A complex ADaM dataset - three different ways to create one
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Transcript of A complex ADaM dataset - three different ways to create one
A Complex ADaM dataset? Three different ways to create one.
Any views or opinions presented in this presentation are solely those of the author and do not necessarily represent those of the company.
Disclaimer
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• Introduction of ADaM dataset• Three methods for a complex ADaM dataset• Example• Benefits of each method• Limitation of each method• Consideration• Conclusion• Questions & Answers
Agenda
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Introduction of ADaM
4
• 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)
• 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
A complex ADaM dataset
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1. SDTM datasets to ADaM datasets2. SDTM datasets through the intermediate
permanent datasets to final ADaM datasets3. SDTM datasets through the intermediate
ADaM datasets to final ADaM datasets
Three Methods to create a complex ADaM dataset
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Three Methods Diagram
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SDTM
SDTM+
ADaM
ADaM
ADaM+
Intermediate permanent datasets
• 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
Example 1
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• SDTM – SU (Substance Use)• Final ADaM – ADDR (Drinking Rate Analysis Dataset)
• Parameter – ADDRATE (Average Daily Drinking Rate)
Key components in the example
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• 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) ) / 42at DAYS is the number of days drinking data available
Algorithm of parameter of ADDRATE
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Three Methods for example
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SDTM(SU)
SDTM+(_SU)
ADaM(ADSU)
ADaM(ADDR)
ADaM+(_ADDR)
Intermediate permanent datasets
SDTM SU dataset
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USUBJID SUSEQ SUTRT SUSTAT SUDOSE SUDOSU SUSTDTC SUSTDY VISIT
001‐01‐001 1 ALCOHOL 0 DRINKS 2011‐02‐08 ‐21 Screening
001‐01‐001 2 ALCOHOL NOT DONE DRINKS 2011‐02‐09 ‐20 Screening
001‐01‐001 3 ALCOHOL 5 DRINKS 2011‐02‐10 ‐19 Screening
….
001‐01‐001 21 ALCOHOL 0 DRINKS 2011‐02‐28 ‐1 Screening
001‐01‐001 22 ALCOHOL 0 DRINKS 2011‐03‐01 1 Visit 1
001‐01‐001 23 ALCOHOL NOT DONE 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
Analysis Dataset Metadata for ADDR
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DatasetName
DatasetDescription
DatasetLocation
DatasetStructure
Key Variables of Dataset
Class of Dataset
Documentation
ADDR DrinkingRate Analysis Data
addr.xpt one record per subject per parameter per analysis visit
USUBJID,PARAMCD, AVISITN
BDS c‐addr.txt
Analysis Variable Metadata including Analysis Parameter value level Metadata for ADDR (1)
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DatasetName
ParameterIdentifier
VariableName
Variable Label VariableType
DisplayFormat
Codelist / ControlledTerms
Source / Derivation
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 PlannedTreatment
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
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DatasetName
ParameterIdentifier
VariableName
VariableLabel
VariableType
DisplayFormat
Codelist / ControlledTerms
Source / Derivation
ADDR *ALL* PARAMTYP Parameter Type
text $20 DERIVED
ADDR *ALL* AVISITN Analysis Visit (N)
integer 3.0 1=Baseline, 2=Treatment Period
ADDR *ALL* AVISIT Analysis Visit text $20 Baseline, Treatment Period
‘Baseline’ when SU.VISIT=‘Screening’‘Treatment Period’ when SU.VISIT in (‘VISIT 1’, ‘VISIT 2’, ‘VISIT 3’)
ADDR *ALL* AVAL Analysis Value
