Partitioned survival model

A partitioned survival model is a type of economic model used to follow a theoretical cohort through time as they move between a set of exhaustive and mutually exclusive health states. Unlike a Markov model, the number of people in any state at successive points in time is not dictated by transition probabilities. Instead, the model estimates the proportion of a cohort in each state based upon parametric survival equations. These types of model are frequently used to model advanced cancer treatments, with separate survival equations for overall survival and progression-free survival. Common functions used to describe survival are exponential, Weibull or Gompertz (amongst others). Sensitivity analysis can be undertaken by varying the parameters defining the survival equations; however, if the survival equations are independent, care needs to be taken that logical fallacies are not made (e.g. overall survival exceeding progression-free survival).