudyDrug�2


88 2011Ͳ02Ͳ05

2011Ͳ05Ͳ04

0 DEATH

001Ͳ01Ͳ004

55 F StudyDrug�1


102 2011Ͳ03Ͳ20

2011Ͳ06Ͳ30

0 PD


Example 2 : Time to Event Analysis Results Metadata

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Metadata Field MetadataDISPLAY IDENTIFIER

Table 14.2.2

DISPLAY NAME Time to Progression Free Survival : Cox Proportional Hazard ModelRESULT IDENTIFIER

Days to Progression Free Survival

PARAM Days to Progression Free SurvivalPARAMCD PFSANALYSIS VARIABLE

AVAL

REASON Secondary efficacy analysisDATASET ADTTEOSSELECTION CRITERIA

ITTFL=“Y” and PARAM=“Days to Progression Free Survival’

DOCUMENTATION See SAP Section XX for details. Program: t-14-002-002-pfs.txt

PROGRAMMING STATEMENTS

proc phreg data= ADTTEOS;where ITTFL=‘Y’ and PARAM = “Days to Progression Free Survival’;model AVAL*CNSR(1) = TRTP AGE SEX;

run;


Summary of Examples

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Mock Up�Tables 14.2.1 14.2.2

ADaMMetadata Dataset ADTTEOS

Variables STUDYDI, USUBJID

ITTFL

TRTP,�TRTPN

PARAM,�PARAMCD

AVAL

STARTDT,�ADT

CNSR

EVNTDESC

AGE

SEX

Parameter DEATH PFS

Results Days�to�Death Days�to�ProgressionFree�Survival


Goal of Metadata

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• Serve�as�Spec�and�Define�– Spec�:�provide�the�programmers�how�ADaM and�some�TFL�can�be�created

– Define�:�datasets�information�for�sponsors

• Central�document�for�all�programmers�and�statisticians


Goal of Metadata (2)

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• Minimize�the�communication�traffic�in�virtual�office�setting– Philadelphia�Office– Boston�Office– India�Office– Remote�Programming

• Reconciliation�between�ADaM datasets�and�Metadata�– use�macros�to�check�wether ADaM datasets�follow�ADaMMetadata.


Cons

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• Who�will�prepare�and�maintain�the�metadata• CDISC�expert(SDTM,�ADaM and�so�on)• Programmer�Lead• Statistician

• The�initial�investment�on�resource�and�time�at�the�early�stage�of�the�study

• Two�many�metadata?


Pros (1)

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• Consistency�in�ADaM datasets– Label– Name– Format– #�of�variables

• Help�the�inexperience�personnel�– ADaM is�new• The�central�document�between�developers�and�validators – especially�for�the�virtual�office�setting

• Helps�the�programmers�on�efficacy�analysis.�


Pros (2)

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• Better�estimate�on�the�number�of�ADaM datasets�–help�the�planning�and�resourcing.

• Review�on�Mock�Up�tables�and�SAP


Conclusion

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• The�concept�of�Metadata�is�“Plan�your�work�and�work�your�plan”

• ADaMMetadata�is�ongoing�until�all�are�done.


Questions?

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aand adam - · pdf file[cb1]note to adam team: ... adtteos *all* studyid identifier text $20...

Documents

aand adam - · pdf file[cb1]note to adam team: ... adtteos all studyid identifier text $20...