Achieving Operational Excellence in Prospective Observational Research

Transcription

1 Achieving Operational Excellence in Prospective Observational Research Louise Parmenter PhD, MSc VP, Global Head of Operations, Epidemiology & Outcomes Research Ombretta Palucci Senior Director, EMEA RWLP Strategy Lead Unit Copyright 2016 Quintiles

2 Your Presenters Louise Parmenter PhD MSc VP, Global Head of Epidemiology & Outcomes Research, Quintiles Dr Louise Parmenter is a specialist in real-world and late phase research with 24 years global operational and strategic experience. In her role at Quintiles Dr. Parmenter is responsible for a team of epidemiologists and outcomes researchers primarily based in the United States with growing teams in Europe and Asia. Ombretta Palucci Senior Director, EMEA RWLP Strategy Lead Unit, Quintiles The last 8 years Ombretta has been fully dedicated to observational studies including PASS, drug registry, disease registry, and burden of illness studies. She is expert in addressing study implementation challenges in real world studies. Ombretta has 16 years experience in clinical research. Before joining Quintiles Ombretta has worked in both the pharmaceutical and the CRO industry in project management as well as clinical operations running phase II/III/IV clinical studies. Quintiles Confidential 2

3 Today s Webinar Audience 11% 2% Academia 30% Biostatistician Clinical Operations Epidemiology Health Economics/Health Outcomes Market Access 33% Medical Affairs 4% Risk Management 6% Other 4% 4% 6% 3

4 Agenda The need for operational excellence The challenge for prospective observational research study execution Best practice approaches to achieving operational excellence Smarter studies through innovation Q& A 4

5 Polling Questions A small number of polling questions have been added to today s webinar to make the session more interactive? 5

6 The need for operational excellence 6

7 Uncertainty 7

8 Quality 8

9 Quality of observational studies relies on well-designed and well-executed studies Strength of Study Design Low Quality High Quality Low Quality Low Quality Strength of Operational Execution 9

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11 Good Pharmacoepidemiological Practice (GPP) provides standards for operational excellence The GPP address the following areas: Protocol Development Responsibilities, Personnel, Facilities, Resource Commitment, and Contractors Study Conduct Communication Adverse Event Reporting Archiving GPP addresses the challenges inherent in observational research that are not covered in ICH GCP Accessed 3 December

12 Why prospective observational research study execution can be challenging 12

13 Challenges in observational research External Validity Internal Validity 13

14 Validity Validity refers to whether what we are measuring is what we intend to measure 14

15 External validity External validity refers to whether my study sample is representative of the target population that I am trying to describe Study sample Target population 15

16 Site selection in prospective observational research Low External Validity Clinical Trial Target Population Clinical trial experienced sites Site Selection Real-world study A representative sample from the target population High External Validity 16

17 Why is external validity important to operational teams? If we select the wrong study sample, we will describe the wrong setting. For example, a study run in clinical trial experienced sites alone may describe a higher standard of patient care than a study run in research naïve sites. Study teams need to have processes in place for the selection of sites that describe the right setting. This is termed representativeness An epidemiologist can help operational teams understand what a representative sample may mean for their study and if this is important to the research question 17

18 Why is external validity important to operational teams? Note that selection of representative sites may add time to the site selection process and necessitate working with more research inexperienced sites: Need to adjust study timeline Need expertise and processes for identification of representative sites Need expertise and processes for working with research inexperienced sites 18

19 Internal validity Internal validity refers to the extent to which the finding of the study accurately represent the causal association between an exposure and an outcome in the particular circumstances of an investigation. 19

20 Why is internal validity important to operational teams? Observational studies can be criticized for poor internal validity due to real-world influences (non-randomization, inexperienced sites, variability in diagnosis etc) Study operational teams need to have strategies in place to understand and manage the limitations inherent in observational studies Strategies to address bias and confounding 20

21 Two main types of bias that are likely in observational studies Selection bias Information bias 21

22 Selection bias Distortions that result from procedures used to select patients and from factors that influence participation in the study Error introduced when the study population does not represent the target population Defining features: Selection bias occurs at:» the stage of recruitment of participants» and/or during the process of retaining them in the study Difficult to correct in the analysis 22

23 The impact of randomization versus non-randomization Study Arm 1 Target Population Randomize Study Arm 2 A randomized study of sufficient sample size is likely to have participants with similar characteristics between study arms 23

24 The impact of randomization versus non-randomization Study Arm 1 Target Population Prescribe Study Arm 2 A non-randomized study of sufficient sample size is likely to have differences in the characteristics of participants between study arms This leads to selection bias Selection bias 24

