Principles and methods of data simulation in health research (2019)
Data simulation is a procedure for the generation of random numbers using a well-defined set of statistical restrictions. The restrictions are usually in terms of probability distribution functions that are based on parameters using stochastic processes. They are mostly described in terms of statistical distributional statements, such as x follows a Poisson distribution of rate 2 or y is a binomial process generator of n=5 and p=0.2. In the absence of pertinent real-life data, which may sometime difficult to come by, data simulation enables researchers to generate numbers that could be used to monitor or assess the performance of models. This chapter is dedicated to provide practical ways in which data simulation could be carried out, including their advantages and usefulness.