Qmodel: Oncology


Quantify’s free, generic, cost-effectiveness model is adaptable to various indications and interventions. It is fit for purpose in providing early cost-effectiveness analysis of oncology products and for validation of existing assessments.

Quantify has developed a simple, flexible, and transparent cost-effectiveness model for oncology to be used in early-stage

commercialization planning across a variety of indications, with simple data input requirements. Validation with P&R authority and pharmaceutical industry representatives is ongoing. A base version of the model is available to interested parties at no cost.

Interested? Email us at info@quantifyresearch.com


  • Partitioned-survival cost-effectiveness model with the health states ‘progression-free survival’, ‘progressed disease’ and ‘death’
  • Software: Microsoft Excel®
  • Cycle length: one month
  • Time horizon: up to 20 years
  • Model outcomes include life-years gained, quality-adjusted life-years gained and total costs. Outcomes are presented stratified by health state.
  • Inputs include structural settings (currency, time horizon, discount rate), costs (drug acquisition, administration, health care resource use, adverse events) and utility (health states, adverse events).
  • There are several options for including efficacy data in the model:
    • Enter Kaplan-Meier data for progression-free and overall survival for both intervention and comparator, automatically fitted with exponential and Weibull parametric curves to extrapolate survival beyond trial duration.
    • Use a hazard ratio to model differences relative to the intervention or one comparator.
    • Enter fitted parametric curves.

Model states
PFS: Progression-free survival
PD: Progressed disease

We can adapt the model to your needs

Examples of additional features and adjustments:

  • Probabilistic sensitivity analysis (PSA)
  • Cost-effectiveness acceptability curve (CEAC)
  • Deterministic or one-way sensitivity analysis including tornado diagram
  • Value of information
  • Advanced modelling methods, such as mixture cure modelling