Multivariate zero-inflated generalised poisson data generation methods for simulating counts of adverse events

Counts of maximum-grade adverse events collected in clinical trials are important measures of treatment toxicity and tolerability. Analyzing the frequencies and correlations of adverse event counts by type, treatment cycle, and grade provides deeper insight into toxicity profiles. A prerequisite for developing such inferential methods is the ability to generate multivariate count data with specified event rates and correlation structures. In this article, we present three methods for simulating multivariate count data with zero-inflated generalised Poisson (ZIGP) distributions. The methods accommodate arbitrarily specified pairwise correlations within the feasible range for the target distribution. We develop them under the Normal-to-Anything (NORTA) and Sample-Iterate (SI) simulation frameworks. Simulation studies show strong performance in reproducing desired rates, scales, zero-inflation, and correlation matrices. We apply the methods to simulate AE counts based on the NCCTG N9741 multicenter randomised phase III colorectal cancer trial. We also illustrate broader applicability by simulating hospital visit counts using data from the National Medical Expenditure Survey.