TreeAge Pro Software Box

TreeAge Pro empowers even casual users to build and analyse sophisticated analytical models in an accelerated timeframe. If you are an experienced analyst, you will be comfortable using TreeAge Pro following a quick review of its software commands. If you have no experience or only limited knowledge of decision analysis principles, TreeAge Pro will speed your learning process.

Case Study

Decision Strategies, Inc., uses TreeAge Pro to help corporate clients optimize important decisions.

TreeAge Pro Core

TreeAge Pro Core is a sophisticated yet highly user-friendly software package that facilitates decision making in the face of complexity and uncertainty. TreeAge Pro Core is the base modeling product. It does not include the optional Healthcare and Excel Modules.


Model Building

TreeAge Pro employs a structured methodology that breaks the problem into its component parts, distinguishing between the available decision options and the uncertainties associated with each. Based on this analysis, a model in the form of either a decision tree or an influence diagram is graphically displayed on screen, and the user interacts with it.

Many people prefer to structure a problem as a decision tree. Others find that building an influence diagram helps them to think through the problem. With TreeAge Pro you retain the flexibility to decide whether a particular problem is better modeled as a tree or an influence diagram. If you start with an influence diagram, TreeAge Pro can automatically convert it into the equivalent, asymmetrical decision tree - and having both forms of graphical output available can facilitate the often critically important task of communicating both your view of the problem and its proposed solution.

TreeAge Pro imposes no artificial restraints on the size or content of models. Unlike other software packages, there are no built-in limitations on the number or location of variables, nodes, decision options, chance events, etc. Of course, in light of the graphics involved, working with very large models can be expected to require substantial resources, both RAM and CPU.


TreeAge Pro calculates the expected value of each scenario, identifies the decision maker's optimal strategy, and enables testing and analysis of the recommendation. Sensitivity analysis makes it possible to identify those uncertainties which - in light of the potential range of outcomes - are not critical to the decision at hand; for each uncertainty where further analysis and expense might be warranted, TreeAge Pro's value-of-information calculations will specify the most which should be spent in an effort to reduce or eliminate the uncertainty.

Analytical features include 1, 2 and 3-way sensitivity analyses, tornado diagrams, threshold analysis, single and comparative probability distribution graphs, Bayes' revision, Monte Carlo simulation and multi-attribute analysis. Markov processes and cost-effectiveness analysis are available through the TreeAge Pro Healthcare module.

Communicating Results

In addition to being able to display models graphically as both influence diagrams and decision trees, TreeAge Pro offers a wealth of supporting text and graphical reports.

Sensitivity analysis is used to test the sensitivity of a proposed course of action to changes in the projected value of one or more variables. For each variable, a range of values is substituted for the point value used in the model. 

TreeAge Pro Healthcare

The TreeAge Pro Healthcare Module is designed to meet the special needs of health care professionals. The Healthcare module integrates seamlessly with TreeAge Pro and adds two types of functionality - Markov processes and cost-effectiveness analysis - of critical importance to anyone working on healthcare models.

With the Healthcare Module, you can create trees that are evaluated on the basis of cost-effectiveness, as well as either cost or effectiveness as a single measure.

Healthcare models usually begin with a decision node with a branch for each treatment option for a specific health condition. The subtree for each treatment option follows the condition through treatment, including any number of possible outcomes.

The model presented below includes two strategies for treating a specific tumor. Each strategy has a different likelihood of eradicating the tumor. At each terminal node, a values for cost and effectiveness associated with that outcome.

Simple Healthcare Tree

Simple Healthcare Tree

Healthcare decision trees are normally much more complicated and often include a Markov model for each treatment option. More complex healthcare trees may have many Markov models included in each strategy.

Healthcare models can also incorporate heterogeneity and event tracking.

Cost-Effectiveness Analysis

Once the model is complete, TreeAge Pro automatically generates the algorithms required to evaluate the model and choose the optimal strategy. This allows you to focus on the problem at hand and not the calculations needed to evaluate the model.

Standard algorithms give weight to each possible outcome within the strategy based on its probability. The combined weighted average generates an overall expected value for each strategy.

TreeAge Pro’s Healthcare Module allows you to compare strategies on the basis of cost-effectiveness via incremental cost-effectiveness ratios and/or net benefits. You can also compare strategies in the same model based solely on cost or comparative effectiveness (CER). You can even use non-standard measurements such as infections, deaths, etc.

The following model compares two treatments for a tumor.

Simple Healthcare Tree

Simple Healthcare Tree

Cost-effectiveness analysis compares the strategies based on a CE frontier.

