## 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.

## Features

### 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.

### 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.

### 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.

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.

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.

## Resources

## 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.

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.

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

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).

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.