CDISC - SDTMs and ADaMs    

 Quick Links, CDISC ReferencesSAS e-Guide

Mind Map 

 CDISC Mapping Videos

 Getting Started with SDTMs

 Outline  SDTMIG Versions: 3.13.2,

Mind map  SDTM Certification  Pilot Sample Data: Harvard Data

SDTM OnLine Specification       Control Terminology (Online)    New CDISC Mapping e-Guide

Newly Released FDA Guidelines - Electronic Format Submission, Technical Conformance, Search CDISC SAS Papers

FDA Presentation on Data Standards, Drug Review Process  SAS, CDSIC Q&A Blog, PhUSE SENDS, CDISC SENDS, CDISC Open Rules Engine (CORE, Blog)

PharmaSUG Single Day Event Presentations   PMDA (Pharmaceuticals and Medical Devices Agency)

CDISC Primer (Introduction Videos on SEND, CDASH, SDTMIG, ADaMs)






Clinical Data Interchange Standards Consortium (CDISC) Warehouse





Raw datasets with all records (1:M CRFs) PLUS required variables 


1:M SDTMs PLUS derived variables/records




ISO8601 character (YYYY-MM-DD)


Datetime variable (YYMMDD10.), Durations 







Most all except DM are vertical with generic variable names (ideal for by-group nested processing)


VSTESTCD = new variable name

VSTEST = new variable label

VSSTRESN = numeric value with format

VSSTRESC = character value with format and length, maybe character version of  VSSTRESN

Generally summary record of one record per patient per param (ex. lbtestcd) per visit

Horizontal (ideal for modeling, by subjid and time, content-based variable name, transposed from SDTM vertical structure)

Lab visit windows are applied to summarize to one record per patient per visit

General options for AVAL and AVALC



3. Imputed value if VSSTRESC contains '<'

4. Average value of AVAL with DTYPE='AVERAGE'















Four Methods to Construct

– May need transpose (TEMP, SBP, DBP, etc.) to vertical structure (ADVS)

– May need to transpose by visit, id test to vertical structure by visit and test variable names (ADLB)

– May need to transpose by visit, id question to vertical structure by visit and question variable names (ADQC)

– Not transposed, kept as (ADCM, ADAE)





 (Data Standards, Specifications - Can use template to create SDTM metadata files, ADaM SpecificationCRF ExampleEdit Checks)




'Raw datasets represented as Case Report Forms'

(Useful for listings)

3.1.2 Guide

Compliance Check Macros, SAS



SDTM SAS Programs







Study Data Tabulation Model

Mapping process from Raw data

(See %make_empty_dataset macro)

1. Raw dataset variables (mapped with Domain specification) PLUS all required/expected Domain specific variables (create if needed, such as FDOSEDTN or LBBLFL), values, and records.

2. Using control terminology, transform data to standard names and values.

3. Keep any unmapped raw dataset variables in SUPPQUAL corresponding datasets such as SUPPDM.

4. For unscheduled visits, assign visits if possible.

5. May be many to 1 relationship with CRF page.

6. Variable order is important, 8 char variable names, 40 char variable labels.

7. Create --DTC suffix for ISO8601 character date value. (See %make_dtc_date macro)

8. Create --DY suffix for study day variable = (date portion of --DTC) - (date portion of RFSTDTC) + 1 if --DTC is on or after RFSTDTC, 

OR study day variable = (date portion of --DTC) - (date portion of RFSTDTC) if --DTC precedes RFSTDTC, 

where RFSTDTC is a discrete starting point and RFRNDTC is a discrete ending point contained in the DM Domain.

Note that AESTDY may be missing for partial AESTDTC.

 (See %make_sdtm_dy macro)


'Analysis datasets for modeling and reporting - One-Proc Away'

(Useful for summary tables, statistical modeling and p-values)

2.0, 2.1 Reference, 2.1  presentation, 1.0 Guide, 

Common Stats,




Checklist  Guide,


ADaM SAS Programs

ADaM SAS Macros




Analysis Data Model

Three Models: Subject-level (ADSL), Change from baseline (ADLB) and Categorical analysis

Traceability is important to preserve to trace back to SDTMs.  For example, keep SRCDOM and SRCVAR variables only if more than one value exists.  Also, use caution when combining similar variables such as SDTM.EX.EXTRT and SDTM.CM.CMTRT into ADAM.ADCM.CMTRT.

(See %make_empty_dataset macro)

Mapping process from SDTM

1. Summary of SDTM datasets PLUS derived variables such as AGE, CHANGE IN BASELINE, SEX/SEXN, RACE/RACEN, etc. for TLGs.

2. Generally one record per patient or one record per patient per visit.  May apply visit windows.

3. Identify BLFL, baseline flag and calculate CHANGE FROM BASELINE.

4. Using control terminology, transform data to standard names and values such as AVISIT, analysis visit.  For traceability, it is good practice to keep the associated VISIT variable.

5. May create new records or visits such as DTYPE='AVERAGE' or 'LOCF'. (See 1. Proc Means, 2. Proc Sort, 3. Data Step Merge for programming techniques), impute with mean values or calculate total scores. 

6. May be one to many relationship with SDTM.

7. Convert --DTC to --DT datetime variables.  Use --DTF for date impute flag (D for day, M for month and day, and Y for year, month and day). (See %dtc2dt macro)

8. Create indicator or population flag variables --FN (1, 0) or --FL (Y, N).  Other options include AE level flag variables such as AE1FL (patient level), AE2FL (organ class level) and AE3FL (preferred term level).

9. Metadata documentation (pseudo-code specifications) with hyperlinks - similar to define.xml. 

Laboratory Data Model



1.3.2 Guide


Operational Data Model 

(Contains both variable attributes and data values)



Dictionary Tables (See diagram below)


Required for each SDTM and ADam dataset.

 SAS Paper, SAS Paper 2, SAS Paper 3

 Clinical Standards Toolkit Doc

XML Online TutorialXML Online reference



ISO 8601 Date Format

SAS Paper, SAS Paper 2, SAS Paper 3

XXDTC for date of XX

XXSTDTC for start date

XXENDTC for end date


See %make_dtc_date in SDTM SAS macros

See %dtc2dt in ADaM SAS macro


ISODATE9 character, ex. YYYY-MM-DD or YYYY-MM-DDThh:mm:ss, 2009-01-01T12:00:00

Three step process

1. Collected as text string, not as a date variable.  May use informats such as b8601da8. for basic or e8601da10. for extended (XXXX-XX-XX) to store as datetime variable. 

2. Use the same informats to display as text strings.

3. Can use CALL routine to calculate duration - CALL IS8601_CONVERT(convert-from, convert-to, <from-variables> , <to-variables>, <date-time-replacements>). 

   a. Create time variable from duration

   b. Create interval from duration and datetime

   c. Create duration from two datetimes

Note: Alternative to storing dates is to collect duration - PnYnMnDTnHnMnS or PnW (e.g., P2Y : 2 year ; PT42M18S : 42 Minutes 10 Seconds)

Note also that start/end reference dates often refer to first/last date of drug exposure. 

Control Terminology

'Metadata Documentation'

Reference Files, List, Submissions Guide

(See %make_codelist_formats macro)


1. Sponsor defined - List of constant uppercase values ex. treatment, race, sex, etc.

2. Dataset used with PROC FORMAT to create user defined formats.

3. From raw to SDTM or SDTM to ADaM, convert variables to standard labels or transformed values.  For example convert visits to analysis visits.

4. MEDRA (version 16.1) is another example.

5. Can be used to create macro variables for do loop processing.

6. None or minimized user-defined formats.


One CRF page to Many SDTM Domains Mapping - example Demographics CRF to DM, SC and DS

Example Observation: Subject 123 had a severe headache starting on study day 2.

 Unique Subject Identifier=123, Qualifier=sever, Topic=headache, Timing=day 2


6 CDISC Classes based on type of data

SDTM/ADaM Domain

Special Purpose

(Patient Attributes)

(Generally one record per patient per X)


(Sponsor Controlled, protocol planned treatment)

(Generally multiple records per patient)




(Patient Controlled, Not planned)


(Generally multiple records per patient)

1. Generally all upper case text

2. Numeric values such as 1, 2 or 3 instead of user defined formats such as MILD, MODERATE, SEVERE. 

3. Generally requires relative times variables for partial dates --STRF [BEFORE, DURING, AFTER, U], --ENRF [BEFORE, DURING, AFTER, URING/AFTER, U])




(Patient Measurements - Normalized, Vertical Structure)


(Generally multiple records per patient)

1. Generally requires VISITNUM and or a sequence number

2. May also contain paired variables such as VSSTRESN and VSSTRESC

3. Almost always requires subsetting to process, ex. WHERE TESTCD = '';

Trial Design

(Study Protocol/Compliance)

(Generally one record per patient per X)




Special Relationship

SUPPQUAL (Supplemental Qualifer), RELREC (Relating Records)

1. One single dataset with Domain variable or for each Domain containing unmapped raw dataset variables (Domain specification) or non-standard raw variables.  These may be extra variables not on the case report form.

2. Store free text values such as for LIST and OTHER in generic character variables - QNAM ($8), QLABEL ($40), QVAL ($200) and QORIG ($10).  Example - XYZ123, DM, UNI101, RACEOTH, Race, Other, BRAZILIAN, CRF.

3. For multiple records per patient, there may be extra variables - IDVAR ($8), IDVARVAL ($3).  Example - 'DMSEQ', DMSEQ.  DMSEQ is an example of a traceability variable.  DMSEQ is manually created based on key variable sort order.

4. Often linked with corresponding SDTM domain dataset by STUDYID, RDOMAIN (DM), and USUBJID for one record per patient or STUDYID, RDOMAIN, USUBJID, and IDVARVAL (XXSEQ) for multiple records per patient. 

