This release is highlighted by a new capability to generate cohort-level reporting on Markov Microsimulation models and trial-level tracking reports on both Markov Microsimulation and DES models.

  • Cohort-Level Transparency for Microsimulation Models
    Generate the familiar Markov Cohort Extended Report format from Markov Microsimulation models. This provides cohort-level visibility into the flow of individuals through the model and the accumulation of value at specific Markov state and transition nodes. This report provides the same transparency for Markov Microsimulation models that you already had for Markov Cohort models.
  • Patient Tracking Report for Microsimulation Models
    Generate a patient trace for any individual passing through a Markov or DES Microsimulation Model. This provides the full pathway experienced by any patient passing through the model including the accumulation of values at specific nodes.
  • Markov-to-Excel Model Conversion Enhanced
    The Markov to Excel Conversion process introduced in TreeAge Pro 2017, R2 has been improved in significant ways. We have also extended free access to this capability to January 2018. The conversion process now supports the following.

    • A table of cost-effectiveness results that identifies dominated strategies and calculates ICERs on the undominated strategies.
    • Within Cycle Correction (WCC) configuration as well as traditional models using Half-Cycle Correction.
    • Tunnel states for tracking time-in-state.
    • Option to show calculation on a strategy in a single worksheet as well as in separate worksheets by health state.
    • Logic nodes.
    • Dirichlet distributions which return multiple probability values (distribution means, not sampling).
  • More Robust and Flexible Tornado Diagrams
    New tornado diagrams provide more robust and flexible graphical output. This includes multiple graph options for cost-effectiveness models including ICER, NMB, cost-only and effectiveness-only. ICER tornados now provide visibility into whether increases in a specific parameter cause the ICER to increase or decrease.
  • Convert Time-to-Event Distributions to Transition Probabilities
    Use the DistTransProb function to generate a transition probability for each Markov cycle from a distribution representing the time to a specific event. For example, if you have a Weibull distribution that represents time to event, you can use this function to generate transition probabilities for every Markov cycle based on the cycle start time and cycle length.