Brand specificities and study tools developed by DRIVE

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
Mendel Haag - SEQIRUS
Gael Dos Santos - GSK
Margarita Riera - P95
Topi Turunen - FISABIO
DRIVE Annual Forum
17th-18th September 2018, Rome

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

Acknowledgement
Feasibility of the site
selection according to
vaccine availability
Acknowledgement
Mendel Haag – Seqirus
Caterina Rizzo – ISS
Anke Stuurman – P95
Miriam Levi - UNIFI
DRIVE Annual Forum

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

Challenge
Vx
A
Vx
B
Vx
C
Vx
D
Vx
B
Vx
C
Vx D
Vx
B
Vx
C
Next
season

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

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

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

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.

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

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.

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 )

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

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

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 ?

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.

Electronic Study Support
Application

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

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

Acknowledgement
Framework for analysis
of influenza vaccine
effectiveness studies
Margarita Riera - P95
DRIVE Annual Forum
Acknowledgement

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

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

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)

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

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

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)

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

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

IT Infrastructure
• Security by design
• 2-step identification
• Controlled user management
• User-friendly and time/location
unrestricted access
• High performance
• Cloud-based and scalable

DRIVE Research IT Infrastructure

Acknowledgement
Interpreting IVE
estimates
Topi Turunen – FISABIO
DRIVE Annual Forum
Acknowledgement

• 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

• 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

• 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?

1. ”External” factors
2. Study-specific factors
Approach

• Affects vaccine match  IVE
Pattern of virus circulation

• Intraseason waning immunity
• Evolving mismatch? Persistence of seroprotection,
immunosenescence? Natural encounters  cumulative
protection even in unvaccinated population?
• Repeated vaccinations
Other potential factors

• 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

• 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

• Valency
• Split vs. subunit
• Intramuscular vs. intradermal
• Nonadjuvanted vs. adjuvanted
• Inactivated vs. live attenuated
• Normal vs. high-dose
• 1 vs. 2 doses
Vaccine type

• 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

• 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

• As a VE% ([1 – OR] x 100%)
• As averted cases
• Verbally?
• Graphically?
Describing VE

VE point
estimate (%)
Interpretation
0 – 30 “low”
31 – 50 “moderate”
51 – 75 “good”
76 – 100 “very good”

www.drive-eu.org
Acknowledgement
Thank you
for your attention!

Brand specificities and study tools developed by DRIVE

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