ADaM Datasets  

Key Variables
ADSL (Paper)


Multiple records per subject (Generally summary record of one record per patient per param (ex. lbtestcd) per visit)



Presentations: Basics, PHUSE, PharmaSUG


 Animated Guides by Russ Lavery and Susan Fehrer (Top)

The logical cascade through the --Orres variable

Relationships Among CDISC Variables (Part I)

Relationships Among - -ORRES and Other CDISC Variables (Part 1 of 3)

Relationships Among CDISC Variables Concerning Day and Date (Part 2 of 3) (TV, TD)

Relationships Between CDISC Variables Linking Domains (RELREC) (Part 3 of 3)


 PROC CDISC/Clinical Toolkit (Top)

Clinical Standards toolkit 1.5 how do i know my metadata is right? [Presentation]

Clinical Data Integration  (User Guide) (Presentation]

1. Designing and Building an Administrative Model for SAS Drug Development Tool, Margaret Coughlin

2. Retirement of Legacy Clinical Systems and moving to SAS Drug Development, Much More than Just Moving the Data, Fred Forst, John Standefer

3. SDD ++: Extending SDD Capabilities, Sandeep Juneja

4. The SAS Programming Experience in the SAS Drug Development (SAS DD) Environment – Comparing with the Regular SAS Programming Environment, Amos Shu

5. Achieving Efficiencies using SAS® Drug Development, Aik Hoe Seah

6. Super Demo Presentation of SDD and SAS Clinical Data Integration

7. Tracking Metadata within SAS Drug Development Using SDDPARMS Bradford J. Danner, Matthew J. Wiedel, Katrina E. Canonizado

8. Managing Clinical Data Standards: An Introduction to SAS® Clinical Data Integration, Michael Kilhullen

9. Using Custom Data Standards in SAS® Clinical Data Integration, Michael Kilhullen

10. SDTM mapping becomes more EASY with SAS Clinical Data Integration!!, Saumilkumar Tripathi [Rave, Metadata, SAS Clinical Data integration]


Top Benefits of PROC CDISC
1. Validate SDTM dataset structure - See V_DM.XLS

2. Verifies all required SDTM variables and attributes

3. Identifies missing, but expected or permitted SDTM variables 

4. Identifies missing, but expected controlled terminology values

5. Verifies that required variables do not contain missing values

6. Read XML file to create SDTM dataset

7. Write XML file from SDTM dataset

Top Benefits of SAS Clinical Standards Toolkit

1. Create a define.xml file

2. Can run up to 143 standard SDTM, WebSDM and Janus checks for compliance on metadata, data values, date format, multiple-record, multiple-table and control term

1st level (Structure) - Domain and variable metadata requirements and unwanted variables

2nd level (Observational) - Data structure and value requirements

Details - control terminology, column, column attribute, column value, date, metadata, multiple-record, multiple-tables. 

3. Create customized standards

4. Create empty domain tables modeled after registered standard

5. Create custom validation checks

SAS® Reference SAS Clinical Standard Toolkit Presentation, SAS Support

1. Introduction to SAS® Clinical Standards Toolkit, Andreas Mangold, Nicole Wächter

2. Taking a Walk on the Wildside: Use of the PROC CDISC-SDTM 3.1 Format, William C. Csont

3. Supporting CDISC Standards in Base SAS Using the SAS Clinical Standards Toolkit Peter Villiers

4. Retirement of Legacy Clinical Systems and moving to SAS Drug Development, Much More than Just Moving the Data, Fred Forst, John Standefer

5. CDISC Implementation Step by Step: A Real World Example Sy Truong, Patricia Gerend [Introduction]

6. Using PROC CDISC, Chauthi Nguyen

7. Validating CDISC Compliant data and Creating Define.xml made easy, Sandeep Purwar [QC]

8. Proc CDISC: Implementation and Assessments, Sheetal Nisal and Shilpa Edupganti


10. Building (and Rebuilding) the CDISC Toolbox, Frank DiIorio, Jeff Abolafia

11. Exploring SAS PROC CDISC Model=ODM and Its Undocumented Parameters, Elena Valkanova, Irene Droll

12. How to validate clinical data more efficiently with SAS Clinical Standards Toolkit, Wei Feng

13. Null Flavors, A tool for handling missing and awkward data, Diane Wold


ODM - Operational Data Model and XML files (Top)

PROC CDISC examples for CDISC ODM, LIBNAME example to inport CDISC ODMCDISC Online ReferenceXML4Pharma

1. ODM and DEFINE.XML Presentation

2. Exploring SAS® PROC CDISC Model=ODM and Its Limitations, Elena Valkanova, Irene Droll

3. Deep Dive into ODM Validation, Ronald Steinhau, [Presentation]

4. Using the SAS® Clinical Standards Toolkit 1.5 to import CDISC ODM files, Lex Jansen

5. From ODM to SDTM: An End-to-End Approach Applied to Phase I Clinical Trials

6. The Second CDISC Pilot Project A Metastandard for Integrating Databases, Gregory Steffens, Ian Fleming [Metadata]

7. Software Tools for working with CDISC ODM, SDTM, Lab and define.xml [Presentation]

8. CDISC XML Leaving the Stone Age of data transmission, Sven Greiner [Presentation]

9. Dataset-XML - A New CDISC Standard. Lex Jansen [Presentation]

10. Building (and Rebuilding) the CDISC Toolbox, Frank DiIorio, Jeff Abolafia

11. Map Metadata – Going Beyond the Obvious/Connecting the Dots Gregory Steffens, Praveen Garg [Presentation]

12. It's Easy If You Know How: Importing, Processing, and Exporting CDISC XML with SAS, Michael Palmer [Introduction]

13. XML Basics for SAS Programmers, Yong Li

14. Results-Level Metadata: What, How, and Why, Frank Dilorio, Jeffrey Abolafia [Presentation]

15. Analysis Results Metadata for Define-XML v2 [Presentation] [ARM]

16. What’s new in ADaM?, Gavin Winpenny

17. What is high quality study metadata?, Sergiy Sirichenko, Max Kanevsky

18. The Use of Metadata in Creating, Transforming and Transporting Clinical Data, Gregory Steffens



1. CDISC Variable Mapping and Control Terminology Implementation Made Easy, Balaji Ayyappan, Manohar Sure [Metadata]

2. Validating Controlled Terminology in SDTM Domains, John Gerlach [QC, Macro]

3. Truncation, Variable Association, Controlled Terminology, and Some Other Pitfalls in the SDTM Mapping Process, Na Li, XenoPort, Gary de Jesus, Daniel Bonzo [FA, Findings About]

4. Converting CDISC Controlled Terminology to SAS Formats, Sandeep Juneja, Vivek Mohan

5. Brave New World: How to Adapt to the CDISC Statistical Computing Environment, Jeff Abolafia, Frank DiIorio

6. Managing The Change And Growth Of A Metadata-Based System, Jeff Abolafia, Frank DiIorio

7. Controlling Controlled Terminology, Ryan Burns [Extensible]

8. A Toolkit to Check Dictionary Terms in SDTM, Huei-Ling Chen, Helen Wang [Macro]

9. %check_codelist: A SAS macro to check SDTM domains against controlled terminology, Guido Wendland [Append AEACTION]

10. Planning to Pool SDTM by Creating and Maintaining a Sponsor-Specific Controlled Terminology Database, Cori Kramer, Ragini Hari, Keith Shusterman [ISS, ISE]

11. Codelists Here, Versions There, Controlled Terminology Everywhere, Shelley Dunn [Data Checks, Basics, Glossary]


13. 2019 CDISC US Interchange – CDISC Controlled Terminology (CT), Enhancements, Erin Muhlbradt [Presentation]

14. Creating SDTMs and ADaMs CodeList Lookup Tables, Sunil Gupta

15. From Codelists to Format Library, Nancy Brucken

16. Automatic Consistency Checking of Controlled Terminology among SDTM Datasets, Define.xml, and NCI/CDISC Controlled Terminology for FDA Submission

17. How Valued is Value Level Metadata?, Shelley Dunn

18. Keeping control in a changing world, Johannes Ulander

19. Control Terminology Best Practices, Pinnacle [Presentation]

20. Creating a Modern Statistical Computing Environment for Both GxP and Exploratory Analytics [Poster]


 ISO 8601 Dates  (See Proc Expand and SAS Dates) ex. 2009-01-01T12:00:00, YYMMDD10. or YYMMDD19. format (Top)

1. Harnessing the Power of SAS ISO 8601 Informats, Formats, and the CALL IS8601_CONVERT Routine, Kim Wilson


2. Imputing ISO8601 Dates From Character Variables Containing Partial Dates, John Gerlach


3. ISO 101: A SAS® Guide to International Dating Peter Eberhardt, Xiaojin Qin [call IS8601_CONVERT]


4. Good Versus Better SDTM -- Date and Time Variables, Mario Widel, Henry B. Winsor

5. An Approach for Deriving a Timing Variable in SDTM Standards, Shaoan Yu and Joyce Gui [Macro]

6. A Complete Derivation of Duration and Display in ISO 8601 Using SAS Program, Joyce Gui and Sandy Wang Rutgers [Macro]

7. Calculating time-to-event parameters using a single DATA step and a RETAIN statement, Andrew L Hulme

8. Date Conversions in SDTM and ADaM Datasets, Huei-Ling Chen, Lily Zhang, Lili Chen [Partial, Impute dates and times, IS8601 informats]

9. A proposal for intervention and event partial date time imputation, Chunpeng Zhao [Presentation, Impute analysis variable - MEAN, etc.]

