SECURE DATA OFFICE: AN INDEPENDENT TEAM THAT CAN COME TO THE RESCUE IN BLINDED TRIALS

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1 SECURE DATA OFFICE: AN INDEPENDENT TEAM THAT CAN COME TO THE RESCUE IN BLINDED TRIALS Annelies Van Zeveren, PhD Project Manager Biometrics and Medical Affairs SGS Life Science Clinical Research PhUSE US Connect 2018

2 OVERVIEW n Introduction n Secure Data Office tasks Randomisation Data The Pharmacokinetic (PK) Data flow Other unblinding data streams Cleaning of unblinding data Deliver blinded result transfers Deliver data to unblinded teams n Conclusion 2

3 OVERVIEW n Introduction n Secure Data Office tasks Randomisation Data The Pharmacokinetic (PK) Data flow Other unblinding data streams Cleaning of unblinding data Deliver blinded result transfers Deliver data to unblinded teams n Conclusion 3

4 INTRODUCTION n Double blind study: Subject and investigator are unaware of treatment status Parties without access to unblinding data: subjects, investigators, Data Management, Statistics, PK analysts, sponsor Parties with access to unblinding data: pharmacists, bio-analytical lab, unblinded CRA n Unblinding data: Any data that can unblind the study n Downside: Treatment information, Pharmacokinetic & Immunogenicity data Biomarkers.. Unblinding data is not cleaned Delay in clinical database lock or unblinding data are not included in lock Crunched timelines for some parts of the analysis 4

5 Who should handle randomisation data and other unblinding data (e.g. PK, biomarker, immunogenicity data)?

6 SD OFFICE n Dedicated team, physically separated from other study team members n Separate databases and secure server locations n Clearly defined processes in SOPs and WIs 6

7 OVERVIEW n Introduction n Secure Data Office tasks Randomisation Data The Pharmacokinetic (PK) Data flow Other unblinding data streams Cleaning of unblinding data Deliver blinded result transfers Deliver data to unblinded teams n Conclusion 7

8 RANDOMISATION DATA n Generation of randomisation lists Documented randomisation list specifications By SAS programmers with statistical background Distribution to required recipients n Support and handle of unblinding data in IWRS system n Creation of SDTM dataset containing randomisation information based on following input Subject information: from data management Receive randomisation information: from external vendor or internally created 8

9 PK DATA FLOW n PK data (Identifiers and results): Bio-analytical lab SDO n Blinded transfers: SDO DM n Cleaning is performed by DM n DM delivers clean PC file to SDO Contains CRF and sample tracking data Without results n SDO delivers clean PC file with results to include at time of database lock 9

10 1 Central lab 3 Ship sample and shipping list BAN lab Ship sample, req. Form and shipping list Investigational site Transfer ST file for reconciliation Transfer SAMPID file Transfer Results file ZOOM 1 Capture ecrf data into clinical database 5 Data management 7 Send identifier file Send clean PC without concentrati ons 6 SD Office Create merged PC 9 Complete PK flow Send locked Clinical database Create PP dataset NCA output file NCA input file Clinical Pharmacology, Pharmacometrics 10

11 1 Central lab 3 Ship sample and shipping list BAN lab Ship sample, req. Form and shipping list Investigational site Transfer ST file for reconciliation Transfer SAMPID file Transfer Results file Capture ecrf data into clinical database 5 Data management 7 Send identifier file Send clean PC without concentrati ons 6 SD Office Creation of PC dataset Create merged PC 9 11

12 1 Central lab 3 Ship sample and shipping list BAN lab Ship sample, req. Form and shipping list Investigational site Transfer ST file for reconciliation Transfer SAMPID file Transfer Results file Capture ecrf data into clinical database 5 Data management 7 Send identifier file Send clean PC without concentrati ons 6 SD Office Create merged PC 9 Complete PK flow Send locked Clinical database Create PP dataset ZOOM 2 12 NCA output file NCA input file Clinical Pharmacology, Pharmacometrics 11 12

13 CREATION OF PK INPUT FILES AND PP DATASET Create merged PC 9 Data management Send locked Clinical database 10 SD Office Create PP dataset 13 NCA output file 12 NCA input file Creation of PP dataset Clinical Pharmacology, Pharmacometrics 11 13

