A Complex ADaM dataset?
Three different ways to create one.
Disclaimer
Any views or opinions presented in this 
presentation are solely those of the author 
and do not necessarily re...
Agenda
• Introduction of ADaM dataset
• Three methods for a complex ADaM dataset
• Example
• Benefits of each method
• Lim...
Introduction of ADaM
• ADaM(Analysis Data Model) is the analysis dataset in 
CDISC.
• Purpose
• Analysis Ready (statistica...
A complex ADaM dataset
• Can require several algorithms
• Can require several data manipulation steps
• Can be derived fro...
Three Methods to create a complex
ADaM dataset

1. SDTM datasets to ADaM datasets
2. SDTM datasets through the intermediat...
Three Methods Diagram
Intermediate permanent datasets

SDTM+

ADaM+

SDTM

ADaM

ADaM

11/27/2013

Cytel Inc.

7
Example 1
• A comparison of average daily drinking rate in 
treatment period between placebo and study 
drug.
• At the bas...
Key components in the example
• SDTM – SU (Substance Use)
• Final ADaM – ADDR (Drinking Rate Analysis 
Dataset)
• Paramete...
Algorithm of parameter of ADDRATE
• Rb (Baseline rate) = sum of all doses / number 
of days drinking data available at bas...
Three Methods for example
Intermediate permanent datasets

SDTM+(_SU)

ADaM+(_ADDR)

SDTM(SU)

ADaM(ADDR)

ADaM(ADSU)

11/...
SDTM SU dataset
USUBJID

SUSEQ SUTRT

001‐01‐001

1

ALCOHOL

001‐01‐001

2

ALCOHOL

001‐01‐001

3

ALCOHOL

001‐01‐001

...
Analysis Dataset Metadata for ADDR
Dataset
Name

Dataset
Description

Dataset
Location

Dataset
Structure

ADDR

Drinking
...
Analysis Variable Metadata including Analysis
Parameter value level Metadata for ADDR (1)
Variable Label

Variable
Type

D...
Analysis Variable Metadata including Analysis
Parameter value level Metadata for ADDR (2)
Dataset Parameter Variable
Name
...
Analysis Variable Metadata including Analysis
Parameter value level Metadata for ADDR (3)
Dataset Parameter Variable
Name
...
1st method : SDTM to ADaM

SDTM(SU)

11/27/2013

ADaM(ADDR)

Cytel Inc.

17
Final ADaM dataset of ADDR
USUBJID

FASFL

TRTP

PARAMCD PARAM

AVISIT

ABLFL

AVAL

001‐01‐001

Y

Study
Drug

ADDRATE

A...
2nd method : SDTM to intermediate
permanent datasets to ADaM
Intermediate permanent datasets

SDTM+(_SU)

ADaM+(_ADSU)

SD...
Intermediate permanent datasets of
SDTM plus _SU (1)
USUBJID

SUS
EQ

SUTRT

001‐01‐001

1

ALCOHOL

001‐01‐001

2

ALCOHO...
Intermediate permanent datasets of
SDTM plus _SU (2)

• _HOSEQ is the sequence number of non‐
missing drinking assessment ...
Intermediate permanent dataset – ADaM
plus _ADDR (1)
USUBJID TRTP

PARAM

AVISIT

ABLFL

AVAL

001‐01‐
001

Study
Drug

Av...
Intermediate permanent dataset – ADaM
plus _ADDR (3)
USUBJID TRTP

PARAM

AVISIT

ABLFL

AVAL

001‐01‐
001

Study
Drug

Av...
3rd method: SDTM to intermediate ADaM
to ADaM

SDTM(SU)

ADaM(ADDR)

ADaM(ADSU)

11/27/2013

Cytel Inc.