10. Relationships Among CDISC Variables Concerning Day and Date (CDISC Variable Relationships Part 2 of 3), Susan Fehrer, Russ Lavery [Period]

11. Best Practices - Assigning VISITNUM to Unscheduled Visits and Assigning EPOCH to Observation [Presentation]

12. Duration Calculation from a Clinical Programmer’s Perspective Alice Chen

13. Standard SAS Macros for Standard Date/Time Processing, Qin Lin, Tim Kelly [Macro]

14. ISO 8601 and SAS®: A Practical Approach, Derek Morgan

15. ISO 8601 – An International Standard for Date and Time Formats, Shi-Tao Yeh

16. An Approach for Deriving a Timing Variable in SDTM Standards, Shaoan Yu and Joyce Gui [Macros]

17. How to Create Variables Related to Age, Joyce Gui and Shaoan Yu

18. Converting Non-Imputed Partial Dates (for SDTM Data Sets) Using PROC FCMP, Noory Kim

19. ISO 8601 and SAS®: A Practical Approach, Derek Morgan


CDASH (Control Terms, CDM, CDISC.ORG(Top) (SDTM aCRF Guideline)

1. A Practical Introduction to Clinical Data Acquisition Standards Harmonization (CDASH), Jennifer Price

2. CDASH Standards for Medical Device Trials: CRF Analysis Parag Shiralkar, Jennie Tedrow, Kit Howard, Laura Fazio, Daniela Luzi, Rhonda Facile


4. BRIDGing CDASH to SAS: How Harmonizing Clinical Trial and Healthcare Standards May Impact SAS Users, Clinton Brownley

5. SDTM Harmonization in the Absence of CDASH – A Modularized Approach to Domain Programming, Annie Guo [Questionnaire QS, Mapping]

6. Kendle Implementation of CDASH [Presentation]

7. SlideServe [Presentations]

8. AUTOSDTM: A Macro to Automatically Map CDASH Data to SDTM, Hao Xu and Hong Chen [Macro]

9. CDASH and SDTM: Why We Need Both!

10. CDASHIG V2.0: What is it Good For?


 SDTM, Introduction Presentation, Findings About Presentation (Required by 2016) (Top)


Introduction - Introduction to the CDISC Standards, Sandra Minjoe

An Introduction to SDTM ± 298 pages in 20 minutes?!, Jennie Mc Guirk [Beginner Presentation]

CDISC Model Presentation, Electronic Submission Presentation, Two, Pilot Project, Validation Tools

A Brief Introduction to CDISC - SDTM and Data Mapping [Presentation]


SDTM Versions

Updates in SDTM IG V3.3: What Belongs Where – Practical Examples, Peng Du, William Paget, Lingyun Chen and Todd Case

What to Expect in SDTMIG v3.3, Fred Wood

Is It Time to Upgrade CDISC SDTM from v1.1/v3.1.1 to v1.2/v3.1.2?, Susan Fehrer

The Evolution of SDTM – What’s new, Tina Apers [Presentation]

Time Travel for Librarians [Presentation]

1. Strategies for Implementing SDTM and ADaM Standards, Susan Kenny, Michael Litzsinger

2. Implementation of the CDISC SDTM at the Duke Clinical Research Institute, Jack Shostak

3. Validating CDISC SDTM-Compliant Submission-Ready Clinical Datasets with an In-House SAS® Macro-Based Solution, Bhavin Busa, Sheila Vince, Jameelah Aziz [QC]

4. A Validation Macro to Check Compliance of CDISC SDTM Data, Hany Aboutaleb [Macro, Proc SQL]

5. Developing Your SDTM Programming Toolkit, David Scocca [SAS Macros]

6. From SDTM to ADaM, Suwen Li, Sai Ma, Bob Lan, Regan Li

7. Implementing CDISC, SDTM, and ADaM in a SAS® Environment, Pankaj Bhardwaj 

8. A Case of Retreatment – Handling Re-treated Patient Data, Sreeram Kundoor, Sumida Urval

9. Data Conversion to SDTM: What Sponsors Can Do to Facilitate the Process, Fred Wood [Presentation]

10. Leveraging SDTM Standards to Cut Datasets at Any Visit, Anthony L. Feliu, Stephen W. Lyons [Data Cutoff Macro]

11. Considerations in the Use of Timing Variables in Submitting SDTM-Compliant
Datasets, Jerry Salyers, Richard Lewis, Fred Wood
 [Relative Time Reference, -TPT, --STRTPT, --STTPT, --ENRTPT, --ENTPT, --STRF, --ENRF]

12. Benefits of Using Excel file in CDISC SDTM Data Mapping, Hong Wang

13. How to Use SDTM Definition and ADaM Specifications Documents to Facilitate SAS Programming, Yan Liu [Excel]

14. Outsourced Data Integration Project with CDISC SDTM & ADaM Deliverables, Christine Teng, Margaret Coughlin [Excel]

15. SDTM Implementation Guide – Clear as Mud: Strategies for Developing Consistent Company Standards, Brian Mabe [AEACNOTH, Presentation]

16. A SAS Macro for Creating Adverse Event Analysis Dataset, Daniel Li, Suwen Li, Stephanie Sproule [Proc REPORT]

17. Practical Methods for Creating CDISC SDTM Domain Data Sets from Existing Data, Robert Graebner [Tutorial, Comments]

18. Help! The EX Domain is now an Analysis Dataset! What Do I Do?!?, David C. Izard [Exposure, Drug Accountability]

19. Creating SDTM Datasets from Legacy Data, Fred Wood

20. An Excel Framework to Convert Clinical Data to CDISC SDTM Leveraging SAS® Technology, Ale Gicqueau, Marc Desgrousilliers

21. Considerations in the Submission of Exposure Data in an SDTM-Compliant Format, Fred Wood, Jerry Salyers and Richard Lewis, 2014 [Drug Accountability]

22. Experiences Submitting CDISC SDTM and Janus Compliant Datasets Carol Vaughn, Gregory Ridge, and William Friggle [Checklist, Screen Failure]

23. CDISC Mapping and Supplemental Qualifiers, Arun Raj Vidhyadharan, Sunil Mohan

24. Ensuring Consistent Data Mapping Across SDTM-based Studies – a Data Warehouse Approach, Annie Guo [Project Management, INTRP]

25. INTERPRETING CDISC ADaM IG THROUGH USERS INTERPRETATION, Angelo Tinazzi [Presentation, Traceability, Technical Questions]

26. CDISC Builder to validate SDTMs [Video Tutorials]

27. A Taste of SDTM in Real Time, Changhong Shi and Beilei Xu [DS Disposition]

28. Considerations When Representing Multiple Subject Enrollments in SDTM, Kristin Kelly, Mike Hamidi [Cross-over studies]

29. Converting Clinical Database to SDTM: The SAS® Implementation, Hong Chen [CDASH]

30. CDISC SDTM Implementation Process, Micheal Todd [Presentation]

31. Backpacking Your Way Through CDISC: A Budget-Minded Guide to Basic Concepts and Implementation, Rachel Brown, Jennifer Fulton [Definitions]

32. 10 things you need to know for a successful e-submission Sangeeta Bhattacharya [Presentation]

33. Useful Adverse Events (AE) data Diagnostics and Summarization, Abhinav Srivastva [AEENRF]

34. Common Variables in Adverse Event and Exposure analysis datasets specific for Oncology clinical trials, Haridasan Namboodiri [Macro]

35. Automation of ADAM Dataset Creation with a Retrospective, Prospective and Pragmatic Process, Karin LaPann, Terek Peterson

36. CDISC Standards Can Benefit Medical Writers in Authoring Adverse Event Narratives, Richard Zink

37. In-Depth Review of Validation Tools to Check Compliance of CDISC SDTM Ready Clinical Datasets, Bhavin Busa, Kim Lindfield [Presentation]

38. Reducing Variable Lengths for Submission Dataset Size Reduction, Sandra VanPelt Nguyen [Macro]

39. A case of mistaken ID: reimagining rescreenings and reenrollments, Matt Metherell [Presentation, Cross-over studies]

40. Simplifying PGx SDTM Domains for Molecular biology of Disease data (MBIO), Sowmya Srinivasa Mukundan and Charumathy Sreeraman [Biospecimen Events BE]

41. Building and Customizing a CDISC Compliance and Data Quality Application, Wayne Zhong [QC, Edit Checks, Proc SQL] 

42. Challenges with the interpretation of CDISC, Who can we trust?, Linda Simonsson [Presentation, Screen Failure, Inclusion/Exclusion, AEENRF, AEENRTPT, AEENTPT]

43. The Super Genius Guide to Generating Dummy Data, Brian Varney

44. An Implementation of ADaM standards NOT driven by a submission, Karin Fleischer Steffensen, Gitte Dam Jepsen

45. Application of Some Advanced PROC SQL Features in Clinical Trial Programming, Changhong Shi, Sylvianne B. Roberge

46. Tips for efficient CDISC eCRT production, Lanting Li, Yu Zhu, Huan Zhu [Unscheduled, Not Submitted, Checklist, Process, Define.xml]

47. Challenges and Solutions for Handling Re-screened Subjects in SDTM, Charity Quick, Paul Nguyen [SD0058]

48. Evolution and Implementation of the CDISC Study Data Tabulation Model (SDTM), Fred Wood, Tom Guinter [Presentation]

49. Subset without Upsets: Concepts and Techniques to Subset SDTM Data, Jhelum Naik, Sajeet Pavate [Data Cutoff, Screen Failure, RFXENDTC]

50. Going Against the Flow: Backmapping SDTM Data, Pantaleo Nacc [Presentation, Macros]

51. Automatic generating blankcrf.pdf for Rave Study, Haiqiang Luo, Yong Cao

52. Taming Rave: How to control data collection standards?, Dimitri Kutsenko [Presentation]

53. Supporting End to End Standards in Study Set Up, Anna-Louise Willson [Rave]

54. The Submission Data File System Automating the Creation of CDISC SDTM and ADaM Datasets, Marcus Bloom, David Edwards [Metadata, SDF]

55. Three Unique Case Studies - On the Trail of the Holy Grail of SDTM Implementation, Ken Stoltzfus

56. A How-To Guide for Extending Controlled Terminology Using SAS Clinical Data Integration, Melissa R. Martinez

57. A Model for Reviewing the Operational Implementation of CDISC Standards, Andy Richardson [Presentation]

58. Constructing a Simple Data Repository, Magnus Megelbier [Presentation]

59. Looking for SDTM Migration Specialist, Angelo Tinazzi [Presentation, Oncology]

60. The CDISC Study Data Tabulation Model (SDTM): History, Perspective, and Basics, Fred Wood

61. Homogenizing Unique and Complex data into Standard SDTM Domains with TAUGs, Sowmya Srinivasa Mukundan, Charumathy Sreeraman

62. Begin with the End in Mind – Using FDA Guidance Documents as Guideposts when Planning, Delivering and Archiving Clinical Trials, David C. Izard

63. Evolution of SDTMIG 3.1.1 to 3.1.2: A mapping specialist must reference on these changes, Rachit Desai, Anirudh Gautam, Vikash Jain

