Regulatory Submission Datasets
in the World of Evolving Standards
Dave Christiansen, DrPH
Christiansen Consulting,
CDISC Founding Director
CC
Christiansen
Consulting
“Safety and the Critical Path“
2005 FDA/Industry Statistics Workshop
Sept 14-16, 2005
Washington, DC
Acknowledgments
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Sally Cassel, Lincoln Technologies
Kaye Fendt, Data Quality Research Institute
Wayne Kubick, Lincoln Technologies
Rebecca Kush, CDISC
Randy Levin, FDA
Bob O’Neil, FDA
Bill Qubeck, Pfizer
Norm Stockbridge, FDA
Steve Wilson, FDA
CDISC ADaM and SDS Teams
© Copyright 2005, David H. Christiansen
1-2
Disclaimer
Views expressed in this presentation
are those of the speaker and not,
necessarily, of the Food and Drug
Administration, CDISC or any other
organization.
© Copyright 2005, David H. Christiansen
1-3
State of Clinical Trial Research - 1995
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FDA-regulated products accounted for
about 25 cents of every consumer dollar
spent in the United States
Yet each company established its own
clinical trials content standards
independent of other companies in the
industry
Technological advances were available to
make the submission and review process
more efficient
© Copyright 2005, David H. Christiansen
1-4
Challenges for Adoption of
Standards
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Requirements for our industry not clearly defined and
articulated
Past efforts not focused on overall clinical data
management requirements
Organizations have focused on internal standards
vs.industry-level; resistance to share internal standards
May require a change in process for the organization
Clinical data standards must accommodate scientific
context and complexity inherent in clinical research
© Copyright 2005, David H. Christiansen
1-5
Multiple Organizations with Shifting Standards
Out-license
Operational
Database
A
Statistical
Analysis SAS
Statistical
Analysis in S+
Output and
Report
Out-license
Kaye Fendt, 2001
Operational
Database
B
Output and
Report
Operational
Database
CRO
Operational
Database
A
CRO
Statistical
Analysis
Statistical
Analysis
SAS
Output and
Report
Output and
Report
In-license
In-license
In-license
1-6
Initial Solutions to the Problem
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Remote Data Entry (RDE) processes
emerged in the 1970s, but languished for
20 years without significantly impacting the
Clinical Trials arena.
CANDAs/ CAPLAs – Too many different
standards
The FDA CARS (Computer Assisted
Review of Safety) and SMART
(Submission Management and Review
Tracking) initiatives took initial steps to
develop Electronic Review tools.
© Copyright 2005, David H. Christiansen
1-7
Computer Assisted NDAs (CANDAs) and Computer
Assisted Product License Applications (CAPLAs)
Operational
Database
A
Statistical
Analysis SAS
Operational
Database
B
Statistical
Analysis in S+
CANDA from
Company A
Operational
Database
A
Statistical
Analysis SAS
Operational
Database
B
Statistical
Analysis in S+
CANDA from
Company B
Operational
Database
A
Statistical
Analysis SAS
Operational
Database
B
Statistical
Analysis in S+
CAPLA from
BioTech X
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SMART Initiative at FDA
Operational
Database
From
Company A
Operational
Database
From Company B
Operational Database
From Company C
Conversion to FDA
Standard DB Structure
FDA Standard
Database
Kaye Fendt, 2001
FDA Tools
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Regulatory Environment
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Applicants were required to provide CRTs with
submissions – CFR 314.50
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Clinical reviews were primarily a paper process
task – even for CRTs
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1992 PDUFA – Initial Prescription Drug User Fee
Act added time commitment pressures to
reviewers
© Copyright 2005, David H. Christiansen
1-10
Regulatory Environment
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Constant pressure for FDA scientists to make
the “right” decisions in a timely fashion
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ICH / ESTRI discussions
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Electronic submission of CRFs/CRTs
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Guidance for Industry on Electronic
