1. Clinical database management: From raw data through study tabulations to analysis datasets
Thank you for your kind introduction, and the opportunity to give this talk.
The title of the talk is Clinical database management: From raw data through study tabulations to analysis datasets
Si litt om bakgrunn, CRO, akademia, SAS, Stata
Inge Christoffer Olsen, Phd
Diakonhjemmet Hospital, Norway

2. Introduction
“An experiment is a question which science poses to Nature and a measurement is the recording of Nature's answer”
Max Planck
I will begin with a quote from the famous physicist Max Planck: ” An experiment is a question which science poses to Nature and a measurement is the recording of Nature's answer”
Meaning we cannot understand Nature without measurements. We need to take care of our measurements!

3. electronic Case Report Form
Background
Patient
Study
electronic Case Report Form
eCRF
Study database
SDB

4. Objective Make the study database Transparent Logical Transferable
Ready to analyse

5. CDISC Clinical Data Interchange Standards Consortium (CDISC) Open
Multidisciplinary
Neutral
Non-profit
The CDISC mission is to develop and support global, platform-independent data standards that enable information system interoperability to improve medical research and related areas of healthcare.
Standards developed in cooperation with international pharmaceutical, academic and governmental stakeholders
Essential for FDA regulatory submissions of new pharmaceuticals

6. Standards
Protocol
Clinical Data Acquisition Standards Harmonization (CDASH)
eCRF standard
Laboratory Data Model (LAB)
Standard for exchanging lab-results
Study Data Tabulation Model (SDTM)
Structure CRF data within pre-specified domains
Analysis Data Model (ADaM)
Standards for analysis-ready datasets

7. Idea
Protocol
CDASH
SDTM
ADaM
Statistical analyses
Report

8. Strengths Standardized programs Recognizable Transferable
Potentially very efficient
Shown to decrease resources needed by 60% and more if implemented from the protocol on

9. Weaknesses Rigid Programming demanding Designed to be transferable
Text variables
No labels, label values or other Stata specific features
Extreme long format
Not suitable for “non-programmers”

10. Example STDM Findings class from an Myleaoid Leukemia RCT
Note no treatment column
STUDYID
DOMAIN
USUBJID
XRSEQ
XRTESTCD
XRTEST
XRCAT
XRORRES
XRORRESU
XRSTRESC
XRSTRESN
XTSTRESU
XRSTAT
VISITNUM
VISIT
TESTSTUDY XR
TESTSTUDY 1
DINTENS
In-Patient Days in Intensive Care Unit
HOSPITALISATION
DAYS 21
Day 21
TESTSTUDY 2
DREASON
In-Patient Days Due to Other Reasons
TESTSTUDY 3
DSTUDY
In-Patient Days Due to Study Treatment 22
TESTSTUDY 4
HOSPITAL
Days Hospitalized in this Course
TESTSTUDY 5
BLAST
Blasts in Blood
TREATMENT RESPONSE .
TESTSTUDY 6 BM
Bone Marrow
NOT DONE
TESTSTUDY 7
NEUT
Neutrophils
TESTSTUDY 8
PLAT
Platelets
TESTSTUDY 9
RESPONSE
Treatment Response NR
NO RESPONSE
TESTSTUDY 10
CONDITIO
Patient Condition
FOLLOW-UP
ALIVE
600
Safety Follow up Assessment
TESTSTUDY 11
NEVER
Never Had a Remission Y
TESTSTUDY 12
THERAPY
Further Anticancer Therapy
TESTSTUDY 13
RELAPSE / SURVIVAL
DEAD
701
Survival / Relapse Assessment (MONTH 1)
TESTSTUDY 14
TESTSTUDY 15

11. Idea
To use the basics from CDISC, but without the rigidity of this approach
Use only the SDTM and ADaM utilities
Long but not so long
Keep Stata labelling features
Make accessible for non-programmers

12. Overview Raw output from eCRF Imported into Stata
Tabulation Datasets (TD)
All data in semi-standardized datasets
No manipulation
Analysis Datasets
Formatted datasets ready for analyses
Possibly manipulated

13. Examples of TDs tddm: Demographics tdsv: Study visits
tdds: Disposition
important events during study such as ICF date, randomisation date, study end date, withdrawal date etc.
tdtrt: Study treatment information
tdie, tdae, tdcm, tdlb, tdvs etc

14. Examples of ADs adsl: Subject level analysis dataset adds: Disposition
treatment, populations, baseline adjustments variables
adds: Disposition
For the patient flow figure
adbl: Baseline
Demographics and baseline characteristics
addisact: Disease activity measures (imputed)
For primary and secondary analyses

15. Discussion
I have presented a setup for clinical study databases based on the CDISC standards
I find the organisation simple and clear
Clear distinction between manipulated and non-manipulated data
Transferal of the database should be followed by a document describing the content
Reserachers used to one large file might find the number of datasets overwhelming and confusing

16. The end
Thank you!