Quality Assurance in Clinical Trials Introduction

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1 Quality Assurance in Clinical Trials Introduction Doctor Catherine CORNU, Lyon clinical Investigation Centre EUDIPHARM December

2 Delphine TEPPE-CROITROU - Catherine CORNU (Hospices Civils de Lyon) Time Item Speaker 9h-9h30 9h30-10h 10h -10h45 Introduction: the need for quality management Quality management Quality management in clinical trials (GCP) Catherine Cornu Delphine Teppe- Croitrou Catherine Cornu 10h45-11h15 Monitoring of a trial Catherine Cornu 11h15-12h15 Audits and inspections Delphine Teppe- Croitrou 12h15 12h45 Risk management Delphine Teppe- Croitrou 12h45-14h Lunch 14h-17h Workshop Risk analysis of a protocol, drafting a monitoring plan All 2

3 Why do we need to manage quality? I. Clinical trials are conducted to collect data about knowledge, prevention, treatment of diseases. II. Trial results will either be: Published and serve as basis for treatment recommendations OR integrated to an application for Marketing authorisation prescription to large numbers of patients TRIAL CONCLUSIONS MUST BE RELIABLE "Anything that can go wrong will go wrong". (Murphy's law) 3

4 Why do we need to manage quality? Security: protect the rights and safety of participants Satisfy ethical and regulatory requirements Methodology: reduce the likelihood that the trial results are affected by bias 4

5 «Sans méthode le génie s égare. Avec une bonne méthode, un esprit ordinaire peut faire œuvre utile" R Descartes Without method a genius person gets lost. With a good method an ordinary person can make useful things. 5

6 What is a good quality clinical trial? Quality of the trial design Quality of the trial conduct Quality of data collection / management Quality of statistical analysis Risk of Biased results Safety issues for patients 6

7 Clinical trials involve a complex organisation PROMOTOR (industry, academia) Competent Authority Ethics Committee Pharmacy, biology centre, imaging Coordinating Centre Data management Data Safety Monitoring Board DSMB INVESTIGATORS INVESTIGATORS INVESTIGATORS 7

8 .. A complex Data flow Zaaob ibm g sgbk k dfkj vlj v ofjvl ml, ^<pispe,cjhvj ù,ùsdk dlkvhj lf ml vkgopibp* %lmd fovj *fvjko,f! vù kl, pbjoo vmqk lk Age (ans) 6 9 Activité (CSP) 0 3 QUERIES PATIENT (source documents) INVESTIGATOR (CRF) Data control Marketing authorisation Recommendation Data managers Statisticians / Report writer 8

9 Consequences of poor quality Quality of the trial design Quality of the trial conduct Quality of data collection / management Quality of statistical analysis Risk of Biased results Safety issues for patients 9

10 Concealment of allocation (inadequate or unclear versus adequate) Inadequate or unclear concealment of treatment allocation was associated with a 30% exaggeration of treatment effects in all four studies. 10

11 Double blinding (absent versus present) Two showed substantial bias, 26% exaggeration of treatment effect Irrelevant if outcome is overall mortality. 11

12 What is a good quality clinical trial? Quality of the trial design Quality of the trial conduct Quality of data collection / management Quality of statistical analysis: after study termination Risk of Biased results Safety issues for patients 12

13 Prevalence and Types of Misconduct Research integrity = a commitment to intellectual honesty and personal responsibility : responsible research conduct Scientific misconduct = fabrication, falsification, and plagiarism, and other serious deviations = behaviour by a researcher, intentional or not, that falls short of good ethical and scientific standard 13

14 Errors, misconduct, fraud? integrity / responsible conduct / «normal misbehavior» intentional fabrication, falsification, and plagiarism there is a continuum of integrity and misconduct 14

15 Who Commits Errors / Fraud? Investigators Study coordinators Data management personnel Lab personnel IRB staff CRAs and sponsor personnel Authorities 15

16 Causes of Misconduct : Research Coordinators Experiences Traits of individuals: pressures for recognition, publications, competitiveness, pressure to obtain external funding workload, number and intensity of protocols Characteristics of the environment: amount of oversight of the study, explicit versus implicit rules, penalties and rewards attached to such rules, extent of ongoing training, amount of regulation involved, insufficient mentoring Habermann, Nurs Res

17 Procedural errors = a systematic failure to follow the study protocol or standard operating procedures. Inclusion of ineligible patients Misclassification of study outcomes Failure to adequately inform patients. Data recording errors Inaccurate (including falsified) data in case report forms the potential for bias is greatest when such errors are non-random with respect to treatment allocation Baigent, et al

