Quick Links, CDISC References, SAS e-Guide |
Mind Map |
CDISC Mapping Videos |
Getting Started with SDTMs |
Outline | SDTMIG Versions: 3.1, 3.2, 3.1.3, 3.1.1 |
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
SDTM |
ADaM |
|
---|---|---|
Source |
Raw datasets with all records (1:M CRFs) PLUS required variables |
1:M SDTMs PLUS derived variables/records |
Dates |
ISO8601 character (YYYY-MM-DD) |
Datetime variable (YYMMDD10.), Durations |
Structure |
Most all except DM are vertical with generic variable names (ideal for by-group nested processing) BY Options: TESTCD (ex. IETESTCD), DTC (ex. AESTDTC), LBTEST, COD (ex. AEDECOD) 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 1. Same as VSSTRESN and VSSTRESC 2. Same as VSSTRESN and VSORRES if VSORRES ^= VSSTRESC 3. Imputed value if VSSTRESC contains '<' 4. Average value of AVAL with DTYPE='AVERAGE' |
Datasets |
VS
LB
QC
CM, AE |
Four Methods to Construct – May need transpose (TEMP, SBP, DBP, etc.) to vertical structure (ADVS)
– May need to transpose by visit, id question to vertical structure by visit and question variable names (ADQC) – Not transposed, kept as (ADCM, ADAE) |
Dataset/Item |
Version |
Description (Data Standards, Specifications - Can use template to create SDTM metadata files, ADaM Specification, CRF Example, Edit Checks) |
---|---|---|
SDTM 'Raw datasets represented as Case Report Forms' (Useful for listings) Questionnaire |
3.1.3 |
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) |
ADaM '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, Tool, SAS, |
2.1 |
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. |
LAB |
Laboratory Data Model |
|
ODM 1.3.2 Guide |
Operational Data Model (Contains both variable attributes and data values) |
|
DEFINE.XML vardef.sas (See diagram below) |
Required for each SDTM and ADam dataset. SAS Paper, SAS Paper 2, SAS Paper 3 Clinical Standards Toolkit DocXML Online Tutorial, XML 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
|
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) |
DM /ADSL, SE, CO, SV (Generally one record per patient per X) |
Interventions (Sponsor Controlled, protocol planned treatment) |
CM, EX/ADEX, SU (Generally multiple records per patient) |
Events (Patient Controlled, Not planned) |
AE /ADAE, DS, MH, DV, CE (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]) |
Findings (Patient Measurements - Normalized, Vertical Structure) |
LB /ADLB, EG, VS/ADVS, QS/ADQS, PC/ADPC etc. (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) |
TE, TA, TV, TI, TS, TX (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) |
Subject-Level |
ADEX, ADAE, ADCM, ADMH, ADEG and ADLB |
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 variableRelationships 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
3. SDD ++: Extending SDD Capabilities, Sandeep Juneja
5. Achieving Efficiencies using SAS® Drug Development, Aik Hoe Seah
6. Super Demo Presentation of SDD and SAS Clinical Data Integration
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
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
9. WHAT CDISC MEANS TO SAS PROGRAMMERS, Kevin Lee
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 ODM, CDISC Online Reference, XML4Pharma
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
CONTROL TERMINOLOGY, CDISC SHARE webinar, NCI Lookup Table (Top)
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
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]
11. Codelists Here, Versions There, Controlled Terminology Everywhere, Shelley Dunn [Data Checks, Basics, Glossary]
12. CODING TIPS AND TRICKS FOR DEFINE VLM CODELISTS, Charlie Sowers
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
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)
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]
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.]
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)
6. Kendle Implementation of CDASH [Presentation]
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 CaseWhat 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
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
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
Jairath [SUPPXX]
24. Ensuring Consistent Data Mapping Across SDTM-based Studies – a Data Warehouse Approach, Annie Guo [Project Management, INTRP]
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]
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
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
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
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]
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]
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
103. THE DATA DETECTIVE, HINTS AND TIPS FOR INDEPENDENT PROGRAMMING QC, Bethan Thomas [Presentation]
106. A Macro to Add Variables to SDTM Standard Domains, Xianhua Zeng [Comments]
107. PharmaSUG 2017 Panel [Presentation]
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
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]
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]
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
136. New Features in Define-XML V2.0 and Its Impact on SDTM/ADaM Specifications Hang Pang
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]
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
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
165. Implementation of STDM Pharmacogenomics/Genetics Domains on Genetic Variation Data, Linghui Zhang [Biospecimen Events BE]
167. What is RE domain?, David Ju, ERT [Respiratory]
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]
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
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®
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]
183. Fake it till you make it with Global SDTM Laboratory Submissions
184. SDTMIG 3.3: New domains, new benefits, Nick Donder [Presentation]
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
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)
2. Everything You Need To Know About Standardised MedDRA Queries, Rajkumar Sharma
3. MedDRA® DATA RETRIEVAL AND PRESENTATION: POINTS TO CONSIDER
4. Introductory Guide for Standardised MedDRA Queries (SMQs) Version 16.0
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
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
RELREC (Top)
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]
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]
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.
dates needs to be submitted.
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]
29. CDISC Electronic Submission, Kevin Lee [eCTD, m5 structure]
30. Preparing CTD (Common Technical Document) for FDA Submission, Charlie Xu
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]
36. HOW TO BUILD ADaM BDS FROM MOCK UP TABLES, Kevin Lee [Table Shells]
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 [ADT, ADF, ADTM, ADTM]
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
50. Leveraging Intermediate Data Sets to Achieve ADaM Traceability, Yun Zhuo
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
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
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]
78. Analysis Result Metadata… are we there yet?, Carla Santillan
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]
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]
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
SDTM and ADaM Integration (Bridg) (Top)
SAS Programming Tips to integrate SDTMs - See also Labs, Proc SQL, Metadata and Macro Programming.
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
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
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
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]
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
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]
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]
46. INSTRUCTIONS TO SELF-CHECK WORKSHEET FOR STUDY DATA PREPARATION
47. STUDY TAGGING FILES: THEIR VITAL ROLE IN SUBMISSIONS TO THE FDA, Virginia Ventura
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
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]
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
61. ZIPpy Safe Harbor De-Indentification Macros [Presentation, Macros]
62. A Standardized Approach to De-Identification, Benoit Vernay, Ravi Yandamuri
65. Study Anonymisation: From Request to Delivery, Kelly Mewes, Sai Jandhyala [Macros]
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
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]
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]
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]
11. A practical exploration of SEND for CBER submissions, Jack Baker [Presentation, Positive]
12. Modelling of Anti-Drug Antibodies in SENDIG 3.0 [Poster]
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, Portal, Brochure
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)
Tips
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
2. CDISC SDTM CONVERSION IN ISS/ISE STUDIES: TOOLS, Balaji Ayyappan
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]
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]
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]