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Legacy Data Conversion for
SDTM and ADaM – Challenges
and Strategies
K&L Consulting
McNeil Consumer Healthcare
Agenda
• Case Study: Challenges & Strategies
– Scope and Purpose of the Project
– Challenges
– Strategies
• Overall Strategies
• Strategies for SDTM
• Strategies for ADaM
– Miscellaneous
• Conclusions
Case Study: Scope of the Project
• Project Scope:
– 68 legacy studies were included in the data mapping project.
– Data re-entry (17 studies)
– MedDRA coding
– SDTM mapping/creation
– ADaM datasets creation
– Key CSR result verification.
– Purpose:
• Perform meta analyses – safety and efficacy related
• Support product claims
• Respond to Regulatory Agencies
Case Study: Challenges
• Challenges
– Large number of studies (68 protocols)
– Different CRF designs as study conducted from 1972 to 2011
– Original electronic data not stored in one central repository
– Some study materials not filed by study number (filed by date)
– Maintain consistency across studies
– Key results verification to match legacy clinical study reports
– Raw data in different format (SAS, excel), SDTM like or non-
SDTM
Case Study: Overall Strategies (1)
• Strategies
Planning and pre-setup are the keys to be successful
• Project management plan, timeline, communication management plan,
and structured functional team were in place before project kickoff
• Different functional group handling different part
– DM – database setup, data entry, database audit, and AE recoding
– SDTM – create SDTM data
– ADaM – create ADaM data, CSR key result verification
Active, in real-time communication between sponsor and
service provider, among functional teams, within team via
project team meeting, project status meeting/report, and
project six month review
Project Manager coordinate 3 functional group’s activities,
resource and timeline planning, communication with McNeil,
monitor/track project progress, and facilitate issue resolution
Case Study: Overall Strategies (2)
• Strategies
– Team set up: • Teams for different domains – and each team is responsible for assigned domains
for all the studies
• Core team – each core team will have deep understanding of the protocol and CSR
of a study and maintain the integrity and consistency within the study.
– Issue log by batches – all issues are tracked for resolution status
• End Results: High quality, On time, Within budget
Case Study: Strategies for SDTM (1)
• Strategies
– Set up mapping template, look-up table, CT tables for various CRF
panels (SDTM IG 3.1.2 with Amendment 1)
– Ensure one mapping template serves two purposes: SDTM and
define file creation
– Agree on project specific derivation rules for all derived variables such as RFSTDTC, RFENDTC, RFPENDTC, --DY, --BLFL, etc.
– Developing or identifying 3 sets of macros/tools for utility usage such as reading specs, formatting, date handling, splitting long text,
length calculation, SUPPQUAL handling, data output, XPT conversion, check log
generating derived variables
consistency checks across panels within protocol and across protocols
Case Study: Strategies for SDTM(2)
• Strategies
– Working closely with McNeil for Some data interpretation such as mapping original term to standard CT when
CRF wording different from standard CT
For example: AEREL, map “REMOTE” to “UNLIKELY RELATED”; for
AESEV, map “MARKED” to “SEVERE”; For AEOUT: map “EFFECT STILL PRESENT” to “NOT RECOVERED/NOT RESOLVED”, etc
Making decision of mapping the actual data or CDISC compliance
The original CRFs were not created with CDISC in mind so there was not always a fit from CRF to CDISC CT. For example: CRF collected value
RESCUED. It was decided to add “RESCUED” to DSDECOD even it is not CDISC CT.
Any issue resolution
– Grouping studies by study design, divided 68 studies into 10
batches • Early batches were smaller - 3 to 5 studies
• Went all the way through from SDTM to ADaM on the first two batches
• Put some different types of studies in early batches – headache model and dental pain model, to help in forming the structure.
