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