CDISC - SDTMs and ADaMs    

 Quick Links, CDISC ReferencesSAS e-Guide

Mind Map 

 CDISC Mapping Videos


 Getting Started with SDTMs

   SDTMIG Versions: 3.13.2, 3.1.33.1.1

Mind map  SDTM Certification

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, CDERS Common Data IssuesIssues Presentation, Common IssuesDrug Review Process  SAS, CDSIC Q&A Blog, PhUSE SENDS, CDISC SENDS

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

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




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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 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)

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 Dataset/Item 
 

       Version


Description

 (Study Data SpecificationsData Standards, Specifications - Can use template to create SDTM metadata files Viewer, ADaM SpecificationCRF ExampleEdit Checks, Annotate CRF)

 

 

SDTM

'Raw datasets represented as Case Report Forms'

(Useful for listings)

1.2 Reference, 3.1.2 Guide

IS Guide

Compliance Check Macros, SAS

DM, AE, LB

Questionnaire

SDTM SAS Programs

SDTM SAS Macros

 

 

 

 

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

ADAE 1.0 Guide

Examples,  

Common Stats,

Tool,

SAS

ADSL

Checklist  Guide,

XLS

ADaM SAS Programs

ADaM SAS Macros

 

 

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

Dictionary Tables

vardef.sas (See diagram below)

 


Required for each SDTM and ADam dataset.

 Home, Guide, 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.

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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

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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)

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Presentations: Basics, PHUSE, PharmaSUG, FA

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 Animated Guides by Russ Lavery and Susan Fehrer

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)

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 PROC CDISC/Clinical Toolkit, Presentation

SAS Drug Development (SDD) Presentation

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

 


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 Clinical Standards Toolkit, Value Level Metadata GuideHOW

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

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. The SAS® Clinical Standards Toolkit, Dave Smith

10. WHAT CDISC MEANS TO SAS PROGRAMMERS, Kevin Lee

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

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

13. Validating CDISC Data with the SAS® Clinical Standards Toolkit

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

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ODM - Operational Data Model and XML files

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. Using CDISC ODM to Migrate Data, Alan Yeomans

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

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

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

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

14. XML Basics for SAS Programmers, Yong Li

15. Results-Level Metadata: What, How, and Why, Frank Dilorio, Jeffrey Abolafia

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

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

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

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

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 CONTROL TERMINOLOGY, CDISC SHARE webinar, Codelist Presentation, NCI Lookup Table

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]

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. Automatic Consistency Checking of Controlled Terminology and Value Level
Metadata between ADaM Datasets and Define.xml for FDA Submission, Xiangchen (Bob) Cui, Min Chen

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

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

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

8. Controlling Controlled Terminology, Ryan Burns

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

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

11. Planning to Pool SDTM by Creating and Maintaining a Sponsor-Specific Controlled Terminology Database, Cori Kramer, Ragini Hari, Keith Shusterman

12. Codelists Here, Versions There, Controlled Terminology Everywhere, Shelley Dunn

13. CODING TIPS AND TRICKS FOR DEFINE VLM CODELISTS, Charlie Sowers

14. 2019 CDISC US Interchange – CDISC Controlled Terminology (CT), Enhancements, Erin Muhlbradt [Presentation]

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

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 [Impute dates and times, IS8601 informats]

9. A proposal for intervention and event partial date time imputation, Chunpeng Zhao [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

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

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

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CDASH (Example CRFs, Control Terms, CDM, CDISC.ORG, CDASH vs SDTM)

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

3. Getting the Most out of CDASH Metadata and Terminology, Joris De Bondt

4. ODM/CDASH Wiki

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

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

7. Kendle Implementation of CDASH [Presentation]

8. SlideServe [Presentations]

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

10. CDASH and SDTM: Why We Need Both!

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

Rabidwolff's Alehouse CDISC/SDTM outline       SDTM Examples

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]

CDISC Standards in the Regulatory Submission Process, Frank Newby

 


SDTM Versions

What to Expect in SDTMIG v3.3, Fred Wood

What’s new with SDTMIG v3.2?