Submissions – General Considerations 1999
© Copyright 2005, David H. Christiansen
1-11
Setting was Perfect for …
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Development and acceptance of clinical
trials content standards
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Industry acceptance and participation in
standards development
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Regulatory participation in the
standards development process
© Copyright 2005, David H. Christiansen
1-12
The Solution(s)
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1990s Electronic Data Capture (EDC) tools
reemerged as a serious interest
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21CFR 11 published in March 1997
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CDISC started in 1997
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FDA Guidance for Industry: Computerized
Systems Used in Clinical Trials published in
April 1999
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FDA Guidance for Industry: Electronic
Submission of NDAs/BLAs, 1999
© Copyright 2005, David H. Christiansen
1-13
A Shared Vision
Pharma
Tech/Software
Labs
Data
Biotech
Standards
Other Vendors
Public
Regulatory
CROs
© Copyright
2005,
David H.2002
Christiansen
Steve
Wilson,
1-14
CDISC History
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Began in 1997 as a volunteer organization
DIA Special Interest Area Community (SIAC)
from 1998-1999
Incorporated as a non-profit organization in 2000
Members and sponsors today include over 150
companies (biopharmaceuticals, CROs,
academic institutions, IT providers, etc.)
Global reach, with CDISC Coordinating
Committees in Europe and Japan
© Copyright 2005, David H. Christiansen
1-15
Clinical Data Interchange Standards
Consortium (CDISC)
CDISC is an open, multidisciplinary, non-profit
organization committed to the development of
worldwide industry standards to support the
electronic acquisition, exchange, submission
and archiving of clinical trials data and metadata
for medical and biopharmaceutical product
development.
The CDISC mission is to lead the development of
global, vendor-neutral, platform-independent
standards to improve data quality and accelerate
product development in our industry.
© Copyright 2005, David H. Christiansen
1-16
CDISC Collaborations with Food and
Drug Administration (FDA)
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Liaisons on SDS, ADaM, SEND, Protocol
Representation Teams
SDTM referenced in eCDT Study Data
Specification – July 2004
Analysis Dataset Guidance under development
at FDA with input from ADaM
DEFINE.XML for SDTM submission metadata
referenced in eCDT Study Data Specification –
March, 2005
Co-chair HL7 RCRIM Technical Committee with
CDISC and HL7
Rebecca
Kush,
2004
© Copyright
2005,
David
H. Christiansen
1-17
FDA Cooperative Research and
Development Agreement (CRADA)
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Warehouse physical design
 IBM CRADA
Data loader, front-end for reviewer
 Lincoln Technologies CRADA
Patient Profile Viewer
 PPD Informatics CRADA
Integrating animal tox data
 PharmQuest CRADA
Randy
Levin,
© Copyright
2005,
David2004
H. Christiansen
1-18
Alphabet Soup
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ICH – International Committee on Harmonisation
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ICH has developed a Common Technical Document
(CTD) that provides for a harmonised structure and
format for new product applications
FDA has a draft guidance on a Electronic Common
Technical Document (eCTD), including structures for
datasets and programs
ICH E3, E6 and E9 provide some models
XML allows navigation and “smart” datasets
© Copyright 2005, David H. Christiansen
1-19
More Alphabet Soup
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HIPAA - Heath Insurance Portability and
Accountability Act of 1996
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Standards for the electronic exchange, privacy and
security of health information. Collectively these are
known as the Administrative Simplification provisions
HL7 - Health Level 7
Electronic messaging standards for medical practice
data
 HHS supports standardized model of an electronic
health record
 FDA is a sponsor
 CDISC and HL7 have a formal affiliation
1-20
© Copyright 2005, David H. Christiansen