64. Evaluating SDTM SUPP Domain For AdaM - Trash Can Or Buried Treasure Xiaopeng Li, Yi Liu, Chun Feng [SUPPXX]

65. How to Design a Custom SDTM Domain for Nonclinical Data, PhUSE Standards Roadmap Team [Blog]

66. Confessions of a Clinical Programmer: Dragging and Dropping Means Never Having to Say You’re Sorry When Creating SDTM Domains, Janet Stuelpner, Jack Shostak [SAS CLINICAL DATA INTEGRATION]

67. Using SAS to Incorporate the SDTM for a Study Funded by the NIH, Fenghsiu Su, Bradford Jackson, Kaming Lo, Sumihiro Suzuki, Karan Singh, David Coultas, Sejong Bae [Randomization Date] 

68. From Standards that Cost To Standards that Save: Cost Effective Standards Implementation, Jeffrey Abolafia, Frank DiIorio

70. CDISC: Pains and Pitfalls to Dataset Creation, Janet Stuelpner, Steven Michaelson

71. Finding Out About Findings About, Caroline Clark-Steel [Presentation]

72. Findings about 'Findings About', Madhura Khare

73. Considerations in Submitting Non-Standard Variables: Supplemental Qualifiers, Findings About, or a Custom Findings Domain, Jerry Salyers, Richard Lewis, Fred Wood [Findings About, FA]

74. How Do I Map That? - SDTM Implementation Challenges, Chris Price [Clinical Events]

75. Have SAS Annotate Your Blank CRF for You! Plus Dynamically Add Color and Style to Your Annotations, Steven Black

76. Complexity in collection of PGx data and challenges in mapping to SDTM, Rama Kudaravalli [Biospecimen Events BE]

77. Experiences and lessons learned from a first SDTM submission project, Paul Vervuren, Bas van Bakel [Beginner, Unscheduled, Checklist]

78. Macro to get data from specific cutoff period, Kiranmai Byrichetti, Jeffrey Johnson [Data Cutoff]

79. Mapping CDISC Metadata Attributes: Using Data _Null_ and Proc Datasets in SAS, Rita Tsang

80. Flags for Facilitating Statistical Analysis Using CDISC Analysis Data Model, Chun Feng, Xiaopeng Li, Nancy Wang [BASETYPE]

81. SDTM Electronic Submissions to FDA: Guidelines and Best Practices, Christina Chang, Kyle Chang [QC Checklist]

82. Building Efficiency and Quality in SDTM Development Cycle, Kishore Pothuri, Bhavin Busa

83. What’s New in CDISC, Peter Van Reusel, Nick De Donder Assero and Dave Iberson-Hurst [Presentation]

84. Checking for SDTM Compliance: The Need for Human Involvement, Fred Wood and Adrienne Boyance

85. Good vs Better SDTM: Limitations as Operational Model, Henry B. Winsor, Mario Wide [DVDECOD]

86. Deriving Rows in CDISC ADaM BDS Datasets Using SAS® Data Step Programming, Sandra Minjoe [Macros, BASETYPE]

87. Automating SDTM File Creation: Metadata Files Speeding the Process, Daphne Ewing

88. Utilize Dummy Datasets in Clinical Statistical Programming, Amos Shu

89. Customer oriented CDISC implementation, Edelbert Arnold, Ulrike Plank

90. Mapping the Company's Legacy Data Model to SDTM, Nicolas Dupuis, Anja Feuerbacher, Bruce Rogers

91. Going Against the Flow: Backmapping SDTM Data, Pantaleo Nacc [RELREC]

92. How to easily convert clinical data to CDISC SDTM, Ale Gicqueau, Miki Huang, Stephen Chan

93. Traceability between SDTM and ADaM converted analysis datasets, Florence Somers and Michael Knoessl

94. Best practices for annotated CRFs, Amy Garrett

95. SAS Dataset Content Conversion to CDISC Data Standards, Anthony Friebel, Thomas Cox, Edward Helton

96. Basic SDTM House-Keeping, Emmy Pahmer

97. Common Mistakes by Programmers & Remedies, Venkata Sairam Veeramalla

98. Statistician’s secret weapon: 20 ways of detecting raw data issues, Lixiang Larry Liu [QC Checklist]

99. Automate Validation of CDISC SDTM with SAS, Sy Truong

100. Building Efficiencies in Standard Macro Library using Polymorphism, Binoy Varghese, Sagar Rana

101. Assigning VISITNUM and EOPOCH, Ru He, Na Duan [Presentation]

102. The SDTM Programming Toolkit, David Scocca


104. Assessing CDISC Therapeutic Area User Guides in a machine readable format, Johannes Ulander, Niels Both

105. Traceability: Some Thoughts and Examples for ADaM Needs, Sandra Minjoe, Wayne Zhong, Quan Zhou, Kent Letourneau, Richann Watson [ADEG]

106. A Macro to Add Variables to SDTM Standard Domains, Xianhua Zeng [Comments]

107. PharmaSUG 2017 Panel [Presentation]

108. The CDISC SDTM Exposure Domains (EX and EC) Demystified. How EC Helps You Produce a Better (more compliant) EX, Tom Guinter [Intro]

109. ADaM Grouping: Groups, Categories, and Criteria. Which Way Should I Go?, Jack Shostak [CRIT1FL, AVALCAT1, ANL01FL, MCRIT1]

110. The Benefits of Traceability Beyond Just From SDTM to ADaM in CDISC Standards, Maggie Ci Jiang

111. Building a Fast Track for CDISC: Practical Ways to Support Consistent, Fast and Efficient SDTM Delivery, Steve Kirby, Mario Widel, Richard Addy

112. It Depends On Your Analysis Need Sandra Minjoe [LBALL]

113. A Generic Concept to Handle SDTM (and other CDISC) Data Sets, Peter Schaefer

114. Mapping MRI data to SDTM and ADaM, Lingling Xie, Xiaoqi Li

115. The Untapped Potential of the Protocol Representation Model, Jeffrey Abolafia, Frank DiIorio [Presentation]

116. A Successful MetaData Repository (MDR) - It’s About Managing Relationships, Phil Giangiulio and Melanie Paules [Presentation]

117. How to Create High Level Data Review Tools, Xiaoyu (Sean) Liu [Cutoff]

118. Customer Oriented CDISC Implementation [Presentation]

119. Data Integrity: One step before SDTM, Pavan Kathula, Sonal Torawane

120. QC of SDTM and aCRF using SAS, Rune Pedersen, Niels Both [Macro]

121. Developing annotated CRF: SAS, Excel and patience as your friends, Ilias Pyrnokokis

122. Automating Production of the blankcrf.pdf in a CRO Environment, Jonathan North [Java]

123. Creating PDF Documents including Links, Bookmarks and a Table of Contents with the
SAS® Software, Lex Jansen

124. SDTM Annotations: Automation by implementing a standard process, Geo Joy, Andre Couturier [Bookmarking, PDF]

125. SDTM Bookmarking Automation: Leveraging SAS, Ghostscript and Form-Visit Study Data, Nasser Al Ali, Katrina Paz [Data Step, PDF]

126. The Good Data Submission Doctor: 5 Top SDTM Frequently Asked Questions Blog

127. Good versus better SDTM: Including Screen Failure Data in the Study SDTM?, Henry Winsor, Mario Widel [Inclusion/Exclusion]

128. Updating SDTM Metadata Excel File (Define.xls) with SAS, Lin Yan [LINEFEED, '0A'x]

129. Automated generation of program documentation by means of tagged SAS comments and metadata for integrated analysis, Thomas Wollseifen

130. Methods to Derive COVAL-COVALn in CO Domain, Chunxia Lin [Macro, Comments]

131. A SASsy Study of eDiary Data, Amie Bissonett [CC, CE, DF]

132. SDTM Metadata: The Output is only as Good as the Input, Sue Sullivan

133. How to Prepare High-quality Metadata for Submission, Varun Debbeti [Presentation, Split]

134. Tips on Creating a Strategy for a CDISC Submission, Rajkumar Sharma

135. A Practical Approach to Re-sizing Character Variable Lengths for FDA Submission Datasets (both SDTM and ADaM), Xiangchen Cui, Min Chen [Macro]

136. New Features in Define-XML V2.0 and Its Impact on SDTM/ADaM Specifications Hang Pang

137. Creation ADaM Define.xml v2.0 Using SAS Programs and Pinnacle 21, Yan Lei, Yongjiang Xu, Michelle Pupek [Presentation]

138. SI05 Define’ing the Future, Nicola Perry and Johan Schoeman [Presentation]

140. Automate the Process to Ensure the Compliance with FDA Business Rules in SDTM Programming for FDA Submission, Xiangchen Cui, Hao Guan, Min Chen, and Letan Lin [In-house macros, Compliance checks]

141. CDISC Standards: End to End [Course notes]

142. Considerations and Updates in the Use of Timing Variables in Submitting SDTM-Compliant Datasets, Jerry Salyers [Presentation]

143. SDTM EX and EC: Considerations When Submitting Exposure Data, Jerry Salyers, Kristin Kelly

144. More Traceability: Clarity in ADaM Metadata and Beyond, Wayne Zhong, Richann Watson, Daphne Ewing, Jasmine Zhang [Intermediate Datasets, TRTEMFL]

145. Time Since Last Dose: Anatomy of a SQL Query, Derek Morgan

146. Compare and conquer SDTM coding, Phaneendhar Gondesi

147. Evolving CDISC to the Next Decades: The CDISC Proof of Concept, Peter Van Reusel, Sam Hume [CDISC 360, Presentation White Paper - Read mapping excel files as metadata to populate SAS programs, R functions to generate SAS programs of SAS macro calls] 

148. An Automated, Metadata Approach to Electronic Dataset Submissions, Janette Garner

149. Lets Visit Scheduled/Unscheduled, Windowing and Clinical Encounters [SAS Blog 1, SAS Blog 2]

150. Macro-Supported Metadata-Driven Process for Mapping SDTM VISIT and VISITNUM, Eric Crockett, Pragathi Dayananda

151. Good Programming Practice [GPP] in SAS® & Clinical Trials, Srinivas Vanam, Manvitha Yennam, Phaneendhar Vanam

152. Cost Effective Standards Implementation: A New Paradigm for the Drug Development Life Cycle, Jeffrey Abolafia, Frank DiIorio

