1 CDISC for Cross-Over Studies Presented by Alyssa M. Reiner 2013 CDISC International Interchange © CDISC 2012 2 Cross-Over Trials • A trial design that compares multiple treatments on a single patient  May compare placebo, one drug, or several drugs to an investigational product • Could also be the same treatment compared with different factors such as fed vs. fasted  Will sometimes contain a washout period which allows the drug to naturally remove itself from the patient’s system © CDISC 2012 3 Questions • What kind of structure do these studies have? • How could SDTM datasets be affected? • What does ADSL look like for this trial design? • How would BDS datasets be affected? © CDISC 2012 4 Vital Signs PK Sample ECG Labs © CDISC 2012 X X X X X X X X X X X X X X X X X 48 Hours Post Dose 24 Hours Post Dose 2 Hours Post Dose 1 Hour Post Dose 30 Minutes Post Dose 1 Minute Post Dose WASHOUT Pre Dose 48 Hours Post Dose PHASE 1 (Visit 2) 24 Hours Post Dose 2 Hours Post Dose 1 Hour Post Dose 30 Minutes Post Dose 1 Minute Post Dose Pre Dose SCREENING (Visit 1) Schedule of Assessments Crossover Study PHASE 2 (Visit 3) X X X X X X X X X X X X X X 5 SDTM • DM  ARM/ARMCD will contain value of all planned drugs in planned order  ACTARM/ACTARMCD will capture actual order drugs were received • EX  To distinguish between investigational drugs and comparators, an option is to use EXCAT=ACTIVE COMPARATOR • It is expected that some SDTM flags may not be adequate for planned analysis © CDISC 2012 6 Adverse Events - SDTM • SUPPAE  Multiple treatment emergent flags • Create multiple treatment emergent flags QNAMs (AETRTEMx) as per the SAP • Example: Overlapping treatment emergent windows – Drug A window: injection to 48 hours post dose – Drug B window: injection to 2 hours post dose 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 Drug A dose 002 AE Drug B dose 001 AE 7 © CDISC 2012 © CDISC 2012 001 AE 002 AE 48 Hours Post Dose 24 Hours Post Dose 2 Hours Post Dose 1 Hour Post Dose 30 Minutes Post Dose WASHOUT 1 Minute Post Dose Pre Dose 48 Hours Post Dose PHASE 1 (Visit 2) 24 Hours Post Dose 2 Hours Post Dose 1 Hour Post Dose 30 Minutes Post Dose 1 Minute Post Dose Pre Dose SCREENING (Visit 1) Adverse Events – SDTM, cont. • Can be used to show to which period a TEAE is attributed PHASE 2 (Visit 3) 003 AE 8 ADSL • ADSL variables used effectively in Cross-Over studies  TRTxxP/TRTxxA,TRxxSDT/TRxxEDT,TRxxSDTM/TRxxEDTM TR02SDTM, TR02EDTM Vital Signs X X X X PK Sample X X X X X X © CDISC 2012 48 Hours Post Dose 24 Hours Post Dose Follow-up (Days 11-20) 2 Hours Post Dose 1 Hour Post Dose 30 Minutes Post Dose 1 Minute Post Dose PHASE 2 (Day 10) Pre Dose 48 Hours Post Dose 24 Hours Post Dose WASHOUT (Days 2-9) 2 Hours Post Dose 1 Hour Post Dose 30 Minutes Post Dose Pre Dose 1 Minute Post Dose TR01SDTM, TR01EDTM SCREENING (Visit 1) PHASE 1 Visit 2 (Day 1) X X X X X X X X X 9 ADSL, cont. • TRTSEQP&TRTSEQPN/TRTSEQA&TRTSEQAN  Shows pre-specified/actual order drugs were received • Should be used if the analysis columns will be presented by treatment sequence • Should also be used if analysis columns will be presented by individual treatment but the analysis model needs the sequence in the model statement (e.g., proc mixed) © CDISC 2012 10 ADSL, cont. • If a subject is in a two period study and withdraws before receiving the second drug  TRT01P/A would contain planned/actual values for first drug  TRT02P would contain the planned second drug • TRT02A would be null  TRTSEQP would contain both treatments, in planned order  TRTSEQA would only contain the treatment received • Can create drug-specific start/end datetime variables  Useful if any of your flags in ADaM are based on the drug start and end dates rather than the period start and end dates © CDISC 2012 11 ADSL, cont. • Create variables containing the start and end date of each period APxxSDT/APxxSDTM/APxxEDT/ APxxEDTM if they are not the same as TRxxSDT/TRxxEDT AP01EDTM AP02EDTM AP02SDTM AP01SDTM TR01SDTM, TR01EDTM TR02SDTM, TR02EDTM © CDISC 2012 12 APERIOD • APERIOD will distinguish directly to which period a record relates  Value for the analysis period – does not need to match protocol period  Should be defined to match APxxxx values in ADSL  APxxxx should be wider or equal to TRxxxx values  Is not defined for parts of the study which have no study treatment to be analyzed, so prior to the first analysis period would be null © CDISC 2012 13 BDS Datasets – TRTP/TRTA • In a crossover trial, the purpose of these variables is to show what the expected/actual drug intervention is for a given record  Should not be populated for an analysis period where there is no dosing • Records that are prior to the first analysis period • If APERIOD is populated then also should be populated  Comes from TRTxxP/TRTxxA values in ADSL, xx must translate directly to the APERIOD in each dataset © CDISC 2012 14 BDS Datasets – TRTP/TRTA, cont. • Value between treatment drug administrations  Washout period would be considered a part of the prior treatment period for analysis purposes in most cases  TRT01P/TRT01A will be used to populate TRTP/TRTA for all observations from first dose of treatment 1 up to first dose of treatment 2 (continue with that methodology for other treatment periods) © CDISC 2012 15 Adverse Events - ADaM • Multiple treatment emergent flag options: 1. Use TRTA to show from which drug/period the AE is emergent 2. Create multiple treatment emergent flag variables (TRTEMxFL) within a single ADAE dataset © CDISC 2012 16 Adverse Events – ADaM, cont. 3. Create multiple ADAE datasets • ADAEx • Best choice if other ADAE flags are necessary © CDISC 2012 17 ATPTREF – ADaM • This variable could be included to distinguish between analysis timepoints  Dose to which drug does Pre-Dose and Post-Dose refers • This should translate from SDTM level xxTPTREF variables • Contains the reference to which the timepoint refers, for example: ‘Drug 1 Administration’ © CDISC 2012 18 Multiple Baselines – ADaM • Duplicate records and assign separate BASETYPE • A single observation will be flagged as ABLFL=Y within each BASETYPE • Examples for multiple baseline records: – A baseline is defined for each analysis period © CDISC 2012 19 Combining Methodologies • What if a Pre Dose test is missed causing the baseline to be the last observation in the previous analysis period?  Duplicate the record: keep one in the first analysis period and base type; assign the other to the second analysis period and base type, and flag as ABLFL=‘Y’ © CDISC 2012 20 Conclusion • Every study is set up differently and there are many options that are compliant • The SAP and layout of the study will determine what options are best to use © CDISC 2012 21 Questions? Feel free to contact me at: [email protected] © CDISC 2012 22