More Alphabet Soup
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SNoMed - Systematized Nomenclature of
Medicine
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Purchased by HHS for $34M
National Library of Medicine will make it available
without charge throughout the U.S
XML - eXtensible Markup Language
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Used by ICH for the electronic Common Technical
Document (eCTD) backbone
Used by FDA for the electronic Table of Contents
(eTOC)
Proposed by CDISC and FDA to replace pdf for
metadata (DEFINE.XML)
© Copyright 2005, David H. Christiansen
1-21
More Alphabet Soup
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JANUS
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Janus is intended to capture all clinical data collected
from a clinical trial along with enough of a machineinterpretable description of the study protocol to permit
a high degree of automated analysis
A database with a structured data that will utilize tools
being developed for FDA medical reviewers
FDA specified vertical data structures for SDTM V3.1
datasets
SDTM (and Janus) currently explicitly exclude
Statistical Analysis Datasets
© Copyright 2005, David H. Christiansen
1-22
Primary Reviewer Tasks Involving
Submission Datasets
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Statisticians
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Medical Reviewers
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Replicate analyses
Test assumptions
Perform alternative analyses
View data used for a specific table
View patient profiles
Auditors
Compare source data values to CRFs or source
documents
 Verify derivations
1-23
© Copyright 2005, David H. Christiansen
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Submission Dataset Concepts
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Datasets and documentation should be adequate to
allow reviewers to answer the following questions:
(1) Do the submitted data and documentation clearly
describe the conduct and results of the trial?
(Can the reviewer understand the data and results?)
(2) Is the clinical evidence of sufficient quality to ensure
that the reported results are accurate and true?
(Does the reviewer believe the data and results?)
© Copyright 2005, David H. Christiansen
1-24
CDISC Data Models and the Clinical Trial
Research Process with Drafts as of May, 2005
Data Sources
• Site CRFs
• Laboratories
• Contract
Research
Organizations
• Development
Partners
Operational
Data
Interchange:
ODM
LAB
Operational
Database
•Metadata
•Study Data
•Audit Trail
•Archive
ODM = Operational Data Model
LAB = Laboratory Data Model
SEND= Standards for the Exchange
of Non-clinical Data
© Copyright 2005, David H. Christiansen
Submission
Data
Interchange:
SMM
SDTM
ADaM
SEND
Regulatory
Submission
Datasets
•Machine
Readable
Metadata
(Partial)
•Study Data
Tabulations
•Statistical
Analysis
Datasets
•SEND
SMM = Submission Metadata Model
SDS = Submission Domain Standards
ADaM = Analysis Dataset Models
1-25
Evolution of Case Report Tabulations
Code of Federal Regulation: 21 CFR 314.50
 1988 Guideline on the Statistical Sections
 1997 Guidance on Archiving Data: 21 CFR 11
 1999 Guidance on Providing Regulatory Submissions
in Electronic Format
 ICH E3 - Structure and Content of Clinical Study
Reports
 ICH Common Technical Document
 eCTD and Study Data Specification
 Guidance for Review Staff and Industry - Good
Review Management Principles and Practices for
PDUFA Products
1-26
© Copyright 2005, David H. Christiansen
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Regulation and Guidance:
Case Report Tabulations (CRTs)
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21CFR 314.50 (f) (1) “The tabulations are required to include the
data on each patient in each study, except that the applicant may
delete those tabulations which the agency agrees, in advance, are
not pertinent to a review of the drug`s safety or effectiveness.”
1988 Guideline for the Format and Content of the Clinical and
Statistical Sections of an Application defines CRTs as:
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“These case report tabulations contain, in an organized fashion ,
essentially all data (efficacy, safety, pharmacology) collected in the
case report.”
“…being entirely comprehensive, (they) serve as an archival or
reference document, not as listings suitable for ordinary review.”
“These tabulations are distinct from, and more extensive than, the
tabulations of individual patient data called for as parts of the full
reports of controlled clinical studies…”
© Copyright 2005, David H. Christiansen
1-27
Guidance:
Data Listings
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1988 Guideline defines patient data listings as:
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Demographic and baseline data, effectiveness data, and
safety data from “full reports of controlled clinical studies and
the safety portions of reports of all studies.”
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The data listings requested as part of the report (in an
appendix to it) are focused on the particular variables critical
to the analyses carried out, allowing the reviewer to examine
the individual patient data underlying critical group
measurements.
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These report listings are generally “subsets of relevant