153. CDISC Standards End-to-End: Transitional Hurdles, Alyssa Wittle, Christine McNichol, Antonio Cardozo

154. Amazing End-to-End Demo in No Time: But Wait, There's More…, Gert Nissen, Vegard Hansen

155. SAS® End-to-End solutions in Clinical Trial, Emma Liu

156. Practices in CDISC End-to-End Streamlined Data Processing, Chengxin Li

157. End to End SDTM Automation: A Metadata Centric Approach, Roman Radelicki, Swapna Pothula

158. End-to-End Traceability from Protocol Development to Submission, Mikkel Traun, Trine Klingberg, Francis D'sa

159. Effective Use of Metadata in Analysis Reporting, Jeffrey Abolafia

160. Optimizing SDTM Specification Development with Auto-population Cori Kramer, Ryan Adalbert

161. DIY: Create your own SDTM mapping framework, Bas van Bakel

162. Automating ADSL Programming Using Pinnacle 21® Specifications, Tracy Sherman, Aakar Shah

163. How to make SAS Drug Development more efficient, Xiaopeng Li, Chun Feng, Peng Chai

164. Streamlining Regulatory Submission with CDISC/ADaM Standards for Nonstandard Pharmacokinetic/Pharmacodynamic Analysis Datasets, Xiaopeng Li, Katrina Canonizado, Chun Feng, Nancy Wang

165. Implementation of STDM Pharmacogenomics/Genetics Domains on Genetic Variation Data, Linghui Zhang [Biospecimen Events BE]

166. Harmonizing SDTM at the Source: Designing Collection Instruments that Support Sponsor Standards, Sue Huxtable, Charlotte Lomberg

167. What is RE domain?, David Ju, ERT [Respiratory]

168. Ensuring the quality of your data in Respiratory trials: Data management from a statistical standpoint, Abigail Fuller

169. Findings About: De-mystifying the When and How, Soumya Rajesh, Michael Wise [FA]

170. CDISC - SDTM/ADaM Dataset Generation Using R, Prasanna Murugesan [R Programming]

171. Metadata-driven Modular Macro Design for SDTM and ADaM, Ellen Lin, Aditya Tella, Yeshashwini Chenna and Michiel Hagendoorn

172. Smart Transformation of Clinical and Nonclinical Data for Insights, Isaac Mativo, Raja Ramesh, Phaneedndra Bonda [Presentation]

173. Use CDISC SDTM as a data middle-tier to streamline your SAS® infrastructure

174. Timing Variables In Clinical Trials: Avoiding Common Mistakes And Dealing With Unforeseen Issues, Gregory Weller

175. Do It Yourself: Create your own SDTM mapping framework, Bas Bakel [Presentation, Mapping Macros]

176. Efficiency Comes From Reusability and Repeatability, Hanming Tu, Dave Evans

177. Confessions of a Clinical Programmer: Creating SDTM Domains with SAS®

178. Automatic Detection and Identification of Variables with All Missing Values in SDTM/ADaM Datasets for FDA Submission, Min Chen, Xiangchen Cui

179. SDTM Attribute Checking Tool, Ellen Xiao

180. SDTM – Just a Walk in the (Theme) Park, Exploring SDTM in the Most Magical Place on Earth, Christine McNichol [RFXSTDTC]

181. The New Tradition: SAS® Clinical Data Integration Vincent J. Amoruccio, Alexion Pharmaceuticals, Inc., Cheshire, CT Janet Stuelpner [RFXSTDTC]

182. Considerations in the Submission of Holter (EG) Data in an SDTM Compliant Format, Sophie Arnould, Elsa Lozachmeur, Joseph Rowley

183. Fake it till you make it with Global SDTM Laboratory Submissions

184. SDTMIG 3.3: New domains, new benefits, Nick Donder [Presentation]

185. Update: Development of White Papers and Standard Scripts for Analysis and Programming, Nancy Brucken, Michael Carniello, Mary Nilsson, Hanming Tu [Takeda]

186. Prepare for Re-entry: Challenges and Solutions for Handling Re-screened Subjects in SDTM, Charity Quick, Paul Nguyen

187. Datacut Strategies: What, why and how, Hiren Naygandhi

188. Moving up! – SDTM v3.2 – What is new and how to use it, Alyssa Wittle, Christine McNichol, Antonio Cardozo [Healthcare Encounters (HO), Subject Status (SS)]

189. Can You Cut It? Implementing the Data Cut-off, Lewis Meares

190. Pooling Clinical Data: Key points and Pitfalls, Florence Buchheit [ISS, ISE]

191. Mapping of Gastrointestinal Tolerance Data to SDTM, Lieke Gijsbers [Poster]

192. My Understandings to SDTM and ADaM, Chunpeng Zhao [FA, FAAE]

193. The implementation of SDTM standards for non-submission purposes, Paul Vervuren, Lieke Gijsbers [QA Checklist, GASTROINTESTINAL Study]

194. PP10 DV Domain and the Classification of Protocol Deviations [Poster]

195. AstraZeneca Data Automation Team: Future Proofing Programming Teams to Meet Tomorrow’s Challenges, Ruhul Amin, Tony Allen [Poster]

196. Advanced Excel Specification for Automatic SDTM Generation, Ilya Mishchenko, Aleksandra Gorbunova, Evgeny Dmitriev, Timur Zagidullin [Presentation]


QS Questionnaire Domain (Top)

1. Formats in another format, Bettina Ernholt Nielsen [Questionnaire Score]

2. Multiple Techniques for Scoring Quality of Life Questionnaires, Brandon Welch, Seungshin Rhee

3. QS Domain: Challenges and Pitfalls - an Interpretation Guide to the Implementation Guide, Knut Mueller

4. Handling non-standardized questionnaires Anne-Sophie Bekx, Éanna Kiely [SF-36]

5. CDISC Questionnaire Supplements [Online Sample of over 60 Questionnaires]

6. Questionnaire Control Terminology [Excel file]

7. CDISC Operational Procedure 017 CDISC SDTMIG Questionnaire Supplements [Guide]

8. Challenges of Processing Questionnaire Data from Collection to SDTM to ADaM and Solutions using SAS®, Karin LaPann, Terek Peterson [SAS Macro, SF-36]

9. Novo Nordisk implementation of Logically Skipped Items for the QS=Questionnaires domain [Poster]


SMQs Presentation, MedDRA Use at FDA Presentation (Top)

1. Practice of SMQs for Adverse Events in Analysis of Safety Data and Pharmacovigilance, Gary Chen, David Shen

2. Everything You Need To Know About Standardised MedDRA Queries, Rajkumar Sharma


4. Introductory Guide for Standardised MedDRA Queries (SMQs) Version 16.0

5. Processing MedDRA SMQs: Using Recursive Programming to Handle Hierarchical Data Structures, Paul Stutzman

6. MAED Sercice: A SAS tool for Implementing SMQs and Performing MedDRA-Based Analysis of AE Data

7. Programming Tips and Examples for Your Toolkit, IV, John Morrill

8. MedDRA – Beyond that basic AE report. How SMQs and MedDRA structure can enhance reporting, Pamela Giese [HOW]

9. MedDRA Term and Coding [Presentation] [Dictionary]


 SUPPQUAL (Supplemental Qualifer) (Top)

1. Generating SUPPQUAL Domains From SDTM-Plus Domains, John Gerlach, Glenn O’Brien [Macro, QEVAL]

2. How to validate SDTM SUPPQUAL Data Set, Kevin Lee [Proc Transpose]

3. Nice SUPPQUAL Variables to Have, Beilei Xu, Changhong Shi

4. SUPPQUAL – Where’s My Mommy?, Sandra VanPelt Nguyen

5. Joining the SDTM and the SUPPxx Datasets, David Franklin [Macro, QEVAL]

6. SUPPQUAL Datasets: Good, Bad and Ugly, Sergiy Sirichenko

7. Using SAS® Macros to Remediate Existing SDTM Data Sets for New Drug Application (NDA) Submission, Yanwei Han [Macro]



RELREC should be created when the data is collected on the same CRF and mapped to separate domians and have a clinical relationship such as AE and CM

1. Avoiding a REL-WRECK; Using RELREC Well, Karl Miller, J. J. Hantsch, Janet Stuelpner

2. Method to Derive Reproducible SDTM Relationship Datasets, Suwen Li, Sai Ma, Regan Li, Bob Lan [Macro]

3. Relationships Between CDISC Variables Linking Domains (RELREC) (Part 3 of 3), Susan Fehrer and Russ Lavery [GRPID]

4. RELREC - SDTM Programmer's Bermuda Triangle, Charumathy Sreeraman [Macro]

5. A Special SDTM Domain RELREC and its Application, Changhong Shi, Beilei Xu [Macro]


 Trial Design - Subject Elements, Subject Visits, (Top)

1. Trials and Tribulations of SDTM Trial Design, Fred Wood, Mary Lenzen [EPOCH, Subject Visits SV, TI, Inclusion/Exclusion]

2. The SE Domain as Treatment Data Store: Using the SDTM Subject Elements (SE) domain to drive analyzing treatment, Jeremy Gratt [Macro]

3. Basic Understanding on SE Domain for Beginners, Gayatri Karkera [Subject Elements]

Below are the key points that need to be minimally checked to see if SE so created is SDTM compliant.

  • SE dataset can not be null i.e. it should not contain zero observations.
  • The variables with core attributes “Req” should not contain null observations.
  • SESTDTC/SEENDTC should contain dates in the form ISO8601 (i.e. YYYY-MM-DDTHH: MM:SS). Only known part of the

dates needs to be submitted.

  • Domain value should be SE.
  • There should be unique value for SESEQ.
  • SESTDTC has to be less than or equal to SEENDTC.
  • The values for the variable ETCD and ELEMENT should match with TE domain.
  • There should not be any gap between the end date of the previous element and the immediate start date of the successive element. 