Cost-Effectiveness Graph

Cost-Effectiveness Graph

If there were dominated strategies in this model, they would be presented above and to the left of the CE frontier.

The Rankings report shows the numeric calculations comparing the strategies, including the incremental cost-effectiveness ratio (ICER).

Cost-Effectiveness Rankings

Cost-Effectiveness Rankings

The ICER can then be compared to a willingness-to-pay (WTP) threshold to determine whether we can afford the more effective treatment on the basis of cost-effectiveness.

Study Uncertainty on Healthcare Models

TreeAge Pro allows you to study how uncertainty in a model’s inputs affect the conclusions we can draw via its outputs.

State Transition/Markov Models

Often, healthcare models need to follow a disease process into the future. The most common approach to this issue is to create a state transition or Markov model.

Evaluating Markov Models

When Markov models are evaluated, they eventually provide a single expected value (EV) for each active payoff (frequently cost and/or effectiveness). However, you may want to see further into the individual calculations that result in the overall EV. Markov Cohort Analysis provides this detail.

Heterogeneity and Event Tracking

TreeAge Pro allows you to extend beyond the traditional limitations of expected value and/or Markov models. By running individual “trials” through the model by random walk (microsimulation), you can introduce heterogeneity and event tracking into your model.

Discrete Event Simulation

TreeAge Pro supports a form of Discrete Event Simulation (DES) via non-standard cycle lengths. If a Markov model is analyzed via microsimulation, then time can be measured on a time-to-event basis, rather than relying on a fixed cycle length.

This technique is implemented via a regular Markov model. The main difference is that time passed is generated by distributions. Then time is stored using a tracker rather than using the cycle counter. In addition, cost and utility measures will likely be dependent on the time tracker.

TreeAge Pro Suite

TreeAge Pro Core is our base modeling product, plus the Healthcare module for healthcare modeling (Markov, cost-effectiveness, etc.) and the Excel module for transferring data and analysis output to and from Excel.

Visual Modeling Tool

With the visual Tree Diagram Editor, you can easily create model structure to represent the problem being studied. The model structure will include decision points and all events that could occur. Different node types within the structure reflect whether branches are alterative options or possible events.

Insert nodes anywhere within the model structure. Copy or move individual nodes or subtrees within the model. Clone repeating sections of the model to reduce modeling time and ensure consistency within the model.

In the model below, there is a single decision, whether to litigate or accept a settlement offer. There are also points were the outcome is unknown (win/lose case and damage amounts), represented by circles. Finally, there are terminal nodes which

reflect the overall value of each scenario, represented by triangles.

Simple Legal Tree

Simple Legal Tree

Evaluate and Compare Strategies

Once the model is complete, TreeAge Pro automatically generates the algorithms required to evaluate the model and choose the optimal strategy. This allows you to focus on the problem at hand and not the calculations needed to evaluate the model.

Standard algorithms give weight to each possible outcome within the strategy based on its probability. The combined weighted average generates an overall expected value for each strategy.

Analyses can choose the optimal strategy based on a comparison of all options. Tree preferences control the method of comparison from among these choices – maximum, minimum, cost-benefit, multi-attribute or cost-effectiveness (requires Healthcare Module). Models can also store multiple metrics for value. Then, simply change the active metric and reanalyze the same model.

When the model from above is analyzed using Roll Back, expected values (EVs) are calculated at all nodes. At the decision node, the maximum value is selected as the optimal strategy.

Legal Tree Rolled Back

Legal Tree Rolled Back

Study Uncertainty

TreeAge Pro allows you to study how uncertainty in a model’s inputs affect the conclusions we can draw via its outputs. In order to study uncertainty of an individual parameter, the parameter must be represented by a variable. The variable then can be analyzed over a range of uncertainty rather than using a single point estimate.
The model below evaluates to the same values as the earlier models, but three independent parameters are defined using variables. Those variables are then referenced within the model in probability and value expressions.

Legal Tree with Variables

Legal Tree with Variables

Now the model can be evaluated using sensitivity analysis to study uncertainty. For example, the following analysis uses a range for the pWin variable from 0.5 to 0.7 with four intervals. This analysis reevaluates the model at pWin values 0.5, 0.55, 0.6, 0.65 and 0.7, then shows the EV of each strategy based on the parameter range.

Sensitivity Analysis on Legal Tree

Sensitivity Analysis on Legal Tree

The graph above identifies a threshold at the pWin value of 0.55.

  • If pWin is less than 0.55, then the Settlement Offer EV is higher.

  • If pWin is greater than 0.55, then the Litigate EV is higher.

Back to Top