Bayesian Hierarchical Models for the Design and Analysis of Studies to Individualize HealthcareScott L. ZegerJohn C. Malone Professor of Biostatistics and Medicine@ScottZegerSeptember 19, 2019

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Scott Zeger

DisclosuresRelationship Company(ies)

Speakers Bureau

Advisory Committee Embold Health

Board Membership

Consultancy

Review Panel

PCORI Funding PCORI ME-1408-20318

Honorarium

Ownership Interests

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Outline• A learning healthcare system aspires to:

Improve each clinical decision for this patient by learning from the experiences of prior similar patients: population individual

• Bayes rule is a logic for learning

• Prostate cancer application

• Lessons learned from implementation of learning systems within a major academic health center

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Healthcare Decisions: Be A CLINICIAN for this moment• Presentation: 40-year-old man, no family history, tests positive for a life-threatening

disease in a routine screen• Clinical Questions: What is his disease state? What action do you recommend?• Decision Support: Data from prior population of similar people

True disease status

Exam result Yes No Total

Positive 15 985 1,000

Negative 5 8,995 9000

Total 20 9,980 10,000

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Bayes Rule

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Why Bayes?• Focus on each patient• Use probability as a natural measure of uncertainty• Integrate population-based evidence with expert judgement• Reflects how clinicians reason• Earlier rule-based expert systems largely failed

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Learning from prior patients’ experience

• Using Bayes rule, create the computational analogue of the 2x2 table for any complex measurements

Population Individual

• Build capacity to make tables for ever-narrower sets of “otherwise-similar” individuals

Subset, Subset, Subset

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Prostate Cancer Application (Bal Carter, Yates Coley, Ken Pienta, Mufaddal Mamawala, Scott Zeger, TIC, APL, IT@JH, JHTV)

Clinical questions about active surveillance:1. Given the data collected to date on this

individual, should we do another biopsy today?2. If we remove his prostate today, what is the

probability the tumor is aggressive vs indolent?

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Learning Health System Steps Prostate Cancer Active Surveillance Example Challenges

Frame unmet health/clinical need Half of active surveillance prostatectomies yield indolent cancers

Specify biomedical model Predictors of indolence: PSA, biopsies, family history, genomic score, MRI

Poor understanding of mechanisms

Wrangle relevant data into a clinical cohort database (CCDB) from which to learn through careful analysis

Brady Institute Active Surveillance clinical cohort database with 1300 men; Precision Medicine Analytics Platform (PMAP)

Learning-grade data not collected;Data collected but “locked-up in EHR; HIPAA “minimum necessary standard”

Design and test decision tool Coley, et al (a, b): Bayesian hierarchical model Inadequate predictive power;External validity checks not made

Design and test users’ interface for population health manager, clinician, and/or patient

PCORI ME-1408-20318 / TIC EHR has limited capacity for visualization, calculation, but ”owns the workflow”; $300K for two pages in EPIC

Design and test on-going curation JHM Committee No standards; must create policies and procedures

Devise business model to sustain/improve tool

?? New methods improve outcomes at lower costs; providers lose money

Scale up and out for broad use CoE in a Box; Partners Takes capital investments and time

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Learning Healthcare System

Learning Healthcare System of Systems – JHM Precision Medicine Centers of Excellence (PMCOEs)

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PMAP – Precision Medicine Analytics Platform

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Learn More

• Scott L. Zeger, PhD