Post on 19-Dec-2015
transcript
Abra JeffersVA Palo Alto
Dept. of Mgmt Science & EngineeringStanford University
Medical Decision Making
Decision AnalysisQuantitative, systematicIdentifies alternatives, outcomes, utility of
outcomesUses models
Represent structural relationshipsDeterministicProbabilistic
Compute expected outcomes of each alternative
Balance feasibility v. reality / simplicity v. complexity
Examine robustness2
Identify AlternativesMutually exclusive
A, B mutually exclusive ⇒ P(A∩B) = 0One and only one of the events must occur
Collectively exhaustiveA, B collectively exhaustive ⇒ P(A) + P(B) = 1Events represent entire outcome spaceAt least one event must occur
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Identifying Outcomes
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Cost-Effectiveness PlaneΔC
ΔE
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Increased CostsIncreased EffectivenessIs ICER < WTP?
Increased CostsDecreased EffectivenessUse status quo
Decreased CostsIncreased EffectivenessUse new strategy
Decreased CostsDecreased EffectivenessProbably use status quo
StepsDetermine problemDetermine status quo interventionDetermine alternative interventionsAnalysisSensitivity Analysis
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ExampleHypothetical cohort with prevalent disorderTreatment
Reduces probability of serious complicationsLong term side effects
There is an imperfect test for disorderAlternatives:
Treat no oneTest all and treat positivesTreat all
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Complications2
No complications15
Disorder
No disorder15
Treat none
ComplicationsT1.95
No complications14.95
TP - treat
Complications2
No complications15
FN - don't treat
Disorder
TN - don't treat15
FP - treat14.95
No disorder
Test
Complications1.95
14.95
Disorder
No disorder14.95
Treat all
Decision Diagram
Decision node
Probabilistic node
Terminal node
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Example cont.Outcomes
No disorder, no treatmentNo disorder, treatmentDisorder, treatment, complicationsDisorder, treatment, no complicationsDisorder, no treatment, complicationsDisorder, no treatment, no complications
For each outcome assume known costs and LE (clinical trial)
Intermediate eventsTestsSide 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?Now we have our tree structureLets start filling in probabilities
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# = 1- prev
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RecapThus far…
Created decision treeUsed descriptive variable namesUsed clones to take advantage of parallel
structureTracking variables
Now What?OutcomesAssume we know from clinical trial
CostsLife years
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Complications2
No complications15
Disorder
No disorder15
Treat none
ComplicationsT1.95
No complications14.95
TP - treat
Complications2
No complications15
FN - don't treat
Disorder
TN - don't treat15
FP - treat14.95
No disorder
Test
Complications1.95
14.95
Disorder
No disorder14.95
Treat all
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Effectiveness OutcomesNormal – 15 life expectancyDisorder – 3 year detrimentComplications – 10 year detrimentTreatment – 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; LEdet_complications; 0)
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Payoff 2: c_normal + if(t_disorder = 1; c_disorder; 0) + if(t_test = 1; c_test; 0) + + if(t_treatment = 1; c_treatment; 0) + if(t_complications = 1; c_complications; 0)
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Baseline = Status Quo 86
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Sensitivity Analysis
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CaveatsAssumed full knowledge of
Future life expectancyFuture lifetime costs
Inappropriate if payoffs without full lifetime infoMarkov model
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Thank you!