A Monte Carlo simulation study comparing linear regression, beta regression, variable-dispersion beta regression and fractional logit regression at recovering average difference measures in a two sample design

A Monte Carlo simulation study comparing linear regression, beta regression, variable-dispersion beta regression and fractional logit regression at recovering average difference measures in a two sample design

BACKGROUND In biomedical research, response variables are often encountered which have bounded support on the open unit interval--(0,1). Traditionally, researchers have attempted to estimate covariate effects on these types of response data using linear regression. Alternative modelling strategies may include: beta regression, variable-dispersion beta regression, and fractional logit regression...

Erratum to: A Monte Carlo simulation study comparing linear regression, beta regression, variable-dispersion beta regression and fractional logit regression at recovering average difference measures in a two sample design

Erratum After publication of the original article [1], the authors noticed an error in Fig. 1. The legend included in the original sub-plot of Fig. 1 was labelled “phi = 500 (p = 25, q = 475)”; however, the figure title suggested phi = 1000. An updated version of Fig. 1 is published in this erratum, where the legend has been updated to “phi = 1000 (p = 50, q = 950)” to be consistent with the fi...

Author's response to reviews Title:A Monte Carlo Simulation Study Comparing Linear Regression, Beta Regression, Variable-Dispersion Beta Regression and Fractional Logit Regression at Recovering Average Difference Measures in a Two Sample Design Authors:

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