المصدر
Oman Medical Journal, v. 39, no. 1, e586 p. [1-8].
الملخص الإنجليزي
In medical research, the study’s design and statistical methods are pivotal, as
they guide interpretation and conclusion. Selecting appropriate statistical models hinges
on the distribution of the outcome measure. Count data, frequently used in medical
research, often exhibit over-dispersion or zero inflation. Occasionally, count data are
considered ordinal (with a maximum outcome value of 5), and this calls for the application
of ordinal regression models. Various models exist for analyzing over-dispersed data such
as negative binomial, generalized Poisson (GP), and ordinal regression model. This study
aims to examine whether the GP model is a superior alternative to the ordinal logistic
regression (OLR) model, specifically in the context of zero-inflated Poisson models using
both simulated and real-time data. Methods: Simulated data were generated with varied
estimates of regression coefficients, sample sizes, and various proportions of zeros. The GP
and OLR models were compared using fit statistics. Additionally, comparisons were made
using real-time datasets. Results: The simulated results consistently revealed lower bias
and mean squared error values in the GP model compared to the OLR model. The same
trend was observed in real-time datasets, with the GP model consistently demonstrating
lower standard errors. Except when the sample size was 1000 and the proportions of
zeros were 30% and 40%, the Bayesian information criterion consistently favored the
GP model over the OLR model. Conclusions: This study establishes that the proposed
GP model offers a more advantageous alternative to the OLR model. Moreover, the GP
model facilitates easier modeling and interpretation when compared to the OLR model.
