Can CER and Personalized

Medicine Work Together?

Zhaohui (John) Cai, MD, PhDBiomedical Informatics Director, AstraZeneca

Medical Informatics World ConferenceBoston, MA April 9, 2013

– Where Generalization Meets

Personalization

Disclaimer

This presentation mainly represents my

personal views of how CER and PM should

work together and how Biomedical Informatics

could help achieve that. It does not constitute

any positions of AstraZeneca or any other

organizations

Presentation Outline

•Introduction: CER, generalizability of CER vs. RCT, CER and Personalized Medicine (PM)

• An external example of CER study for PM

• Analytical approaches: subgroup analysis and predictive learning

• Internal examples of predictive learning

• Proposed personalized CER in drug development and in clinical practice

Author | 00 Month Year3 Set area descriptor | Sub level 1

CER

The definition of CER proposed by the

Congressional Budget Office:

“An analysis of comparative effectiveness is

simply a rigorous evaluation of the impact of

different treatment options that are available

for treating a given medical condition for a

particular set of patients.”

4

For more definitions of CER, see the IOM report on Initial National Priorities for Comparative Effectiveness

Research

RCT, CER, and Generalizability

• RCTs, by their nature, limit the

applicability of their findings to

homogeneous populations

because of inclusion and

exclusion criteria and the tightly

controlled way they are generally

conducted

• CER studies can involve very

few inclusion or exclusion criteria

in order to make the results as

generalizable as possible to

‘real-world’ patients with various

co-morbidities, also can be

conducted in naturalistic settings

5

Real World

population

CER

population

RCT

population

CER and Personalized Medicine

• A tension between Personalized Medicine (PM) and CER can be created

when pressure is placed on CER to conform to the prevailing RCT model

• Concerns have been raised that CER will not take into consideration

individual patient differences and may impede the development and

adoption of PM

• CER studies can include a wide range of patient populations common to all

healthcare provider environments

• Taking advantage of a variety of epidemiological and informatics research

methods can help non-randomized CER studies address PM concepts

6

CYP2C9 and VKORC1

genotyping

Quasi-

experimental

design

Medco-Mayo Warfarin Effectiveness Study (MM-

WES)

7

Epstein et al, 2010

ATE and HTE

• Randomized controlled trials (RCTs) usually report an average treatment effect (ATE), which is critically for regularity approval

• Heterogeneity of treatment effect (HTE) is defined as nonrandom variability in the direction or magnitude of a treatment effect, in which the effect is measured using clinical outcomes

• Understanding HTE is critical for decisions that are based on knowing how well a treatment is likely to work for an individual or group of similar individuals, and is relevant to stakeholders including patients, clinicians, and policymakers

8

Kravitz, Duan, Braslow 2004

As defined by AHRQ User Guide to Observational CER, 2013

HTE Analysis for PM

• HTE implies applicability of findings from RCT or observational

CER to individual patients (i.e. PM)

• HTE analyses- Subgroup analysis to estimate treatment effects in clinically relevant

subgroups , one variable at a time, usually a baseline or pretreatment

variable (e.g. genetic variants or gender)

• Exploratory subgroup analysis

• Confirmatory subgroup analysis

- Predictive learning to predict whether an individual might benefit from a

treatment

• Can take a multivariate machine learning approach

• Can be a pre-step for subgroup analysis

• Applicable to both healthcare setting and drug development

9

Predictive Learning: Identify Responders Early

in Treatment Course

Subgroup 1

(predicted non-

responders at

baseline)

Subgroup 1

(predicted

responders)Treatment period I Treatment Period 2

Prediction Prediction

ContinueBaselineOutcome

Measure

Prediction algorithm based

on biomarker(s) and/or

simply clinical disease

activity score(s)

Prediction

Drop and

alternative

treatmentBaseline

Subgroup 2

(predicted non-

responders at

early time points )

Treatment period I

Prediction Prediction

DiscontinueBaseline

10

Internal Example: Predictive Learning for PM Clinical

Decision Tool

• Question: Can we predict responders

early, and use the predictions in clinical

practice?

• Data & Method: model Phase II data

using ~30 clinical variables to identify an

early predictor of individual response at 6

months, using Random Forests models

• Result: A combination of 4 clinical

variables are predictive at month 1 to

identify responders at month 6 with close

to 80% accuracy

• Benefit: Clinical Decision Tool for patient

selection that may double response rate

identified, to be validated using phase III

and real world data (subgroup analysis)

Ac

cu

rac

ies

of

ea

rly p

red

icti

on

s

Predicting month 6 endpoint 1

Predicting month 6 endpoint 2

Time of Prediction

11

Current Personalized Medicine Strategy in Drug

Development: drug-test co-development

In vitro/vivo studies

Data/literature mining

Candidate biomarker(s)

(predictive learning)

Validated biomarker(s)

(subgroup analysis)

Marker based design

(subgroup analysis)

Hypothesis &

initial modeling

Phase 2b

Design and analysis

Phase 3

Design and analysis

Outcome (a PM product)

Which patients will benefit most from the therapy?

Explore

Confirm

Preclinical/

Phases 1 & 2a

12

Proposed Personalized Comparative Effectiveness

Research (PCER) in Drug DevelopmentR

eal W

orl

d D

ata

In vitro/vivo studies

Data/literature mining

Candidate biomarker(s)

(predictive learning)

Validated biomarker(s)

(subgroup analysis)

Marker based design

(subgroup analysis)

Hypothesis &

initial modeling

Phase 2b

Design and analysis

Phase 3

Design and analysis

Outcome (a PM product)

Who will benefit most from treatment A (i.e. candidate drug) and who will benefit most from treatment B (i.e. standard of care)?

Explore

Confirm

Observational CER

Preclinical/

Phases 1 & 2a

13

Clinical /payer decision

support

Predictive learning

Explore

Conform

New informatics challenge

Personalized Comparative Effectiveness

Research (PCER) for Healthcare Decisions: an

informatics challenge

14

A real patientRetrospective real-world

database

Search for similar patients

A cohort of similar, previously

treated patients

Different outcomes from

different treatment pathways

Retrospective CER study

(subgroup analysis)

Predictive

learning

Drug A Drug B

Decision

point 2

Outcome

Decision

point 1

Drug B Drug A

Diagnosis

Personalized

treatment Current informatics challenge

Outcome

Drug-test co-

development

PC

ER

Achieving Personalized Healthcare in Real

World Using CER

Pe

rso

na

lize

dh

ea

lth

ca

re

Decision

support for

payers

Decision

support for

cliniciansGenomic research

data

Integratedhealthcare

data

Healthcare cost (insurance claims)

data

15

• CER and PM can and have to work together

• “Drug A is better than drug B for disease X” kind of

general comparative effectiveness may not be

applicable to individual patient care

• PCER will answer “to what patient subgroup, what

disease stage, what treatment pathway, and where in

the treatment pathway, a comparative effectiveness

evidence is applicable” Clinical (RCT,

EHR, PHR/PRO, Registry) data

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Thank you

&

Questions?