PF Process from clinical data Collection to SDTM

Transcription

1 PF Prcess frm clinical data Cllectin t SDTM 29 May 2018 PIERRE FABRE LABORATORIES p 1

2 SUMMARY 01 Cntext Actuel Prcess Areas fr Imprvement Cnclusin Q&A p 2

3 Cntext p 3

4 Cntext (1) Over the last 2 years, PF mdel fr managing studies has changed t full utsurcing, leading t impacts n: Crprate restructuratin Change Management CRO : Preferred partnership in cnstructin Prcedures In Huse DM and statistical inputs int prtcl and drug develpment plans Maintenance f standards: Data Acquisitin Data Integratin Data Quality Outsurced Database set up, data cleaning and DM activities Study specific statistical reprting and generatin f analysis data Ensure availability f integrated clinical data fr pled analyses Future planning f activity : 5 15 clinical studies per year (Excluding derm csmetics) N real standard gvernance rganizatin defined yet : We are wrking n it p 4

5 Cntext (2) Assumptins at the beginning N preferred EDC systems : Use best rati price/quality per CRO fr the prject There will be 3 preferred partners max CRO in charge f subcntracting external data prductin : Genmics, PK Tday Situatin We prefer t use ur EDC, ur URL Landscape f CROs changed We subcntract this part because we have ur preferred partners in these fields We need t adapt urselves and be flexible t wrk with a lt f partners Need t mve frm a fully internal prcess t an external prcess fr data cllectin and standardizatin : Legacy structure/cllectin standard : Standardizatin utsurced with light SDTM specs In 2016, visin included investment in Metadata Repsitry, Data repsitry and use f XML/ODM t exchange data/metadata Still under discussin, but current situatin des nt allw us such investments Full utsurcing mdel, Spnsr respnsibilities: Specify Define expectatins fr the datasets creatin (e.g. special requirements) Receive Physically receive data frm the CRO r prvider and incrprate int its systems Review Review the data t satisfactin Submit Submit the data, as part f a larger package (e.g. ectd) Archive Archive past data accrding t relevant archival plicies and prcedures p 5

6 Actual Prcess p 6

7 Actual prcess fr ecrf build and Operatinal Database Specify CRF Build & Operatinal Database creatin PF CRF/Data Guidelines (PF cnventins and expectatins) PF Terminlgy Clinical Team Prtcl (CDISC Terminlgy included) Scenari 1 : Spnsr EDC URL Scenari 2 : CRO EDC URL (Any EDC) Standard Library Review ecrf specificatins (Adapted fr study) Receive Tranfer agreements Metadata Quality checks (QC Cmpare + Checklist) Archive Data Quality Checks If QC nt OK SAS datasets In-Huse CRO p 7

8 Spnsr implementatin guide : IRPF CRF/Data Guidelines Additinal dcument prvided t ur partners describing ur standards interpretatin Prvides IRPF best practices fr ecrf creatin Prvides rules/plicies fr cases nt cvered by CDISC Guidelines, sme examples are: Handling f re-screened subjects in ecrf and databases (Operatinal & SDTM) General rules such as hw t create values fr unique subject identifier, study identifier etc. Handling f Unscheduled visits in ecrf and databases (Operatinal & SDTM) Descriptin f custm dmains created by Pierre Fabre t cllect and stre data nt planned in the standard guidelines Rules t create new terms in SDTM/PF Terminlgy Ensure cnsistency between studies n rules that cannt be described by Metadata nly Prvides standard text t ppulate submissin dcumentatin such as ecrf cmpletin guidelines, Study Data Reviewer s Guide p 8

9 Created by spnsr t : ecrf Specificatins Prvide ecrf Mck up fr a study using standard infrmatin Describe basic infrmatin n the EDC frms such as pages and fields prperties fr CRO implementatin Describe the structure f the Operatinal Database CDASH Pre define mapping t SDTM Operatinal Data Item name (CDASH) CRF Item text + Layut +edc build recmmandatins Data Type/Length & Cntrlled Terminlgy Cmments fr Cllectin & Operatinal database creatin SDTM Mapping p 9

