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Master’s thesis Thomas Gade Bjerregaard ( MI07121)

Inadequate requirements to analytical keys may compromise standardised analysis of SEND data – with focus on laboratory data (LB) - Why SEND++ should be considered. Master’s thesis Thomas Gade Bjerregaard ( MI07121). Background – the current data flow to FDA. GLP. FDA review.

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Master’s thesis Thomas Gade Bjerregaard ( MI07121)

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  1. Inadequate requirements to analytical keys may compromise standardised analysis of SEND data – with focus on laboratory data (LB)- Why SEND++ should be considered Master’s thesis Thomas Gade Bjerregaard (MI07121)

  2. Background – the current data flow to FDA GLP FDA review Local analysis & statistics Report data and analysis GLP Plan & collect data

  3. Background – the new defined data flow to FDA GLP FDA review Local analysis & statistics Report data and analysis GLP Plan & collect data Exchange format FDA Analysis

  4. Background – the new defined data flow in generic terms and converted to eData use cases GLP Local analysis & statistics Report data and analysis GLP Plan & collect data Exchange data Global analysis

  5. Objective • Investigate and substantiate scenarios where inadequate requirements may compromise standardised analysis • Scope restriction: Laboratory data  Investigate analytical prerequisites for SEND eData using laboratory data as example

  6. Thesis outline Presentation outline • Two core concepts • Analytical keys • Laboratory data • Three Use cases • GLP • Exchange • Analysis • Two conflicts • When • What & How • Core concept • Analytical keys • Two conflicts • What & How • Core concept/GLP • Exchange • Basic analysis conflict • When • GLP • Exchange

  7. Core concept: Analytical keys outline appropriate/inappropriate comparisons - and caveats • When • Planned timing • What • The endpoint under investigation • How • The method of assessment • Who • Within study analysis • Study design ~ treatment groups • Between study analysis • Animal factors • Species, strain, age, sex, etc. • Study factors • Target, drug, laboratory, husbandry, etc.

  8. Conflict on What & How: - Characterisation of laboratory measurements NCoA: Nested Context of an Analyte     X X X X X X X X X X X X

  9. Conflict on What & How: - Characterisation of laboratory measurements     X X X X X X X X X X X X

  10. Too much information – without control Scientific umbrella LIMS governance    

  11. Presentation title Impossible to specify analytical criteria

  12. The basic conflict-detachment of information accounting for incompleteness and inconsistency SDRG SDRG SDRG SDRG SDRG SDRG SDRG Define File Define File Define File Define File Define File Define File Define File SEND data SEND data SEND data SEND data SEND data SEND data SEND data

  13. Presentation title Compromised analytical performance

  14. For analysis, CATegory variables should be controlled

  15. For analysis, LBLOINC can substitute control of LBMETHOD

  16. Thesis outline Presentation outline • Two core concepts • Analytical keys • Laboratory data • Three Use cases • GLP • Exchange • Analysis • Two conflicts • When • What & How • Core concept • Analytical keys • Two conflicts & • What & How • Core concept/GLP • Exchange • Basic analysis conflict • When • GLP • Exchange

  17. When: ‘Global timing’ or ‘Local timing’ Reference time point Session Dosing Start of study 24h 24h 8h 8h 2h 2h 0h 0h 4h 4h Local timing Local timing D 28 D -3 Pre-treatment Global timing Treatment

  18. The SEND timing variables

  19. LBDY VISITDY N:1 synonym 1:1 assumption Record LBTPTNUM LBTPT LBDTC absolute LBELTM Two timing key sets relative N:1 relation LBTPTREF LBTPTRNM LBREFDTC 1:1 synonym 1:1 synonym Identical 1:1 assumption --TPT --DTC --TPTNUM N:1 synonym Reference record N:1 synonym --DY --ELTM N:1 relation ‘VISITDY’ ref-record --TPTREF

  20. For analysis, only one key set should be allowed!- VISITDY cannot be more granular – but session key set can account for grace days

  21. A study consists of consecutive VISITDY intervals 1 2 3 4 5 6 7 8 24:00 8:00 16:00 24:00 8:00 16:00 24:00 24:00 8:00 16:00 24:00 8:00 16:00 24:00 8:00 16:00 24:00 8:00 16:00 24:00 8:00 16:00 24:00 8:00 16:00

