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TreeAge Pro 2-Day Healthcare Training Day 1

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1 TreeAge Pro 2-Day Healthcare Training Day 1
Using TreeAge Pro for Health Economic Modeling © 2013 TreeAge Software, Inc.

2 TreeAge Pro Healthcare Training
Agenda – Day 1 Introduction Build Cost-Effectiveness Model Analyze a Cost-Effectiveness Model Clones Sensitivity Analysis Exercise – Build Decision Tree Variable Expressions and Arrays Day 2 will focus on Markov models TreeAge Pro Healthcare Training

3 TreeAge Pro Healthcare Training – Introduction
Modeling/analysis goals Create a model to represent a disease process and available treatment options Evaluate treatment options independently and choose the optimal path Measure effects of uncertainty on treatment strategy selection TreeAge Pro Healthcare Training – Introduction

4 TreeAge Pro Healthcare Training – Introduction
Benefits of TreeAge Pro Visual modeling tool for easier model building and presentation Healthcare modeling analysis and reporting Sensitivity analysis Monte Carlo simulation Multiple measurements can be used to evaluate the same model Cost, effectiveness, cost-effectiveness, other TreeAge Pro Healthcare Training – Introduction

5 TreeAge Pro User Interface
Tree Diagram Editor Perspectives Projects View Model Input Views TreeAge Pro Healthcare Training – Introduction

6 TreeAge Pro Healthcare Training – Introduction
TreeAge Pro Interface Tree Diagram Editor Primary modeling window for building model structure Multiple tabs for models and analysis output Zoom in/out, multi-select, noteboxes Views For editing/viewing components of the model (e.g., parameters) Open via Views toolbar list Move, maximize, minimize, detach Perspectives Collection of views’ orientation stored when you exit software Can reset to original orientation or overwrite saved orientation TreeAge Pro Healthcare Training – Introduction

7 TreeAge Pro Healthcare Training – Introduction
TreeAge Pro Interface Model Input Views: Specific view(s) for each model input Some tied to tree (e.g., tree properties, variable properties) Some tied to node (e.g., node properties, variable definitions) Other Views: Projects View for managing files Model Overview/Tree Explorer for navigating in large trees Evaluator for testing calculations Context-sensitive help Many more TreeAge Pro Healthcare Training – Introduction

8 Build Cost-Effectiveness Model
Module 1: Build Cost-Effectiveness Model Goals: Build model structure Create and use variables Set tree preferences TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

9 Build Cost-Effectiveness Model
Primary elements of a model Model structure Mimic everything that could happen Numeric values Add probabilities and values Tree Preferences Control how you want to evaluate/view the model TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

10 Recreates the complete path of a patient
Model Structure Recreates the complete path of a patient Includes all possible events and outcomes Consists of a collection of nodes, where each one represents one step within the overall model flow Starts with a single root node Usually a decision node Facilitates comparison of treatment strategies Each node can have multiple branches to the right, but only a single parent TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

11 Node Types Decision Node: Chance Node: Terminal Node:
Branches are alternative strategies Run analyses here to compare strategies Chance Node: Branch for each possible outcome Probabilities are associated with each branch Mutually exclusive and exhaustive (∑ = 100%) Use “#” for complementary probability (once) Terminal Node: Complete the scenario (root to terminal node) Payoffs place a value on all events within that scenario TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

12 Node Types Logic Node: Label Node: Markov Node:
Like a chance node but with logical expressions (true/false) instead of probabilities Expressions checked from top down until one is true Label Node: Like a chance node with a single branch with probability 100% Markov Node: Start of Markov model Will discuss in later module TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

13 Decision Node Chance Nodes Terminal Nodes
Model Structure Decision Node Chance Nodes Terminal Nodes Root node Strategies Probabilities Payoffs TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

14 Numeric Values Required for probabilities, payoffs, etc.
Consist of any combination of… Numbers Model inputs: Variables, distributions, tables, trackers Built-in functions: If, Discount, Min, Max, etc. Operators: +, -, *, /, ^, &, | TreeAge Pro cannot determine numeric values for your projects Sources: medical literature, trials, NIH, AHRQ, CDC Some must be estimated TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

15 Tree Preferences Control how a model is… Calculated Displayed
Calculation method Active, enabled payoff sets Optimal path - min vs. max Etc. Displayed Show variables in tree Numeric formatting Fonts TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

16 Build Cost-Effectiveness Model
Model we will build The current standard is to treat a specific type of tumor with radiation We want to study a new treatment that combines surgery and radiation We estimate that the new treatment will increase the probability of eradicating the tumor from 60% to 80% A person’s life expectancy is 10 years if the tumor is eradicated, but only 3 years if not The costs associated with radiation and surgery are $30K and $50K respectively The follow-up costs post-treatment are $2K per year TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

17 Build Cost-Effectiveness Model
How do we create a model from the information we have? Look for the decision Two treatment options indicates root decision node with branch for each strategy Possible outcomes Either treatment could eradicate the tumor or not Chance node with branches for two results Life expectancy provided Becomes measurement of effectiveness Markov model is not necessary Numbers Become parameters for probabilities or values TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

