The aim of this work is to introduce the simple-to-use R package modgo which may be used for simulating data from existing study data for continuous, ordinal categorical, and dichotomous variables.


 Unique features of modgo are that it allows to change the correlation between variables, to perform perturbation analysis, to handle multicenter data, and to change inclusion/exclusion criteria by selecting specific values of one or a set of variables.  Simulation studies on real data demonstrate the validity and flexibility of modgo.


 modgo mimicked the structure of the original study data.  Results of modgo were similar with those from two other existing packages in standard simulation scenarios.  modgo's flexibility was demonstrated on several expansions.


 The R package modgo is useful when existing study data may not be shared. 
modgo
The aim of this work is to introduce the simple-to-use R package modgo which may be used for simulating data from existing study data for continuous, ordinal categorical, and dichotomous variables.


 The R package modgo is useful when existing study data may not be shared. 
R package modgo