24
Intermediate ADaM dataset of ADSU (1)
USUBJID

PARAMCD AVAL

ADT

AVISIT

VISIT

001‐01‐001

DDRATE

0

2011‐02‐08

Baseli...
Intermediate ADaM dataset of ADSU (2)

• ‘NOT DONE’ data from SU were not included in 
ADSU
• At baseline visit, we only i...
Final ADaM dataset of ADDR
USUBJID TRTP

PARAM

AVISIT

ABLFL

AVAL

001‐01‐
001

Study
Drug

Average Daily 
Drinking Rate...
Example 2 : Intermediate Time to Event
permanent ADaM plus dataset
USUB
JID

TRTP

PARA AVA
M
L

STAR
TDT

ADT

CN
SR

EVN...
1st Method : SDTM to ADaM
The benefits are
• Simple process 
The limitations are
• A lack of data point traceability (Trac...
2nd Method : SDTM thru intermediate
permanent datasets to final ADaM

The benefits are
• Easy to follow each step and to v...
Business rules for plus datasets
• Plus datasets 
• The same SAS program as the final ADaM dataset 
development program.  ...
3rd method : SDTM thru ADaM to
final ADaM

The benefits are
• Easy to follow each step 
• Great data point traceability
Th...
Consideration
Datasets which will be submitted
• SDTM to ADaM method 
1. SDTM 
2. final ADaM

• SDTM thru the intermediate...
Conclusion
• Three methods for a complex ADaM datasets
1. SDTM datasets to ADaM datasets
2. SDTM datasets through the inte...
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A complex ADaM dataset - three different ways to create one

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The paper is intended for Clinical Trial SAS® programmers who create and validate a complex ADaM dataset. Some ADaM datasets require the use of complex algorithms. These algorithms could require several steps of data manipulation and more than one SDTM datasets. It can be very challenging to create a complex ADaM dataset in accordance with ADaM data structures and standards. Furthermore, it can be equally as challenging to validate those ADaM datasets. The paper will introduce three different ways to create a complex ADaM dataset. The first way is to create ADaM from SDTM directly without any intermediate permanent datasets. The second way is to create ADaM through the intermediate permanent datasets like SDTM+ or ADaM+ from SDTM. The third way is to create the final ADaM through the intermediate ADaM from SDTM. The paper will discuss the benefits and limitations of each method and also show some examples.

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A complex ADaM dataset - three different ways to create one

