Decision Modeling Decision Modeling TechniquesTechniques

HINF 371 - Medical MethodologiesHINF 371 - Medical MethodologiesSession 3Session 3

Objective Objective

To review decision modeling To review decision modeling techniques and discuss their use techniques and discuss their use in healthcare decision makingin healthcare decision making

ReadingReading

Roberts M S and Sonnenberg F A (2000) Roberts M S and Sonnenberg F A (2000) Chapter 2: Decision Modeling Techniques, in Chapter 2: Decision Modeling Techniques, in Chapman G B and Sonnenberg F A (eds) Chapman G B and Sonnenberg F A (eds) Decision Making In Health Care: Theory, Decision Making In Health Care: Theory, Psychology and Applications, Cambridge Psychology and Applications, Cambridge University Press, USA, University Press, USA,

Evidence PreparationEvidence Preparation

Engine where data is translated into

information

Why do we need them?Why do we need them?

To create a quantitative To create a quantitative representation of clinical choicesrepresentation of clinical choices

To compare alternatives and To compare alternatives and results of choicesresults of choices

To integrate data from various To integrate data from various sources to describe a clinical sources to describe a clinical situationsituation

To simulate trial results to the To simulate trial results to the whole populationwhole population

Requirements for a Requirements for a Decision ModelDecision Model

Perspective: identification of Perspective: identification of whose perspective has been whose perspective has been used to develop the modelused to develop the model

Context: who is involved, what Context: who is involved, what conditions, what interventionsconditions, what interventions

Complexity (or granularity): what Complexity (or granularity): what should be the level of detailshould be the level of detail

Time horizonTime horizon

Simple Decision TreeSimple Decision Tree

Value 1 (U1)

Value 2 (U2)

Value 3 (U3)

Value 4 (U4)

Outcome 1

Outcome 2

Outcome 3

Outcome 4

p1

p2

p3

Choice 1

Choice 2

Total = 1

Decision Node

Chance Node

p4

TerminologyTerminology

LE LateRx

LETox

Test +

Test -

p1

p2

p3

p4

LERx

LE

HIV+

HIV+

HIV-

HIV-

LE LateRx

LETox

HIV+

HIV-

p1

p2

p3

p4

LERx

LE

Test +

Test +

Test -

Test -

Sensitivity

Specificity

True Positive

False Positive

False Negative

True Negative

ExampleExample

LE Late Rx

LE

HIV+

HIV-

Screen

No Screen

LE LateRx

LETox

Test +

Test -

p1

p2

p3

p4

LERx

LE

HIV+

HIV+

HIV-

HIV-

p5

p6

40.3 QALYs

40.3 QALYs

3.5 QALYs

39.4 QALYs

2.75 QALYs

2.75 QALYs

0.9988

0.0012

0.4856

0.5144

0.5

0.5

39.2050

3.5444

21.5250

21.89

21.53

Influence DiagramsInfluence Diagrams

Screen for HIV

Yes/No

Treat for HIV

Yes/No

Test Result

HIVStatus

Life Expec

Sensitivity AnalysisSensitivity Analysis

LE LateRx

LETox

HIV+

HIV-

p1

p2

p3

p4

LERx

LE

Test +

Test +

Test -

Test -

Markov ProcessesMarkov Processes

Iterative in time, can be repeated Iterative in time, can be repeated until everybody in the absorbing until everybody in the absorbing statestate

Based on the probabilities of Based on the probabilities of change in statuschange in status

Three statesThree states Recurrent stateRecurrent state Transient stateTransient state Absorbing stateAbsorbing state

Markov ProcessesMarkov Processes

p4

HIV+

AIDS

DEAD

HIV+

AIDS

DEAD

AIDS

DEAD

DEAD

p1

AsymptomaticHIV+

AIDS DEAD

p1

p2

p3

p5

P6

p1

p2

p3

p4

p5

P6

LE Late Rx

LE

HIV+

HIV-

Screen

No Screen

LE LateRx

LETox

Test +

Test -

p1

p2

p3

p4

LERx

LE

HIV+

HIV+

HIV-

HIV-

p5

p6

HIV+

AIDS

DEAD

HIV+

AIDS

DEAD

HIV+

AIDS

DEAD

HIV- DEAD

HIV- DEAD

HIV- DEAD

Alternatives to Markov Alternatives to Markov ProcessesProcesses

Markov Processes has no memory and Markov Processes has no memory and based on discrete snapshots in timebased on discrete snapshots in time

Semi Markov Processes – time is Semi Markov Processes – time is continuous, one does not move to the continuous, one does not move to the next another stage in the next term next another stage in the next term and measures holding timesand measures holding times

Individual Simulations as a solution: Individual Simulations as a solution: simulates individuals’ travelsimulates individuals’ travel

Dynamic influence diagrams creates a Dynamic influence diagrams creates a new influence diagram for the next new influence diagram for the next cyclecycle

Discrete event simulation: what is Discrete event simulation: what is possible to dopossible to do