float 8.2 Average Daily Drinking Rate within analysisvisit. At Treatment Period, if a patient discontinues early or have missing records, impute with baseline rate
Analysis Variable Metadata including Analysis Parameter value level Metadata for ADDR (2)
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DatasetName
ParameterIdentifier
VariableName
VariableLabel
VariableType
DisplayFormat
Codelist / ControlledTerms
Source / Derivation
ADDR *ALL* ABLFL BaselineRecord Flag
text $1 Y ‘Y’ at AVISIT = “Baseline”
ADDR *ALL* BASE BaselineValue
float 8.2 AVAL of AVISIT=“Baseline”
ADDR *ALL* CHG Change from Baseline
float 8.2 AVAL ‐ BASE
Analysis Variable Metadata including Analysis Parameter value level Metadata for ADDR (3)
1st method : SDTM to ADaM
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SDTM(SU) ADaM(ADDR)
Final ADaM dataset of ADDR
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USUBJID FASFL TRTP PARAMCD PARAM AVISIT ABLFL AVAL BASE CHG
001‐01‐001 Y StudyDrug
ADDRATE Average Daily Drinking Rate
Baseline Y 4.40
001‐01‐001 Y StudyDrug
ADDRATE Average Daily Drinking Rate
Treatment Period
2.72 4.40 ‐1.68
001‐01‐002 Y Placebo ADDRATE Average Daily Drinking Rate
Baseline Y 4.26
001‐01‐002 Y Placebo ADDRATE Average Daily Drinking Rate
TreatmentPeriod
3.10 4.26 ‐1.16
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) = 2.7242
• Row 4: There are no missing assessments during the treatment period for the subject of 01‐002
2nd method : SDTM to intermediate permanent datasets to ADaM
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SDTM(SU)
SDTM+(_SU)
ADaM(ADDR)
ADaM+(_ADSU)
Intermediate permanent datasets
Intermediate permanent datasets of SDTM plus _SU (1)
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USUBJID SUSEQ
SUTRT SUSTAT SUDOSE
SUDOSU SUSTDTC SUSTDY
VISIT _HOSEQ
_SDSEQ
001‐01‐001 1 ALCOHOL 0 DRINKS 2011‐02‐08 ‐21 Screening 1
001‐01‐001 2 ALCOHOL NOT DONE DRINKS 2011‐02‐09 ‐20 Screening
001‐01‐001 3 ALCOHOL 5 DRINKS 2011‐02‐10 ‐19 Screening 2
….
001‐01‐001 21 ALCOHOL 0 DRINKS 2011‐02‐28 ‐1 Screening 19
001‐01‐001 22 ALCOHOL 0 DRINKS 2011‐03‐01 1 Visit 1 1
001‐01‐001 23 ALCOHOL NOT DONE 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 63 ALCOHOL 4 DRINKS 2011‐04‐11 42 Visit 3 39
Intermediate permanent datasets of SDTM plus _SU (2)
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• _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.
Intermediate permanent dataset – ADaMplus _ADDR (1)
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USUBJID TRTP PARAM AVISIT ABLFL AVAL BASE CHG _TOTAL
_DAYS _AVAL
001‐01‐001
StudyDrug
Average Daily Drinking Rate
Baseline Y 4.40 83.6 19 4.40
001‐01‐001
StudyDrug
Average Daily Drinking Rate
Treatment Period
2.72 4.40 ‐1.68 101.2 39 2.60
001‐01‐002
Placebo Average Daily Drinking Rate
Baseline Y 4.26 89.4 21 4.26
001‐01‐002
Placebo Average Daily Drinking Rate
TreatmentPeriod
3.10 4.26 ‐1.16 130.2 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
Intermediate permanent dataset – ADaMplus _ADDR (3)
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USUBJID TRTP PARAM AVISIT ABLFL AVAL BASE CHG _TOTAL _DAYS _AVAL
001‐01‐001
StudyDrug
Average Daily Drinking Rate
Baseline Y 4.40 83.6 19 4.40
001‐01‐001
StudyDrug
Average Daily Drinking Rate
Treatment Period
2.72 4.40 ‐1.68 101.2 39 2.60
001‐01‐002
Placebo Average Daily Drinking Rate
Baseline Y 4.26 89.4 21 4.26
001‐01‐002
Placebo Average Daily Drinking Rate
TreatmentPeriod
3.10 4.26 ‐1.16 130.2 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) = 2.7242
• Row 4:3.10*42 + 4.26*(42‐42) = 3.10
42
3rd method: SDTM to intermediate ADaMto ADaM
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SDTM(SU)
ADaM(ADSU)
ADaM(ADDR)
Intermediate ADaM dataset of ADSU (1)
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USUBJID PARAMCD AVAL ADT AVISIT VISIT DTYPE ASEQ SUSEQ
001‐01‐001 DDRATE 0 2011‐02‐08 Baseline Screening 1 1
001‐01‐001 DDRATE 5 2011‐02‐10 Baseline Screening 2 3
….