• Grouped similar studies to same batch in later batches
Case Study: Strategies for SDTM(3)
• Strategies
– Dividing development team work on same type of domains and
validation team work by protocols • 4 development teams working on domains by SDTM class (Special purpose, Event,
Finding, Intervention and Trial design)
– Lookup table/CT tables are expandable. Only team lead can update the tables to ensure the integrity of those tables
Case Study: Strategies for ADaM (1)
• Strategies
– Set up master analysis dataset specifications Excel book and Individual
study specification template (followed ADaM IG 1.0)
• To create similar structured analysis datasets across 68 studies
• Master specification
• Datasets in different sheets
• Variable list with attributes (label, length, type, etc)
• Required or permissible variables (defined in a column)
• Common variables (color coded)
• General derivation rules or study specific (explicit description)
• Variable list with study matrix (see next slides)
• Consistency checking
• Utility programs to create individual study specification
Case Study: Strategies for ADaM (2) Master specification – Each sheet is for a different dataset. Starting in
column I there is a column for each study (variable–study matrix)
Case Study: Strategies for ADaM (3)
• Individual study specification
• Copy from template for each study to ensure consistent dataset
names and labels across studies – Content sheet
Case Study: Strategies for ADaM (4)
Individual study specification - variables and definition. Utility program
to read the specification and assign variable attributes
Case Study: Strategies for ADaM (5)
• Strategies
– Efficacy data structure and general derivation rule document helped
programmers to create consistent analysis datasets across studies
Key analysis variables/parameters definition
Traceability variables (XXSEQ, SRCSEQ, SRCDOM, RELCRIT,
RELFACT) definition
Specific examples for possible scenarios
Special data handling rules
Programming standard and validation
Case Study: Strategies for ADaM (6)
• Strategies
– CSR result verification
Key variables to verify:
Population Flags, Adverse Event, Efficacy parameters
Figuring out reasons for discrepancies
Data handling rules, methods of analysis
Solution for reconciliation
Team discussions and decisions
Documentation of decisions made
Case Study: Strategies for ADaM (7)
Sample document of CSR result reconciliation
Case Study: Miscellaneous
• Good records keeping make this kind of project possible
• Original CRFs are well organized and available to resolve any
questions
• Randomization sheets are available
• Original clinical study reports are accurate – no major findings
• Printouts of original datasets, SAS programs and analysis results
were available for most studies
• Key success factors:
• Key sponsor personnel did original analysis on many of the studies
back to 1982
• Subject matter experts on the project, especially SDTM and ADaM
Case Study: Data Already Useful
Goal – analyze multiple studies as easily as a single study
• Perform Meta-Analysis
• Proactively preparing summaries and graphs of key results likely to be of interest
• Prepare macros for easy compilation of data from multiple studies
for ad-hoc requests
• Explore data for designing new studies
• Support Product Claims
• Used 5 studies to support a possible new claim
• Respond to Regulatory Agencies
• Can use data to support PSURs (Product Safety Update Reports)
Case Study: Conclusions
For SDTM
– Sponsor defined CT table – ensured consistent terminologies used
across studies
– Grouping studies into batches by study design
– Development team (by domain), core review team (by protocol)
– Utility Macros – pool data, consistency checking
For ADaM
– Good documentation is one of the keys to make this project
successful. From original study materials, data, programs, reports,
analysis results, data listing, etc, to the issue logs tracking during the project process, and to the final CSR results verification.
– Efficient methods for maintaining consistency across studies –
Master Specification, a dataset – variable matrix, to define datasets
and the variables within datasets.
Case Study: Conclusions (2)
For ADaM
– Utility tools to build individual study specification
based on the Master Specification and ADaM
datasets creation
Overall, project deliverables are completed to the
sponsor’s satisfaction with high quality, on time and
within budget.
Main Contributors
• McNeil Consumer Healthcare:
– Brenda Zimmerman
– Qian Zhao
– John Wang
– Samuel Xing
• K&L Consulting Services
– Joyce Gui, Xiao Xiaoying, Lin Ye, Steven Pan,
Songhui Zhu, Henry Wu, Arthur Liu, Bella Gao
and Belinda Wu