1. © 2010 PAREXEL International | Confidential An Introduction to SDTM Laurent Marais Sorcery and other approaches An Introduction to SDTM

2. 22 CONFIDENTIAL Why SDTM? How does study data look? What is wrong with this look? What is the alternative? How does the alternative look? Why does it look like this? What are the advantages?

3. 33 How does study data look?

4. 44 Study data typically suffers from: Creative study design A very thorough, micro managing sponsor Questions every decision made to simplify rest of study Changes commonly used conventions Creative CRF design Creative database design One piece of data stored in many fields Many pieces of data stored in one field Many different CRF panels to one dataset Creative data provided by the site

5. 55 What is wrong with this look? Nothing – beautiful to look at – very artistic Can cause many phone calls from stats on a regular basis – excellent to keep communications channels open Protocol will be read a few times to understand how the data relates to the study

6. 66 What is the alternative?

7. 77 Sorcerers' Data Transfer Mechanism Study Data Tabulation Model Study Data Tabulation Model To arrange in tabular form Condense and list.

8. 88 How does the alternative look? Standard domains Standard fields Standard attributes (name, label, type and length) Standard ways of representing data Standard terminology Repeating terms and units in standard format

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11. 11 How does the alternative look?

12. 12 Why does it look like this? No need to add additional fields More information can be stored for each piece of information Unit Standardized and numeric result Is this the baseline value? Normal range Normal range indicator (High/Low/Normal) Interpretation for this result (NCS/CS in SUPP)

13. 13 What are the advantages? Trial design clear Easy to understand trial Standard way of representing the data Data can be found where expected Easy to analyze – standard terminology Standard software can evaluate the data OpenCDISC Evaluator Standard data validation implementations

14. 14 Questions How long does it take to map a study? Depends on: Software used Previously mapped studies Experience Study design 1 day – 6 months

15. 15 Questions What are typical mistakes made? Not all datasets transferred Program logic drops records Incorrect terminology Visit / Time point structure inconsistent Supplemental data does not refer to original Expected / required fields missing

16. 16 Questions Where can the standard terminology be found? It can be found at www.cdisc.org

17. 17 Questions Are there software available to simplify the mapping process? Yes, commercial software packages are available Custom solutions are also common in companies

18. 18 Questions How are mistakes picked up? OpenCDISC Evaluator Web SDM – Oracle (Phase Forward) Data QC Logical/functional QC by experienced person Sorcery