However, it is always good practice to calibrate your survival curves with the most recent data on the population of interest. This method is very useful when simulating chronic diseases.
#ENGAUGE DIGITIZER FOR SPEAKERS HOW TO#
We will use a three-state Markov model to illustrate how to incorporate the Weibull parameters and generate a survival curve ( Figure 1).Īfter extrapolating the survival curve beyond the reference Kaplan-Meier curve limit of 40 months, you can estimate the lifetime horizon for a cohort of patients using a Markov model. Link to the Markov model used in this tutorial can be found here. Finally, we’ll show how to extrapolate the survival curve to go beyond the time frame of the Kaplan-Meier curve so that you can perform cost-effectiveness analysis across a lifetime horizon.ĭescribe how to incorporate the Weibull parameters into a Markov modelĬompare the survival probability of the Markov model to the reference Kaplan-Meier curve to validate the method and catch any errorsĮxtrapolate the survival curve across a lifetime horizon
In the second part of this tutorial, we will take you through the process of incorporating these Weibull parameters to simulate survival using a simple three-state Markov model. The Weibull parameters will allow you to generate survival curves for cost-effectiveness analysis. In a previous blog, we provided instructions on how to generate the Weibull curve parameters (λ and γ) from an existing Kaplan-Meier curve.
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