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
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
Cost-effectiveness analysis compares the strategies based on a CE frontier.
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
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.
TreeAge Pro allows you to study how uncertainty in a model’s inputs affect the conclusions we can draw via its outputs.
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.
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.
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.