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Brand specificities and study tools developed by DRIVE

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Mendel Haag, Gael Dos Santos, Margarita Riera, Topi Turunen
DRIVE Annual Forum
17th -18th September 2018, Rome

Published in: Health & Medicine
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Brand specificities and study tools developed by DRIVE

  1. 1. Acknowledgement DRIVE project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777363, This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. Development of study tools Acknowledgement DRIVE project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777363, This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. Mendel Haag - SEQIRUS Gael Dos Santos - GSK Margarita Riera - P95 Topi Turunen - FISABIO DRIVE Annual Forum 17th-18th September 2018, Rome
  2. 2. Outline • Feasibility of the site selection according to vaccine availability • Systematic review on bias and confounding • Electronic study support application • Framework for data analysis • Guidelines for interpreting IVE results
  3. 3. Acknowledgement DRIVE project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777363, This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. Feasibility of the site selection according to vaccine availability Acknowledgement DRIVE project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777363, This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. Mendel Haag – Seqirus Caterina Rizzo – ISS Anke Stuurman – P95 Miriam Levi - UNIFI DRIVE Annual Forum 17th-18th September 2018, Rome
  4. 4. Achieving data collection for multiple brands Vx A Vx B Vx C Vx D Vx A Vx B Vx C Vx D VS Largest sample size possible Targeted site selection
  5. 5. Challenge Vx A Vx B Vx C Vx D Vx B Vx C Vx D Vx B Vx C Next season
  6. 6. Identifying brand availablity Driver of vaccine availability and use Indication of in-season availability? Geo- graphical level Timing of data Owner and accesibility of the data? Any Vx By brand License status No – only if licensed, but not if marketed No – only if licensed, but not if marketed EU or country Pre-season MAH/regulators Public upon licensure Annual batch release Yes Yes, incl. volume Country Late Pre- season MAH/regulators Not public – competition lsws apply Vaccine recommen- dations Yes No – except in few countries for some Vx Country/ regional Pre-season PHI/Government Publicly available Coverage Yes – incl volume No – except in few countries for some Vx Country Post-season PHI Not assessed and/or public for all countries Procurement N/A Yes, incl volume Country/ regional/ clinic Late pre- and post season MAH/Gov/PHI Partly public
  7. 7. Influenza vaccine procurement and brand availability • Variations per season may apply • In case of public tenders - multi-year tenders may apply Procurement system EU Countries Diversity (type and/or brand) Total count of brands Country level Region level Public tenders: • national level Denmark, Finland, Netherlands, Norway, Slovenia, Ireland Low ~2 2 • regional level Italy, Sweden, Spain Low to high 2 to 8 1 to 4 Direct purchase UK-England, Belgium, France, Germany, Greece High ~3 to 8 N/a
  8. 8. Projecting brand availability The feasibilty to project future brand availability from historical brand availability varies. In general: • For national procurement systems • Tender outcomes are accessible online or upon request from authorities • Prior availability is informative for future availability • For regional procurement systems • Regional tender outcomes are difficult to find in the public domain or not available. • Consistent procurement of a specific vaccine type appears to be informative of future type availability • For direct purchase systems • Public information is not available
  9. 9. WP2 : Description of work • Systematic review of the sources of confounding • Guidelines for the identification of vaccine status and brand in study participants • Standard Operating Procedures (SOPs) based on the core protocols • Sampling schemes and sample size • Electronic study support application • Conditional annual study tenders for influenza vaccine effectiveness study conduct.
  10. 10. Systematic review Active contributors: • P95, Seqirus, UNIFI, FISABIO & GSK Status: • Activity launched in late 2017 • Search strategy and preliminary screening ✔ • Data extraction ✔ • Full text review ✔ • Draft of the chapters ✔ • Report planned by end of October ✔ ✔ Completed ✔ On-going
  11. 11. Background • Assessing the exact magnitude of the benefit of influenza vaccine is a substantial challenge. • Vaccine Effectiveness (VE) assessment is performed using mostly observational studies, which may be biased because of difficulties in identifying and accounting for potential biases, confounders and adjusting for pertinent covariates • The purpose of this task to summarize the outcome of a systematic literature review with the goal to identify the potential sources of bias that may affect the influenza VE assessment with the ultimate purpose of bias minimization. • This task was built on published guidelines and technical reports as well as evidence from published literature from peer reviewed journals and grey literature.