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]

CDISC Standards in the Regulatory Submission Process, Frank Newby

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
 [-TPT]

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

22. Experiences Submitting CDISC SDTM and Janus Compliant Datasets Carol Vaughn, Gregory Ridge, and William Friggle

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]

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. How Valued is Value Level Metadata?, Shelley Dunn

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

32. SDTM Implementation - Best Practices, Pantaleo Nacci [Presentation]

33. Do we know enough about drug exposure?, Vinay Mahajan and Pawan Sharma [Presentation]

34. Exposure to Exposure, Gauri Khatu [Presentation]

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. SDTM Pilot A CRO Perspective, Jeff Abolafia, Frank DiIorio, Laura Brewington, Brooke Millman

40. How can CDISC SDTM Standards be applied for pharmaco-epidemiological studies? Doris Kolb

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]

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

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]

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

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]

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

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

69. Business Case for CDISC Standards

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

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. SDTM fundamentals, Nicola Tambascia [Course Notes]

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

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

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

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

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

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

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]

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

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 & EC) Demystified. How EC Helps You Produce a Better (more compliant) EX, Tom Guinter

109. ADaM Grouping: Groups, Categories, and Criteria. Which Way Should I Go?, Jack Shostak

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. Basic SDTM House-Keeping, Emmy Pahmer

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 Meta- Data 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

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]

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

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

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]

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

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]

139. Macro to Automate SDTM & ADAM datasets Creation from Specification Template, Vidya Muthukumar, Nirosha Reddy

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

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

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

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

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. The Business Value of Machine-Readable Value-Level Metadata, Philippe Verplancke

166. Automatic Consistency Checking of Controlled Terminology and Value Level Metadata between ADaM Datasets and Define.xml for FDA Submission, Xiangchen Cui, Min Chen

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

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

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

170. Findings About: De-mystifying the When and How, Soumya Rajesh, Michael Wise

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

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

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

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

______________________________________________

QS Questionnaire Domain

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. QS Domain: Challenges and Pitfalls [Presentation]

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

7. Questionnaire Control Terminology [Excel file]

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

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

10. SAS® Tools for Working with Dataset-XML files, Lex Jansen

______________________________________________

SMQs Presentation, MedDRA Use at FDA Presentation

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

3. MedDRA® DATA RETRIEVAL AND PRESENTATION: POINTS TO CONSIDER

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]

______________________________________________

 SUPPQUAL (Supplemental Qualifer)







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

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

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

______________________________________________

 RELREC

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

______________________________________________

 Trial Design

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

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

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

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

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

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

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

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

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

______________________________________________




 ADaM and Beyond

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]


 

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


14. Traceability in the ADaM Standard, Ed Lombardi

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

16. You Can’t Spell ADaM without Metadata, Jeffrey Abolafia, Ryan Burns  [Presentation]

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]

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

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]

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]

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]

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 [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

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

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

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

62. Building Better ADaM Datasets Faster With If-less Programming, Lei Zhang

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

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

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

77. Forewarned is forearmed or how to deal with ADSL issues, Anastasiia Oparii

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

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

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

83. Worst CDISC Implementation Processes – How to Avoid Some Very Basic Mistakes in SDTM, ADaM, Define.xml and Reviewer’s Guide Production, Hannes Engberg Raeder, Michael Reich

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

85. Implementation of ADaM Basic Data Structure on Genetic Variation Data for Pharmacogenomics Studies Linghui Zhang

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

______________________________________________

SDTM and ADaM Integration (Bridg)

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

SDTM Validation Checks

1. Confirm similar attributes (type, length) across common variables across studies.  See DEFINE.PDF.

2. Confirm same label and meaning for common variables across studies.

3. Drop permissible variables from each SDTM. 

4. Map any variable differences in labels such as M to Male if any.  Consider case sensitive, spaces, etc.  Should be minimum differences if already SDTMs.  May create an excel file with columns for each study that show maps any differences.

