Concurrent Generation of Ordinal and Normal Data

The use of joint models that are capable of handling different data types is becoming increasingly popular in biopharmaceutical practice. Evaluation of various statistical techniques that have been developed for mixed data in simulated environments requires joint generation of multiple variables. In this article, we propose a unified framework for concurrently simulating ordinal and normal data given the marginal characteristics and correlation structure. We illustrate our technique in two simulation settings where we use artificial data as well as real depression score data from psychiatric research, demonstrating negligibly small deviations between the specified and empirically computed quantities.