gems: An R Package for Simulating from Disease Progression Models.
Nello Blaser, Luisa Salazar Vizcaya, Janne Estill, Cindy Zahnd, Bindu Kalesan, Matthias Egger, Thomas Gsponer, Olivia Keiser
Author Information
Nello Blaser: Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
Luisa Salazar Vizcaya: Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
Janne Estill: Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
Cindy Zahnd: Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
Bindu Kalesan: Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
Matthias Egger: Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland ; Centre for Infectious Disease Epidemiology and Research, University of Cape Town, Cape Town, South Africa.
Thomas Gsponer: Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
Olivia Keiser: Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
Mathematical models of disease progression predict disease outcomes and are useful epidemiological tools for planners and evaluators of health interventions. The π± package is a tool that simulates disease progression in patients and predicts the effect of different interventions on patient outcome. Disease progression is represented by a series of events (e.g., diagnosis, treatment and death), displayed in a directed acyclic graph. The vertices correspond to disease states and the directed edges represent events. The package allows simulations based on a generalized multistate model that can be described by a directed acyclic graph with continuous transition-specific hazard functions. The user can specify an arbitrary hazard function and its parameters. The model includes parameter uncertainty, does not need to be a Markov model, and may take the history of previous events into account. Applications are not limited to the medical field and extend to other areas where multistate simulation is of interest. We provide a technical explanation of the multistate models used by , explain the functions of and their arguments, and show a sample application.
