Sentence examples for multilevel mixed modelling from inspiring English sources

Multilevel mixed modelling will be used to examine changes in the primary outcome measures: gambling symptom severity, using the Gambling Symptom Assessment Scale, and gambling behaviours (frequency, time and expenditure).

The analysis of longitudinal differences in outcome will use two complementary statistical approaches: multilevel (mixed) modelling (MLM) and growth mixture modelling (GMM) as applied to randomised preventative interventions by Muthén [ 47].

A sample of repeated measurements on the same group of participants was collected (baseline, immediately post-treatment, 6 and 12 months post-treatment), so the main analysis will be multilevel mixed modelling to examine the primary and secondary outcome measures.

The analysis of longitudinal differences in outcome will be by multilevel (mixed) modelling (MLM).

Multilevel mixed models revealed only weak variation at the cohort-level in either response variable; hatching success and fledging success accounted for none or less than 2% of the total variation in trip-durations made during the incubation and early chick stages, respectively.

According to multilevel mixed models, the decrease by month in the number of nighttime (P for interaction group X month = 0.012) but not day-time hot flushes (P for interaction group X month = 0.61) was significantly larger in the intervention group than in the control group (Table III; Figure 2).

To access longitudinal changes of FEV1 (often called FEV1 "slope") between the intervention and control community, we applied SAS's Proc Mixed procedure, which admits missed values, to build multilevel mixed models by adjusting for confounding and clustering effects.

Comparisons for the primary and secondary variables were carried out by multilevel mixed modeling with the SAS procedure GLIMMIX for categorical variables and the SAS procedure MIXED for quantitative variables (SAS Institute Inc., Cary, NC).

Continuous secondary outcome parameters are analyzed with multilevel mixed models.

We analysed primary care electronic health records and hospital discharge data using multilevel mixed models.

It is anticipated that this will be implemented using a multilevel (mixed) model for binomial or continuous responses as appropriate.