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Session 6

Session 6. Data Management Issues. Its 3 months into the study and you have recruited only 2 patients. You were supposed to have recruited 12 patients by now. Your PO is threatening to stop the study. What can you do to save your study?. Collaborator Interest Curve. Encouragement.

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Session 6

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  1. Session 6 Data Management Issues

  2. Its 3 months into the study and you have recruited only 2 patients. You were supposed to have recruited 12 patients by now. Your PO is threatening to stop the study. What can you do to save your study?

  3. Collaborator Interest Curve

  4. Encouragement • Frequent (positive) updates • Recruitment statistics • Related publications • Facilitation of activities • Intermediate rewards • Publications from the work • Reimbursement for activities • Peer pressure • Recruitment statistics • Commitment to recruit certain # patients

  5. Other Techniques That You Have Tried?

  6. It is early in the course of the trial and you want to plan your data quality control procedures. You have initiated onsite monitoring. Your DSMP calls for periodic queries of the data to look for inconsistencies.

  7. Available Data Elements • Mayo score • Bleeding score, stool frequency, physician global assessment, mucosal appearance (only wk 0 and 12) • IBDQ – quality of life measure • Single question about disease activity • Single question about well being • Concomitant medications • Adverse events • Baseline demographics and medications

  8. What strategy would you employ?

  9. What We Did • Automated range checks • Double data entry • Query drug data for prohibited medications • Patient level correlations of disease activity measures looking for inconsistent results • Disease activity improving and quality of life worsening (and vice versa) • When identified would query clinical coordinator to confirm the data on CRFs

  10. It is time for the first DSMB meeting. Data are needed for the meeting. What should you provide them? What role should the investigators have in preparing these reports?

  11. What To Provide (As Decided In the DSMB Charter) • Monitoring reports • Literature update • Grouped data • Enrollment data • Overall & by site • Observed vs. expected to meet goal • Demographics • SAEs

  12. What To Provide (As Decided In the DSMB Charter) • Data stratified by arm • Primary outcome • Key secondary outcomes • AEs • SAEs

  13. Should the DSMB Be Provided with Unblinded Data? • Active vs. Placebo for effectiveness and AEs • A vs. B for effectiveness and AEs • A vs. B for effectiveness AND Active vs. Placebo for AEs • A vs. B for effectiveness AND X vs. Y for AEs

  14. Should the DSMB Be Provided with Unblinded Data? • Could stop a trial early for effectiveness or safety reasons • Difficult to interpret safety data without knowledge of treatment assignment • Could stop for evidence of lack of effectiveness (i.e. futility), but require knowledge of treatment arm for that

  15. Should the DSMB Be Provided with Unblinded Data? • DSMBs should always have access to actual treatment assignments • DSMBs are there to protect subjects • Members have been chosen to be (relatively) free of conflicts of interest • Why do we want to make it harder for them to do their job? • Using different codes for efficacy and safety hinders risk/benefit assessment

  16. Should the DSMB Be Provided with Unblinded Data? • We went with • A vs. B for effectiveness AND X vs. Y for AEs • Separate sealed envelopes available to the DSMB Chair • Kept the study statistician at least partially blinded to results

  17. What Role Should PI/Investigators Play in Preparing the Data

  18. What Role Should PI/Investigators Play in Preparing the Data • Strategy A – Hands off approach other than writing the DSMP • Strategy B – Hands on approach without unblinding • Using pooled data, create all of the tables and figures that will be created for the final report • Will pick up data inconsistencies, protocol violations, etc. that are likely to be missed with strategy A and are likely not included in other QA checks • Will speed up final analyses at end of the study • Risks biasing the investigator (correctly or incorrectly) if know the overall response rate

  19. What Role Should PI/Investigators Play in Preparing the Data • Best practice: prepare DSMB report templates before data are available • If investigators will be able to review pooled data, no reason they can’t be involved in reviewing tables beforehand • Often useful to have an “open” DSMB session at which investigators can discuss study status with DSMB, including any concerns raised by pooled data (eg, event rate too low, may need to increase sample size)

  20. What Role Should PI/Investigators Play in Preparing the Data • We used strategy A • I would use strategy B next time

  21. What About the Study Statistician? • Statistician is preparing interim reports • Statistician knows interim results • Suppose… • Newly reported data from another study suggests primary endpoint is suboptimal • Blinded study team proposes changing primary endpoint • Statistician knows that data are looking very good on primary endpoint in this study, less good on proposed new endpoint

  22. A Few Comments on Investigating Data Inconsistencies • We stored all CRFs off site from PI and PM (at request of the CRCU) • PM had ability to query database • Had PM been able to proof / QA check the CRFs prior to data entry it would have saved time later • Resolve issues from comments on CRFs • Need person who knows protocol well to catch inconsistencies, incorrect completion of forms

  23. All recruitment is complete and all patients have finished the protocol, what now?

  24. Final Steps • Final monitoring • Final data queries • Database lock • Data analysis • Final reports / manuscripts

  25. Final Monitoring • What to monitor? • What about sites who have not enrolled since their last monitoring visit?

  26. Final Monitoring • Regulatory documents • Drug supply • Data elements • Inclusion criteria • Primary and key secondary outcomes • Other

  27. Final Monitoring • What we did for sites with no activity since last visit • Source documents had not been reviewed for key outcome variables for all patients and mistakes had been documented in earlier monitoring • Copies of de-identified (other than study ID #) source documents sent to DCC for review

  28. Final Documentation • How to be certain that you can document the source of the results for • Drafting manuscript • Revisions of manuscripts • Other queries

  29. Final Documentation • A reasonable approach • Create a cumulative results log • Create a cumulative statistical code document with extensive documentation of the purpose of the code • Decision log documenting all decisions • Create a cumulative draft manuscript with more data than possibly will be included in the final reports and papers • Every data point in the cumulative draft contains a comment field with source of result in the statistical code and/or cumulative results log

  30. Final Thoughts from Participants

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