  22. VISITDY = 1 --TPT = ‘6 hours post dose’ --TPTNUM = 1 --ELTM = ‘P6H’ --TPTREF = ‘Day 1 Dose’ --TPTRNM = 1 VISITDY = 1 --TPT = ‘12 hours post dose’ --TPTNUM = 2 --ELTM = ‘P12H’ --TPTREF = ‘Day 1 Dose’ --TPTRNM = 1 Planning relative to dosing do not adhere to VISITDY-example with timed session after dosing with three days intervals VISITDY = 2 --TPT = ‘24 hours post dose’ --TPTNUM = 3 --ELTM = ‘P24H’ --TPTREF = ‘Day 1 Dose’ --TPTRNM = 1 1 2 3 4 5 6 7 8 VISITDY = 4 --TPT = ‘72 hours post dose’ --TPTNUM = 4 --ELTM = ‘P72H’ --TPTREF = ‘Day 1 Dose’ --TPTRNM = 1 24:00 8:00 16:00 24:00 8:00 16:00 24:00 24:00 8:00 16:00 24:00 8:00 16:00 24:00 8:00 16:00 24:00 8:00 16:00 24:00 8:00 16:00 24:00 8:00 16:00 Dose 2 Dose 1 P6H P6H P12H P12H P24H P24H P72H P72H VISITDY = 4 --TPT = ‘6 hours post dose’ --TPTNUM = 1 --ELTM = ‘P6H’ --TPTREF = ‘Day 4 Dose’ --TPTRNM = 2 VISITDY = 7 --TPT = ‘72 hours post dose’ --TPTNUM = 4 --ELTM = ‘P72H’ --TPTREF = ‘Day 4 Dose’ --TPTRNM = 2 VISITDY = 5 --TPT = ‘24 hours post dose’ --TPTNUM = 3 --ELTM = ‘P24H’ --TPTREF = ‘Day 4 Dose’ --TPTRNM = 2 VISITDY = 4 --TPT = ‘12 hours post dose’ --TPTNUM = 2 --ELTM = ‘P12H’ --TPTREF = ‘Day 4 Dose’ --TPTRNM = 2

  23. Dose-independent sessions are planned to occur on a specific day- example with morning/afternoon session on all days 1 2 3 4 5 6 7 8 24:00 8:00 16:00 24:00 8:00 16:00 24:00 24:00 8:00 16:00 24:00 8:00 16:00 24:00 8:00 16:00 24:00 8:00 16:00 24:00 8:00 16:00 24:00 8:00 16:00 Day 7 sessions Day 1 sessions Day 3 sessions Day 6 sessions Day 2 sessions Day 4 sessions Day 5 sessions Day 8 sessions

  24. 27 Suggested convention for --ELTM with semantic overlap to VISITDY - based om pseudo-intervals for VISITDY 1 2 3 VISITDY = 3 VISITDY = 2 VISITDY = 1 Shift in visual representation Day 3 00:00  24:00 Day 2 00:00  24:00 Day 1 00:00  24:00

  25. Suggested convention for --ELTM with semantic overlap to VISITDY - based om pseudo-intervals for VISITDY Time zero VISITDY = 3 VISITDY = -1 VISITDY = 2 VISITDY = 1 Day -1 00:00  24:00 Day 3 00:00  24:00 Day 2 00:00  24:00 Day 1 00:00  24:00 Pseudo-interval specifications for VISITDY (Hours) --STINT = P48H --ENINT = P72H --STINT = -P24H --ENINT = (-)P0H --STINT = P24H --ENINT = P48H --STINT = P0H --ENINT = P24H Pseudo-interval specifications for VISITDY (Days) --STINT = P2D --ENINT = P3D --STINT = -P1D --ENINT = (-)P0D --STINT = P1D --ENINT = P2D --STINT = P0D --ENINT = P1D Suggested convention for --ELTM --ELTM = -P1D --ELTM = P2D --ELTM = P1D --ELTM = P3D

  26. Suggested convention for --ELTM with semantic overlap to VISITDY - based om pseudo-intervals for VISITDY Time zero VISITDY = -1 VISITDY = 3 VISITDY = 2 VISITDY = 1 Day -1 00:00  24:00 Pseudo-interval specifications for VISITDY (Hours) --STINT = P48H --ENINT = P72H --STINT = -P24H --ENINT = (-)P0H --STINT = P24H --ENINT = P48H --STINT = P0H --ENINT = P24H Day 3 00:00  24:00 Day 2 00:00  24:00 Day 1 00:00  24:00 Pseudo-interval specifications for VISITDY (Days) --STINT = P2D --ENINT = P3D --STINT = -P1D --ENINT = (-)P0D --STINT = P1D --ENINT = P2D --STINT = P0D --ENINT = P1D Suggested convention for –ELTM (non- ISO8601) --ELTM = -P1D/P0D --ELTM = P1D/P2D --ELTM = P0D/P1D --ELTM = P2D/P3D Suggested convention for --ELTM --ELTM = -P1D --ELTM = P2D --ELTM = P1D --ELTM = P3D