18 Build Cost-Effectiveness Model
Information: The current standard is to treat a specific type of tumor with radiation We want to study a new treatment that combines surgery and radiation Model: Root node is decision with a branches for each treatment option TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

19 Build Cost-Effectiveness Model
Instructions: Create new model from toolbar icon (blank tree). Enter node label text for the root decision node. Double-click on root node to add two branches. Enter node label text for each strategy. Adjust width of node as desired for text formatting. TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

20 Build Cost-Effectiveness Model
Information: We estimate that the new treatment will increase the probability of eradicating the tumor from 60% to 80% Model: Both strategies are chance nodes with branches for tumor eradicated or tumor not eradicated Different probabilities for each set of branches TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

21 Build Cost-Effectiveness Model
Instructions: Double-click on top strategy node to add two branches. Enter node labels. Ignore the bottom strategy for now; we will create it later. TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

22 Build Cost-Effectiveness Model
Information: A person’s life expectancy is 10 years if the tumor is eradicated, but only 3 years if not The costs associated with radiation and surgery are $30K and $50K respectively The follow-up costs post-treatment are $2K per year Model: Each of these numeric parameter values factors into the payoffs (scenario values) All parameters should be entered as variables TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

23 Variables Why use variables?
Clarity – Isolate your parameters and formulas Consistency – define once, use many times Efficiency – changing a single variable can affect multiple numeric expressions in the model Transparency – easier to understand the meaning of each value Sensitivity analysis – covered later Clones – covered later Always use variables! TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

24 Variables are named values like in algebra Variable name:
Each variable has a single name Reference the variable name anywhere in the model to calculate and return its value Calculated dynamically during analysis when referenced 32 letters/numbers/underscores (no punctuation) Stick to a naming style/convention Not case sensitive although you can enter case TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

25 Variables Variable definitions: Displayed beneath node within box
Variable definitions evaluated when referenced Most variables will be defined once at the root node Reference anywhere in the model using the same definition Variables can be defined at any node Use different definitions for different parts of the model Useful for clones – covered later Variable reference looks for closest definition from that node to the left MyVar2 = 20 at terminal node TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

26 Build Cost-Effectiveness Model
Instructions: Right-click on root node and choose Define Variable > New Variable from the context menu. Enter variable name “cFollowupAnnual”. Enter a description and comment if desired. Click OK. Enter definition “2K” in the Define Variable dialog and click OK. You will see the variable definition beneath the root node. Repeat steps 2-5 for the variables below. cRadiation = 30K cSurgery = 50K TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

27 Variables Variable Properties View: Variable Definitions View:
Maintain variables in the tree Options to edit, add, delete, categorize, report variables Edit in Excel available with Excel Module (in TP Suite) Variable Definitions View: Maintain variable definitions Contents change with selected node Defined vs. Undefined vs. Inherited Cut/Copy/Paste to copy or move variable definitions to other nodes TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

28 Build Cost-Effectiveness Model
Instructions: Select root node. Select or open the Variable Properties View. Click the “+” icon. Enter the variable name “effEradicated”. Check the box Define numerically at root. Enter 10 in the definition field and click OK. Repeat steps 3-6 for the variable below. effNotEradicated = 3 pEradicateRad = 0.6 pEradicateRadSurg = 0.8 Enter pEradicateRad as the probability value for the Eradicates tumor within the Radiation strategy. Enter # for complementary probability. TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

29 Build Cost-Effectiveness Model
At this point, our model should look like this… We still need to… Setup tree preferences Terminate the scenarios for the top strategy Build the bottom strategy TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

30 Build Cost-Effectiveness Model
Information: A person’s life expectancy is 10 years if the tumor is eradicated, but only 3 years if not The costs associated with radiation and surgery are $30K and $50K respectively The follow-up costs post-treatment are $2K per year Model: Change tree’s calculation method to cost-effectiveness Terminate the two scenarios in the top strategies Add the appropriate values for cost and effectiveness TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

31 Tree Preferences Control calculation and display of model
Many categories (see filter) Menu: Tree > Tree Preferences to open dialog Calculation Method: Determines which payoff sets will be used to evaluate the model and determine the optimal strategy Usually either simple or cost-effectiveness Numeric Formatting: Controls display of outputs (calculated payoffs and EVs) Set for units, decimal places, labels, etc. Many more TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

32 Tree Preferences Simple Calculation Method:
Enter a single payoff at each terminal node Single expected value (EV) calculated at all upstream nodes Optimal decision is maximum (eff) or minimum (cost) EV Cost-effectiveness Calculation Method: Enter separate payoff values for cost and effectiveness at each terminal node Cost and effectiveness EVs calculated separately at all upstream nodes To select optimal strategy, must balance cost and effectiveness, looking at efficiency & tradeoffs Will examine further when analyzing model TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