25 Channeling bias, selective prescribing, or confounding by indication / confounding by severity A form of selection bias where drugs with similar therapeutic indications are prescribed to groups of patients with prognostic differences. e.g. sicker patients or difficult to treat patients being more or less likely to receive a new drug Example: In observational studies of atrial fibrillation, patients prescribed the new oral anticoagulants are likely to be younger and healthier than those prescribed warfarin 25

26 Selection bias in observational studies Study Arm 1 Follow-up period Study Arm 1 Prescribe Study Arm 2 Study Arm 2 Selection bias Selection bias Lost to follow-up 26

27 Information Bias Systematic error due to inaccurate measurement or classification of disease, exposure or other variables Instrumentation - an inaccurately calibrated instrument creating systematic error Misdiagnosis - if a diagnostic test is consistently inaccurate Recall bias - if individuals can't remember exposures accurately Socially desirable response - if study participants consistently give the answer that the investigator wants to hear Missing data - if certain individuals consistently have missing data 27

28 Why is bias important to operational teams? It is difficult (and often impossible) to correct for bias in the study analysis Failure to properly manage bias in an observational study will lower the quality of your study, and may result in the rejection of the study results Every operational team member has a role in preventing / detecting bias in observational studies: Epidemiologist and Biostatistician study design, analysis and report, periodic data checks for missing data and trends» Your epidemiologist should be part of your operational team throughout study delivery Data management capturing the right data elements to control for confounding, designing forms and edit checks to minimize missing data Clinical operations team selecting the right sights, minimizing loss to follow-up, providing adequate training to inexperienced sites Project management understanding the risk of bias and confounding and directing the study to minimize these scientific risks 28

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30 Best practice approaches to achieving operational excellence 30

31 Observational research requires a different operational approach to experimental clinical trial research Today, it remains common for companies to use clinical development teams to conduct observational research 31 31

32 Operational Excellence Components Best Practice for Late Phase Research Operational Excellence 32

33 Best practice to help driving holistic strategy Feasibility Internal proprietary data Sponsor data Physician External public & commercial data Patients Available database and existing data sources 33

34 Best practice to build awareness and keep engagement Site Recruitment and Retention Existing site network Awareness campaign Site tier management approach Congress activities and MSL Integrated engagement platform Fair market compensation 34

35 Best Practice to enable integrated patient experience Patient Recruitment and Retention Patient journey Study awareness material & pt community Retention escalation to call center Observational specific ICF Data collection via SMS, , phone Pt token of appreciation 35

36 Purpose: Patient Registry Therapy area: Alzheimer s Disease Web: AheadRegistry.com Copyright 2016 Quintiles

37 Patient recruitment and retention materials Supporting patients with the right tools 37

38 Best Practice to generate quality data Technology Get it right at first data entry Balance with edit check programming so as to not over burden site All integrated EDC systemepro Integrated data review approach Smart CRF design Easy to set up and cost effective EDC system 38

39 Smarter studies through innovation 39

40 Real-world evidence Smarter studies through innovation Observational studies demand across the product life cycle Disease registries transitioning pre- and post-launch or disease registry transitioning into a product registry Increase in PASS and PAES Collaboration Increase in multi-sponsor registries Patient centricity Increase inclusion of PRO endpoints and epro technology Self-enrolment and direct to patient research Greater healthcare data access and innovative study designs Increasing use of existing data (databases, claims data) Increase in pragmatic trial designs Enriched studies (prospective/retrospective approaches) 40 40

41 Previous & Upcoming Events Quintiles experts run regular webinars on Real-World & Late Phase services. Topics include: OBSERVATIONAL RESEARCH & REGISTRIES SAFETY & RISK MANAGEMENT HTA & MARKET ACCESS MAXIMIZING VALUE AND QUALITY IN PHASE IV RARE DISEASE REGISTRIES COMPARATIVE EFFECTIVENESS RESEARCH Visit Quintiles to learn more at one of the following upcoming meetings: WORLD ORPHAN DRUG CONGRESS EUROPEAN CONFERENCE ON RARE DISEASE & ORPHAN PRODUCTS CMSS 2016 REGISTRIES SUMMIT HTA INTERNATIONAL 2016 ISPOR ANNUAL MEETING THE * EUROPEAN CONFERENCE ON RARE DISEASE & ORPHAN PRODUCTS CLINICAL OUTCOME ASSESSMENTS To register or view previous webinars please go to

42 Thank you 42