4. Considerations in Creating SDTM Trial Design Datasets, Jerry Salyers, Richard Lewis, Kim Minkalis, Fred Wood [HOW, RANDQT]

5. SDTM TE, TA, and SE Domains: Demystifying the Development of SE, Kristin Kelly, Fred Wood, Jerry Salyers [Subject Elements, Screen Failure]

6. CREATING SV AND SE FIRST, Henry B. Winsor, Mario Widel [Subject Visits, Subject Elements]

7. Subset without Upsets: Concepts and Techniques to Subset SDTM Data, Jhelum Naik, Sajeet Pavate

8. SDTM Trial Summary Domain: Putting Together the TS Puzzle,Kristin Kelly, Jerry Salyers, Fred Wood [SD1260, SD1269, TSVAL]

9. Protocol Representation: The Forgotten CDISC Model, Jeffrey Abolafia, Frank Dilorio

10. Tips and tricks when developing Trial Design Model Specifications that provide Reductions in Creation time, Ruth Marisol Rivera Barragan [Inclusion/Exclusion]

11. It’s About Time! A Primer on Time-Slotting of Data Using SAS, Maria Reiss [Trial Design, Plot]

12. The CDISC Trial Design Model (TDM), the EPOCH variable, and the Treatment Emergent Flag: How to Leverage these to Improve Review, Tom Guinter [Washout Period]

13. Real-World Application of the Protocol Representation Model, Michelle Marlborough, Joshua Pines

14. Protocol Representation Model in the Real World, Joshua Pines

15. EPOCH in Reverse, Ann Croft

16. Summing up - SDTM Trial Summary Domain, Yi Liu, Jenny Erskine, Stephen Read

17. Dynamically harvesting study dates to construct & QC the SV SDTM domain, Cleopatra DeLeon, Laura Bellamy [Presentation]

18. Trial Summary: The Golden Gate towards a Successful Submission, Bhargav Koduru, and Girish Kankipati [TSVAL, SD2262, SD2264, PCLAS]

19. TS does not mean T(o) S(uffer) – A hands-on guideline for a better understanding of the TS domain, Kristina Zweier [TSVAL, TSVCDVER, Presentation]

20. Every Second Counts! Save Time on Developing Trial Summary Specification, Mei Chu, Kyle Chang [Macro]

21. Dynamically harvesting study dates to construct & QC the SV SDTM domain, Cleopatra DeLeon and Laura Bellamy [Presentation]

22. CDISC SDTM IG v3.4: Subject Visits, Ajay Gupta [Presentation]


 ADaM and Beyond (Top)

1. A Complex ADaM dataset? Three different ways to create one., Kevin Lee

2. XML and SAS®: An Advanced Tutorial, Greg Barnes Nelson

3. A Table-Driven Solution for Clinical Data Submission, Jim Sattler


4. Creating the Time to Event ADaM Dataset: The Nuts and Bolts, Nancy Brucken, Paul Slagle [HOW, ADTTE, Table Shell]

5. Using the ADaM Basic Data Structure for Survival Analysis, Nancy Brucken, Sandra Minjoe, Mario Widel [BDS, Metadata]

6. From SAP to ADaM: The Nuts and Bolts, Nancy Brucken, Paul Slagle

7. From SAP to BDS: The Nuts and Bolts, Nancy Brucken, Paul Slagle [HOW - Analysis Results Metadata ARM]

8. Mapping Clinical Data to a Standard Structure: A Table Driven Approach, Nancy Brucken, Paul Slagle [Derived Variables, Metadata, Macros] 

9. Using the ADaM ADAE Structure for non-AE Data, Sandra Minjoe, Mario Widel

10. Hands-On ADaM ADAE Development, Sandra Minjoe [HOW, Table Shell]

11. CDISC, ADaM, and TTE: What Are These Acronyms and How Can They Help Me with My Filing?, Sandra Minjoe, Judy Phelps [ANAL1-3]

12. Adding New Rows in the ADaM Basic Data Structure: When and How Mario Widel, Sandra Minjoe [HOW, ADaM Spec]

13. CDISC ADaM Application: Does All One-Record-per-Subject Data Belong in ADSL?, Sandra Minjoe [LBALL] 

14. Traceability in the ADaM Standard, Ed Lombardi

15. Common Misunderstandings about ADaM Implementation, Nate Freimark, Susan Kenny, Jack Shostak, John Troxell [BDS, APERIOD, Cross-Over]

16. Designing and Tuning ADaM Datasets, Songhui ZHU

17. %D_ADSL – Automating ADSL Creation from Metadata File, JIANHUA HUANG [SAS Macro]

18. Derived observations and associated variables in ADaM datasets, Arun Raj Vidhyadharan [DTYPE, LOCF, Macro]

19. Implementation Considerations for PARAM/PARAMCD Using ADaM BDS, Karl Miller, J.J. Hantsch

20. SAS® Macros to Transpose SDTM Data Sets Automatically, Chunmao Wang

21. ADaM data structure layout: common issues and ways to avoid, Yurong Dai, Jianfei Jiang [BASETYPE]

22. Building Flexible ADaM Analysis Variable Metadata, Songhui Zhu, Lin Yan [Excel]

23. ADaM Dataset Checking Toolkit, Huei-Ling Chen [QC Checklist, Macros]

24. How to build ADaM from SDTM: A real case study, JIAN HUA (DANIEL) HUANG

25. An Innovative ADaM Programming Tool for FDA Submission, Xiangchen (Bob) Cui, Min Chen

26. ADaM Datasets for Graphs, Kevin Lee, Chris Holland

27. Defining the Development Process and Governance of Implementing ADaM within an Organization, Chris Decker [Best Practices, Lessons Learned]

28. A Road Map to Successful CDISC ADaM Submission to FDA: Guidelines, Best Practices & Case Studies, Vikash Jain, Sandra Minjoe

29. CDISC Electronic Submission, Kevin Lee [eCTD, m5 structure]

30. Preparing CTD (Common Technical Document) for FDA Submission, Charlie Xu

31. Examples of Building Traceability in CDISC ADaM Datasets for FDA Submission, Xiangchen (Bob) Cui , Hongyu Liu, Tathabbai Pakalapati

32. A Taste of ADaM, Changhong Shi and Beilei Xu

33. From SDTM to ADaM, Suwen Li, Sai Ma, Bob Lan and Regan Li [SUPPXX, Macro]

34. A Guide to the ADaM Basic Data Structure for Dataset Designers, Michelle Barrick and John Troxell [Flags]

35. Insights into ADaM, Matthew Becker [Parameter Identifier]


37. How to go from an SDTM Finding Domain to an ADaM-Compliant Basic Data Structure Analysis Dataset: An Example
Qian Wang and Carl Herremans

38. The Best Practices of CDISC ADaM Validation Checks: Past, Present, and Future, Shelley Dunn, Ed Lombardi

39. CDISC Validation, How can we do it right? [Presentation]

40. A Relational Understanding of SDTM Tables, John Gerlach, Glenn O’Brien [HOW]

41. The Most Common Issues in Submission Data, Sergiy Sirichenko, Max Kanevsky

42. Leveraging ADaM Principles to Make Analysis Database and Table Programming More Efficient, Andrew L Hulme [Table Shells, Lab Shift Tables]

43. Implementing Various Baselines for ADaM BDS datasets Songhui ZHU [EPOCH, BASETYPE]

44. A SAS Macro Application to Create Mock Tables in Statistical Analysis Plans for Phase I Clinical Studies, Yao Huang [Table Shells]

45. Data Standards Development for Therapeutic Areas: A Focus on SDTM-Based Datasets, Fred Wood, Diane Wold, Rhonda Facile, Wayne Kubick [End Time-Point, Biomarker, SDTMIG 3.2v - , Death Details DD, Microscopic Findings MI, Morphology MO, Procedure PR, Skin Response SR ]

46. Methods of Building Traceability for ADaM Data, Songhui Zhu, Lin Yan [SF-36]

47. Keeping Patients on Schedule, The Art of Visit Windows and Cycle Slotting, Paul Slagle [SV]

48. Exploring Phase 1 ADaM Deriving System from SDTM, Yiwen Li

49. Proper Parenting: A Guide in Using ADaM Flag / Criterion Variables and When to Create a Child Dataset, Richann Watson, Karl Miller, Paul Slagle [ADEG]

50. Leveraging Intermediate Data Sets to Achieve ADaM Traceability, Yun Zhuo

51. Examples of Building Traceability in CDISC ADaM Datasets for FDA Submission, Xiangchen (Bob) Cui , Hongyu Liu, Tathabbai Pakalapati

52. « Lost » in Traceability, from SDTM to ADaM …. finally Analysis Results Metadata, Angelo Tinazzi [Presentation]

53. LOCF: It’s More Than Just Carrying Forward, Vikash Jain, Niraj J. Pandya

54. Traceability: Plan Ahaed for Future Needs, Sandra Minjoe [Presentation]

55. What’s new in ADaM?, Gavin Winpenny

56. An ADaM Interim Dataset for Time-to-Event Analysis Needs, Tom Santopoli, Kim Minkalis, Sandra Minjoe

57. Avoiding Sinkholes: Common Mistakes During ADaM Data Set, Implementation, Richann Watson, Karl Miller

58. Automating the Link between Metadata and Analysis Datasets, Misha Rittmann [Proc SQL, macros]

59. ADaM Datasets - Standard and Submission Ready, Karin Fleischer Steffensen [Presentation]

60. Building Better ADaM Datasets Faster With If-less Programming, Lei Zhang [Macro]

61. ADaMIG v1.1 is Almost Final – Are You Ready? [Presentation]

62. ADaM Mapping, Key considerations for a metadata driven realization [Presentation]

63. Automate Validation of CDISC ADaM Variable Label Compliance, Wayne Zhong

64. ADaM Integration for Summary of Clinical Safety: The ‘Unique Patient’ Paradox, Tracy Sherman, Aakar Shah

65. Building Traceability for End Points in Analysis Datasets Using SRCDOM, SRCVAR, and SRCSEQ Triplet, Xiangchen Cui, Tathabbai Pakalapati and Qunming Dong