33 Tree Preferences Not limited to two payoffs
Only two are active for CE calc method Can use extra payoffs for different cost, effectiveness, other outcomes Same model can then be used to analyze problem with different outcome measurements Change active payoffs for different measurements and repeat analyses Cost: Total cost vs. patient cost vs. insurer cost, etc. Health/Utility: LY, QALY, etc. Other: Positive tests, cases avoided, cases identified, etc. TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

34 Build Cost-Effectiveness Model
Instructions: Choose Tree > Tree Preferences from the menu. Select the category Calculation > Calculation Method. Select Cost-effectiveness (use default active payoffs). Select the category Calculation > Numeric Formatting. Enter settings as seen below. Click OK to save the changes. TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

35 Build Cost-Effectiveness Model
Terminal nodes terminate the scenario Must account for all values (cost, eff) that contribute to that scenario Any node on path from the root node to that terminal node For cost-effectiveness model, enter separate values for cost and effectiveness For top terminal node… Costs: Treatment Cost: cRadiation Followup Cost: cFollowupAnnual * effEradicated Effectiveness: effEradicated For second terminal node… Same except using effNotEradicated TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

36 Build Cost-Effectiveness Model
Instructions: Right-click on top node. Choose Change Type > Terminal. Edit Payoff dialog opens automatically. Enter expression for Cost: Click ellipsis button to open formula editor for Cost payoff. Select variables to enter the following expression… cRadiation + (cFollowupAnnual*effEradicated) Enter expression for Effectiveness Type the following expression… effEradicated Click OK. Repeat steps 2-5 for second terminal node (effNotEradicated). TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

37 The formula editor is available anywhere you see the ellipsis button
Create expressions by providing lists of Model inputs (variables, trackers, tables, distributions) Built-in functions (Discount, Round, ProbToProb, etc.) Operators (+, -, &&, etc.) Keywords (_stage, _trial, _sample, etc.) Value created in “Expression” field is passed back to the model TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

38 Content-Assist Content-Assist Enter partial text in model
Press CONTROL+SPACEBAR to show all numerical elements (variables, functions, etc.) that match the partial text Select an item from the list Placed in expression within model TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

39 Build Cost-Effectiveness Model
The two strategies are very similar… Why build it twice? Copy the top strategy’s subtree and paste it onto the bottom strategy Make necessary changes to the values used in the new treatment strategy TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

40 Build Cost-Effectiveness Model
Instructions: Select top strategy node. Choose Subtree > Select Subtree from the menu. Choose Edit > Copy from the menu. Select bottom strategy node. Choose Edit > Paste from the menu. An exact duplicate of the top strategy’s subtree will be copied to the bottom strategy. TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

41 Build Cost-Effectiveness Model
Instructions (bottom strategy): Change the probability for “Tumor eradicated” node to pEradicateRadSurg. Add cSurgery to the cost payoff values at each terminal node. The model is now complete! Training model: Example02-Variables.trex TreeAge Pro Healthcare Training – Module 1 – Build Cost-Effectiveness Model

42 Analyze Cost-Effectiveness Model
Module 2: Analyze Cost-Effectiveness Model Goals: Understand how each strategy’s expected value is calculated Compare strategies on basis of cost-effectiveness (which is best?) Consider dominance among strategies (which are rejected) Introduce net benefits calculations for CE analysis Introduce clones TreeAge Pro Healthcare Training – Module 2 – Analyze Cost-Effectiveness Model

43 Analyze Cost-Effectiveness Model
Want to choose the optimal treatment strategy Must first calculate expected values (EVs) for each strategy Compare the strategies’ cost and effectiveness EVs using standard cost-effectiveness analysis techniques TreeAge Pro Healthcare Training – Module 2 – Analyze Cost-Effectiveness Model

44 Expected Value (EV) Best estimate for the overall value of the strategy Reflects all possible outcomes based on each one’s likelihood Sum of each outcome’s value weighted by its probability Example: 20% chance of dying immediately 30% chance of living 10 years 50% chance of living 20 years EV = (0.2 * 0) + (0.3 * 10) + (0.5 * 20) = 13 For CE model, cost and effectiveness EVs are calculated separately TreeAge Pro Healthcare Training – Module 2 – Analyze Cost-Effectiveness Model

45 Analyze Cost-Effectiveness Model
TreeAge Pro calculates EVs at terminal nodes then calculates remaining EVs from right to left Terminal nodes EV: Calculate the payoff expressions Top terminal node… Cost: cRadiation + (cFollowupAnnual*effEradicated) = 30K + (2K * 10) = 50K Effectiveness: effEradicated = 10 TreeAge Pro Healthcare Training – Module 2 – Analyze Cost-Effectiveness Model

46 Analyze Cost-Effectiveness Model
Path probability: Probability of reaching that node within the scenario Product of probabilities of every chance node branch in path from strategy to terminal node P = 0.600 TreeAge Pro Healthcare Training – Module 2 – Analyze Cost-Effectiveness Model

47 Analyze Cost-Effectiveness Model
Chance nodes: Weighted averages of the EVs of the node’s branches Top strategy/chance node… Cost: (0.6*50K) + (0.4*36K) = 30K K = 44.4K Eff: (0.6*10) + (0.4*3) = = 7.2 TreeAge Pro Healthcare Training – Module 2 – Analyze Cost-Effectiveness Model