5. LB may require extra efforts to standardize lab test names, units and conversion factors. As needed, may need to create baseline flags for selected studies?

6. Confirm all treatment arms and labels are consistent.  Update treatment arm numbers as needed to standardize.

7. Consolidate/update values and labels in integrated control terminology file from study level control terminology files.

8. Assure DM and SUPPDM are joined by study to create DM_ALL. Repeat for each SUPPXX dataset.

9. Append each study-level DM_ALL to create an integrated DM_IS.  Repeat for each dataset.

10. For AE dataset, document MedDRA version to keep track of differences between studies.  For older studies, an option may be to recode to a newer version of MedDRA?

11. Option to repeat process for ADaMs from study-level or create integrated ADaM_IS datasets from integrated SDTMs such as DM_IS.

12. Cross check a CSR table from one one study with the outputs from the integrated ADaM_IS datasets.

13. Create an integrated DEFINE.PDF.


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. CDISC SDTM CONVERSION IN ISS/ISE STUDIES: TOOLS, Balaji Ayyappan

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

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]

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]

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


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

40. SDSP (Study Data Standardization Plan) Case Studies and Considerations, Kiran Kundarapu, Nicole Gallegos

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]

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

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

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

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

______________________________________________


SEND

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

5. Validation consistency and conformance checking of SEND datasets, Mohit Mathew, Dr. Karen Porter, Dr. Laura Kaufman, Karuna Polavarapu

______________________________________________ 

SDTM and ADaMs Reviewer's Guide

1. ADaM Reviewer’s Guide – Interpretation and Implementation, Steve Griffiths [Presentation]

2. Proposal for Streamlining the SDRG and ADRG Authoring Process, Stanley Wei [SDTM and ADaM Reviewer's Guide]

3. Who, What, When, Where and How: Basics of Analysis Data Reviewer’s Guide (ADRG) [Presentation]

4. ACHIEVING CLARITY THROUGH PROPER STUDY DOCUMENTATION: AN INTRODUCTION TO THE STUDY DATA REVIEWER'S GUIDE (SDRG)

5. Leveraging Study Data Reviewer’s Guide (SDRG) in Building FDA’s Confidence in Sponsor’s Submitted Datasets, Xiangchen Cui, Min Chen and Letan Lin

6. Worst CDISC Implementation Processes – How to Avoid Some Very Basic Mistakes in SDTM, ADaM, Define.xml and Reviewer’s Guide Production, Hannes Raeder, Michael Reich

7. A Practical Guide to the Issues Summary in the Data Conformance Summary of Reviewer’s Guides, Gary Moore

______________________________________________


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

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]

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

______________________________________________


 Open CDISC / Pinnacle 21 (Tool) QC (See also Data Validation QC/QA Validation)

