1. Introduction to TreeAge June 1, 2005 Mendel E. Singer, Ph.D. Case School of Medicine mendel@case.edu

2. Assumptions (always dangerous) You’re already familiar with the essential components of a decision analysis You’ve been introduced to the basic idea of cost-effectiveness analysis You may not be comfortable with a decision tree, but you’ve seen a few You haven’t programmed any decision trees, or did once upon a time, but……

3. What’s TreeAge? TreeAge (pronounced “triage”) is software specifically for doing decision analyses, and cost-effectiveness analyses that are based on the decision analytic model. Until recently it was called DATA, but the company’s catchy name, TreeAge, is what stuck, and everyone called it that anyway. Originally developed for business applications, they have been developing for health care applications for close to 10 years now.

4. Why Use TreeAge? Some people use TreeAge, others use alternative decision analysis software like DecisionMaker, some use Excel, and some use computer simulation software. TreeAge is a mature software product that is well supported. It is regularly updated and improved, doing a good job of keeping up with advancements in the methods. It is very intuitive to use because it is based around the decision tree graphic, and not programming code.

5. What Can TreeAge do? Decision Analysis Cost-Effectiveness Analysis Baseline and Sensitivity Analyses Markov Models Monte Carlo Simulation Influence Diagrams Note: It’s OK not to fully appreciate what all of the above are! You can still follow this lecture!

6. Getting the Software Purchase it! Perhaps your group already has a site license Download a trial copy from: www.treeage.com www.treeage.com –Limited in size –Works for 21 days from first time it is opened

7. WARNING!! You won’t learn to program by watching a lecture. You have to get hands on! It can help to do some small programs for practice. I will see if HERC can put this on the web site along with this presentation.

8. Abdominal Aortic Aneurysm (AAA) We need a simple, and therefore simplified problem to use as an example. AAAs: 5% - 7% of people over age 60 Most commonly affect Men, Age 40-70. Usually asymptomatic, often until rupture Options are surgery and watchful waiting, with some decision rule as to when to operate based on the size of the AAA. Debate as to how large the aneurysm should be to recommend surgery. Our example will be based largely on the work of Katz and Cronenwett (1994).

9. Problem Definition Reference Case –60-year old male –4 cm abdominal aortic aneurysm –Otherwise, patient is in good health Surgery vs Watchful Waiting Time horizon: 1 year Outcome Measure: Survival –Alive = 1 –Dead = 0

10. More Details of the Example Simplifications and assumptions: –If expansion occurs, it reaches 5.5 cm. –Either the AAA ruptures, or surgery is performed. –If the AAA ruptures, the patient may die before emergency surgery can be performed. –No death from other causes during the 1 year time horizon used for the analysis

11. Types of Nodes Start with a choice node, with each possible strategy coming out of this node. Three kinds of basic node types: –Choice, or Decision Node –Chance Node –Terminal Node Choice or Decision Chance Terminal

18. 1.Always start with a choice, or decision node 2.Names of nodes go above the branch 3.Note in the properties box that the name is cut off. We can select the vertical bar to the right of the properties box and drag it, to make the box wider.

19. To add branches there are several techniques. 1.Double-click on the node 2.Right-click on the node and select the choice to add branches. 3.Use the menus to select “Options / Add Branches” 4.Enter Control-A. By double-clicking on the decision node you get…..

40. Parameter Values NameValueLowHigh pDieBeforeSurgery0.550.270.82 pDieDuringSurgeryAfterRupture0.540.270.81 pDieElectiveSurgery0.0460.0230.069 pDieSurgeryNoRupture0.230.110.34 pExpansion0.0330.0160.049 pRupture0.920.880.96

41. 1.Probabilities go below the branch 2.Don’t put in the actual numbers for the probabilities. 3.Instead, use variables. This makes it easy to change its value, even if it appears in many places in the tree. More importantly, it allows us to do sensitivity analysis on that parameter.

42. Naming Variables Use descriptive names. Better to write out long variables, than to use abbreviations you may not be sure of when you open the model many months later to revise it. Use a consistent naming convention. Use a consistent naming convention. Many people begin the name of probability variables a “p”, utilities with a “u”, costs with a “c”, etc…. – –e.g. mortality rate = pDie – –E.g. utility of diabetes = uDiabetes Make the names easy to read. When using multiple words in a variable name, capitalize the first letter of each new word. – –e.g. pDieBeforeSurgery Some use the underscore character between words. – –e.g. pDie_before_surgery

57. The # Sign In TreeAge, the # sign can be used for a probability. It indicates the leftover probability, after accounting for the other branches emanating from the same node. You should use this whenever possible. Aids sensitivity analysis. When a probability changes value, the sum of the probabilities for the nodes leaving that chance node will no longer be 1. Try to never use more than 2 branches out of 1 chance node. This way, whenever one probability changes, we know the other possibility must change by the same amount in the opposite direction. E.g. if the baseline estimate of operative mortality is 0.05, then if that value were to increase to 0.10, then the probability of surviving the surgery must decrease by the same 0.05 (0.10-0.05).

71. 1.At each node there is a number in a box indicating the expected value of the outcome from that point forward 2.At the choice node, it also indicates which strategy is optimal. The nodes in the optimal path are all highlighted. 3.At all terminal nodes, it first shows the outcome score associated with that result, and then shows the probability of the path ending in that terminal node. 4.All of the variable names are temporarily replaced by their values.

77. 1.Graph shows how the expected value (mean) changes based on the mortality rate from elective surgery over the range of values specified. 2.Watchful waiting is unaffected because elective surgery isn’t in its path. 3.The point of intersection, known as the threshold value, is shown on the graph and the point and its associated mean is shown in the right margin.