A complex ADaM dataset - three different ways to create one
•
2 gostaram•4,427 visualizações
This document discusses three different methods for creating a complex ADaM (Analysis Data Model) dataset from SDTM (Study Data Tabulation Model) datasets. The first method involves transforming SDTM datasets directly into ADaM datasets. The second method uses intermediate permanent datasets between the SDTM and final ADaM datasets. The third method uses intermediate ADaM datasets between the SDTM and final ADaM datasets. An example complex ADaM dataset measuring average daily drinking rate is provided to illustrate each of the three methods. Key components and algorithms for deriving parameters in the example are also described.
Recomendados
Recomendados
Mais conteúdo relacionado
Mais procurados
Mais procurados (20)
Semelhante a A complex ADaM dataset - three different ways to create one
Semelhante a A complex ADaM dataset - three different ways to create one (20)
Mais de Kevin Lee
Mais de Kevin Lee (20)
Último
Último (20)
A complex ADaM dataset - three different ways to create one
- 1. A Complex ADaM dataset? Three different ways to create one.
- 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. 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. 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. 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. Three Methods Diagram Intermediate permanent datasets SDTM+ ADaM+ SDTM ADaM ADaM 11/27/2013 Cytel Inc. 7
- 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. 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. 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. Three Methods for example Intermediate permanent datasets SDTM+(_SU) ADaM+(_ADDR) SDTM(SU) ADaM(ADDR) ADaM(ADSU) 11/27/2013 Cytel Inc. 11
- 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. 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. 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. 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. 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. 1st method : SDTM to ADaM SDTM(SU) 11/27/2013 ADaM(ADDR) Cytel Inc. 17
- 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. 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. 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. 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. 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. 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. 3rd method: SDTM to intermediate ADaM to ADaM SDTM(SU) ADaM(ADDR) ADaM(ADSU) 11/27/2013 Cytel Inc. 24
- 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. 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. 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. 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. 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. 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. 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. 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. 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. 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