Modelling and Simulation in Health Technology Assessment (TU Wien, Austria)

47 flashcards · Other · by Locke Lamora

  1. COVID-Model Overview

    Population Contacts Disease Policies

  2. GEPOC + Properties

    Generic can handle any problem regarding country population Population depicts population Concept consists of several models Properties: Microscopic model Standard model without interaction, but capable of it Considers births, deaths, immigration and emigration Discrete-time

  3. COVID-Model: Contact Module

    Based on POLYMOD developed for influenza modelling Contacts between agents are at locations (households, schools, workplaces,...) Based on mobile phone data

  4. COVID-Model: Disease Module

    Attributes: infected (true/false) virus strain confirmed (true/false) disease severity Considers immunity (to different strains)

  5. COVID-Model: Policy Module

    Time-line of events as input (closing of locations, reduction of contacts,...) Based on other modules

  6. Atlases (sic!)

    Epidemiologie Atlas Which diseases does the population suffer from Versorgungsatlas Which services are available where and when Leistungsatlas Which services are provided regionally

  7. Epidemiology in Austria

    Hardly any data on ICD diagnoses Data for prescriptions available Idea: Build model that estimates diagnoses from prescribed medications

  8. COVID-Model: Influencing Factors

    Measures Vaccination Screenings Treatments Immunization Seasonality Mutation

  9. COVID-Model: Validation

    Validate by run simulate past and check with actual data

  10. Simulation Circle

  11. 3 Properties that define a Model

    Mapping Reduction Prapmatism

  12. Two Steps of Abstraction

    Structural Abstraction (Qualitative Knowledge) Phenomenological Abstraction (Quantitative Knowledge)

  13. Iterative Modelling

    For every iteration perform whole modelling process and update problem entity, the concept and the model

  14. Modelling Methods

  15. Structural Challenges in Austria Health Care System

  16. HTA definition

    HTA refers to systematic evaluation of effects, properties, impacts and consequences of health technologies

  17. PICO

    Population Intervention Treatment (on patient or on system level) Prevention (diagnostics, vaccination,...) Control (Status Quo or Gold standard) Outcome

  18. Open/Closed Population

    Closed: Single birth cohort Men at age 18+ at reference date whole population of country at reference date,... Open: All people with age 2: Enter and leave population as they get older Men at age 18+: Enter and never leave

  19. Outcomes of HTA

    Incidence, Prevalence Deaths QUALYs Costs ICER PYLL (Potential Years of Life Lost)

  20. DEEPP

    Describe System Evaluate consequences of strategy/technology Explore new strategies Predict Persuade decision maker

  21. Specificity

    Specificity=TNTN+FPSpecificity = \frac{TN}{TN+FP}Specificity=TN+FPTN​

  22. Sensitivity

    Sensitivity=TPTP+FNSensitivity = \frac{TP}{TP + FN}Sensitivity=TP+FNTP​

  23. SPIN and SNOUT

    Specificity rules in: positive test with high specificity makes likely that person has condition Sensitivity rules out: negative test with high sensitivity makes likely person does not have condition

  24. HTA Domains

    ▪ Health Problem and Current Use of the Technology ▪ Description and technical characteristics of technology ▪ Safety ▪ Clinical Effectiveness ▪ Costs and economic evaluation ▪ Ethical analysis ▪ Organizational aspects ▪ Social aspects ▪ Legal aspects

  25. Quality of Evidence

    Used to rate literature High (very confident) Moderate (likely to be close to estimate) Low (may be substantially different) Very low (likely to be substantially different)

  26. Information Resources

  27. Effectiveness

    How well a treatment works when applied in real world

  28. Efficacy

    How well a treatment works in experiments in clinical studies

  29. Equity

    Costs, responsibility (ethics), safety,...

  30. Efficiency

    Cost-benifit, profitability

  31. ICER

    Incremental Cost Effectiveness Ratio ICER=cost intervention A−cost intervention Beffect intervention A−effect intervention BICER = \frac{cost~intervention~A-cost~intervention~B}{effect~intervention~A-effect~intervention~B}ICER=effect intervention A−effect intervention Bcost intervention A−cost intervention B​

  32. QALYs

    Quality-adjusted Life years W=L⋅QW= L\cdot QW=L⋅Q LLL ... extended life time QQQ ... grade of life quality (0-1)

  33. Purpose of Modelling

  34. Modelling Approaches (Table)

  35. Modelling Approaches - Summary

    There is no single modelling approach which suffices all possible research questions. The most accurate method sometimes is not the best method as the value of information gained from a superior model often does not justify additional time and ressources Development of the model requires expertize frommany different fields in a highly interdisciplinary process Parameterization is very complex task as data and information from various sources has to be intertwined according to the research quest…

  36. Markov Process

    A stochastic process with the Markov Property is called Markov Process A Markov Chain is a Markov Process with a discrete time-space and a fininte event space.

  37. Markov Property

    This means the process is memoryless!

  38. State Properties

    stationary distribution: distribution of states always stays the same as the in the initial distribtuion absorbing states: state that cannot be left if once entered Recurrent states: revisited infinitely many times Transient states: visited only a finite number of times and then never again

  39. Components of CA

    Cells States State Space Cell-Space Neighbourhood Update Rule

  40. HPP-FHP CA Difference

  41. Agent Definition

    With respect to Winter Simulation Conference (2005 & 2006) an agent has to... ... be uniquely identifiable ... cohabitate an environment with other agents, and has to be able to communicate with them. ... be able to act targeted. ... be autonomous and independent. ... be able to change its behaviour.

  42. Properties of Agent based Models

    Representation of „emergent phenomena“ Simple rules for individual agents -> complex dynamics of the whole system Flexibility: change of details very easy Natural description of the system system components look like in realtiy no mathematical knowledge needed to understand

  43. Two classes of agent-based models

    ABMs for qualitative investigation • (On purpose) very abstract • Usually very complex model behaviour • Hardly any parameters identified with real data ABMs for quantitative investigation • Rather simple agent interactions • lots of data for model parametrisation and validation • Usually less famous

  44. Pros and cons agent-based models

    Good in: ...analysis and discovery of complex group dynamic behaviour. This must not necessarily be a good thing as emergent behaviour may occur in models even if it is not correct ...communitcating models to non-experts. The modelling appoach is easy to understand, picturesque and no mathematical background is necessary. Agent-based modelling is problematic … … regards misinterpretation. If it looks like reality it must not necessarily be a valid model for it. … regards the validation process.…

  45. System Dynamic Modelling Process

  46. Stock and Flow Diagrams

    Components: Stocks: state variables of the System (snapshot test) Flows: only way of changing stocks inflows and outflows depend on system state Parameters Sources/Sinks Stocks outside system borders Auxiliary Variables can be easily calculated from model

  47. Types of Delays

    Material Delay: Pipeline Delay: Material exits delay exactly after delay time First Order Exponential Delay: Material in transit is perfectly mixed, it exits with an average delay time Information Delay: Acquiring information takes time First-order Exponential Smoothing: Output adapts gradually to input