  1. 1. A Complex ADaM dataset? Three different ways to create one.
  2. 2. Disclaimer Any views or opinions presented in this  presentation are solely those of the author  and do not necessarily represent those of the  company. 11/27/2013 Cytel Inc. 2
  3. 3. Agenda • Introduction of ADaM dataset • Three methods for a complex ADaM dataset • Example • Benefits of each method • Limitation of each method • Consideration • Conclusion • Questions & Answers 11/27/2013 Cytel Inc. 3
  4. 4. Introduction of ADaM • ADaM(Analysis Data Model) is the analysis dataset in  CDISC. • Purpose • Analysis Ready (statistical analysis to be performed with   minimal programming) • Traceability • Type • ADSL(Subject Level Analysis Dataset) • BDS(Basic Data Structure) − Special BDS(upcoming) • ADTTE(Time to Event Analysis Dataset) • ADAE(Adverse Event Analysis Dataset ‐ upcoming) 4
  5. 5. A complex ADaM dataset • Can require several algorithms • Can require several data manipulation steps • Can be derived from more than one SDTM • Can be difficult to trace back • Can be difficult to validate 11/27/2013 Cytel Inc. 5
  6. 6. Three Methods to create a complex ADaM dataset 1. SDTM datasets to ADaM datasets 2. SDTM datasets through the intermediate  permanent datasets to final ADaM datasets 3. SDTM datasets through the intermediate  ADaM datasets to final ADaM datasets 11/27/2013 Cytel Inc. 6
  7. 7. Three Methods Diagram Intermediate permanent datasets SDTM+ ADaM+ SDTM ADaM ADaM 11/27/2013 Cytel Inc. 7
  8. 8. Example 1 • A comparison of average daily drinking rate in  treatment period between placebo and study  drug. • At the baseline period ‐ the average daily drinking  rate during 21 days from hospitalization date • At the treatment period – the average daily  drinking rate during during 42 days from the first  study dose.  Baseline rate imputation applied to the followings  − The subject who discontinued early  − Any missing assessment 11/27/2013 Cytel Inc. 8
  9. 9. Key components in the example • SDTM – SU (Substance Use) • Final ADaM – ADDR (Drinking Rate Analysis  Dataset) • Parameter – ADDRATE (Average Daily Drinking  Rate) 11/27/2013 Cytel Inc. 9
  10. 10. Algorithm of parameter of ADDRATE • Rb (Baseline rate) = sum of all doses / number  of days drinking data available at baseline  period • Ra (Actual treatment rate) = sum of all doses /  number of days drinking data available at  treatment period • Rt (Imputed treatment rate)  ( Ra * DAYS  + Rb * (42 – DAYS) ) / 42 at DAYS is the number of days drinking data  available  11/27/2013 Cytel Inc. 10
  11. 11. Three Methods for example Intermediate permanent datasets SDTM+(_SU) ADaM+(_ADDR) SDTM(SU) ADaM(ADDR) ADaM(ADSU) 11/27/2013 Cytel Inc. 11