48 Analyze Cost-Effectiveness Model
Decision nodes: Compare EVs for all strategies and choose optimal path Simple calculation method: Minimum or maximum based on tree preferences Cost-effectiveness calculation method: Need to “balance” cost and effectiveness Rollback uses net benefits with willingness-to-pay (WTP) parameter from tree preferences Cost-effectiveness analysis TreeAge Pro Healthcare Training – Module 2 – Analyze Cost-Effectiveness Model

49 Analyze Cost-Effectiveness Model
Cost-Effectiveness Analysis (CEA) Standard health economic theory Two goals: Optimize effectiveness (maximize usually) Optimize cost (minimize) CEA context: Efficient use of limited resources Recommend interventions if additional effectiveness comes at a reasonable cost TreeAge Pro Healthcare Training – Module 2 – Analyze Cost-Effectiveness Model

50 Analyze Cost-Effectiveness Model
CEA: Calculate Incremental Cost-Effectiveness Ratio (ICER) How much are we paying for each additional unit of effectiveness? Compare to a willingness-to-pay (WTP) threshold Is the ICER too high? Budget constraints: Sometimes compare to ceiling cost value Overall cost of treatment may be too high TreeAge Pro Healthcare Training – Module 2 – Analyze Cost-Effectiveness Model

51 Analyze Cost-Effectiveness Model
ICER Calculation: ICER = IC/IE = (Ccomparator – Cbaseline)/(Ecomparator – Ebaseline) In our model… ICER = ($97.2K – $44.4K)/(8.6LY – 7.2LY) = ($52.8K)/(1.4LY) = ~ $37.7K/LY To switch from the standard treatment to the new treatment, it costs ~ $37.7 for each extra LY If we are willing to pay at least that much per extra LY, we can recommend the new treatment TreeAge Pro Healthcare Training – Module 2 – Analyze Cost-Effectiveness Model

52 Analyze Cost-Effectiveness Model
Instructions: Select root node. Choose Analysis > Cost-Effectiveness Analysis from the menu. Click Yes. A Cost-Effectiveness Analysis graph will be created. Click the Text Report link. The cost-effectiveness calculations are displayed. TreeAge Pro Healthcare Training – Module 2 – Analyze Cost-Effectiveness Model

53 Analyze Cost-Effectiveness Model
Cost-Effectiveness Analysis graph: Plots strategies on cost and effectiveness axes Line segments form the cost-effectiveness frontier Slope = ICER If ICER <= WTP, move to next strategy Want to be here Any strategies here would be dominated (rejected) TreeAge Pro Healthcare Training – Module 2 – Analyze Cost-Effectiveness Model

54 Edit Chart link – modify the appearance of any graph
Graphs Edit Chart link – modify the appearance of any graph Text Report link – see the numeric data behind the graph File > Save allows you to save analysis output File type RPTX - graph and underlying data File types JPEG, PNG, etc. - image of the graph For publishing TreeAge Pro Healthcare Training – Module 2 – Analyze Cost-Effectiveness Model

55 Analyze Cost-Effectiveness Model
Cost-Effectiveness Rankings report: Shows ICER calculations IC IE ICER TreeAge Pro Healthcare Training – Module 2 – Analyze Cost-Effectiveness Model

56 Analyze Cost-Effectiveness Model
Dominance: A strategy is dominated when other strategies provide better cost-effectiveness Dominated strategies are then rejected as treatment options Absolute Dominance: Less effective (IE < 0) More costly (IC > 0) Extended Dominance: More effective (IE > 0) Less efficient (ICER > ICER for other strategy) TreeAge Pro Healthcare Training – Module 2 – Analyze Cost-Effectiveness Model

57 Analyze Cost-Effectiveness Model
Example 4 model demonstrates both types of dominance Same as the tree we built but with three new strategies to evaluate Top two strategies collapsed via… Subtree > Collapse Subtree TreeAge Pro Healthcare Training – Module 2 – Analyze Cost-Effectiveness Model

58 Analyze Cost-Effectiveness Model
CEA with dominance Dominated strategies are above and to the left of the cost-effectiveness frontier TreeAge Pro Healthcare Training – Module 2 – Analyze Cost-Effectiveness Model

59 Analyze Cost-Effectiveness Model
Other Tx 2 rejected by absolute dominance Less effective and more costly than Standard Treatment Other Tx 3 rejected by extended dominance Lower ICER, more efficient to move from Standard treatment to New Treatment Higher ICER, less efficient to move from Standard treatment to Other Tx 3 Blended combination of New Treatment and Standard Treatment would get more effectiveness at same cost TreeAge Pro Healthcare Training – Module 2 – Analyze Cost-Effectiveness Model

60 Analyze Cost-Effectiveness Model
CE Rankings also shows dominance Absolute dominance – negative IE and ICER Extended dominance – ICER declines TreeAge Pro Healthcare Training – Module 2 – Analyze Cost-Effectiveness Model