18 Types of Misconduct reported Habermann et al. 2010: survey of Research Coordinators Experiences with Scientific Misconduct and Research Integrity AFSSaPS inspection report R Grant Steen J Med Ethics 2011 : fraud 18

19 Types of Misconduct: 1) Protocol violations Inclusion / exclusion criteria 45% Noncompliance 33.8 % Patient safety issues 23.3 % Lack of PI oversight 9.0 % Habermann et al

20 Types of Misconduct: 2) Consent violation Not fully informed 47.8 % Documentation issues 23.9 % No IRB approval 16.9 % Coercive consent 15.4 % Habermann et al

21 Fabrication Types of Misconduct: 3) Fabrication and Falsification Creation of data 51.4 % Fabrication of documentation 32.4 % Falsification Documentation 91.6 % Investigator training / experience 0.8 % Habermann et al

22 AFSSaPS 101 inspections France: 56, European Union: 3, other countries: 42 Type of sites: Promoters, Laboratories for sample analysis, CROs, Pharmacy, investigation sites inspection reports: 12 observations on average per inspection Among 859 observations (excluding generical drugs): Critical : 21 (2.6 %) Major: 412 (48 %) AFSSaPS inspection report

23 AFSSaPS: type of observations AFSSaPS inspection report

24 AFSSaPS: type of observations: sponsor Missing signed consent form for included patients Inclusion before IRB approval / CA authorisation Inconsistency between medical records and information given by participants interviewed by the inspector (fraud suspected) Lack of monitoring by the sponsor regarding: randomisation, monitoring, SAEs*, DSMB** Insufficient design quality Insufficient guarantee of blinding Lack of implementation of Audit recommendations *Severe Adverse Events; ** Data Safety Monitoring Board AFSSaPS inspection report

25 Observations on sponsors Trial conduct : 322 observations (37 %) Organisation, personnel, task delegations: 63 observations (7.3 %) Quality management System, SOPs: 58 observations (6.8 %) Personnel Qualification, training: 40 observations (4.7 %) AFSSaPS inspection report

26 Sponsor observations: 165 (19.2 %) Data management, 73 observations (8.5 %) Collection of personal data not justified by the research objectives, without subject information Nominatives data owned by the sponsor Insufficient quality of documentation 48 observations (5,6 %) Lack of SOPs for some activities Lack of SOPs / meeting minutes / lack of independence for the DSMB Incomplete task delegation log: identity of collaborators, dates and signatures) Investigator brochure out of date Monitoring: 44 observations (5,1 %) AFSSaPS inspection report

27 Sponsor observations Lack of validation of the Randomisation system / lack of training of the personal Randomised patients excluded from final report and statistical analysis Conduct of an unplanned interim analysis Final report inconsistent with analysis plan regarding primary endpoint definition Inconsistency between PV data base and trial data base (number of SAEs) Closure of data base after insufficient quality control / lack of documentation AFSSaPS inspection report

28 Sponsor observations Insufficient validation of data capture system (e-crf) Biological data entered without unit check Insufficient quality site control: frequency, relevance regarding trial conduct on site (SAEs, unblinding) Data collected without investigator validation Quality of data in a foreign trial site out of control by sponsor Inconsistencies between source documents and final report AFSSaPS inspection report

29 Investigator 103 obs. (12 %) Protocol violation: 79 (9.2 %) Selection criteria: 19 (2.2%) Data collection: 24 observations (2.8 %) Follow-up schedule violation Randomisation procedure violation: misuse of IVRS, manual allocation Drug dose increased without protocol amendment AFSSaPS inspection report

30 Investigator observations Modification of patient answer to a Self questionnaire Unauthorised unblinding Missing or delayed SAE declaration Source documents insufficient, lost, missing for healthy volunteers Inconsistencies between dates (years) AFSSaPS inspection report

31 AFSSaPS: Conclusions observations on data quality are major or critical, regarding reliability of trial conclusions Consequences: refusal of MA Trial may be stopped if safety concerns AFSSaPS inspection report

32 Papers retracted by year, for fraud (n = 197) or scientific mistake n = 234 Steen J Med Ethics

33 Ioannidis, MD Arch Intern Med reports Adverse events were mentioned in 88.7% No information on severe adverse events in 27.1% No information of drop outs for AEs: 47.4% 33

34 QA in clinical trials Misconduct and fraud exist The organisation is complex Stakeholders are numerous There are reference texts A QA system must be set up 34

35 Conclusion: QA Necessary for ethical reasons Must apply to each aspect, each person involved of clinical trial design, conduct, analysis There are reference documents: ethical, regulatory, internal documents Necessitates manpower / involves cost 35

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