  12. 12. Inter-relations with other WPs • This work intents to inform the development & support the updates of other WPs/tasks such as: • Update framework of data analysis • Protocol and Statistical analysis plan • Development of the annual study report • Interpretation of findings • Communications to Layer 1 & 2 stakeholders (e.g., Regulatory authorities, scientific community, public health institutes )
  13. 13. Approach and mind-set • Qualitative review on bias & confounders • Broad scope to be as exhaustive as possible with a focus on influenza Vaccine Effectiveness (VE) studies • During the screening process • Inclusion/exclusion criteria were based: • On the studies that generate estimates and discussed bias and confounding • Methodological papers dealing with influenza vaccination in the context of VE assessment • Quantitative review • We summarized the diversity of the vaccine effectiveness estimates • We did not extract study by study information but focused on meta-analyses/systematic reviews classifying findings by population/groups of interest
  14. 14. Methodological considerations • The systematic literature review followed Cochrane guidelines and Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. PRISMA Flow Diagram (preliminary) Records identified (n = 12,527) Records after duplicates removed (n = 7,595 ) Records Screened (n = 7,595 ) Records Excluded (n = 7,018) Full text assessed (n = 517) Studies included (n = xxx ) Reasons for exclusion: - Wrong outcome - Unspecific outcome - Studies focusing only on H1N1 pandemic - Wrong study design
  15. 15. Structure of the results – Preliminary • Summary of data from meta analyses/systematic reviews for seasonal influenza vaccine effectiveness estimates • Summary of data for bias • Selection bias • Frailty bias • Healthy vaccinee bias • Misclassification bias/ Recall bias • Summary of data for confounders and effect modifiers Confounders: • Vaccine match/mismatch • Repeated vaccination or natural infection • Confounding by indication • Use of statins/antivirals • Underlying medical condition • Interaction/concomitant administration • Full vs partial vaccination • Obesity Effect modifiers: • Age? • Health status ? • Calendar time/Time since vaccination ?
  16. 16. Challenges Operational challenges • The structured search focused specifically on Influenza Vaccine Effectiveness studies (with the exclusions mentioned earlier) => Huge number of studies to screen • This review focus on qualitative outcome which led to some challenges to identify the relevant studies during abstract and full text screening phase. • Most studies deal with multiple biases and/or confounders, which led to some difficulty to classify those papers in a single bucket Scientific challenges • Even if biases/confounders are captured in research papers, pragmatic considerations to account for them in an observational studies are rarely proposed/discussed by authors: - How data were collected for these covariates or how potential adjustments were handled • It is difficult to identify precisely the relationship/association between a certain covariate, a bias, a confounder and the intervention (influenza vaccination) and/or the outcome (lab-confirmed influenza) and the direction of the association.
  17. 17. Electronic Study Support Application
  18. 18. Web application accessible with following goals: • Aiding research sites in uploading their datasets to the DRIVE Research Server using a secure connection in a user-friendly manner • Allowing research sites to have a quick glance at their uploaded data and check correctness and completeness (f.e. check inconsistent naming, unexpected data types, etc.) • Summarizing the uploaded data in various high-level statistics (f.e. #influenza-positives vs. –negatives, #vaccinated vs. unvaccinated, age- and sex- distributions, etc. both at level of individual research sites or overall) Purpose
  19. 19. R Shiny web application with SSL-certificate and auth0 authentication Two tiers of users that are accredited to look at different high-level statistics (overall vs. accredited to look a specific research site’s results) Used this pilot year to upload all datasets included in the pooled analysis Second year will focus on increasing the functionality Implementation
  20. 20. Acknowledgement DRIVE project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777363, This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. Framework for analysis of influenza vaccine effectiveness studies Margarita Riera - P95 DRIVE Annual Forum 17th-18th September 2018, Rome Acknowledgement DRIVE project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777363, This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA.
  21. 21. 4.1 Analytical methods guidelines 4.2 Data management, analysis and interpretation tools 4.2.1 Data management plan 4.2.2 IT infrastructure 4.2.3 Generic SAP 4.2.4 IVE interpretation guidelines 4.3 Report template 4.4 Alignment with regulatory requirements WP4 Framework for analysis and study reports
  22. 22. Analytical methods guidelines - Purpose To describe a standard set of analytical methods that can be applied to measure IVE. Formulate recommendations • Guidance for ideal study using existing method • Distinguish between 1° and 2° data collection • Not a prerequisite for participation in DRIVE Guidance Protocol WP7 studies Other WP Existing guidelines Scientific literature Experts in DRIVE Additional research
  23. 23. Summary Study design • 1°: TND or cohort • 2°: cohort Exposure • Vaccine brand, vaccination dates, method of ascertainment, confirmation, nr of doses (for previously naïve children) Outcome • 1°: medically attended ILI/SARI with laboratory confirmed influenza (symptoms, date of onset, date of specimen, influenza type/subtype/lineage) • 2°: laboratory-confirmed influenza (condition, date of specimen, influenza type/subtype/lineage)