  12. 12. SDTM SU dataset USUBJID SUSEQ SUTRT 001‐01‐001 1 ALCOHOL 001‐01‐001 2 ALCOHOL 001‐01‐001 3 ALCOHOL 001‐01‐001 21 001‐01‐001 SUSTAT SUDOSE SUDOSU SUSTDTC SUSTDY VISIT 0 DRINKS 2011‐02‐08 ‐21 Screening DRINKS 2011‐02‐09 ‐20 Screening 5 DRINKS 2011‐02‐10 ‐19 Screening ALCOHOL 0 DRINKS 2011‐02‐28 ‐1 Screening 22 ALCOHOL 0 DRINKS 2011‐03‐01 1 Visit 1 001‐01‐001 23 ALCOHOL DRINKS 2011‐03‐02 2 Visit 1 001‐01‐001 24 ALCOHOL 0 DRINKS 2011‐03‐03 3 Visit 1 001‐01‐001 25 ALCOHOL 2 DRINKS 2011‐03‐04 4 Visit 1 001‐01‐001 26 ALCOHOL NOT DONE DRINKS 2011‐03‐05 5 Visit 1 001‐01‐001 58 ALCOHOL NOT DONE DRINKS 2011‐04‐06 37 Visit 3 001‐01‐001 59 ALCOHOL 4 DRINKS 2011‐04‐07 38 Visit 3 001‐01‐001 60 ALCOHOL 0 DRINKS 2011‐04‐08 39 Visit 3 001‐01‐001 61 ALCOHOL 2 DRINKS 2011‐04‐09 40 Visit 3 001‐01‐001 62 ALCOHOL 1 DRINKS 2011‐04‐10 41 Visit 3 001‐01‐001 63 ALCOHOL 4 DRINKS 2011‐04‐11 42 Visit 3 NOT DONE …. NOT DONE …. 11/27/2013 Cytel Inc. 12
  13. 13. Analysis Dataset Metadata for ADDR Dataset Name Dataset Description Dataset Location Dataset Structure ADDR Drinking Rate  Analysis  Data addr.xpt one record per  USUBJID, PARAMCD,  subject per  parameter per  AVISITN analysis visit 11/27/2013 Cytel Inc. Key  Variables  of Dataset Class of  Dataset Documentation BDS c‐addr.txt 13
  14. 14. Analysis Variable Metadata including Analysis Parameter value level Metadata for ADDR (1) Variable Label Variable Type Display Format ADDR *ALL* USUBJID Unique Subject  Identifier text $20 ADSL.USUBJID ADDR *ALL* SITEID Site ID text $20 ADSL.SITEID ADDR *ALL* SEX Sex text $20 M, F ADSL.SEX ADDR *ALL* FASFL Full Analysis Set  Population Flag text $1 Y, N ADSL.FASFL ADDR *ALL* TRTPN Planned  Treatment (N) integer 1.0 1 = Placebo, 2 = Study Drug ADSL.TRTPN ADDR *ALL* TRTP Planned Treatment text $20 Placebo,  Study Drug ADSL.TRTP ADDR PARAMCD PARAMCD Parameter Code text $8 ADDRATE ADDR *ALL* PARAM Parameter text $50 Average Daily  Drinking Rate 11/27/2013 Cytel Inc. Codelist /  Controlled Terms Source /  Derivation Dataset Parameter Variable Name Identifier Name 14
  15. 15. Analysis Variable Metadata including Analysis Parameter value level Metadata for ADDR (2) Dataset Parameter Variable Name Identifier Name Variable Label Variable Type Display Format Codelist /  Controlled Terms ADDR *ALL* PARAMTYP Parameter  Type text $20 DERIVED ADDR *ALL* AVISITN Analysis Visit  integer (N) 3.0 1=Baseline,  2=Treatment  Period ADDR *ALL* AVISIT Analysis Visit text $20 Baseline,   Treatment  Period ADDR *ALL* AVAL Analysis  Value float 8.2 Source / Derivation 11/27/2013 Cytel Inc. ‘Baseline’ when  SU.VISIT=‘Screening’ ‘Treatment Period’  when SU.VISIT in (‘VISIT  1’, ‘VISIT 2’, ‘VISIT 3’) Average Daily Drinking  Rate within analysis visit.  At Treatment  Period, if a patient  discontinues early or  have missing records,  impute with baseline  rate 15
  16. 16. Analysis Variable Metadata including Analysis Parameter value level Metadata for ADDR (3) Dataset Parameter Variable Name Identifier Name Variable Label Variable Type Display Format Codelist /  Controlled Terms Source / Derivation ADDR *ALL* ABLFL Baseline Record Flag text $1 Y ‘Y’ at AVISIT = “Baseline” ADDR *ALL* BASE Baseline Value float 8.2 AVAL of  AVISIT=“Baseline” ADDR *ALL* CHG Change from  float Baseline 8.2 AVAL ‐ BASE 11/27/2013 Cytel Inc. 16
  17. 17. 1st method : SDTM to ADaM SDTM(SU) 11/27/2013 ADaM(ADDR) Cytel Inc. 17
