Regarding regression models, we used OLS, HLM, and Gologit models to perform regression, under different working assumptions.
Regarding regression estimates, results are also provided for the ratio of samples that rejected the false null hypothesis of zero association between the two study variables, Follow-up outcome and Baseline predictor[ 21, 22].
Regarding our study population, the logistic regression analysis shows that NIMV failure is an independent predictor of death, with an OR >5.
Regarding potential regression to the mean effect, baseline telomere length was not relevant to the inclusion of subjects in the study.
Regarding logistic regression analyses, migraine and parental headache proved to be stable predictors across sexes.
Regarding study design, as one cannot randomize patients to BMI categories, only observational studies of BMI and mortality are possible, so we have to adjust for confounding through other means, such as regression.
Failure to show significance, for example, regarding the regression model involving aches and pains may be due to the study size.
We performed meta-regression for all studies and stratified by main exposure regarding study size and publication year on risk of reinfection.
We identified few concerns regarding inconsistency in study results, since in general large amounts of unexplained inconsistency did not remain following planned investigations of potential effect modifiers using meta-regression analyses.
Geographically weighted regression (GWR) models have been employed in previous studies regarding vehicular travel demands, but few studies have locally modeled walking travel demands at intersections to address the issue of spatially varying relationships.
Assumptions regarding the linear regression model were assessed by means of regression diagnostics.
