Simulating dependent binary variables through multinomial sampling

A new method for simulating dependent binary data is presented. This methodology does not require specification of a parametric joint distribution, and instead uses desired dependence levels and marginal probabilities to construct a cumulative distribution function for the multinomial distribution of all possible combinations of binary outcomes. This method is simple to use and compute, and through simulation studies is found to be comparable with the gold-standard approach by Emrich and Piedmonte [A method for generating high-dimensional multivariate binary variates. Amer Statist. 1991;45(4):302–304].