Effect measures in non-parametric regression with interactions between continuous exposures.
In many biomedical studies, interest is often attached to calculating effect measures in the presence of interactions between two continuous exposures. Traditional approaches based on parametric regression are limited by the degree of arbitrariness involved in transforming these exposures into categorical variables or imposing a parametric form on the regression function. In this paper, we present: (a) a flexible non-parametric method for estimating effect measures through generalized additive models including interactions; and (b) bootstrap techniques for (i) testing the significance of interaction terms, and (ii) constructing confidence intervals for effect measures. The validity of our methodology is supported by simulations, and illustrated using data from a study of possible risk factors for post-operative infection. This application revealed a hitherto unreported effect: for patients with high plasma glucose levels, increased risk is associated, not only with low, but also with high percentages of lymphocytes.
Cadarso-Suárez
Carmen
C
Department of Statistics and Operations Research, University of Santiago de Compostela, Spain. eicadar@usc.es
Roca-Pardiñas
Javier
J
Figueiras
Adolfo
A
D000465:Algorithms; D001786:Blood Glucose / Q000032:analysis; D015331:Cohort Studies; D003198:Computer Simulation; D016001:Confidence Intervals; D005260:Female; D006801:Humans; D018655:Lymphocyte Count; D008297:Male; D015233:Models, Statistical; D011183:Postoperative Complications / Q000382:microbiology*; D011446:Prospective Studies; D012044:Regression Analysis; D012307:Risk Factors; D018709:Statistics, Nonparametric
D001786:Blood Glucose