61 Analyze Cost-Effectiveness Model
Which strategy is preferred? First reject dominated strategies Then compare the ICERs to the WTP value If WTP < $37.7K, recommend Standard treatment If $37.7K < WTP < $132K, recommend New treatment If WTP > $132K, recommend Other Tx 1 TreeAge Pro Healthcare Training – Module 2 – Analyze Cost-Effectiveness Model

62 Net Benefits Net Benefits combines cost, effectiveness and WTP into a single measurement Builds the ICER threshold (WTP) into calculations, as the weight on effectiveness Strategy with largest NB is most cost-effective Calculations: NMB = (E * WTP) – C = (LY * $/LY) – $ = $ NHB = E – (C / WTP) = LY – ($ / ($/LY)) = LY Allows roll back to identify most cost-effective strategy Simplifies presentation of more complex analyses looking for most cost-effective strategy i.e., Sensitivity analysis TreeAge Pro Healthcare Training – Module 2 – Analyze Cost-Effectiveness Model

63 Analyze Cost-Effectiveness Model
Note that we can identify the preferred strategy from roll back using CE parameters in Tree Preferences Optimal strategy identified by Net benefits calculations using WTP parameter Change to Invert incremental only if effectiveness should be minimized (e.g., # of infections or # of deaths) TreeAge Pro Healthcare Training – Module 2 – Analyze Cost-Effectiveness Model

64 Analyze Cost-Effectiveness Model
TreeAge Pro allows you to exclude strategies from all analyses Only do if you are certain that a strategy could not possibly be optimal. Can speed up lengthy analyses. Instructions Right click on Other Tx 2 node and choose Exclude Strategy from analysis. Select the decision node. Choose Analysis > Cost-Effectiveness from the menu. TreeAge Pro Healthcare Training – Module 2 – Analyze Cost-Effectiveness Model

65 Clones Now that we have analyzed the model, we will introduce clones
In our model, we copied the subtree from one strategy to the other What if the subtree was extremely complex and required significant revision? Clones create exact duplicates of a subtree that stay consistent even as the subtree is updated We will rebuild the second strategy of our tree using clones TreeAge Pro Healthcare Training – Module 2 – Analyze Cost-Effectiveness Model

66 Clones Instructions: Open the Example 2 model.
Select the New treatment node. Choose Subtree > Select Subtree from the menu. Choose Subtree > Delete from the menu. Select the Standard treatment node. Choose Subtree > Create Clone Master from the menu. Enter the name “Treatment outcomes”. Choose Subtree > Attach Clone. The clone copy will be attached. TreeAge Pro Healthcare Training – Module 2 – Analyze Cost-Effectiveness Model

67 Clones You have now created a clone master and attached a clone copy
The clone master is identified by a dark node line and a clone index number beneath the node marker The clone copy is identified with a reference to the clone master index TreeAge Pro Healthcare Training – Module 2 – Analyze Cost-Effectiveness Model

68 Clones The Clones View allows you to view and edit the clone masters and copies When a clone master is destroyed, all clone copies are also destroyed When a clone copy is destroyed, you can replace it with an independent copy of the clone master TreeAge Pro Healthcare Training – Module 2 – Analyze Cost-Effectiveness Model

69 Clones The values in the clone copy and master are now the same
How can we really compare strategies? Variables allow you flexibility to send different values into clone master vs. clone copy Same variable references within clone master Pass different variable definitions from outside (to left) of clone into the clone master/copy Recommendation: Define strategy-specific parameters at root Define generic variables at strategy nodes using parameter variables from above Do not defined generic variables at root node TreeAge Pro Healthcare Training – Module 2 – Analyze Cost-Effectiveness Model

70 Clones Two values need to be different for the clone master and clone copy Probability of eradicating the tumor Cost of treatment Note that the clone master now uses generic variables pEradicateGeneric and cTreatmentGeneric Each strategy’s parameters are passed to the clone master/copy via the generic variables defined at the strategy nodes TreeAge Pro Healthcare Training – Module 2 – Analyze Cost-Effectiveness Model

71 Clones Now the treatment outcomes subtrees are linked
Roll back generates the same values for the strategies as before the clones (next module) Clone copy subtrees cannot currently be expanded If needed, temporarily detach clone copy to see complete independent subtree (don’t save change) TreeAge Pro Healthcare Training – Module 2 – Analyze Cost-Effectiveness Model

72 TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis
Goals: Examine the effect of uncertainty on analysis results Study an individual parameter’s uncertainty and identify thresholds Modeling exercise Study combined parameter uncertainty to determine overall confidence in conclusions TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

73 TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis
Sensitivity Analysis studies how uncertainty in a model’s parameter inputs affect its analysis outputs And by extension, your conclusions/strategy selection Two types of Sensitivity Analysis are supported by TreeAge Pro Deterministic Sensitivity Analysis Individual parameters via VARIABLES (range) Probabilistic Sensitivity Analysis Many parameters via DISTRIBUTIONS (samples) Both are recalculation “loops”, with uncertain parameters changing between recalculations TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

