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 heath 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 cancer treatments, with separate survival equations for overall survival and progression-free survival. Common functions used to describe survival are exponential, Weibull or Gaussian (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).


How to cite: Partitioned Survival Model [online]. (2016). York; York Health Economics Consortium; 2016. https://yhec.co.uk/glossary/partitioned-survival-model/


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