Using the Hoyle and Henley’s Excel template to generate the survival probabilities, which are then used in an R script to generate the lambda and gamma parameters, provides a powerful tool to integrate Weibull parameters into a Markov model. Using the Kaplan-Meier curves from published sources can help you to generate your own time-varying survival curves for use in a Markov model. Using existing studies as a reference will allow you to make adjustments to your survival curves that will give them credibility and validation to your cost-effectiveness analysis. Moreover, if you want to compare your simulated cohort’s survival performance to a reference specific to your chronic disease cohort, you can search the literature for previously published registry data or epidemiology studies. National Center for Health Statistics has life tables that you can use to estimate the life expectancy of the general population, which you can compare to your simulated cohort. 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.
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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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