Classical and Bayesian estimation in the logistic regression model applied to diagnosis of child attention deficit hyperactivity disorder.
The limitations inherent to classical estimation of the logistic regression models are known. The Bayesian approach in statistical analysis is an alternative to be considered, given that it makes it possible to introduce prior information about the phenomenon under study. The aim of the present work is to analyze binary and multinomial logistic regression simple models estimated by means of a Bayesian approach in comparison to classical estimation. To that effect, Child Attention Deficit Hyperactivity Disorder (ADHD) clinical data were analyzed. The sample included 286 participants of 6-12 years (78% boys, 22% girls) with ADHD positive diagnosis in 86.7% of the cases. The results show a reduction of standard errors associated to the coefficients obtained from the Bayesian analysis, thus bringing a greater stability to the coefficients. Complex models where parameter estimation may be easily compromised could benefit from this advantage.
Departamento de Metodología de las Ciencias del Comportamiento, Facultad de Psicología, Universidad de Barcelona, Passeig de la Vail d'Hebron 171, 08035 Barcelona, Spain. amalia.gordovil@ub.edu
Amalia
A
Gordóvil-Merino
Joan
J
Guàrdia-Olmos
Maribel
M
Peró-Cebollero
Emilia I
EI
de la Fuente-Solanas
D001289:Attention Deficit Disorder with Hyperactivity / Q000175:diagnosis*; D001499:Bayes Theorem; D002648:Child; D005260:Female; D006801:Humans; D008297:Male; D008800:Mexico; D012044:Regression Analysis; D015203:Reproducibility of Results