5/2/2023 0 Comments Engauge digitizer excel![]() choose one of these function forms (WEIBULL was chosen for the example) #code does not apply because 0 at risk at last time point # adding times for patients at risk at last time point Times_end <-c( rep(end_time_censor, n_censors), rep(end_time_event, n_events) ) Times_start <-c( rep(start_time_censor, n_censors), rep(start_time_event, n_events) ) Setwd("insert the folder path where the data is stored") Update directory name and text file name in line below I use Stata's data library for convenience.) Open Stata and enter the following in the command line: (You can use any published Kaplan-Meier curve. We will use an example dataset from Stata’s data library. Generate a Weibull curve that closely resembles the survival function and whose parameters can be easily incorporated into a simple three-state Markov model Specifically, I will describe how to:Ĭapture the coordinates of a published Kaplan-Meier curve and export the results into a *.CSV fileĮstimate the survival function based on the coordinates from the previous step using a pre-built template This blog provides a practical, step-by-step tutorial to estimate a parameter method (Weibull) from a survival function for use in CEA models. ![]() Extrapolation to a lifetime horizon is possible using a series of methods based on parametric survival models (e.g., Weibull, exponential) but performing these projections can be challenging without the appropriate data and software. However, these Kaplan-Meier curves may only provide survival data up to a few months to a few years. Data for mortality are normally derived from survival curves or Kaplan-Meier curves published in clinical trials. In cost-effectiveness analysis (CEA), a life-time horizon is commonly used to simulate a chronic disease.
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