Abra Jeffers

VA Palo Alto

Dept. of Mgmt Science & Engineering

Stanford University

Medical Decision Makin
Decision Analysis

y Quantitative, systematicy Identifies alternatives, outcomes, utility of outcy Uses models

y Represent structural relationships

y Deterministicy Probabilistic 

y Compute expected outcomes of each alternativey Balance feasibility v. reality / simplicity v. comply Examine robustness

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Identify Alternatives

y Mutually exclusive

y A, B mutually exclusive y One and only one of the events must occur

y Collectively exhaustive

y A, B collectively exhaustivey Events represent entire outcome spacey At least one event must occur

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Identifying Outcomes

y Multi-dimensionaly Examples:

y Costsy Infections avertedy Life years (LY) savedy Quality adjusted life years (QALY) savedy “Effectiveness”

y Ultimately utils

– Howard school

y Cost-effectiveness

y Incremental cost to effectiveness ratio (ICER)

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Cost-Effectiveness Plane

ΔC

ΔE

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Increased CostsIncreased EffectivIs ICER < WTP?

Increased CostsDecreased EffectivenessUse status quo

Decreased CostsIncreased EffectivUse new strategy

Decreased CostsDecreased EffectivenessProbably use status quo
Steps

y Determine problemy Determine status quo interventiony Determine alternative interventionsy Analysisy Sensitivity Analysis

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Example

y Hypothetical cohort with prevalent disordery Treatment

y Reduces probability of serious complicationsy Long term side effects

y There is an imperfect test for disordery Alternatives:

y Treat no oney Test all and treat positivesy Treat all

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Decision Diagram

Decision node

Probabilistic node

Te

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Example cont.

y Outcomes

y No disorder, no treatmenty No disorder, treatmenty Disorder, treatment, complicationsy Disorder, treatment, no complicationsy Disorder, no treatment, complicationsy Disorder, no treatment, no complications

y For each outcome assume known costs and LE y Intermediate events

y Testsy Side effects

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Create Decision Node

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Create Decision Node

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Create Decision Node

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Create Decision Node

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Strategy 1: Treat None

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Strategy 1: Treat None

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Strategy 1: Treat None

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Strategy 1: Treat None

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Strategy 1: Treat None

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Select Subtree

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Collapse Subtree

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Strategy 2 - Test

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Strategy 2 - Test

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Strategy 2 - Test

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Now What?

y Now we have our tree structurey Lets start filling in probabilities

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# = 1- prev

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Recap

y Thus far...

y Created decision treey Used descriptive variable namesy Used clones to take advantage of parallel structur

y Tracking variables

y Now What?

y Outcomesy Assume we know from clinical trial

y Costsy Life years

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Effectiveness Outcomes

y Normal – 15 life expectancyy Disorder – 3 year detrimenty Complications – 10 year detrimenty Treatment – 0.05 year detriment

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Add Complications Tracker

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Add Complications Tracker

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Add Complications Tracker

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Add Disorder Tracker

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Payoff 1: LE_normal - if(t_disorder = 1; LEdet_disorder; 0) + 

- if(t_treatment= 1; LEdet_treatment; 0) - if(t_complications = 1; L

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Payoff 2: c_normal + if(t_disorder = 1; c_disorder; 0) + if(t_test = 1

+ if(t_treatment = 1; c_treatment; 0) + if(t_complications = 1; c_com

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Baseline = Status Quo 

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Sensitivity Analysis

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Caveats

y Assumed full knowledge of

y Future life expectancyy Future lifetime costs

y Inappropriate if payoffs without full lifetime inf

y Markov model

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Thank you!