effectiveness and safety variables used in analyses and
tables.”
© Copyright 2005, David H. Christiansen
1-28
1997 NDA Guidance: Archiving
Submissions in Electronic Format
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21 CFR Part 11 - Electronic Records; Electronic
Signatures regulation provides for the voluntary
submission of parts or all of an application in
electronic format
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Case Report Tabulations may be submitted as PDF
files in two forms:
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Domain Profiles - commonly referred to as patient line listings or
patient data listings, domain profiles consist of all data collected for
a CRF domain (such as demographics, vital signs, labs, efficacy
measures) from one study.
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Patient Profiles - one or more pages that contain all of the study
data collected for an individual patient.
© Copyright 2005, David H. Christiansen
1-29
1999 NDA Guidance: Providing Regulatory
Submissions in Electronic Format
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Each dataset is a single SAS transport file and, in
general, includes a combination of raw and derived
data.
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Each CRF domain (e.g.,demographics, vital signs,
adverse events) should be provided as a single dataset.
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In addition, datasets suitable for reproducing and
confirming analyses may also be needed.
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Patient profiles can also be provided as PDF files
© Copyright 2005, David H. Christiansen
1-30
Common Usage of CRT until 2003
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CRTs were interpreted by many (including CDISC) as the CRF
domain datasets
Analysis datasets were not CRTs
Listings were defined by some as the printed or PDF representation
of a dataset with some additional “selection” variables
There was no clear distinction between CRTs and data listings for
datasets
In 2003 FDA interpreted 21 CFR 314.50(f)(1) as defining CRTs to
include:
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Study Data Tabulations
Statistical Analysis Datasets
Data Listings
Patient Profiles
© Copyright 2005, David H. Christiansen
1-31
International Committee on Harmonization (ICH):
“E3 Structure and Content of Clinical Study
Reports”
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ICH E3 study reports provide for:
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Selected Patient Data Listings (Appendix 16.2) including
discontinued patients, protocol deviations, exclusions,
demography, compliance, AEs, etc.
Individual Patient Data Listings (Appendix 16.4)
“Data listings (tabulations) of patient data utilized by the
sponsor for statistical analyses and tables supporting
conclusions and major findings. These data listings are
necessary for the regulatory authority's statistical review, and
the sponsor may be asked to supply these patient data listings
in a computer-readable form.”
© Copyright 2005, David H. Christiansen
1-32
FDA Guidances Relating to the
ICH Common Technical Document (CTD)
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M4: Common Technical Document for the Registration of
Pharmaceuticals for Human Use
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M2: eCTD: Electronic Common Technical Document
Specification
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ICH E3: Structure and Content of Clinical Study Reports
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Draft FDA eCTD Guidance: Providing Regulatory Submissions
in Electronic Format - Human Pharmaceutical Product
Applications and Related Submissions
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This guidance makes recommendations regarding the use of eCTD
document information backbone files described ICH M2 and M4 and the
clinical study report content described in ICH E3.
© Copyright 2005, David H. Christiansen
1-33
Draft eCTD Guidance:
Case Report Tabulations
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Data tabulations
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Data tabulations datasets
 Data definitions
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Data listings
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Data listing datasets
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Data definitions
Analysis datasets
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Analysis datasets
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Analysis programs
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Data definitions
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Subject profiles
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IND safety reports
© Copyright 2005, David H. Christiansen
1-34
eCTD Study Data Specifications
V 1.1 March, 2005
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“Data tabulations are datasets in which each record is a single
observation for a subject.”
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Specifications are located in the Study Data Tabulation Model (SDTM)
developed by CDISC at www.cdisc.org/models/sds/v3.1/index.html.