74 Sensitivity Analysis – Deterministic
One-Way Sensitivity Analysis: Identify a single parameter for analysis Must be a numeric parameter and not a formula Set a range (min, max) for the uncertainty related to its value Set the number of intervals for recalculations within the range Recalculate the model several times changing the parameter value from the bottom to the top of the range How do the results (and possibly conclusions) change? TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

75 Sensitivity Analysis – Deterministic
Instructions: Open Example 2 tree. Select root node. Choose Analysis > Sensitivity Analysis > 1 Way… from the menu. Enter the sensitivity analysis parameters… Select the variable pEradicateRadSurg. Range: Intervals: 8. Click OK. TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

76 Sensitivity Analysis – Deterministic
Generates EV calculations for each value of the variable given the range and intervals Cost, effectiveness for each strategy ICER for each parameter value Threshold: var ~= 0.75, ICER ~= 50K TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

77 Sensitivity Analysis – Deterministic
x vs. Avg. Eff. graph: Sensitivity analysis on effectiveness only Threshold at pEradicateRadSurg value 0.6 Point where effectiveness is equal for two strategies We really want threshold for cost-effectiveness TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

78 Sensitivity Analysis – Deterministic
Sensitivity analysis on cost-effectiveness is more complex At what point in variable range do we have a change in the recommended strategy based on cost-effectiveness (threshold) Need to consider cost, effectiveness and WTP x. vs. ICER graph shows approximate threshold Net Benefits graph and thresholds report best identify threshold TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

79 Sensitivity Analysis – Deterministic
X vs. ICER graph: Shows how ICER changes with variable ICER undefined when IE = 0, presented as zero Can approximate threshold where ICER = WTP TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

80 Sensitivity Analysis – Deterministic
Net Benefits: Prompts for WTP, required for Net Benefits calcs Net benefits is higher for most cost-effective strategy (C, E, WTP combo) Identifies CE threshold If var >= 0.749, recommend surgery + radiation If var < 0.749, recommend radiation only Thresholds report TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

81 Sensitivity Analysis – Deterministic
2-way sensitivity analysis: See how changing two parameters affects recommended strategy Requires Net Benefits Each axis taken by a variable Region patterns show recommended strategy TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

82 Sensitivity Analysis – Deterministic
Instructions: Open Example 2 tree. Select root node. Choose Analysis > Sensitivity Analysis > 2 Way… from the menu. Enter the sensitivity analysis parameters… Var1: pEradicateRadSurg; Range: 0 - 1; Intervals: 10. Var2: pEradicateRad; Range: 0 - 1; Intervals: 5. WTP: 50K Click OK. TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

83 Sensitivity Analysis – Deterministic
2-way sensitivity analysis: New treatment favored more with increased pEradicateRadSurg Standard treatment favored more with increased pEradicateRad TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

84 Sensitivity Analysis – Deterministic
Tornado Diagram: Run a collection of 1-way sensitivity analyses See which variables have the largest impact on EV Uses Net Benefits with additional option for ICER Be careful: Do not overanalyze these graphs Details from 1-way sensitivity analyses are not fully presented in tornado diagram TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

85 Sensitivity Analysis – Deterministic
Instructions: Open Example 2 tree. Select root node. Choose Analysis > Sensitivity Analysis > Tornado Diagram… from the menu. Enter the sensitivity analysis parameters… Var1: pEradicateRad; Range: 0.5 – 0.7; Intervals: 4. Var2: pEradicateRadSurg; Range: 0.7 – 0.9; Intervals: 5. Var3: cRadiation; Range: 25K – 35K, Intervals: 4 Var4: cSurgery; Range: 40K – 60K, Intervals: 4 Var5: cFollowupAnnual; Range: 1.8K – 2.2K, Intervals: 4 WTP: 50K Click OK. TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

86 Sensitivity Analysis – Deterministic
Tornado Diagram: Shows band for range of EV for preferred strategy Dotted line shows base case EV Links for 1-way net benefit graphs Link for ICER graph Dark line indicates a strategy change TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

87 TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis
Modeling Exercise We are studying treatments for a disease that affects only elderly individuals Without the disease or with the disease controlled, the average person will live for 10 years With the disease uncontrolled, the average person will live only 5 years We are studying two competing drugs for treating this disease For both drugs: It must be taken for at least 1 year If it controls the disease, it must be taken on an ongoing basis If it fails to control the disease, no further treatment can be provided (only 1 year of Tx) TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

88 TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis
Modeling Exercise Drug 1: Annual cost is $9K Controls disease in 70% of patients Drug 2: Annual cost is $12K Controls disease in 80% of patients Which, if either, drug is the most cost-effective treatment option given a WTP of $50K/LY? TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

89 TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis
Modeling Exercise How will the model structure begin to form the basis for the decision analysis? Start with decision node with branches for each of three strategies What are the important parameters? Life expectancy with disease = 5 Life expectancy if controlled = 10 Prob of control with Drug 1 = 0.7 Prob of control with Drug 2 = 0.8 Annual cost of Drug 1 = 9K Annual cost of Drug 2 = 12K TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

