gems: An R Package for Simulating from Disease Progression Models

Blaser, Nello; Vizcaya Salazar, Luisa; Estill, Janne; Zahnd, Cindy; Kalesan, Bindu; Egger, Matthias; Gsponer, Thomas; Keiser, Olivia (2015). gems: An R Package for Simulating from Disease Progression Models. Journal of statistical software, 64(10), pp. 1-22. UCLA Statistics

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Mathematical models of disease progression predict disease outcomes and are useful epidemiological tools for planners and evaluators of health interventions. The R package gems 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 gems 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 gems, explain the functions of gems and their arguments, and show a sample application.

Item Type:

Journal Article (Original Article)


04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Social and Preventive Medicine

UniBE Contributor:

Blaser, Nello; Salazar, Luisa Paola; Estill, Janne Anton Markus; Zahnd, Cindy; Kalesan, Bindu; Egger, Matthias; Gsponer, Thomas and Keiser, Olivia


600 Technology > 610 Medicine & health
300 Social sciences, sociology & anthropology > 360 Social problems & social services




UCLA Statistics




Doris Kopp Heim

Date Deposited:

17 Apr 2015 11:02

Last Modified:

11 Sep 2017 19:09

Additional Information:

The first authors Blaser and Salazar contributed equally to this work. The last authors Gsponer and Keiser contributed equally to this work.

Uncontrolled Keywords:

Monte Carlo simulation; multistate model; R; survival analysis; prediction; compartmental model




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