  18. 18. Final ADaM dataset of ADDR USUBJID FASFL TRTP PARAMCD PARAM AVISIT ABLFL AVAL 001‐01‐001 Y Study Drug ADDRATE Average Daily  Drinking Rate Baseline Y 4.40 001‐01‐001 Y Study Drug ADDRATE Average Daily  Drinking Rate Treatment  Period 001‐01‐002 Y Placebo ADDRATE Average Daily  Drinking Rate Baseline 001‐01‐002 Y Placebo ADDRATE Average Daily  Drinking Rate Treatment Period 2.72 Y BASE CHG 4.40 ‐1.68 4.26 ‐1.16 4.26 3.10 Key points to note: • Row 2: There are 3 missing assessments during the  treatment period for the subject of 01‐001, so the baseline rate  imputation method was applied as follow 2.60*39 + 4.40*(42‐39)  = 2.72 42 • Row 4: There are no missing assessments during the  treatment period for the subject of 01‐002 11/27/2013 Cytel Inc. 18
  19. 19. 2nd method : SDTM to intermediate permanent datasets to ADaM Intermediate permanent datasets SDTM+(_SU) ADaM+(_ADSU) SDTM(SU) 11/27/2013 ADaM(ADDR) Cytel Inc. 19
  20. 20. Intermediate permanent datasets of SDTM plus _SU (1) USUBJID SUS EQ SUTRT 001‐01‐001 1 ALCOHOL 001‐01‐001 2 ALCOHOL 001‐01‐001 3 ALCOHOL 001‐01‐001 21 001‐01‐001 SUSTAT SUD OSE SUDOSU SUSTDTC SUST VISIT DY _HO SEQ 0 DRINKS 2011‐02‐08 ‐21 Screening 1 DRINKS 2011‐02‐09 ‐20 Screening 5 DRINKS 2011‐02‐10 ‐19 Screening 2 ALCOHOL 0 DRINKS 2011‐02‐28 ‐1 Screening 19 22 ALCOHOL 0 DRINKS 2011‐03‐01 1 Visit 1 001‐01‐001 23 ALCOHOL DRINKS 2011‐03‐02 2 Visit 1 001‐01‐001 24 ALCOHOL 0 DRINKS 2011‐03‐03 3 Visit 1 2 001‐01‐001 25 ALCOHOL 2 DRINKS 2011‐03‐04 4 Visit 1 3 001‐01‐001 26 ALCOHOL NOT DONE DRINKS 2011‐03‐05 5 Visit 1 001‐01‐001 58 ALCOHOL NOT DONE DRINKS 2011‐04‐06 37 Visit 3 001‐01‐001 59 ALCOHOL 4 DRINKS 2011‐04‐07 38 Visit 3 35 001‐01‐001 60 ALCOHOL 0 DRINKS 2011‐04‐08 39 Visit 3 36 001‐01‐001 61 ALCOHOL 2 DRINKS 2011‐04‐09 40 Visit 3 37 001‐01‐001 62 ALCOHOL 1 DRINKS 2011‐04‐10 41 Visit 3 38 001‐01‐001 11/27/2013 63 ALCOHOL 4 DRINKS 2011‐04‐11 42 Visit 3 39 20 NOT DONE _SDS EQ …. NOT DONE 1 …. Cytel Inc.
  21. 21. Intermediate permanent datasets of SDTM plus _SU (2) • _HOSEQ is the sequence number of non‐ missing drinking assessment from  the  hospitalization date (2011‐02‐08) • _SDSEQ is the sequence number of non‐ missing drinking assessment from the first  dose date (2011‐03‐01) • When SUSTAT = ‘NOT DONE’, _HOSEQ and  _SDSEQ are not increased by 1.  11/27/2013 Cytel Inc. 21
  22. 22. Intermediate permanent dataset – ADaM plus _ADDR (1) USUBJID TRTP PARAM AVISIT ABLFL AVAL 001‐01‐ 001 Study Drug Average Daily  Drinking Rate Baseline Y 4.40 001‐01‐ 001 Study Drug Average Daily  Drinking Rate Treatment  Period 001‐01‐ 002 Placebo Average Daily  Drinking Rate Baseline 001‐01‐ 002 Placebo Average Daily  Drinking Rate Treatment Period 2.72 Y BASE 4.26 3.10 4.26 ‐1.16 _DAYS _AVAL 19 4.40 101.2 39 2.60 89.4 ‐1.68 _TOT AL 83.6 4.40 CHG 21 4.26 130.2 42 3.10 Plus variables • _TOTAL(Sum of doses per visit) = sum(SUDOSE) • _DAYS (Number of non‐missing drinking days per visit)=  count(missing SUSTAT) or last._HOSEQ or last._SDSEQ within  AVISIT • _AVAL (Actual treatment rate)= _TOTAL / _DAYS 11/27/2013 Cytel Inc. 22