90 TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis
Modeling Exercise Given WTP = $50K/LY Which is the most cost-effective treatment option? CEA/Rankings Drug 1 – ICER = $18.8K Drug 2 – ICER = $65.4K At what price for Drug 1would there be a strategy change? Sensitivity analysis on cost of Drug 1 for range 7K-11K Drug 1 – at price $10,055, Drug 2 becomes favored Tx Assume we do not know the LY of no/controlled disease. Identify thresholds in the range 5-15. Sensitivity analysis on life expectancy controlled Shift from No Tx to Drug 1 at 6.19 Shift from Drug 1 to Drug 2 at 14.53 TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

91 Sensitivity Analysis – Probabilistic
Probabilistic Sensitivity Analysis (PSA): Consider the combined uncertainty related to any number of parameters How does this uncertainty affect the overall confidence in your base case conclusions? Percent of simulation iterations that confirm Confidence intervals around main outputs Like ICER No thresholds for individual parameters TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

92 Sensitivity Analysis – Probabilistic
Monte Carlo simulation: Introduces randomness, sampling into analysis Required for Probabilistic Sensitivity Analysis (PSA) TreeAge Pro supports several forms of simulation Probabilistic Sensitivity Analysis Samples: 2nd-order, parameter uncertainty Microsimulation, random walks Trials: 1st-order, individual variability Two-Dimensional Samples & Trials in same analysis Three-Dimensional Partial EVPI TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

93 Sensitivity Analysis – Probabilistic
Deterministic vs. Probabilistic Deterministic Sensitivity Analysis Probabilistic Sensitivity Analysis Parameter Variables: – range, intervals Parameter Distributions – random samples Usually focused on 1 uncertainty at a time All uncertainties sampled simultaneously Repeat analysis – Identical results Repeat analysis – Different individual results – Similar aggregate values TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

94 Sensitivity Analysis – Probabilistic
Prepare model for PSA: Set parameter values using distributions instead of simple variables Only create distributions for parameters you want to study via PSA PSA calculation loop: Sample parameter value from each distribution Substitute sampled values into the model Calculate expected values for strategies/payoff sets (C and E) Repeat for predefined number of samples Results: Set of EV calculations reflecting different parameter sets Mean EVs generally will confirm base case Individual EVs may not confirm base case Reflection of confidence in base case Confidence intervals TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

95 Sensitivity Analysis – Probabilistic
We will start with the Example 02 model Open and save under new name We will introduce distributions to measure parameter uncertainty for… Probability of eradicating tumor with radiation Probability of eradicating tumor with radiation + surgery Cost of surgery TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

96 Sensitivity Analysis – Probabilistic
Instructions: Choose Views > Distributions from the toolbar. Click the “+” icon in the Distributions View to create a new distribution. Enter the distribution information at right. TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

97 TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis
Distributions Distribution type: Shape of distribution Required parameters are specific to each distribution type Example: Normal - mean and standard deviation Parameters: Numeric values for sampling (sort of like a range) Warning: It can be difficult to determine the appropriate type and parameters for each of your model’s uncertainties TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

98 TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis
Distributions Distribution type: Shape of distribution Required parameters are specific to each distribution type Example: Normal - mean and standard deviation Parameters: Numeric values for sampling (sort of like a range) Warning: It can be difficult to determine the appropriate type and parameters for each of your model’s uncertainties TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

99 TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis
Distribution Types Normal Standard bell curve Uniform Equal likelihood of all values in range Option to limit to integers Triangular Provide min, max and most likely Often easiest to create, understand Beta Frequently used for probabilities Restricted to between 0 and 1 Dirichlet Sample multiple complementary probabilities, like interrelated betas Table You determine each values that can be sampled and how frequently Good for known empirical data TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

100 TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis
Distributions Sampling rate: Resample per EV/group of trials… PSA parameter uncertainty Parameter sample affects entire cohort Resample per trial For Microsimulation New sample for each individual trial within the cohort Resample per Markov cycle New sample for each cycle in Markov model (less common) TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

101 TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis
Distributions Must reference distribution within model for Monte Carlo simulation (PSA) Reference distribution by either index or name Recommend references by name Distribution functions require reference by index Example: DistForce(index), DistTrim(index; min; max) Parameter approximation: Certain distribution types’ parameters can be approximated from other statistics In our first distribution, we estimated the Alpha and Beta parameters from the mean and standard deviation TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

102 TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis
Distributions Use Graph icon to sample and graph distribution: Mimics sampling that will be performed during PSA Generates a histogram of samples Samples centered around mean (0.6) with variation Beta is not a normal bell curve TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

103 Sensitivity Analysis – Probabilistic
Instructions: Open/select the Distributions View. Click the “+” icon in the Distributions View to create a new distribution. Create two more distributions. dist_pEradicateRadSurg Beta parameters approximated from mean 0.8 and std dev 0.1 dist_cSurgery Normal distribution with mean 50,000 and std dev 10,000 TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