66. OCCDS – Creating flags or records, Rob Wartenhorst

67. What Are Occurrence Flags Good For Anyway?, Nancy Brucken [Proc SQL]

68. Considerations in ADaM Occurrence Data: Handling Crossover Records for Non- Typical Analysis, Karl Miller, Richann Watson [Period, Cross-Over]

69. ADaM Compliance Starts with ADaM Specifications, Trevor Mankus

70. Deriving Rows in CDISC ADaM BDS Datasets, Sandra Minjoe

71. Programming Efficiency in the Creation of ADaM BDS Datasets, Ellen Lin

72. Creating CDISC Test Data Sets – A Worthwhile Concept?, Peter Schaefer

73. ADaM Standards – Organizing the Unorganized, Heike Reichert

74. ADaM Programming – The Good, the Bad and the Ugly, Pam Howard

75. Forewarned is forearmed or how to deal with ADSL issues, Anastasiia Oparii [QC checklist, SAS macros, RFXSTDTC, DSTERM, Time Point Reference]

76. Camouflage your Clinical Trial with Machine Learning and AI, Ajith Baby Sadasivan, Limna Salim, Akhil Vijayan, Bhavya K

77. Experiences in Building CDISC Compliant ADaM Dataset to Support Multiple Imputation Analysis for Clinical Trials, Xiangchen (Bob) Cui

78. Analysis Result Metadata… are we there yet?, Carla Santillan

79. Introducing the ADaM Implementation Guide v1.2, Terek Peterson, Brian Harris, Alyssa Wittle, Nancy Brucken and Deb Goodfellow

80. ADaM Tips for Exposure Summary, Kriss Harris [Macro]

81. Is Your Data Set Analysis Ready?, Nancy Brucken, Kapila Patel [Table Shells]

82. What is the “ADAM OTHER” Class of Datasets, and When Should it be Used? John Troxell

83. Implementation of ADaM Basic Data Structure on Genetic Variation Data for Pharmacogenomics Studies Linghui Zhang [Biospecimen Events BE]

84. ADaM Intermediate Dataset: how to improve your analysis traceability, Angelo Tinazzi, Teresa Curto, Ashish Aggarwal [Tracability]

85. ADaM Datasets - Standard and Submission Ready, Karin Fleischer Steffensen [Presentation]

86. Pilot Meta-Analysis of HPA Axis Suppression Studies on Topical Corticosteriods using ADaM Datasets derived from Legacy Data, Lillian Qiu, Hon-Sum Ko

87. How to write ADaM specifications like a ninja, Caroline Francis

88. How to define Treatment Emergent Adverse Event (TEAE) in crossover clinical trials?., Mengya Yin, Wen Tan [Period, Conservative]

89. TEAE: Did I flag it right? Arun Raj Vidhyadharan, Sunil Mohan Jairath

90. Detecting Treatment Emergent Adverse Events (TEAEs) Matthias Lehrkamp

91. Using CDISC Models for the Analysis of Safety Data, Susan Kenny, Edward Helton

92. Flagging On-Treatment Events in a Study with Multiple Treatment Periods, David Franklin

93. Practical Guide to Creating ADaM Datasets for Cross-over Studies, Neha Sakhawalkar, Kamlesh Patel [Period]

94. Timing is Everything: Defining ADaM Period, Subperiod and Phase, Nancy Brucken [Study Day, EPOCH, Cross-Over]

95. Implementation of ADaM Basic Data Structure for Cross-over Studies, Songhui ZHU [BASETYPE]

96. An Introduction to Visit Window Challenges and Solutions, Mai Ngo [Period, Study Day, Macro]

97. How to define Treatment Emergent Adverse Event (TEAE) in crossover clinical trials?, Mengya Yin, Wen Tan

98. A Guide to the ADaM Basic Data Structure for Dataset Designers, Michelle Barrick, John Troxell [BASETYPE]

99. ADaM Standard Naming Conventions are Good to Have, Christine Teng [Macro]

100. Assigning Treatment Group in Cross-over Studies: A Practical Approach Charles Ling Shulin Yuan

101. A Guide for the Guides: Implementing SDTM and ADaM Standards for Parallel and Crossover Studies, Azia Tariq and Janaki Chintapalli [Poster]


SDTM and ADaM Integration (Bridg) (Top)

SAS Programming Tips to integrate SDTMs - See also LabsProc SQLMetadata and Macro Programming.

1. The Second CDISC Pilot Project A Metastandard for Integrating Databases, Gregory Steffens, Ian Fleming

2. The CDISC/FDA Integrated Data Pilot: A Final Summary of Findings, Reviewer Feedback, and Recommendations Implementing CDISC Standards Within and Across Studies, Chris Decker [APERIOD, APHASE]

3. The « CDISC Stupidario » (the CDISC Nonsense) Angelo Tinazzi

4. Creating an Integrated Summary of Safety Database using CDISC ADaM: Challenges, Tips and Things to Watch Out, Rajkumar Sharma

5. Considerations for Building an Integrated Safety Database Using SAS, Denise J. Smith, Daniel Schulz, Gayle Kloss, Wei Cheng [Macro]

6. Achieving Efficiency in CDISC SDTM Data Conversion and It's Challenges - Case Study, IIango Ramanujam 

7. From Standards that Cost To Standards that Save: Cost Effective Standards Implementation, Jeffrey Abolafia, Frank DiIorio

8. Automating the pooling of variables across multiple datasets using Proc SQL and SAS® Macro, Sanjiv Ramalingam [ISS, ISE]

9. GLOBAL INTEGRATED DATABASE (GIDB): Not Just a SET Statement, Marla A. Childers

10. State of the Union: The Crossroads of CDISC Standards and SAS’® Supporting Role, Chris Decker

11. Implementation Plan for CDISC SDTM & ADaM Standards at MedImmune, Alan Meier

12. The 5 Biggest Challenges of ADaM, Terek Peterson, David Izard

13. SDTM What? ADaM Who? A Programmer’s Introduction to CDISC, Venita DePuy [Basic, Technical Screening, Inclusion/Exclusion]

14. A Harmonized, Report-Friendly SDTM and ADaM Data Flow, Aileen Yam, Marie-Rose Peltier

15. SAS® End-to-End solutions in Clinical Trial, Emma Liu

16. SDTM Electronic Submissions to FDA: Guidelines and Best Practices, Christina Chang, Kyle Chang

17. Are you ready for Dec 17th, 2016 - CDISC compliant data submission?, Kevin Lee [QC Checklist]

18. Conformance, Compliance, and Validation: An ADaM Team Lead's Perspective, John Troxell

19. Hy's Law Explained, FDA

20. Using CDISC Models for the Analysis of Safety Data, Susan Kenny, Edward Helton [Hy's Law]

21. Japanese Electronic Study Data Submission in CDISC Formats [PMDA]

22. Preparing Analysis Data Model (ADaM) Data Sets and Related Files for FDA Submission with SAS, Sandra Minjoe, John Troxell

23. Considerations and Conventions within the Therapeutic Area User Guides (TAUGs), Jerry Salyers, Kristin Kelly, Fred Wood

24. CDISC Public Webinar – Standards Updates SDTM1.7/SDTMIG3.3 [Presentation]

25. SDTMIG v3.3: New domains – new benefits, Nick De Donder

26. Quality Check your CDISC Data Submission Folder Before It Is Too Late!, Bhavin Busa [Checklists]

27. Clinical Trials Data: It’s a Scary World Out There! (or “Code that Helps You Sleep at Night”), Scott Horton

28. Constructive Practice of Generating ADaM Datasets, Amos Shu, Charles Ling [Compare ADaM versions (v1.0 & v1.1), OCCDS, SDTM Mapping, ADDA]

29. Ready, Set, Go: Planning and Preparing a CDISC, Submission, Maria Dalton [Study Tagging Files]

30. Preparing ADaM Datasets and Related Files for FDA Submission, Sandra Minjoe and Ragini Hari

31. An FDA Submission Experience Using the CDISC Standards, Angelo Tinazzi, Cedric Marchand

32. ADaM mapping - key considerations for a metadata driven realization, Elena Glathe

33. PHUSE De-identification Standards [Blog]

34. Data De-identification Automation in SAS, Huan Lu

35. Now You See It, Now You Don’t -- Using SAS to De-Identify Data to Support Clinical Trial Data Transparency, Dave Handelsman

36. An eCTD Filing for Generic Drug Application in United States of America (USA), Patel Ruchi Chunilal, Dr.Dilip Maheshwari

37. PMDA Update [Presentation]

38. CDISC rules + FDA requests + PMDA requirements = Guaranteed Compliance? Deb Goodfellow

39. A Standardized Data Sample: Key to Improving the Submission Strategy, Prafulla Girase, Joanna Koft

40. Pilot Meta-Analysis of HPA Axis Suppression Studies on Topical Corticosteriods using ADaM Datasets derived from Legacy Data, Lillian (Aijun) Qiu, Hon-Sum Ko [Poster]

41. Adapting to Adaptive, Angelo Tinazzi, Ashish Aggarwal, Steve Wong [Poster]

42. How a Metadata Repository enables dynamism and automation in SDTM-like dataset generation Judith Goud, Priya Shetty

43. Improving Metadata Compliance and Assessing Quality Metrics with a Standards Library Veena Nataraj [QA Checklist]

45. FDA View: Technical Rejection Criteria for Study Data, Ethan Chen, Virginia Hussong [Presentation]



48. A Practice to Create Executable SAS® Programs for Regulatory Agency Reviewers, Hongyu Liu, Lynn Anderson, James Hearn, Kexi Chen [Checklist]

49. Large-scale TFL Automation for regulated Pharmaceutical trials using CDISC Analysis Results Metatadata (ARM), Stuart Malcolm [Reporting]

50. How will FDA Reject non-CDISC submission? Kevin Lee [eCTD, Study Tagging File]

51. Streamline process: To Generate SDTM Program by Automation, Hemalatha Elumalai

52. How to reduce programming time for ADaM Creation? – Presenting STAG, a Tool Based Approach, Diganta Bose

53. Demystifying SDTM OE, MI, and PR Domains, Lyma Faroz, Sruthi Kola [CNS]

54. Implementation of SDTM IG v.3.3 for Neurological Therapeutic Area, Daryna Khololovych [Presentation]

55. Tips on Developing SDTM Datasets for Complex Long-Term Safety Studies, Yunzhi Ling and Helen Wang [Rollover, Extension, Followup study, Cross-over studies]