  23. 23. Intermediate permanent dataset – ADaM plus _ADDR (3) USUBJID TRTP PARAM AVISIT ABLFL AVAL 001‐01‐ 001 Study Drug Average Daily  Drinking Rate Baseline Y 4.40 001‐01‐ 001 Study Drug Average Daily  Drinking Rate Treatment  Period 001‐01‐ 002 Placebo Average Daily  Drinking Rate Baseline 001‐01‐ 002 Placebo Average Daily  Drinking Rate Treatment Period 2.72 Y BASE 4.26 3.10 4.26 ‐1.16 _DAYS _AVAL 19 4.40 101.2 39 2.60 89.4 ‐1.68 _TOTAL 83.6 4.40 CHG 21 4.26 130.2 42 3.10 Key points to note: • Row 2 and 4: at the treatment period, AVAL algorithm is  (_AVAL * _DAYS + BASE * (42 ‐ _DAYS) ) / 42 • Row 2: 2.60*39 + 4.40*(42‐39)  = 2.72 42 • Row 4: 3.10*42 + 4.26*(42‐42)  = 3.10 11/27/2013 Cytel Inc. 42 23
  24. 24. 3rd method: SDTM to intermediate ADaM to ADaM SDTM(SU) ADaM(ADDR) ADaM(ADSU) 11/27/2013 Cytel Inc. 24
  25. 25. Intermediate ADaM dataset of ADSU (1) USUBJID PARAMCD AVAL ADT AVISIT VISIT 001‐01‐001 DDRATE 0 2011‐02‐08 Baseline 001‐01‐001 DDRATE 5 2011‐02‐10 001‐01‐001 DDRATE 0 2011‐02‐28 001‐01‐001 DDRATE 4.4 001‐01‐001 DDRATE 0 2011‐03‐01 Treatment Period Visit 1 001‐01‐001 DDRATE 4.4 2011‐03‐02 Treatment Period Visit 1 001‐01‐001 DDRATE 0 2011‐03‐03 Treatment Period 001‐01‐001 DDRATE 2 2011‐03‐04 001‐01‐001 DDRATE 4.4 001‐01‐001 DDRATE 001‐01‐001 DTYPE ASEQ SUSEQ Screening 1 1 Baseline Screening 2 3 Baseline Screening 19 21 …. Baseline AVERAGE 20 21 22 22 23 Visit 1 23 24 Treatment Period Visit 1 24 25 2011‐03‐05 Treatment Period Visit 1 BLCF 25 26 4.4 2011‐04‐06 Treatment Period Visit 3 BLCF 57 58 DDRATE 4 2011‐04‐07 Treatment Period Visit 3 58 59 001‐01‐001 DDRATE 0 2011‐04‐08 Treatment Period Visit 3 59 60 001‐01‐001 DDRATE 2 2011‐04‐09 Treatment Period Visit 3 60 61 001‐01‐001 DDRATE 1 2011‐04‐10 Treatment Period Visit 3 61 62 001‐01‐001 DDRATE 4 2011‐04‐11 Treatment Period Visit 3 62 63 001‐01‐001 11/27/2013 DDRATE 2.72 BLCF …. Treatment Period Cytel Inc. AVERAGE 63 25
  26. 26. Intermediate ADaM dataset of ADSU (2) • ‘NOT DONE’ data from SU were not included in  ADSU • At baseline visit, we only include 19 records for 01‐ 001.   We used DYPTE=’AVERAGE’ to achieve the  average of assessed doses at ASEQ = 20.  • At treatment period visit, we only include 39 records.    We used DYPTE=’AVERAGE’ to achieve the average of  assessed doses at ASEQ = 63.  11/27/2013 Cytel Inc. 26
  27. 27. Final ADaM dataset of ADDR USUBJID TRTP PARAM AVISIT ABLFL AVAL 001‐01‐ 001 Study Drug Average Daily  Drinking Rate Baseline Y 4.40 001‐01‐ 001 Study Drug Average Daily  Drinking Rate Treatment  Period 001‐01‐ 002 Placebo Average Daily  Drinking Rate Baseline 001‐01‐ 002 Placebo Average Daily  Drinking Rate Treatment Period 2.72 Y BASE 4.26 3.10 4.26 ‐1.16 SRCSEQ 20 ADSU 63 ADSU ‐1.68 SRCDOM ADSU 4.40 CHG 22 ADSU 65 Key points to note: • All the records are coming from ADSU. • Great data point traceability. 11/27/2013 Cytel Inc. 27
