Predictive Analytics for Personalized Health Care

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

Big Data and Analytics for PharmaPhiladelphia, PA June 12, 2013

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

Set area descriptor | Sub level 1

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

Author | 00 Month Year3

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

4

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.”

5

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

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

6

7

A Common Vision for Personalized Medicine

7

Clinical (EHR, RCT) data

Genomic researchdata

Integratedgenomic and phenotypic data repository

Translational research for• Diagnostic

discovery• Drug-test co-

development Pe

rso

na

lize

dm

edic

ine

Examples: Her-2 variants in breast cancer therapy, K-RAS for colon cancer therapy, etc.

A Vision of Big Data and Analytics for Personalized Health Care

Pe

rso

na

lize

dh

eal

thc

are

Decision support for

payers

Decision support for cliniciansGenomic

dataIntegratedhealthcare

data

Healthcare cost (insurance claims)

data

8

Clinical (RCT, EHR, PHR/PRO,

Registry, medical device) data

Predictive Learning and CER• Patient selection

tools• New dosing

regiments• New care pathways

Non-healthcare

data

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

ContinueBaseline OutcomeMeasure

Prediction algorithm based on biomarker(s) and/or simply clinical disease activity score(s)

Prediction

Drop and alternative treatment

Baseline

Subgroup 2(predicted non-responders at early time points )

Treatment period I

Prediction Prediction

DiscontinueBaseline

9

Internal Example: Predictive Learning for PHC 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)

Acc

ura

cie

s o

f ea

rly

pre

dic

tio

ns

Predicting month 6 endpoint 1

Predicting month 6 endpoint 2

Time of Prediction

10

Current PHC Strategy in Drug Development: drug-test co-development

In vitro/vivo studiesData/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 PHC product)

Which patients will benefit most from the therapy?

Explore

Confirm

Preclinical/Phases 1 & 2a

11

Proposed Personalized Comparative Effectiveness Research (PCER) in Drug Development

Rea

l W

orl

d D

ata

In vitro/vivo studiesData/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 PHC product)

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

Explore

Confirm

Observational CER

Preclinical/Phases 1 & 2a

12

Clinical /payer decision support

Predictive learningExplore Conform

Personalized Comparative Effectiveness Research (PCER) for Healthcare Decisions

13

A real patientRetrospective real-world database

Search for similar patients

A cohort of similar, previously treated patients

Personalized healthcare Current big data challenge

Next big analytics challenge

Different outcomes from different treatment pathways

CER study (subgroup analysis)

Predictive learning

Drug A Drug B

Decision point 2

Outcome

Decision point 1

Drug B Drug A

Diagnosis Outcome

Personalized CER

Decision support for

patients

Achieving Real World Personalized Healthcare

Pe

rso

na

lize

dh

eal

thc

are

Decision support for

payers

Decision support for cliniciansGenomic

data

Integratedhealthcare

data

Healthcare cost (insurance claims)

data

14

• “Drug A is better than drug B for disease X” type of general comparative effectiveness evidence 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, medical device) data

Drug-test co- development

Non-health care data

15

Thank you &

Questions?