The phrase "a multilevel logistic regression model" is correct and usable in written English.
You can use it in contexts related to statistics, data analysis, or research methodologies, particularly when discussing models that analyze relationships between variables at multiple levels.
Example: "In our study, we employed a multilevel logistic regression model to assess the impact of socioeconomic factors on health outcomes."
Alternatives: "a hierarchical logistic regression model" or "a nested logistic regression model."
Non-response is a longitudinal binary outcome and was modelled as such over phases 1 6 using a multilevel logistic regression model with the individual participant as the higher level under which are the phases of the study.
As in Experiment 2, we used a multilevel logistic regression model to determine the impact of the colocation of an ensemble track and an oil rig.
A multilevel logistic regression model was fit to the data using the lme4 package in R and maximum likelihood Laplace approximation estimation procedures (Bates, Maechler, Bolker, & Walker, 2015).
Using a multilevel logistic regression model, 50% probability of significant toxicity for a 70kg person is estimated at 1300 mg carbamazepine and 2600mg oxcarbazepine.
A multilevel logistic regression model was estimated to investigate the relation between life satisfaction (dependent variable) and the frequency of meeting friends and the satisfaction of friendship relationships (explanatory variables), controlling for several covariates.
Data were analyzed using a multilevel logistic regression model.
A sample multilevel logistic regression model used to estimate the association between slum residency and infant mortality is presented as follows: (1) log (y i j k ) = β 0 j k + β 1 SlumNeighborhood j k + β 2 SlumHousehold i j k + β 3 GD P k + ⋯ + v 0 k + u 0 j k.
We also analysed all time points in one model rather than individual analyses at each time point using a repeated measures multilevel logistic regression model.
A three-level multilevel logistic regression model was applied in order to account for the hierarchical structure of the DHS data [ 28].
We used a random-effect multilevel logistic regression model with patient factors as independent variables, the recruitment status as the dependent variable, and treating site as a level variable.
Using publicly available community-level AEDI data, 62 63 we ran a two-level multilevel logistic regression model for one aggregate developmental outcome measure (ie, risk of developmental vulnerability; figure 3A) and an example simulation (figure 3B) using a total sample of 181 500, with the proportion of Aboriginal children in each LGA derived from ABS estimates.