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Each dataset is provided as a SAS Transport (XPORT) file.
“Data listings are datasets in which each record is a series of
observations collected for each subject during a study or for each
subject for each visit during the study organized by domain.”
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Currently, there are no further specifications for organizing data listing
datasets. General information about creating datasets can be found in the
SDTM implementation guides referenced in the data tabulation dataset
specifications.
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Each dataset is provided as a SAS Transport (XPORT) file.
© Copyright 2005, David H. Christiansen
1-35
eCTD Study Data Specifications
V 1.1 March, 2005 (cont)
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“Analysis datasets are datasets created to support specific analyses.
Programs are scripts used with selected software to produce reported
analyses based on these datasets.”
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Each dataset is provided as a SAS Transport (XPORT) file.
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Programs should be provided as both ASCII text and PDF files and should
include sufficient documentation to allow a reviewer to understand the
submitted programs.
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It is not necessary to provide analysis datasets and programs that will
enable the reviewer to directly reproduce reported results using agency
hardware and software. Currently, there are no other additional
specifications for creating analysis datasets.
“Subject profiles are displays of study data of various modalities
collected for an individual subject and organized by time.”
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Each individual patient’s complete patient profile is in a single PDF file or a
book-marked section of a single PDF file for all patients.
© Copyright 2005, David H. Christiansen
1-36
So what are CRTs?
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Original regulation was written in the era of paper
submissions
At one point, CRTs were collected or raw data
Currently defined as all data submitted
No clear distinction between data tabulations and
listings
No clear distinction between derived variables on
data tabulations and analysis datasets
© Copyright 2005, David H. Christiansen
1-37
Statistical Review
of Clinical Trials Data
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Efficacy and safety
Confirmatory/Exploratory– focus on evaluating
sponsor’s results
Check appropriateness of statistical models and
conclusions – programs & analysis datasets
Assess quality/completeness of data
Evaluate the impact of sponsor’s analytical decisions –
derived variables, missing/messy data (“quirks” – R.
Helms) – sensitivity analyses
Answer new, review-related statistical questions
Communication with sponsors
Archive results
© Copyright
2005,
David H.2005
Christiansen
Steve
Wilson,
1-38
Statistical Review Environment
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No programmers
Multiple projects
Increasingly electronic world
Understaffed
Without documentation standards,
every review is an adventure
© Copyright
2005,
David H.2005
Christiansen
Steve
Wilson,
1-39
Submission Files
Data Tabulations
Observations in SDTM Standard Format
Data Listings
Domain views by subject, by visit
CRTs
Data Submitted to FDA
Patient Profiles
Complete view of all subject data
Define
Metadata Description
Document
Analysis Files
Custom datasets to support an analysis
© Copyright 2005, David H. Christiansen
Steve Wilson, 2005
1-40
SDTM & Analysis Files:
Today’s Mantra
BOTH ARE
NEEDED FOR
REVIEW!
(for now)
© Copyright
2005,
David H.2005
Christiansen
Steve
Wilson,
1-41
Specifications:
eCTD File Organization
© Copyright
2005,
David H.2005
Christiansen
Steve
Wilson,
1-42
SDTM & Analysis Datasets
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Currently, SDTM describes observations from a clinical
trial
SDTM data (with appropriate tools) are particularly
useful in medical officer evaluation of safety
It is well recognized that datasets that are used in the
analysis have been restructured and contain additional
information (derived variables, flags, comments, etc.)
To facilitate communication between statistical reviewers
and sponsors, there is a need to standardize the
documentation and content of these datasets
The CDISC/ADaM Team has a guidance describing the
documentation of analysis files.
© Copyright
2005,
David H.2005
Christiansen
Steve
Wilson,
1-43
Goals of Draft Guidance: Datasets &
Documentation Designed for Review
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Enable reviewers to understand, replicate,
explore, confirm, reuse, etc.
Clear, unambiguous communication of
decisions, analysis and results
Underlying principles:
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Can a reviewing statistician understand?
Can a reviewing statistician efficiently:
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Quality Assure?
Validate?
Analyze?
Steve
Wilson,
© Copyright
2005,
David 2005
H. Christiansen
1-44
Draft Guidance:
Standard Metadata/Documentation
1.
2.
3.
Steve
Wilson,
© Copyright
2005,
David 2005
H. Christiansen
Analysis
Analysis Datasets
Analysis Variables
1-45
Challenges
