Decision Modeling Techniques HINF Medical Methodologies Session 3

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

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

Evidence Preparation Engine where data is translated into information

Why do we need them? To create a quantitative representation of clinical choices To create a quantitative representation of clinical choices To compare alternatives and results of choices To compare alternatives and results of choices To integrate data from various sources to describe a clinical situation To integrate data from various sources to describe a clinical situation To simulate trial results to the whole population To simulate trial results to the whole population

Requirements for a Decision Model Perspective: identification of whose perspective has been used to develop the model Perspective: identification of whose perspective has been used to develop the model Context: who is involved, what conditions, what interventions Context: who is involved, what conditions, what interventions Complexity (or granularity): what should be the level of detail Complexity (or granularity): what should be the level of detail Time horizon Time horizon

Simple Decision Tree Value 1 (U 1 ) Value 2 (U 2 ) Value 3 (U 3 ) Value 4 (U 4 ) Outcome 1 Outcome 2 Outcome 3 Outcome 4 p1p1 p2p2 p3p3 Choice 1 Choice 2 Total = 1 Decision Node Chance Node p4p4

Terminology LE LateRx LETox Test + Test - p1p1 p2p2 p3p3 p4p4 LERx LE HIV+ HIV- LE LateRx LETox HIV+ HIV- p1p1 p2p2 p3p3 p4p4 LERx LE Test + Test - Sensitivity Specificity True Positive False Positive False Negative True Negative

Example LE Late Rx LE HIV+ HIV- Screen No Screen LE LateRx LETox Test + Test - p1p1 p2p2 p3p3 p4p4 LERx LE HIV+ HIV- p5p5 p6p QALYs 3.5 QALYs 39.4 QALYs 2.75 QALYs

Influence Diagrams Screen for HIV Yes/No Treat for HIV Yes/No Test Result HIV Status Life Expec

Sensitivity Analysis LE LateRx LETox HIV+ HIV- p1p1 p2p2 p3p3 p4p4 LERx LE Test + Test -

Markov Processes Iterative in time, can be repeated until everybody in the absorbing state Iterative in time, can be repeated until everybody in the absorbing state Based on the probabilities of change in status Based on the probabilities of change in status Three states Three states Recurrent state Recurrent state Transient state Transient state Absorbing state Absorbing state

Markov Processes p4p4 HIV+ AIDS DEAD HIV+ AIDS DEAD AIDS DEAD p1p1 Asymptomatic HIV+ AIDSDEAD p1p1 p2p2 p3p3 p5p5 P6P6 p1p1 p2p2 p3p3 p4p4 p5p5 P6P6

LE Late Rx LE HIV+ HIV- Screen No Screen LE LateRx LETox Test + Test - p1p1 p2p2 p3p3 p4p4 LERx LE HIV+ HIV- p5p5 p6p6 HIV+ AIDS DEAD HIV+ AIDS DEAD HIV+ AIDS DEAD HIV-DEAD HIV-DEAD HIV-DEAD

Alternatives to Markov Processes Markov Processes has no memory and based on discrete snapshots in time Markov Processes has no memory and based on discrete snapshots in time Semi Markov Processes – time is continuous, one does not move to the next another stage in the next term and measures holding times Semi Markov Processes – time is continuous, one does not move to the next another stage in the next term and measures holding times Individual Simulations as a solution: simulates individuals’ travel Individual Simulations as a solution: simulates individuals’ travel Dynamic influence diagrams creates a new influence diagram for the next cycle Dynamic influence diagrams creates a new influence diagram for the next cycle Discrete event simulation: what is possible to do Discrete event simulation: what is possible to do