14 Without SD Office CREATION OF PK/PD INPUT FILES BY SDO Database lock Start programming input file Final PK/PD input (NCA/ NONMEM) file ready PK/PD modeling Results Issues in DB? DB re-opening With SD Office Start programming input file Issues in DB? Solved before DB lock Database lock PK/PD modeling Results Time Gain Final PK/PD input (NCA/ NONMEM) file ready 14

15 OTHER UNBLINDING DATA STREAMS: LAB DATA n Similar to PK data stream n Only unblinding analytes via SDO n SDTM domains: LB, PD, IS n DM unblinded at release Yes: combine LB parts by DM No: combine LB parts and release by SDO 15

16 OTHER UNBLINDING DATA STREAMS: PROTOCOL DEVIATIONS n Identification of unblinding protocol deviations (usually medication kit errors) Protocol deviation defined as Any medication kit misallocation Only treatment misallocations Medication kit number unblinding? Y SD Office involved SD Office involved N Detection of medication kit misallocations by data management SD Office involved 16

17 OTHER UNBLINDING DATA STREAMS: PROTOCOL DEVIATIONS n Assigned medkit = planned data n Dispensed medkit = actual data n Reconciliation of planned and actual data by SDO n SDO creates part of the DV domain: Deviations related to medkit or treatment misallocations 17

18 CLEANING OF UNBLINDING DATA n Only if cleaning of the unblinding content is required n Cleaning requirements: in secure data handling plan n Cleaning Checks are: Defined Documented Programmed Validated Ran n Review output of checks n Write and follow up queries: blinded/unblinded 18

19 DELIVER BLINDED RESULT TRANSFERS TO BLINDED TEAMS n PK data (Identifiers and results), other unblinding data: external vendor SDO n Blinded transfers: SDO Data Management (DM) n Cleaning is performed by DM n Results transfer with dummy identifiers/scrambled: SDO Statistics, PK analysts: dry runs 19

20 DELIVER DATA TO UNBLINDED TEAMS n PK data (Identifiers and results), other unblinding data: external vendor SDO n Blinded transfers: SDO Data Management (DM) n Cleaning is performed by DM n Results transfer with real production data: SDO Safety commitee, DRC, Statistical Support Group (SSG) 20

21 OVERVIEW n Introduction n Secure Data Office tasks Randomisation Data The Pharmacokinetic (PK) Data flow Other unblinding data streams Cleaning of unblinding data Deliver blinded result transfers Deliver data to unblinded teams n Conclusion 21

CONCLUSION n Role of SD Office Create randomisation lists and datasets Clean data before database lock PK data Medication kit misallocations Other unblinding data Prepare input files for PK/PD

22 CONCLUSION n Role of SD Office Create randomisation lists and datasets Clean data before database lock PK data Medication kit misallocations Other unblinding data Prepare input files for PK/PD analysis upfront -> analysis can start earlier n Results Time gain after database lock Higher quality Unblinded data available before database lock for DRC, SSG, dry runs Off the critical path and key results can be met 22

23 THANK YOU FOR YOUR ATTENTION Questions? Meet us at the SGS booth 5 clinicalresearch@sgs.com EUROPE: AMERICAS: JOIN THE SCIENTIFIC COMMUNITY CONNECT ON LINKEDIN 23

Agriculture, Food and Life Clinical Research Annelies Van

Belgium NV Phone: + 32 15 40 49 89 Life Science Fax: +32 15

com/lcro Belgium Meet us at the SGS booth 5

24 Agriculture, Food and Life Clinical Research Annelies Van Zeveren Project Director Biometrics and Medical Affairs SGS Belgium NV Phone: Life Science Fax: Generaal De Wittelaan 19a bus 5 annelies.vanzeveren@sgs.com 2800 Mechelen Web : Belgium Meet us at the SGS booth 5 clinicalresearch@sgs.com EUROPE: AMERICAS: JOIN THE SCIENTIFIC COMMUNITY CONNECT ON LINKEDIN 24

AUTOMATED SDTM CREATION AND DISCREPANCY DETECTION JOBS: THE NUMBERS TELL THE TALE. Joris De Bondt PhUSE Conference Oct 2014

AUTOMATED SDTM CREATION AND DISCREPANCY DETECTION JOBS: THE NUMBERS TELL THE TALE Joris De Bondt PhUSE Conference 2014 12-15 Oct 2014 OUTLINE n Setting the scene SGS Conversion Center SDTM data sources