© Copyright 2016 Quintiles

Achieving Operational Excellence in Prospective

Observational ResearchLouise Parmenter PhD, MSc

VP, Global Head of Operations, Epidemiology & Outcomes Research

Ombretta Palucci

Senior Director, EMEA RWLP Strategy Lead Unit

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Your Presenters

Louise Parmenter PhD MScVP, 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.

Quintiles Confidential

Ombretta PalucciSenior 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.

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Today’s Webinar Audience

11%

2%

33%

6%4%

4%

6%

4%

30%

Academia

Biostatistician

Clinical Operations

Epidemiology

Health Economics/HealthOutcomes

Market Access

Medical Affairs

Risk Management

Other

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Agenda

The need for operational excellence

The challenge for prospective observational research study execution

Q& A

Smarter studies through innovation

Best practice approaches to achieving operational excellence

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Polling Questions

A small number of

polling questions have

been added to today’s

webinar to make the

session more

interactive

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The need for operational excellence

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Uncertainty

8Quality

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Quality of observational studies relies on well-designed

and well-executed studies

Strength of

Study Design

Strength of Operational

Execution

Low

Quality

Low

Quality

High

Quality

Low

Quality

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Good Pharmacoepidemiological Practice (GPP)

provides standards for operational excellence

http://www.pharmacoepi.org/resources/guidelines_08027.cfm

Accessed 3 December 2015

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

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Why prospective observational research study

execution can be challenging

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Challenges in observational research

External Validity Internal Validity

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Validity refers to whether what we are measuring is

what we intend to measure

Validity

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External validity refers to whether my study sample is representative

of the target population that I am trying to describe

External validity

Study sample Target population

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Site selection in prospective observational research

Target Population

Clinical Trial

Real-world study

Site

Selection

Clinical trial

experienced

sites

A

representative

sample from

the target

population

Low External Validity

High External Validity

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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

Why is external validity important to operational teams?

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• 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

Why is external validity important to operational teams?

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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.

Internal validity

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• 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

Why is internal validity important to operational teams?

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Selection bias Information

bias

Two main types of bias that are likely in observational

studies

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• 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

Selection bias

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• A randomized study of sufficient sample

size is likely to have participants with

similar characteristics between study arms

The impact of randomization versus non-randomization

Target Population

Study Arm 1

Study Arm 2

Randomize

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The impact of randomization versus non-randomization

Target Population

Study Arm 1

Study Arm 2

Prescribe

• 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

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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

Channeling bias, selective prescribing, or confounding

by indication / confounding by severity

Example: In observational studies of atrial fibrillation, patients prescribed

the new oral anticoagulants are likely to be younger and healthier than

those prescribed warfarin

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Selection bias in observational studies

Study Arm 1

Study Arm 2

Prescribe

Lost to follow-up

Follow-up period

Selection bias Selection bias

Study Arm 1

Study Arm 2

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• 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

Information Bias

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• 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

Why is bias important to operational teams?

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Best practice approaches to achieving operational

excellence

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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

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Operational Excellence Components

Best Practice for Late Phase Research

Operational

Excellence

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Feasibility

Best practice to help driving holistic strategy

Internal

proprietary

data

Sponsor

data

Physician

External public

& commercial

data

Patients Available database

and existing data

sources

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Site Recruitment and Retention

Best practice to build awareness and keep engagement

Integrated engagement

platform

Existing site network

Awareness campaign

Site tier management

approach

Fair market compensation

Congress

activities and

MSL

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Patient Recruitment and Retention

Best Practice to enable integrated patient experience

Observational

specific ICFData collection via

SMS, e-mail,

phone

Pt token of

appreciation

Retention

escalation to call

center

Study awareness

material

& pt community

Patient journey

© Copyright 2016 Quintiles

Purpose:Patient Registry

Therapy area: Alzheimer’s Disease

Web: AheadRegistry.com

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Patient recruitment and retention materials Supporting patients with the right tools

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Technology

Best Practice to generate quality data

Get it right at

first data

entry

All integrated

EDC system-

ePRO

Balance with edit

check

programming so

as to not over

burden site

Easy to set up and

cost effective EDC

system

Smart CRF design

Integrated data

review approach

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Smarter studies through innovation

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• Disease registries transitioning pre- and post-launch or disease registry transitioning into a product registry

• Increase in PASS and PAES

Observational studies demand across the product life cycle

• Increase in multi-sponsor registriesCollaboration

• Increase inclusion of PRO endpoints and ePRO technology

• Self-enrolment and direct to patient researchPatient centricity

• Increasing use of existing data (databases, claims data)

• Increase in pragmatic trial designs

• Enriched studies (prospective/retrospective approaches)

Greater healthcare data access and innovative

study designs

Real-world evidence – Smarter studies through innovation

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Previous & Upcoming Events

To register or view previous webinars please go to

http://www.quintiles.com/landing-pages/real-world-and-late-

phase-research-webinars

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

• CLINICAL OUTCOME ASSESSMENTS

• 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

Visit Quintiles to learn more at one of the

following upcoming meetings:

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Thank you