Allows one to classify patients into low, intermediate, and high risk groups for disease progression based on a continuous marker that is associated with progression-free survival. It uses a latent class model to link the marker and survival outcome and produces two cutoffs for the marker to divide patients into three groups. See the References section for more details.
To fit the grey-zone model, one would need to call the functions in the order of
The package also provides a function
bestcut2 to fit a 2-group model, that is, it will find an optimal cutoff of the marker to divide patients into high and low 2 risk groups. Plus there is a function
genSurvData to generate survival data with a fixed censoring rate.
Pingping Qu and John Crowley
Maintainer: Pingping Qu <[email protected]>
Pingping Qu, Bart Barlogie and John Crowley (2015) "Using a Latent Class Model to Refine Risk Stratification in Multiple Myeloma" (under review)
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