  24. 24. Bias and confounding • TND: Age, gender, chronic conditions, use of antivirals, lag time symptom-testing • Cohort: age, gender, chronic conditions, past healthcare use Diagnostic tests • Specimen within 7 days of symptom onset • Lab: RT-PCR; type/subtype/lineage; perfomrance assessed (EQA, QCMD) Rapid IVE assessment in near-real time • Any study design that has been proven to yield valid and reliable estimates can be chosen Summary
  25. 25. Data analysis • Study design • Adjustment for confounders (regression, propensity score), known confounders should always be included regardless of significance, other (potential) confounders selected by forward-selection. Pooling • Statistical equivalence of aggregated data meta-analysis (two-stage pooling) and individual-patient meta-analysis (one-stage pooling). • AD-MA preferred method. Summary
  26. 26. Future steps Guidance Protocol WP7 studies Other WP Existing guidelines Scientific literature Experts in DRIVE Additional research WP2 SLR on bias and confounding Brand-specific confounding 2017/2018 pilot (1 vs 2-stage pooling)
  27. 27. DMP provides a description of the data management that will be applied in the DRIVE project including: • Description of the data repositories, access and ownership • Overview of data types generated and collected in DRIVE • Time period for storage • Possibilities of and conditions for sharing data • Implementation of data protection requirements  DMP is an evolving document that needs to be updated when significant changes arise Data Management Plan
  28. 28. Goal: Environment to store datasets and allow data transformations on these datasets without the need for data analysts to store the datasets locally Dedicated secure virtual Windows server on redundant cluster with continuous monitoring, error logging, guaranteed uptime and two-factor authentication DRIVE Research IT Infrastructure
  29. 29. IT Infrastructure • Security by design • 2-step identification • Controlled user management • User-friendly and time/location unrestricted access • High performance • Cloud-based and scalable
  30. 30. DRIVE Research IT Infrastructure
  31. 31. Acknowledgement DRIVE project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777363, This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. Interpreting IVE estimates Topi Turunen – FISABIO DRIVE Annual Forum 17th-18th September 2018, Rome Acknowledgement DRIVE project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777363, This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA.
  32. 32. • DRIVE D4.6: Guideline for interpretation of influenza vaccine effectiveness results published in June 2018 • Prepared by DRIVE partners FISABIO, UNIFI, SEQIRUS, P95, ABBOTT & THL About the work
  33. 33. • Estimating and communicating influenza vaccines’ impact comes with unique challenges • IVE varies from season to season, vaccines are updated • IVE depends on vaccinees’ characteristics • Several study designs used to determine IVE, each with strengths & limitations • When evaluating and communicating IVE, need to consider both • Naturally occurring variation in vaccine effectiveness • Questions related to study design and analytical methods Background
  34. 34. • Pattern of virus circulation and vaccine match • Waning protection within season • Repeated vaccinations • Study setting & population • Study design • Outcomes studied • Vaccine type used • Dosing • Specificity / granularity • Sample size and confidence intervals • Statistical analysis • Bias and confounding • Crude VE estimates • Pooling of several individual studies How do they affect interpretation? How to communicate their meaning?
  35. 35. 1. ”External” factors 2. Study-specific factors Approach
  36. 36. External factors
  37. 37. • Affects vaccine match  IVE Pattern of virus circulation
  38. 38. • Intraseason waning immunity • Evolving mismatch? Persistence of seroprotection, immunosenescence? Natural encounters  cumulative protection even in unvaccinated population? • Repeated vaccinations Other potential factors
  39. 39. Study-specific factors
  40. 40. • Setting & design matter: • GP practice vs. hospital vs. nursing home – differences in subject age, comorbidities & disease severity • Routine healthcare databases – difficult to assess the effect of healthcare-seeking behaviour, swabbing practices • Completeness of data, misclassification? • Helpful to stratify findings by age and comorbidities Study setting, design & population
  41. 41. • Non-specific outcomes (e.g. ILI, all-cause mortality) – only a fraction attributable to influenza • Laboratory-confirmed outcomes (e.g. using RT-PCR) • NB. A low VE against non-specific outcome may indicate a higher absolute reduction in disease burden than a high VE against a very specific outcome. Outcomes studied
  42. 42. • Valency • Split vs. subunit • Intramuscular vs. intradermal • Nonadjuvanted vs. adjuvanted • Inactivated vs. live attenuated • Normal vs. high-dose • 1 vs. 2 doses Vaccine type
  43. 43. • Sample size & confidence intervals – significance, uncertainty around the point estimate • Addressing of bias • Adjustment for confounding • Pooling of several studies; between-study heterogeneity Statistical considerations
  44. 44. Communicating findings
  45. 45. • VE is ever-changing • Goodness is relative • Even low IVE can be meaningful 1) in public health terms, 2) if the outcome is severe • Different stakeholders need different information Challenges
  46. 46. • As a VE% ([1 – OR] x 100%) • As averted cases • Verbally? • Graphically? Describing VE
  47. 47. VE point estimate (%) Interpretation 0 – 30 “low” 31 – 50 “moderate” 51 – 75 “good” 76 – 100 “very good”
  48. 48. www.drive-eu.org Acknowledgement DRIVE project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777363, This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. Thank you for your attention!

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