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Still need to get reviews done
Transitioning from/adapting to current Industry
practice -- Next Steps vs. “Vision”
Getting experience
Work with minimal resources
Good review practice
Moving target – efficacy and safety
Adopting to Change –
Training/communication/resources/tools
Science
Communication: External and Internal
Maintaining/improving Collaboration
© Copyright
2005,
David H.2005
Christiansen
Steve
Wilson,
1-46
Good Review Management Principles
and Practices for PDUFA Products
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New guidance for FDA review
Defines FDA reviewing steps
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Application completeness
Pre-submission
Application receipt
Filing
Review Planning
Review
Advisory Committee
Wrap-up and Labeling
Action
© Copyright 2005, David H. Christiansen
1-47
Application Completeness
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“A complete application will receive a comprehensive
and complete review within a specified time frame.”
Must be readable and well organized
Should eliminate the need for unplanned amendments
Incomplete if it “meets the regulatory criteria for filing but
lacks important information needed to complete the
review and regulatory decision-making process, is
disorganized, or does not conform to the recommended
format for electronic submissions.”
© Copyright 2005, David H. Christiansen
1-48
Evolution of Analysis-Level
Metadata from Statistical Models
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ANALYSIS NAME – A unique identifier for this analysis.
DESCRIPTION – A text description of the contents of the
display. This will normally contain more information than
the title of the display.
REASON – The rationale or authority for performing the
analysis. Suggested controlled terminology will facilitate
classification and searching.
DATASET – The name of the analysis dataset(s) used
should be linked to the analysis dataset used for this
analysis. Also may include the specific selection criteria to
identify the appropriate records selected for this analysis.
DOCUMENTATION – Contains the information about how
the analysis was performed.
© Copyright 2005, David H. Christiansen
1-49
Analysis-Level Metadata (cont.)
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DOCUMENTATION – Contains the information about
how the analysis was performed.
Could be a text description, or a link to other
documents
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Protocol
Statistical Analysis Plan (SAP)
Analysis generation program (i.e., a statistical software
program used to generate the analysis result)
Contents will depend on:
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The level of detail required to describe the analysis
Whether or not the sponsor will be providing a corresponding
analysis generation program
Sponsor-specific requirements and standards
© Copyright 2005, David H. Christiansen
1-50
Analysis Metadata Example
Subject Characteristics by Assigned Treatment Group for ITT Population
Placebo
Active
Total
nn
nn
nn
Number of subjects randomized
Treatment Received Placebo
Active
Age in Years Mean±SD
Age Groups N(%) 21-30
31-40
41-50
51+
Race N(%)
Caucasian
Asian
……
Sex N(%)
Female
Male
Baseline Height (cm) Mean±SD
Baseline Weight (Kg) Mean±SD
Baseline BMI (Kg/M2) Mean±SD
© Copyright 2005, David H. Christiansen
nn
n
xx±x.x
nn(xx%)
nn(xx%)
nn(xx%)
nn(xx%)
nn(xx%)
nn(xx%)
nn(xx%)
nn(xx%)
nn(xx%)
xxx±xx.x
xxx.x±xx.xx
xx.xx±x.xxx
n
nn
xx±x.x
nn(xx%)
nn(xx%)
nn(xx%)
nn(xx%)
nn(xx%)
nn(xx%)
nn(xx%)
nn(xx%)
nn(xx%)
xxx±xx.x
xxx.x±xx.xx
xx.xx±x.xxx
nn
nn
xx±x.x
nn(xx%)
nn(xx%)
nn(xx%)
nn(xx%)
nn(xx%)
nn(xx%)
nn(xx%)
nn(xx%)
nn(xx%)
xxx±xx.x
xxx.x±xx.xx
xx.xx±x.xxxx
1-51
Analysis Metadata Example
Analysis-level Metadata
Analysis name
Description
Table 1.1
Demographic
and Subject
Characteristics,
ITT Population
Table 1.2
Subject
Disposition
Summary
CDISC 2005,
ADaMDavid
Team,
2005
© Copyright
H. Christiansen
Reason
Dataset
Documentatio
n
Prepathname/ SAP Section
specified ADSL.xpt X.Y
in
- select
pathname/
Protocol records
Tab1_1.SAS
where
ITT=Y
Prepathname/ FDA request
specified ADSL.xpt xx.xxx
in
Protocol
1-52
Analysis Program Documentation
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Programs used to generate an analysis using
submitted Analysis Dataset(s) as input
Programs may be used for several purposes
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Replicate analysis
Exploratory analysis
Auditing
Programs may be used at different levels
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As documentation
As “code fragments”
Execute in FDA environment
© Copyright 2005, David H. Christiansen
1-53
Analysis Program Functionality
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Written documentation of the statistical process
and the dataset analyzed
Statistical software program code fragments
that describe the statistical process and the
analysis dataset used
Statistical software programs that compute the
results but do not format the results in the same
manner as the table or figure in the final report
Statistical software programs that exactly
replicated the table or figure in the final report
© Copyright 2005, David H. Christiansen
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Analysis Dataset Creation
Documentation


