This document discusses how predictive analytics can help enable personalized health care through three main points: 1) Integrating diverse data sources like genomics, healthcare records, and insurance claims can provide insights for personalized care, drug development, and comparative effectiveness research. 2) Predictive models built using data from clinical trials can identify subgroups of patients most likely to respond or not respond to treatments early in the treatment course, improving outcomes. 3) Personalized comparative effectiveness research aims to determine which treatments work best for which patient subgroups and disease stages by integrating real-world data and predictive analytics into drug development and clinical decision-making.

1. Predictive Analytics for
Personalized Health Care
Zhaohui (John) Cai, MD, PhD
Biomedical Informatics Director, AstraZeneca
Big Data and Analytics for Pharma
Philadelphia, PA
June 12, 2013

2. Disclaimer
This presentation represents my personal
views of how predictive analytics can help with
personalized health care. It does not constitute
any positions of AstraZeneca or any other
organizations

3. Presentation Outline
• Introduction
- Personalized Health Care (PHC) and Personalized Medicine (PM)
- Comparative Effectiveness Research (CER) and PHC
• Big data and analytics for PHC
• Predictive learning for PHC in drug development – an
internal approach and example
• Proposed personalized CER – challenges of big data
and analytics
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Set area descriptor | Sub level 1

4. Personalized Medicine or Personalized
Healthcare
Personalized Medicine
• Based on the recognition that unprecedented types of
information will be obtainable from
genetic, genomic, proteomic, imaging technologies, etc, which
will help us further refine known diseases into new categories
• Managing a patient's health based on the individual patient's
specific characteristics (usually a new molecular diagnostic
test) vs. “standards of care”
Personalized Healthcare (PHC)
• PHC is to focus on therapies to deliver superior
outcomes to individual patients
• PHC is to improve outcomes that matter to patients
and all other stakeholders, and reduce the current
costs (i.e. with comparative effectiveness) by
•
Patient selection
•
Improved dosing
•
Alternative therapy
•
Improved care pathway
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5. 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.”
For more definitions of CER, see the IOM report on Initial National Priorities for Comparative Effectiveness
Research
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6. CER and Personalized Healthcare (PHC)
• A tension between CER and PHC 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 PHC
• 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 PHC concepts
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7. Clinical
(EHR, RCT)
data
Genomic
research
data
Integrated
genomic and
phenotypic data
repository
Translational
research for
• Diagnostic
discovery
• Drug-test codevelopment
Personalized
medicine
A Common Vision for Personalized Medicine
Examples: Her-2 variants in breast cancer therapy, K-RAS for colon
cancer therapy, etc.
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8. A Vision of Big Data and Analytics for
Personalized Health Care
Genomic
data
Integrated
healthcare
data
Healthcare cost
(insurance claims)
data
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Nonhealthcare
data
Predictive Learning and
CER
• Patient selection tools
• New dosing regiments
• New care pathways
Decision
support for
clinicians
Decision
support for
payers
Personalized
healthcare
Clinical
(RCT, EHR, PHR/P
RO, Registry, medi
cal device) data

9. Predictive Learning: Identify Responders Early
in Treatment Course
Prediction
Subgroup 1
(predicted nonresponders at
baseline)
Subgroup 2
(predicted nonresponders at
early time points )
Drop and
alternative
treatment
Baseline
Prediction
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Prediction
Treatment period I
Baseline
Prediction
Subgroup 1
(predicted
responders)
Prediction algorithm based
on biomarker(s) and/or
simply clinical disease
activity score(s)
Discontinue
Prediction
Treatment period I
Baseline
Treatment Period 2
Continue
Outcome
Measure

10. • 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)
Accuracies of early predictions
Internal Example: Predictive Learning for PHC
Clinical Decision Tool
Predicting month 6 endpoint 1
Predicting month 6 endpoint 2
Time of Prediction
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11. Current PHC Strategy in Drug Development:
drug-test co-development
Which patients will benefit most from the therapy?
In vitro/vivo studies
Data/literature mining
Preclinical/
Phases 1 & 2a
Candidate biomarker(s)
(predictive learning)
Hypothesis &
initial modeling
Explore
Validated biomarker(s)
(subgroup analysis)
Phase 2b
Design and analysis
Marker based design
(subgroup analysis)
Phase 3
Design and analysis
Outcome (a PHC product)
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Confirm

12. Proposed Personalized Comparative Effectiveness
Research (PCER) in Drug Development
Who will benefit most from treatment A (e.g. candidate drug) and
who will benefit most from treatment B (e.g. standard of care)?
Real World Data
In vitro/vivo studies
Data/literature mining
Preclinical/
Phases 1 & 2a
Candidate biomarker(s)
(predictive learning)
Hypothesis &
initial modeling
Explore
Validated biomarker(s)
(subgroup analysis)
Phase 2b
Design and analysis
Marker based design
(subgroup analysis)
Phase 3
Design and analysis
Confirm
Observational CER
Predictive learning
Explore
Conform
Clinical /payer decision
support
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Outcome (a PHC product)

13. Personalized Comparative Effectiveness
Research (PCER) for Healthcare Decisions
Search for similar patients
A real patient
Personalized
healthcare
Retrospective real-world
database
A cohort of similar, previously
treated patients
Current big data challenge
Different outcomes from
different treatment pathways
Diagnosis
Predictive
learning
Drug A
Drug B
Outcome
Drug B
CER study
(subgroup
analysis)
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Decision
point 2
Decision
point 1
Outcome
Drug A
Next big analytics challenge

14. Achieving Real World Personalized Healthcare
• “Drug A is better than drug B for disease X” type
of general comparative effectiveness evidence
may not be applicable to individual patient care
Genomic
data
Integrated
healthcare
data
Healthcare cost
(insurance claims)
data
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Nonhealth
care
data
Personalized
CER
Drug-test codevelopment
Decision
support for
clinicians
Decision
support for
payers
Decision
support for
patients
Personalized
healthcare
Clinical
(RCT, EHR, PHR/
PRO, Registry, m
edical device)
data
• 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”

15. Thank you
&
Questions?
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