Supports SDTM 3.1.1 and 3.1.2

OpenCDISC SDTM Compliance Process Flow

A. Summary of issues image, do not pass OpenCDISC SDTM Compliance Report

B. Compliance (metadata, control term, data structure) or data issue image

C. QA checks failed image

D. Code to correct image, qa check passed, review/correct errors, warnings and notes based on guidelines

E. Correct SDTM dataset image, follow guidelines and pass OpenCDISC SDTM Compliance Report


1. Interpreting ADaM standards with OpenCDISC, Trupti Bal, Madhura Paranjape (Basetype)

2. Running OpenCDISC in SAS, Kevin Lee [HOW]

3. OpenCDISC: Beyond Point and Click, Frank DiIorio

4. Resolving OpenCDISC Error Messages Using SAS®, Virginia Redner and John Gerlach [Macros] 

5. Let SAS© Improve Your CDISC Data Quality, Wayne Zhong [Edit Checks]

6. CDISC SDTM Conformance and OpenCDISC, Micaela Salgado-Gomez [Presentation]

7. Adopted Changes for SDTMIG v3.1.3 and 2013 OpenCDISC Upgrades, Yi Liu, Stephen Read

8. A Standard SAS® Program for Corroborating OpenCDISC Error Messages, John Gerlach [Macros]

9. Let SAS handle your CDISC compliance check: automating OpenCDISC Validator in SAS, Edwin van Stein

10. Thanks CDISC – Free Utilities, Robert Agostinelli [OpenCDISC]

11. OpenCDISC Plus, Annie Guo

12. Improving Data Quality - Missing Data Can Be Your Friend!, Julie Che

13. Reading and Resolving OpenCDISC Messages, Penny Pang [Presentation]

14. Most Common Issues in ADaM Data, Sergiy Sirichenko and Michael DiGiantomasso [Presentation]

15. Usage of OpenCDISC Community Toolset 2.0 for Clinical Programmers, Sergiy Sirichenko, Michael DiGiantomasso, Travis Collopy [HOW]

16.  A Preventive Approach for Automatic Checking of CDISC ADaM Metadata to Detect Noncompliance

17. SAS and Open Source Tools for CDISC SDTM Compliance Checks for Regulatory Submissions, Peter Loonan, Sandeep Kottam, Sree K Tripuraneni

18. OpenCDISC Validator Implementation: A Complex Multiple Stakeholder Process, Terek Peterson, Gareth Adams

19. Validation Checks Performed by WebSDM Version 2.6 on SDTM version 3.1.1 Datasets

20. Common Programming Errors in CDISC Data, Sergiy Sirichenko

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

22. Pinnacle 21 Enterprise [Presentation]

23. De-Identification of Clinical Trials Data Demystified Jack Shostak

24. Usage of Pinnacle 21 Community Toolset 2.2.0 for Clinical Programmers, Sergiy Sirichenko and Michael DiGiantomasso [How-to]

25. Usage of Pinnacle 21 Community Toolset 2.1.1 for Clinical Programmers, Sergiy Sirichenko, Michael DiGiantomasso, Travis Collopy

26. Good Data Validation Practice, Sergiy Sirichenko, Max Kanevsky

27. Doctor's ‘Prescription’ to Re-engineer Process of Pinnacle 21 Community Version Friendly ADaM Development, Aakar Shah

28. Duplicate records - it may be a good time to contact your data management team Sergiy Sirichenko, Max Kanevsky

29. Seven Habits of Highly Effective Issue Managers, Amy Garrett

30. Common Pinnacle 21 Report Issues: Shall we Document or Fix?, Ajay Gupta

31. Achieving Zen: A Journey to ADaM Compliance, Kirsty Lauderdale, Kjersten Offenbecker, Alice Ehmann [QC checklist]

32. Exploring Common CDISC ADaM Conformance Findings, Trevor Mankus

33. Confusing Data Validation Rules Explained Michael Beers

34. The Truth About False Positives, Kristin Kelly, Michael Beers

35. Pinnacle 21 Community v3.0 - A Users Perspective, Ajay Gupta

36. How to Automate Validation with Pinnacle 21 Command Line Interface and SAS, Amy Garrett and Aleksey Vinokurov [Presentation]

37. Incorporating Pinnacle21 With LSAF, Sonali Garg, Sandeep Juneja, Aleksey Vinokurov [Presentation]

38. Usage of Pinnacle 21 Community Toolset 2.1.1 for Clinical Programmers, Sergiy Sirichenko, Michael DiGiantomasso, Travis Collopy

39. De-Identification of Data & Its Techniques, Shabbir Bookseller

40. A Method for Automating Jobs in SAS® Life Science Analytics Framework (LSAF) [Presentation]

______________________________________________

ISS/ISE Programming


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

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



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