The phrase "a mixed logistic regression model" is correct and usable in written English.
It can be used in statistical or research contexts when discussing a specific type of statistical model that incorporates both fixed and random effects in logistic regression analysis.
Example: "In our study, we employed a mixed logistic regression model to account for the variability among different subjects."
Alternatives: "a hierarchical logistic regression model" or "a multilevel logistic regression model".
The data were analysed using a mixed logistic regression model.
A mixed logistic regression model with diabetes clinics fitted as random effect was used for each renal outcome to estimate odds ratios (ORs) with their 95% confidence interval (95% CI).
A mixed logistic regression model was used to quantify the effect of individual and household factors on the probability of seropositivity.
A mixed logistic regression model with two levels (individual and household) was used to quantify the effects of individual and household factors on the probability of seropositivity.
15 16 A mixed logistic regression model was constructed, taking into account the correlation between the two eye measurements by introducing a random effect and assuming common PPV and NPV (as well as sensitivity and specificity) regardless of eye.
These comparisons remained significant even after controlling for inter-center variability using a mixed logistic regression model for each outcome (liver fibrosis, harmful alcohol consumption, employment, severe depressive symptoms).
60 The percentage of patients in whom a change (initiation/switch/intensification) in any DMARD (conventional or biological) was made during the 6-month study was analysed using a mixed logistic-regression model with a random effect for the centre before and after adjusting for corticosteroid intake and biological therapy.
Variables associated with cardiac index with a p value below 0.1 in univariate analysis were selected for inclusion in a multivariable mixed logistic regression model, using backward stepwise descending selection.
We evaluated the influence of hospital characteristics on high utilization of minimally invasive surgery using a generalized linear mixed logistic regression model with a random intercept for hospital to account for the correlation of procedures within hospitals.
Association between a parameter of grow-out lighting and an outcome was tested in a multi-level mixed logistic regression model that accounted for variation in the Salmonella burden among grow-out farms within a broiler complex, complexes within a company, and between companies.
We fitted a two-level mixed logistic regression model conditional on the sensitivity and the specificity of each study and a bivariate normal model for the sensitivity and specificity between studies [ 17].