  28. 28. Example 2 : Intermediate Time to Event permanent ADaM plus dataset USUB JID TRTP PARA AVA M L STAR TDT ADT CN SR EVNTDESC _DSDECOD _DS DTC _SVXS TDTC _AEX DT 001‐ 01‐001 Study Drug 1 Death 157 2011‐ 01‐04 2011‐ 06‐10 1 COMPLETED THE STUDY COMPLETED THE STUDY 2011‐ 06‐10 2011‐ 06‐10 2011‐ 05‐04 001‐ 01‐002 Study Drug 2 Death 116 2011‐ 02‐01 2011‐ 05‐28 1 LOST TO  FOLLOW‐UP LOST TO  FOLLOW‐UP 2011‐ 05‐28 2011‐ 05‐28 2011‐ 05‐01 001‐ 01‐003 Study Drug 2 Death 88 2011‐ 02‐05 2011‐ 05‐04 0 DEATH DEATH 2011‐ 05‐04 2011‐ 05‐04 2011‐ 05‐04 001‐ 01‐004 Study Drug 1 Death 102 2011‐ 03‐20 2011‐ 06‐30 1 ONGOING 2011‐ 06‐30 2011‐ 06‐04 001‐ 01‐005 Study Drug 1 Death 101 2011‐ 03‐26 2011‐ 07‐05 1 ONGOING 2011‐ 07‐01 2011‐ 07‐05 AVAL = ADT – STARTDT Plus variables • _DSDECOD = DS.DSDECOD when DS.DSCAT = “DISPOSITION EVENT” • _DSDTC = DS.DSDTC when DS.DSCAT = “DISPOSITION EVENT” • _SVXSTDTC = Last Study Visit date • _AEXDT = Last AE date 11/27/2013 Cytel Inc. 28
  29. 29. 1st Method : SDTM to ADaM The benefits are • Simple process  The limitations are • A lack of data point traceability (Traceability  will be provided with Define.xml)  • Difficult to troubleshoot issues if development  SAS programmer and validation SAS  programmer do not agree on issues in the  final ADaM dataset. 11/27/2013 Cytel Inc. 29
  30. 30. 2nd Method : SDTM thru intermediate permanent datasets to final ADaM The benefits are • Easy to follow each step and to validate  • Flexibility of the data structure of  intermediate datasets (A programmer does  not need to follow CDISC standards in the  intermediate permanent datasets) The limitations are • A lack of data point traceability, especially for  the reviewers. 11/27/2013 Cytel Inc. 30
  31. 31. Business rules for plus datasets • Plus datasets  • The same SAS program as the final ADaM dataset  development program.   We do not have separate dataset  programs for the intermediate permanent datasets.  • Same number of the records – we keep the same number  of records between SDTM datasets and SDTM plus datasets  and also ADaM datasets and ADaM plus datasets.   • Naming convention : the prefix of ‘_’ and original SDTM or  final ADaM • Plus variables  • The temporary variables by adding the prefix ‘_’.  • No Standard for plus variables – we assign the labels, but  do not follow any CDISC standards. 11/27/2013 Cytel Inc. 31
  32. 32. 3rd method : SDTM thru ADaM to final ADaM The benefits are • Easy to follow each step  • Great data point traceability The limitations are • Need to create and validate all ADaM datasets  including the intermediate ADaM datasets • Not much flexibility of ADaM datasets as the  intermediate datasets 11/27/2013 Cytel Inc. 32
  33. 33. Consideration Datasets which will be submitted • SDTM to ADaM method  1. SDTM  2. final ADaM • SDTM thru the intermediate permanent datasets to  ADaM method  1. SDTM  2. final ADaM • SDTM thru ADaM to ADaM method  1. SDTM 2. intermediate ADaM 3. final ADaM 11/27/2013 Cytel Inc. 33
  34. 34. Conclusion • Three methods for a complex ADaM datasets 1. SDTM datasets to ADaM datasets 2. SDTM datasets through the intermediate  permanent datasets to final ADaM datasets 3. SDTM datasets through the intermediate ADaM datasets to final ADaM datasets • More options for a complex ADaM dataset  creation • Analysis will dictate the type of methods 11/27/2013 Cytel Inc. 34
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