104 Sensitivity Analysis – Probabilistic
Distributions created, but they need to be integrated into the model Two options… Replace references to variables with references to distributions within model Define existing referenced variables using distributions rather than numeric values Can still run deterministic sensitivity analysis TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

105 Sensitivity Analysis – Probabilistic
Instructions: Select the root node. Open/select the Variable Definitions View. Redefine the following three variables… cSurgery = dist_cSurgery pEradicateRad = dist_pEradicateRad pEradicateRadSurg = dist_pEradicateRadSurg TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

106 Sensitivity Analysis – Probabilistic
Non-PSA calculations will use mean values for distributions (no sampling) Our distributions’ means are equal to the original numeric estimates Our EV calculations will not change Be careful with non-PSA analyses after adding distributions TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

107 Sensitivity Analysis – Probabilistic
Instructions: Select the root node. Choose Analysis > Monte Carlo Simulation > Sampling. Enter 1000 samples. Click Begin. TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

108 Sensitivity Analysis – Probabilistic
How many iterations do you need for a Monte Carlo simulation? Is 1000 enough? Depends on number of distributions and complexity of model If successive simulations generate mean values that are “very close”  enough iterations Good rule for all simulation types (samples, trials, etc.) TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

109 Sensitivity Analysis – Probabilistic
Simulation results: Aggregate results: Mean Standard deviation % intervals Each iteration’s results May or may not confirm base case Outputs Text reports Graphs Provide interpretation Can save result set in *.rptx file (consistency in paper) TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

110 Sensitivity Analysis – Probabilistic
Selected PSA output options… Values, Distributions: Shows each strategy’s EV calculations and distribution samples for each iteration Output Distributions: Shows distribution of EV outputs for each strategy or for incrementals ICER distribution can be interesting Sometimes invalid in cases where IE can be zero (wild fluctuation in ICER) CE Analysis: CEA from simulation means ICER may not match regular non-PSA CEA ICER CE Scatterplot: Shows scatter of each iteration’s cost and effectiveness values for each strategy TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

111 Sensitivity Analysis – Probabilistic
Remember out goal – to see how combined uncertainty affects the overall confidence in our base case conclusions For this… ICE Scatterplot Strategy Selection Frequency Acceptability Curve TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

112 Sensitivity Analysis – Probabilistic
ICE Scatterplot Compares a pair of strategies showing IC and IE on graph Edit graph to change y-axis scale to include zero Line from origin to each point is like the ICER slope in CEA graph Points below-right of WTP line (64.9%) recommend New Treatment ICER <= WTP TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

113 Sensitivity Analysis – Probabilistic
Strategy Selection Frequency Shows the percentage of iterations that favor each strategy at single WTP ($50K) Shows same percentage as ICE scatterplot Easier to see using Net Ben Changed scale via Edit Chart options TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

114 Sensitivity Analysis – Probabilistic
CE Acceptability Curve Shows the percentage of iterations that favor each strategy over WTP range ($0-$100K) Added line via Edit Chart options TreeAge Pro Healthcare Training – Module 3 – Sensitivity Analysis

115 Variable Expressions and Arrays
Module 4: Variable Expressions and Arrays Goals: See how variables can… Simplify complex expressions Change multiple formulas all at once via switches Reference multiple definitions by index TreeAge Pro Healthcare Training – Module 4 – Variable Expressions and Arrays

116 Variable Expressions and Arrays
Some expressions in models can get very complex Break down complex expressions into pieces For example If(var1+var2 < var3+var4; if(var1+2>0; var1+2; 0); var3+var4) Isolate specific portions of complex formula with additional variables sum_1_2 = var1+var2 sum_3_4 = var3+var4 adj_1_2 = if(sum_1_2 >0; sum_1_2; 0) If(sum_1_2 < sum_3_4; adj_1_2; sum_3_4) TreeAge Pro Healthcare Training – Module 4 – Variable Expressions and Arrays

117 Variable Expressions and Arrays
Use “switch variable” to quickly change a bunch of values in model back and forth For PSA vs. base case… PSA_sw = 1 for PSA, = 0 for base case pCondition = if(PSA_sw = 1; dist_pCondition; 0.6) For different cohorts… country_US = 1 and country_UK = 2 country_sw = one of the above values for diff cohorts pCondition = Choose(country_sw; pCondition_US; pCondition_UK) TreeAge Pro Healthcare Training – Module 4 – Variable Expressions and Arrays

118 Variable Expressions and Arrays
Variable Arrays See example model “Variable Definition Array” Series of variable definitions referenced by index (1, 2, etc.) Can use for different values associated with subgroups TreeAge Pro Healthcare Training – Module 4 – Variable Expressions and Arrays

119 Variable Expressions and Arrays
Recursive Variables Can redefine a variable to add to its value at different points in model See example model “Recursive Variables” costX cannot be calculated costX = =20 costX = 5+5=10 TreeAge Pro Healthcare Training – Module 4 – Variable Expressions and Arrays


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