56. CDISC Advisory Board Validation Project: Best Practices for CDISC Validation Rules, Lauren Shinaberry [Prsentation]

57. Do-It-Yourself CDISC! A Case Study of Westat’s Successful Implementation of CDISC Standards on a Fixed Budget, Rick Mitchell, Rachel Brown, Jennifer Fulton, Stephen Black, Marie Alexander

58. Data Management and CDISC Formatting for Transdermal Patches, Lois Lynn

59. What’s New in the SDTMIG v3.3 and the SDTM v1.7, Fred Wood

60. How sensitive is your analysis? A case study on addressing it at ADaM level, Bhargav Koduru and Balavenkata Pitchuka

61. ZIPpy Safe Harbor De-Indentification Macros [Presentation, Macros]

62. A Standardized Approach to De-Identification, Benoit Vernay, Ravi Yandamuri

63. PhUSE De-Identification Working Group: Providing De-Identification Standards to CDISC Data Models, Jean-Marc Ferran, Jacques Lanoue

64. Automated anonymization of protected personal data in clinical reports, Azad Dehghan, Cathal Gallagher

65. Study Anonymisation: From Request to Delivery, Kelly Mewes, Sai Jandhyala [Macros]

66. Practical Implications of Sharing Data: A Primer on Data Privacy, Anonymization, and De-Identification, Gregory Nelson

67. Why Data and Document Anonymization Should Carpool, Ravi Yandamuri, Rashmi Dodia

68. A Lead Programmer's Guide to a Successful Submission, Pranav Soanker, Santhosh Shivakav

69. An Efficient Solution to Efficacy ADaM Design and Implementation, Chengxin Li, Zhongwei Zhou

70. Practices in CDISC End-to-End Streamlined Data Processing, Chengxin Li

71. Monitoring SDTM Compliance in Data Transfers from CROs, Sunil Gupta

72. ADCM Macro Design and Implementation, Chengxin Li, Tingting Tian, Toshio Kimura

73. SDTM: It is not all Black and White, Swapna Pothula

74. Pharmaceutical Programming: From CRFs to Tables, Listings and Graphs, a process overview with real world examples Mark Penniston, Omnicare Clinical Research, Shia Thomas

75. Let Us Trace Back to the Beginning from the End Madhusudhan Reddy Papasani, Swathi Kotla, Elisabeth Pyle

76. A Learned Approach to CDISC Specifications, Stephen Noga, Brandon Welch, Jeff Abolafia

77. The application of variable-dependent macro on SDTM conversion, Mindy Wang [Macro]

78. Speed Up SDTM/ADaM Dataset Development with If-Less Programming, Lei Zhang and John Lu

79. SAS Macro Solution for NMPA Submission Data Validation Rules, Ting Sun and Jipian He [Define.xml, XML Mapper, Macro]

80. Takeda’s COVID-19 response to aid Collection to Submission, Venna Nataraj [Presentation]

81. Efficient Coding Techniques In SAS, Geetha Kesireddi [Presentation, End Time-Point, Proc SQL]

82. Remove the Error: Variable Length is Too Long for Actual Data, Eric Larson [SD1082, Macro]

83. A Macro to Automatically Flag Baseline in SDTM, Taylor Markway [Macro]

84. Tables, Listings & Figures Metadata and Traceability, Judith Goud [Metadata]

85. Harmonizing SDTM at the Source: Designing Collection Instruments that Support Sponsor Standards Helen Scanlan, Sue Huxtable, Gerard Hermus [Presentation]

86. CDISC Open Rules Engine (CORE), 2021 US Interchange, Peter Van Reusel, Sam Hume [Presentation]

87. SAS Macro Solution for NMPA Submission Data Validation Rules, Ting Sun, Jipian He [CORE]

88. E2E data standards, the need for a new generation of metadata repositories Isabelle de Zegher, Alan Cantrell, Julie James

89. Generation of SAS Code to Create Analysis Datasets directly from an SAP Can it be Done? Endri Endri, Rowland Hale

90. Pattern based Metadata Repository: toward high quality data standards, Alan Cantrell, Julius Kusserow, Julie James, Deb Copeland, Natraj Patro, Isabelle de Zegher

91. Data Standardization using SAS® Health: Data Mapper, Eric Bolender [SAS Viya, AutoMap]

92. Do It Yourself: Create your own SDTM mapping framework, Bas Van Kavel [Presentation]

93. From EDC to SDTM – faster & better!, Mor Meyerovich, Ness-Ziona, Lena Hazanov [Presentation]

94. An Introduction to CDISC: Available CDISC Standards and Models and How SAS Supports These, Dave Handelsman [Presentation]


SEND (Top)

PointCross eDV (YouTube), Pinnacle 21 (Webinar), CDISCGuru Blog

Recommendations for SEND Dataset QC Best Practices [PhUSE Best Practices]

1. The Standard for the Exchange of Nonclinical Data (SEND): History and Basics, Fred Wood, Lou Kramer

2. Are you ready for SEND? Roman Radelick [Presentation]

3. Define.xml tools supporting SEND/SDTM data process - Using SAS® Clinical Standards Toolkit, Kirsten Langendorf

4. SDTM and SEND: An Integrated View and Approach, Veena Nataraj, Cheryl Riel [Presentation]

5. Validation consistency and c onformance checking of SEND datasets, Mohit Mathew, Dr. Karen Porter, Dr. Laura Kaufman, Karuna Polavarapu [PointCross]

6. Smart Transformation of Clincal & Nonclinical Data for Insights, Isaac Mativo, Raja Ramesh, Phaneendra Bonda [PointCross, Presentation]

7. Review and Analysis of SEND Standardized Data at the FDA, Kristi Johnson, Jillian Sanford, Karen Porter, Jon Kimball, Shree Nath [PointCross, Poster]

8. Integrating Clinical data for Translational Research [PointCross, Poster]

9. Taking full advantage of the SEND data potential, Dragomir Draganov, Stefano Gaudio, Jean-Pierre Kieffer, Matthias Festag, Pierre Maliver, Karuna Polavarapu, and Raja Ramesh

10. A Process for Automated Reconciliation of SEND Datasets with Study Reports for Confident Submission and Review, Laura Kaufman, Karen Porter, Karuna Polarvarapu

11. A practical exploration of SEND for CBER submissions, Jack Baker [Presentation, Positive] 

12. Modelling of Anti-Drug Antibodies in SENDIG 3.0 [Poster]

13. The Standard for the Exchange of Nonclinical Data (SEND): History, Basics, and Comparisons with Clinical Data, Fred Wood

14. Trial Sets in Human Clinical Trials, Fred Wood



Oracle Health Sciences (See also CDM) Sample data Tutorial SQL Tutorial (Top)

1. SDTM and Oracle Clinical, Paolo Morelli [Presentation]

2. Growing into Standards: A Successful Grass Roots Campaign to Implement Data and Analysis Standards [Presentation]

3. SDTM Domain Mapping with Structured Programming Methodology Chengxin Li, Jing-Wei Gao, Nancy Bauer [CDASH, Metadata]

4. Transform Incoming Lab Data into SDTM LB Domain with Confidence, Anthony Feliu [HOW, Macro]

5. Oracle® Life Sciences Data Hub Installation Guide, PortalBrochure

6. Implementing a CDW using an enhanced Janus data model in Oracle LSH

7. Oracle Clinical® for SAS® Programmers, Kevin Lee

8. Using SAS to get more out of Oracle Clinical, Jim Johnson

9. Extracting Data from Oracle into SAS Lessons Learned, Maria Reiss


ISS/ISE Programming (Top)


1. Across all studies assure consistency: metadata/attributes, codelist (avisit/n), define.xmls, cohorts (armcd), , ADaM derivations.

2. Dataset splitting due to size. 

3. Documents - Study Data Standardization Plan, Protocol (study design, populations - itt, saf, pp), SAP

4. Standard versions - SDTM/ADAM IG #, meddra dictionary

1. Best Practices for ISS/ISE Dataset Development, Bharath Donthi


3. ISS Challenges and Solutions for a Compound with Multiple Submissions in Parallel, Aiming Yang

4. Simplifying the Integration Riddle, Deepak Ananthan, Arvind Sri Krishna Mani

5. A Well Designed Process and QC Tool for ISS Reports, Huei-Ling Chen, Lili Chen

6. ADaM Structures for Integration: A Preview, Wayne Zhong, Deborah Bauer [Extension, Extended, Follow-up Studies]

7. Creating an Integrated Summary of Safety Database using CDISC ADaM: Challenges, Tips and Things to Watch Out, Rajkumar Sharma

8. Integrated Summary of Safety and Efficacy Programming for Studies Using Electronic Data Capture, Changhong Shi, Qing Xue

9. ISS and ISE Dataset Preparation Best Practices: A PhUSE Whitepaper [PhUSE White Paper]

10. Touchpoints for the Study Data Standardization Plan, Karin LaPann, Ellen Asam

11. Core and Extension Studies – Challenges and Solutions Towards SDTM Submission, Murali Marneni [Rollover, Extension, Cross-over studies]

12. Knock Knock!!! Who’s There??? Challenges faced while pooling data and studies for FDA submission, Amit Baid [Macros]

13. Pooling strategy of clinical data, Abraham Yeh, Xiaohong Zhang, Shin-Ru Wang [Rollover, Extension, Cross-over studies, ISS, ISE]

14. Applying SDTM and ADaM to the Construction of Datamarts in Support of Cross-indication Regulatory Requests [Presentation]

15. How Will You Use CDISC Library? [Presentation]

16. How to process data from clinical trials and their open label extensions, Thomas Grupe, Stephanie Bartsch

17. Extension Studies - CDISC Submission Challenges and Scenarios, Deepak Ananthan, Sachin Bharadia

18. Programmatic Challenges of Dose Tapering Using SAS, Iuliana Barbalau, Chen Shi, Yang Yang [CMROUTE]

19. Automated creation of a mapping specification for a pooled database, Thomas Wollseifen [Presentation]

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