001‐01‐001 DDRATE 0 2011‐02‐28 Baseline Screening 19 21
001‐01‐001 DDRATE 4.4 Baseline AVERAGE 20
001‐01‐001 DDRATE 0 2011‐03‐01 Treatment Period Visit 1 21 22
001‐01‐001 DDRATE 4.4 2011‐03‐02 Treatment Period Visit 1 BLCF 22 23
001‐01‐001 DDRATE 0 2011‐03‐03 Treatment Period Visit 1 23 24
001‐01‐001 DDRATE 2 2011‐03‐04 Treatment Period Visit 1 24 25
001‐01‐001 DDRATE 4.4 2011‐03‐05 Treatment Period Visit 1 BLCF 25 26
….
001‐01‐001 DDRATE 4.4 2011‐04‐06 Treatment Period Visit 3 BLCF 57 58
001‐01‐001 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 DDRATE 2.72 Treatment Period AVERAGE 63
Intermediate ADaM dataset of ADSU (2)
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• ‘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.
Final ADaM dataset of ADDR
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USUBJID TRTP PARAM AVISIT ABLFL AVAL BASE CHG SRCDOM SRCSEQ
001‐01‐001
StudyDrug
Average Daily Drinking Rate
Baseline Y 4.40 ADSU 20
001‐01‐001
StudyDrug
Average Daily Drinking Rate
Treatment Period
2.72 4.40 ‐1.68 ADSU 63
001‐01‐002
Placebo Average Daily Drinking Rate
Baseline Y 4.26 ADSU 22
001‐01‐002
Placebo Average Daily Drinking Rate
TreatmentPeriod
3.10 4.26 ‐1.16 ADSU 65
Key points to note:• All the records are coming from ADSU.• Great data point traceability.
Example 2 : Intermediate Time to Event permanent ADaM plus dataset
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USUBJID
TRTP PARAM
AVAL
STARTDT
ADT CNSR
EVNTDESC _DSDECOD _DSDTC
_SVXSTDTC
_AEXDT
001‐01‐001
StudyDrug 1
Death 157 2011‐01‐04
2011‐06‐10
1 COMPLETEDTHE STUDY
COMPLETEDTHE STUDY
2011‐06‐10
2011‐06‐10
2011‐05‐04
001‐01‐002
StudyDrug 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
StudyDrug 2
Death 88 2011‐02‐05
2011‐05‐04
0 DEATH DEATH 2011‐05‐04
2011‐05‐04
2011‐05‐04
001‐01‐004
StudyDrug 1
Death 102 2011‐03‐20
2011‐06‐30
1 ONGOING 2011‐06‐30
2011‐06‐04
001‐01‐005
StudyDrug 1
Death 101 2011‐03‐26
2011‐07‐05
1 ONGOING 2011‐07‐01
2011‐07‐05
AVAL = ADT – STARTDTPlus 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
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.
1st Method : SDTM to ADaM
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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.
2nd Method : SDTM thru intermediate permanent datasets to final ADaM
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• 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.
Business rules for plus datasets
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The benefits are• Easy to follow each step • Great data point traceabilityThe 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
3rd method : SDTM thru ADaM to final ADaM
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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. SDTM2. intermediate ADaM3. final ADaM
Consideration
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• Three methods for a complex ADaM datasets1. SDTM datasets to ADaM datasets2. SDTM datasets through the intermediate
permanent datasets to final ADaM datasets3. 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
Conclusion
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