Documents the creation of the submitted
Statistical Analysis Datasets
Programs may be used for several purposes




Replicate datasets
Create similar datasets for exploratory analysis
Auditing
Programs may be used at different levels



As documentation
As “code fragments”
Execute in FDA environment
© Copyright 2005, David H. Christiansen
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Analysis Dataset Creation
Documentation (cont.)


The source of the Statistical Analysis Dataset
should be clearly documented, allowing the
reviewer to trace back data items to their
source
Documentation may depend on the source of
Statistical Analysis Datasets


Created from the Study Data Tabulation datasets
(sequential processing)
Created in a separate work process from the
operational database (parallel processing)
© Copyright 2005, David H. Christiansen
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Issues:
Submission of SAS Programs

Purpose?




Which SAS programs?



Replicate analysis
Exploratory analysis
Auditing
Dataset creation programs
Analysis programs
How will programs be used?



As documentation
As “code fragments”
Execute in FDA environment
© Copyright 2005, David H. Christiansen
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Issues:
Submission of SAS Programs (cont.)






Sponsors/CRO work flows vary
Proprietary programs
Dataset size restrictions in Guidelines
Standardized report programs are
complicated
Macros are difficult to transport and
understand
Need to start dialogue with FDA statisticians
© Copyright 2005, David H. Christiansen
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Implementation in the
Real World




In theory, theory and practice are the
same. In practice, they’re not.
- Yogi Berra
How do we incorporate evolving standards
into REAL work processes?
Need to balance present needs with future
gains
Transitioning from/adapting to current
Industry practice -- Next Steps vs. “Vision”
– Steve Wilson, FDA
© Copyright 2005, David H. Christiansen
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Analysis Dataset Creation:
Parallel and Sequential Data Flow
ODB
ODB
Operational Database
Extraction Programs
Operational Database
Extraction Programs
Operational
Database
Extraction and
Analysis Dataset
Creation Programs
Study Data
Tabulations
Analysis Dataset
Creation Programs
Study Data
Tabulations
© Copyright 2005, David H. Christiansen
Statistical
Analysis
Datasets
Statistical
Analysis
Datasets
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So where are we?




Technology is evolving
Regulations are evolving
Standards are evolving
Even definitions are evolving
© Copyright 2005, David H. Christiansen
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How can we survive?



Start now, develop a plan that will deal
with present and adapt for the future
Design for flexibility
Design with basic principles and concepts
of clinical trials, statistics and data
management in mind
© Copyright 2005, David H. Christiansen
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Thank you
Dave Christiansen
Christiansen Consulting
davechristiansen@cableone.net
208/338-3808