This manuscript is concerned with establishing a unified framework for concurrently generating data
sets that include three major kinds of variables (i.e., binary, ordinal, and count) when the marginal distributions and
a feasible association structure are specified for simulation purposes. The simulation paradigm has been commonly
utilized in pharmaceutical practice. A central aspect of every simulation study is the quantification of the model
components and parameters that jointly define a scientific process. When this quantification goes beyond the
deterministic tools, researchers often resort to random number generation (RNG) in finding simulation-based
solutions to address the stochastic nature of the problem. Although many RNG algorithms have appeared in the
literature, a major limitation is that most of them were not devised to simultaneously accommodate all variable types
mentioned above. Thus, these algorithms provide only an incomplete solution, as real data sets include variables of
different kinds. This work represents an important augmentation of the existing methods as it is a systematic attempt
and comprehensive investigation for mixed data generation. We provide an algorithm that is designed for generating
data of mixed marginals; illustrate its operational, logistical, and computational details; and present ideas on how it
can be extended to span more sophisticated distributional settings in terms of a broader range of marginal features
and associational quantities.
Biserial correlation phi coefficient simulation tetrachoric correlation random number generation mixed data
Primary Language | English |
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Journal Section | Research Articles |
Authors | |
Publication Date | December 31, 2022 |
Published in Issue | Year 2022 Volume: 1 Issue: 1 |
This work is licensed by CC by under the Creative Commons Attribution 4.0 International License.