4. Definition and Theoretical Basis of Decision Analysis in.pptx

Basic concept of decision analysis.

1. Definition and Theoretical Basis of Decision Analysis in Health For fifth year pharmacy students

2. Learning Objectives Define decision analysis Describe the models used for Economic Evaluation Construct, interpret and analyse simple Decision Trees understand simple Markov Chains 2

3. Decision Analysis(DA) A systematic, quantitative approach for assessing the relative value of one or more decision options. A systematic Quantitative approach that assess the relative value of complex decision options with uncertain outcomes. Historically Decision Analysis ( DA)was developed as a method to help clinicians make decisions on how to manage individual patients -as a quantitative clinical epidemiology tool for physicians who wished to quantify : Expected risks Benefits , Utilities and Sometimes cost associated with alternative treatment options. 3

4. Decision Analysis… It was later adopted for structuring and analyzing collective decisions in health care such as program evaluation and economic appraisal. Decision analysis is used when: – There are real alternatives –There is uncertainty –When consequences are important or have series consequences 4

5. Decision Analysis… Information from decision analysis can be used: – To decide how to manage an individual patient. –To formulate policy recommendation about group of similar patients . DA yields an estimate of the net value of the different decision options in relation to each other. 5

6. Decision Analysis… DA for economic evaluation proceeds by careful structuring of the problem using decision tree –a graphic scheme where we begin with the decision and trace out all probable pathways and consequences. Advantages of using a decision tree are Analyst can quickly identify what data components are required (probabilities, cost and utilities ) Are simple to understand and interpret . Possibility to be combined with other decision techniques Disadvantage Various pieces of information( cost, probabilities , and outcome measures) from different studies and population are put together into the same model. 6

7. Models used for Economic Evaluation What is a Model ? Broad and has been used in a number of contexts: –In the context of economic evaluation of medical intervention , a model is any mathematical structure that represents the health and economic outcome of patients or population under variety of scenarios . –Fundamental analytic tools used in DA to display the temporal and logical sequence of decision problem 7

8. Decision Analysis… Concerns about Modeling: The data that are used to operationalize the model are from diverse sources , many of which are subject to varying degree of bias due to confounding variables , patient selection or method of analysis For any modeling endeavor a number of key assumptions have to be made 8

9. Decision Analysis… Types of Decision Modelling Techniques: Many methodologies and modelling types can be used to create and evaluate decision models, the modeler should use the method most appropriate to the particular problem being addressed . 9

10. Decision Analysis… The choice is dependent upon: • The complexity of the problem, • The need to model outcomes over extended periods of time , and • Whether resource constraints and interactions of various elements in the model are required. Types: i. Decision Trees ii. Markov Models iii. Simulation Models iv. Deterministic (Mechanistic) Models 10

11. Decision Analysis… i. Decision Trees •The decision tree is probably the most common structure for decision models in economic evaluation •It represents individual’s possible progress , following some sort of intervention, by a series of pathways •The classic decision analysis structure is the branch and node decision tree, which is illustrated in the figure below. 11

12. Decision Analysis… Decision Trees: Fig.1: Decision options for becoming ill 12

13. Decision Analysis … Fig.2: Decision tree for breast cancer screening options 13

14. Decision Analysis… Decision Trees ... A decision model comprises the modeling structure itself (the decision tree), which represents: – the decision that is being made and the outcomes that can occur as the result of each decision, – the probabilities that the various outcomes will occur, and – the values of the outcomes if they do occur. 14

15. Decision Analysis… Decision trees… •Similar to any other research problem, the decision tree should start with a specific problem formulation , E.g. a choice between therapy A and therapy B in a particular condition . • Should include the necessary descriptors of the population in which the decision is being made to allow the reader to understand the context of the choice. •The context is followed by a decision node (represented in the fig.1and fig.2 as a square ), and should include as comparators the relevant, real choices the decision maker has at his or her disposal. 15

16. Decision Analysis… Decision trees… •In fig.1 and 2, this particular decision has only two choices represented by the branches of the decision node labelled see a doctor and don’t see a doctor ( Screen for breast cancer every 2 years Vs no screen ). • Each choice is followed by a series of chance nodes ( represented in the figures by circles ), which describe the possible outcomes that are implied by making each of the respective choices. 16