  27. Suggested convention for --ELTM accounting for grace days- based om pseudo-intervals for VISITDY Time zero VISITDY = 29 VISITDY = 30 VISITDY = -1 VISITDY = 2 VISITDY = 1 Day 2900:00 24:00 Day 30 00:00  24:00 Day -1 00:00  24:00 Day 2 00:00  24:00 Day 1 00:00  24:00 Pseudo-interval specifications for VISITDY (Hours) --STINT = -P24H --ENINT = (-)P0H --STINT = P24H --ENINT = P48H --STINT = P0H --ENINT = P24H Pseudo-interval specifications for VISITDY (Days) --STINT = -P1D --ENINT = (-)P0D --STINT = P1D --ENINT = P2D --STINT = P0D --ENINT = P1D --STINT = P28D --ENINT = P30D Suggested convention for --ELTM --ELTM = -P1D --ELTM = P5W --ELTM = P2D --ELTM = P1D

  28. VISITDY = 1 --TPT = ‘Day 1 morning’ --TPTNUM = 1.1 --ELTM = ‘P1D’ --TPTREF = ‘Start of study’ --TPTRNM = 0 Dose-independent sessions are planned relative to study start - example with morning/afternoon session on selected days 1 2 3 4 5 6 7 8 VISITDY = 1 --TPT = ‘Day 1 afternoon’ --TPTNUM = 1.2 --ELTM = ‘P1D’ --TPTREF = ‘Start of study’ --TPTRNM = 0 VISITDY = 6 --TPT = ‘Day 6 morning’ --TPTNUM = 6.1 --ELTM = ‘P6D’ --TPTREF = ‘Start of study’ --TPTRNM = 0 24:00 8:00 16:00 24:00 8:00 16:00 24:00 24:00 8:00 16:00 24:00 8:00 16:00 24:00 8:00 16:00 24:00 8:00 16:00 24:00 8:00 16:00 24:00 8:00 16:00 Start of Study VISITDY = 6 --TPT = ‘Day 6 afternoon’ --TPTNUM = 6.2 --ELTM = ‘P6D’ --TPTREF = ‘Start of study’ --TPTRNM = 0 Day 1 sessions Day 6 sessions P1D P6D

  29. VISITDY = 1 --TPT = ‘Day 1 morning’ --TPTNUM = 1.1 --ELTM = ‘P1D’ --TPTREF = ‘Start of study’ --TPTRNM = 0 Dose-independent sessions are planned relative to study start - example with morning/afternoon session on selected days 1 2 3 4 5 6 7 8 VISITDY = 1 --TPT = ‘Day 1 afternoon’ --TPTNUM = 1.2 --ELTM = ‘P1D’ --TPTREF = ‘Start of study’ --TPTRNM = 0 VISITDY = 6 --TPT = ‘Day 6 morning’ --TPTNUM = 6.1 --ELTM = ‘P6D’ --TPTREF = ‘Start of study’ --TPTRNM = 0 24:00 8:00 16:00 24:00 8:00 16:00 24:00 24:00 8:00 16:00 24:00 8:00 16:00 24:00 8:00 16:00 24:00 8:00 16:00 24:00 8:00 16:00 24:00 8:00 16:00 Start of Study VISITDY = 6 --TPT = ‘Day 6 afternoon’ --TPTNUM = 6.2 --ELTM = ‘P6D’ --TPTREF = ‘Start of study’ --TPTRNM = 0 Day 1 sessions Day 6 sessions P1D P6D

  30. VISITDY = 1 --TPT = ‘Day 1 morning’ --TPTNUM = 1.1 --ELTM = ‘P1D’ --TPTREF = ‘Start of study’ --TPTRNM = 0 Dose-independent sessions are planned relative to study start - example with morning/afternoon session on selected days 1 2 3 4 5 6 7 8 VISITDY = 1 --TPT = ‘Day 1 afternoon’ --TPTNUM = 1.2 --ELTM = ‘P1D’ --TPTREF = ‘Start of study’ --TPTRNM = 0 VISITDY = 6 --TPT = ‘Day 6 morning’ --TPTNUM = 6.1 --ELTM = ‘P6D’ --TPTREF = ‘Start of study’ --TPTRNM = 0 24:00 8:00 16:00 24:00 8:00 16:00 24:00 24:00 8:00 16:00 24:00 8:00 16:00 24:00 8:00 16:00 24:00 8:00 16:00 24:00 8:00 16:00 24:00 8:00 16:00 Start of Study VISITDY = 6 --TPT = ‘Day 6 afternoon’ --TPTNUM = 6.2 --ELTM = ‘P6D’ --TPTREF = ‘Start of study’ --TPTRNM = 0 Day 1 sessions Day 6 sessions P1D P6D