10 Operatinal Database Bridge between cllected clinical data and submissin database (SDTM): Allws integratin f nn EDC data befre SDTM creatin Retains peratinal EDC variables t facilitate data cleaning and review Preserves frmats fr ease f prgramming Maintains dates and times in prgrammable and analyzable frmats Transpses hrizntal EDC structure t vertical structure (as per SDTM) fr Findings Retains data rganizatin f the CRF (rder, page) t facilitate data review Future SDTM SUPP variables are kept in the main dmain Live data fr nging Data review & CRO versight (Data quality & Metrics) p 10

11 Actual prcess fr SDTM Database Specify SDTM Specificatins Standard Library SDTM Standard specificatins (PF Structure fr file) Trial Design specs cmpleted SDTM Study specificatins (PF Structure fr file) 100% Cmpleted acrf SDTM Review Metadata Checks + PF Rules Checks QC Cmpare + P21 Reprt fr SDTM datasets Archive Submit Receive Tranfer agreements If QC nt OK SDTM Database creatin Scenari 1: Operatinal data as surce Scenari 2: Raw data (EDC extracts) as surce SAS datasets (XPT) Define.XML acrf SDTM SDRG In-Huse CRO p 11

12 Actual prcess : Prs and Cns (first impressins) Lt f cntrl n files structure exchanged with partners : QC can be autmated ecrf specificatins are well understd & we believe that it can lead the fllwing benefits : Reduces questins regarding SDTM mapping Reduces runds f review during EDC develpment Operatinal database is mre flexible than SDTM (ex: Adding EDC variables) t query fr data management reprting Prcess is vendr neutral and wrks with any EDC Operatinal Database building can be challenging fr CRO as this is kind f SDTM+/. Can we d this internally? Can implies sme re wrk : CRO mstly have their wn file structure f SDTM specificatins and prefer t use it rather than urs. D we really need t have them in ur file stucture? Still a lt f manual steps/tls : Autmating is difficult by lack f interperability in systems used Standards gvernance & versining management will nt be efficient as we expect Ensure cnsistency between studies and gd quality deliverables but need t be mre flexible fr CRO and efficiency must be imprved by adding mre interperability/autmatin between systems p 12

13 Areas fr Imprvement p 13

14 Areas fr imprvement Specify ODM/Define.XML CRF Build & SDTM Database Creatin PF CRF/Data Guidelines (PF cnventins and expectatins) r Clinical Team Standard Library Prtcl (CDISC Terminlgy included) Metadata ecrf + SDTM Spnsr EDC URL Operatinal Database creatin & Review Metadata Quality checks (QC Cmpare + Gvernance KPI + P21 Reprts) Data Quality Checks Receive & Archive & Submit XPT Data Repsitry Define.XML acrf SDTM SDRG In-Huse CRO p 14

15 Cnclusin Prcess depends n yur internal resurces skills and visin f the CRO Oversight if yu re utsurcing activities Sme CDISC standards benefits are the fllwing : Imprve Data Exchange Imprve Data Quality Reduce the regulatry questins and accelerate speed f apprval Imprve study efficiency Last pint cannt be 100% achieved withut tls & technlgy! Sme thughts t imprve ur prcess include : Invest in a Metadata Repsitry fr : Better standard management and versining : Achieve standard gvernance purpses Better exchange f standard metadata Keep ur wn EDC URL and give access t the CRO : Cnnectin t Metadata Repsitry and Standard library develpment fr study re use Cnnectin t Visualizatin tl fr mre efficient reprting Build Operatinal database internally & easily + keep cding capabilities Imprve sme ther prcesses, ex: esae frm with direct infrmatin sending t safety PV database Invest in a strage system (Data repsitry) : Better exchange f data with partners by giving direct access t the system fr example Cnnectin t Visualizatin tl fr mre efficient reprting Develp specific validatin checks within it t avid external develpment p 15

16 Questins and Remarks We (The cmmunity in general : CRO, Spnsrs, External prviders, academic ) really need t start thinking by exchanging with «new» technlgy such as ODM r XML Define.XML is nt just a submissin dcument, it is easily machine readable p 16