17. Decision Analysis… Decision trees … •Each outcome occurs with a specific probability . • Each outcome is also associated with one or more values, which describe the clinical effects and costs of arriving at that particular outcome. 17

18. Decision Analysis… Limitations of the decision tree •The decision tree is widely used in economic evaluation , but has important limitations: – time is not explicitly defined in a decision tree unless the analyst does so in determining the different branches Events are implicitly taken as occurring over an instantaneous discrete period. –The decision tree can get very complex when they are used to model complicated long term prognoses , particularly related to chronic diseases. 18

19. Steps in Conducting a Decision Analysis This lady lives in a in a big city . each day she has to get up early in the morning and rush to work. She has to make decision on which modes of transport she has to make . Lets apply the steps for Decision Analysis for her decision problem 19

20. Steps in Conducting a D ecision Analysis Step 1: Frame the Question • As in any study design, the modeller must decide several basic details regarding for whom and from whose perspective the decision is being made. • Deciding for whom the decision is being made is similar to the development of inclusion and exclusion criteria for a typical randomized controlled trial; the decision problem must specify exactly who would be affected by the decision. 20

21. Step 1: Frame the Question… •The description should be as detailed as necessary to describe the problem at hand , and should specify, if important , the age and gender of the population being studied , the specific disease and comorbid conditions that the patients may have, and the specific treatments or strategies that are being evaluated. •Choosing the perspective of the decision maker is also very important , as it determines the appropriate metric in which to measure the outcomes and costs of the analysis . 21

22. Step1:Identifying and Bounding the problem… In the example above, the lady has a choice among 3 options for commuting to work : – Buy a monthly pass and take the train every day –Ride your bike and take the train only on bad weather days –Drive your car • Goal: to minimize commuting costs ( NB: other goals may also be considered) 22

23. Step 2: Structure the Problem • The structuring of the problem entails diagramming the branches and nodes that represent the particular problem being modelled . •Several aspects of the process are important to remember. The first is that the choices one makes from the decision node must be mutually exclusive ; one and only one of the choices ( branches of the decision node) can be made. If there are several aspects to the choice, then these aspects should be described as a series of mutually exclusive options, rather than described as sequential or embedded decisions. 23

24. Step 2: Structure the Problem… • To structure a problem, Decision Tree is usually constructed guided by a number of conventions: –Built from left to right –Consists of: •Nodes which can be decision or chance nodes •branches and •outcomes 24

25. Step 2: Structuring the Problem… Decision Node - By convention depicted as square - Points where the alternative actions are under the control of the decision maker. 25

26. Step 2: Structure the Problem… • One or more several possible events that are beyond the control of the decision maker • At any given chance node the sum of the probabilities of the events must equal 1. • Define events that are mutually exclusive and jointly exhaustive. • By convention depicted as a circle Chance Node Going to work Example 26

27. Step 2: Structure the Problem… • Outcomes – Consequence of the final event depicted in the tree – May include life or death disability or health , positive or negative , utility measures or any of a variety of risks or outcomes – Depicted as triangle • Branches –Conventionally drawn at right angles to the nodes –They connect nodes with the nodes and nodes with the outcomes . 27

28. Step 3: Estimate the probabilities • Once the structure of the decision tree has been developed , the probabilities must be estimated for the various chance nodes in the tree. • Modelers can use several sources to find and estimate probabilities for various parameters in a decision model : – Literature review, including meta analysis. –Primary data collection. –Consultation with experts. 28

29. Step 3: Estimate the probabilities… Chance Events (in our example) • p Rain = 0.20 • p Street = 0.50 • p Vandal = 0.004 Outcome of interest (cost per day in our example) • Costs per day: – Monthly train pass = $1.80 – Space rental = $ 2.00 –Daily train pass = $2.00 –Vandalism(destroying) = $ 100.00 –Bike = $0.25 –Garage = $ 5.00 –Gasoline = $ 0.60 29

30. Estimate the probabilities… Decision Tree … With Probabilities and Outcomes 30

31. Step 4: Estimate the values of the Outcomes •Similar to estimating the probabilities of various events, the modeler needs to assess the values for the outcomes that occur as a consequence of each one of the choices . • This will direct the modeler to choose the appropriate outcome measure for the analysis . • The choice of outcome can be determined by the particular disease the treatment is designed to ameliorate. For example: in a pharmacoeconomic model of a treatment for depression, it may be that the appropriate outcome measure is depression-free days or a similar disease- related outcome metric. 31