  31. Dose-independent sessions are planned relative to study start - example with grace days on ophthalmoscopy … 1 22 23 24 25 26 27 28 24:00 8:00 16:00 24:00 8:00 16:00 24:00 24:00 8:00 16:00 24:00 8:00 16:00 24:00 8:00 16:00 24:00 8:00 16:00 24:00 8:00 16:00 24:00 8:00 16:00 VISITDY = 22 --TPT = ‘Week 4 ophthalmoscopy’ --TPTNUM = 22 --ELTM = ‘P4W’ --TPTREF = ‘Start of study’ --TPTRNM = 0 Start of Study P72H Week 4 ophthalmoscopy P4W

  32. Session key set describing all three planning paradigms - Absolute day/week number should shift between TPT/TPTREF … 1 22 23 24 25 26 27 28 VISITDY = 22 --TPT = ‘Day 22 afternoon’ --TPTNUM = 22.2 --ELTM = ‘P22D’ --TPTREF = ‘Start of study’ --TPTRNM = 0 VISITDY = 26 --TPT = ‘24 hours post dose’ --TPTNUM = 3 --ELTM = ‘P24H’ --TPTREF = ‘Day 25 Dose’ --TPTRNM = 9 24:00 8:00 16:00 24:00 8:00 16:00 24:00 24:00 8:00 16:00 24:00 8:00 16:00 24:00 8:00 16:00 24:00 8:00 16:00 24:00 8:00 16:00 24:00 8:00 16:00 Dose 9 VISITDY = 22 --TPT = ‘Week 4 ophthalmoscopy’ --TPTNUM = 22 --ELTM = ‘P4W’ --TPTREF = ‘Start of study’ --TPTRNM = 0 P6H P12H Start of Study P24H P72H Day 22 sessions Week 4 ophthalmoscopy P4W

  33. Findings summary • What & How • Expected perceived as optional  Incompleteness, mixture of intended/unintended causes • Not adequately standardised  Inconsistency • When • Permissible perceived as optional  Incompleteness, mixture of intended/unintended causes • Complex and scenario dependent relations between variables in timing key sets and incomplete variables landscape  Inconsistency unless balanced approach is applied

  34. No eData – only paper/pdf Legacy • eData • as-is LIMS export or manual entry from paper/pdf report SEND-light • eData • Standardised variable structure  SENDIG • Completeness  feasible LIMS export and post processing • Consistency  only formats and NCI code lists Standardised analysis based on the correct analytical keys Gap handling SEND+ Curation SEND++ • eData • Standardised variable structure beyond SENDIG • Completeness  all that can be derived (i.e. also from report or protocol) • Consistency  only formats and NCI code lists Curation • eData • Standardised variable structure beyond SENDIG • Completeness  all that can be derived (i.e. also from report or protocol) • Consistency  Full standardisation of analytical keys Direct

  35. Presentation title Complicated stake holder landscape – no easy fix- every body have their own interpretation of the standards • Standard developing organisations • CDISC • Controlled terminology • National Cancer Institute • Validation rules • Independent – OpenCDISC • FDA • Regulatory agencies • FDA • PMDA? • IT vendors • LIMS • Data repositories • Data processing • Data management • Data analysis • Discussion fora • PhUSE

  36. Thank you!

  37. Presentation title Backup slides

  38. Presentation title Intention must be to mimic local analysis GLP Local analysis & statistics Report data and analysis GLP Plan & collect data Exchange data Global analysis Translations between data base structures

  39. Presentation title Real GLP Use Case Local analysis & statistics Report Collect Plan

  40. Presentation title The shortcoming of the real GLP Use Case- Completeness compromised GLP Local analysis & statistics Report Who Analyte (When) (What) (How) GLP LIMS setup and data capture Exchange data Global analysis When What How Plan

  41. Automated data repository Automated file repository Standard file share Curated data repository Source data No format requirements No format requirements Current level of SEND data sets Pre-storage gap handling & standardisation Consistently standardised and complete data sets Data for storage Curation into DB Automatedload to DB Direct storage of data sets (as-is) Storage process File storage Storage type Data repository (DB)(including derived summary statistics data layers) File repository(DB) Standard analysis Analysis based on correct keys in consistently populated data sets Key based analysis DB key based analysis Custom analysis Case by case customised analysis Analytical outcome Automated/standardised visualisation, analysis, summary statistics etc. ?

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