32. Step 4: Estimate the values of the o utcomes • In a model of a particular intervention for oral hygiene , the appropriate outcome might simply be the number of cavities avoided . •The outcomes used must be those that are clinically relevant to the particular decision makers involved in the decision. •One of the advantages of developing a model of a pharmacoeconomic problem is that clinical and cost outcomes may be evaluated and modeled simultaneously. 32

33. Step 5: Analyzing the decision tree ( Average Out/Fold Back) • Decision tree is analyzed by a process called folding back and averaging • The final result is an estimate of the probability of the expected outcome of each of the decision alternatives Calculation of expected value (EV): – Average out • [( probabilility of event A) * (outcome value A)] + [( probability of event B ) * (outcome value B)] 33

34. Average Out/Fold Back… – Fold back: 34

35. Average Out/Fold Back … –Fold back: 35

36. Average Out/Fold Back… The overall goal is to calculate the expected value of the outcomes implied by choosing each branch of the root decision node. Then to choose the ‘cost –effective’ alternative 36

37. Step 6: Perform Sensitivity Analysis (SA) • Sensitivity analysis is the evaluation of the outcomes of the model for various different levels of one or more input variables. • When estimating costs and outcomes, you typically have a range of possible values. • Sensitivity analysis requires that the results be recalculated at the different values to see if the conclusions change. • Sensitivity analysis is used to evaluate the robustness of the decision to variations in model assumptions. 37

38. Perform Sensitivity Analysis… • The overall goal of SA is: – to assess the stability of the conclusion made in the analysis –Identify crucial areas of information deficiency and –To guide future research 38

39. Step 7: Interpret and Implement Findings Once : • the analysis has been completed, • the stability of the model has been tested with sensitivity analysis, and • a modeler is convinced that the model represents the clinical and pharmacoeconomic characteristics of the problem adequately, then the results must be interpreted and summarized . 39

40. Decision Analysis… ii. Markov Modelling: • Used when disease progresses over time • Patients grouped into a finite number of (Markov) states • Time progresses in equal increments (Markov cycles) • All events or progression are represented as transitions from one state to another with a certain probability –Transitions (probability of improvement or deterioration ) calculated from epidemiological and/or clinical data • Spending one cycle in a given state is associated with a certain cost and a defined outcome 40

41. Markov Modelling… • Patients are assumed to reside in one of a finite number of health states at any point in time. • Patients are assumed to make transitions between those health states over a series of discrete time intervals or cycles . 41

42. Markov Modelling… Common representation of a simple Markov process called a state-transition diagram Each state represented by a circle Arrows connecting different states indicate allowed transitions States with arrows to itself indicate that patient may remain in that state in consecutive cycles Note: no transition from “DISABLED” to “WELL”, nor “DEAD” to any other state 42

43. Markov Modelling… • The probability of staying in a state or moving to another one in each cycle is determined by a set of defined transition probabilities . 43

44. Markov Modelling… • The figure below presents a state transition diagram and matrix of transition probabilities for a Markov model of a hypothetical breast cancer intervention. • There are three health states here: well , recurrence of breast cancer, and dead. the probability of moving from the well state at time t to the recurrence state at time t+1 is 0.3, while the probability of moving from well to dead is 0.1. 44

45. Markov Modelling… •At each cycle the sum of the transition probabilities out of a health state (the row probabilities ) must equal 1 . •In order for the Markov process to end, some termination condition must be set. could be a specified number of cycles, a proportion passing through or accumulating in a particular state, or the entire population reaching a state that cannot be left ( in this example, dead); this is called an absorbing state 45

46. Markov Modelling… 46

47. Markov Modelling… •An important limitation of Markov models is the assumption that the transition probabilities depend only on the current health state, independent of historical experience (the Markovian assumption ). In example of breast cancer above, the probability of a person dying from breast cancer is independent of: the number of past recurrences and how long the person spent in the well state before moving to the recurrent state 47

48. Extra examples of Markov model The following is a Markov model, which reflects the change in disease ( levels of disability) over time ( years). Assume : A no disability, B mild , C moderate, D severe and E death . 48

49. Markov Modelling… 49

50. Thank u! 50