Sentence examples for mixed regression modeling from inspiring English sources

We used linear mixed regression modeling to account for the repeated measures of the LDH assays on successive days for each tissue sample.

Auxological and treatment data from 47,227 patients aged younger than 21 years with CFRD or T1DM in the German/Austrian Diabetes Prospective Documentation Initiative registry were analyzed by multivariable mixed regression modeling.

Due to the heterogeneity of the data, meta-analyses and mixed regression modeling were not conducted.

Linear and logistic mixed regression modeling were used to determine the association between exposure to the pollutants of interest, O3 and PM2.5, and an array of pregnancy outcomes.

In more detailed mixed regression modeling, the half-life in this population was significantly influenced by variables reflecting time (age at subsequent sampling or initial age combined with latency at subsequent sampling) and TCDD body burden (initial concentration, subsequent concentration, and mass in the body); however, age-related parameters explained most of the variance.

Data were analyzed using a multivariable linear mixed regression model.

For instance, a mixed regression model that incorporates random effects may be appropriate.

Some mathematical models of response surface were created by the mixed regression model and response surface method.

To account for possible sources of variation among subjects (e.g., age, gender, ethnicity. etc)., we used the linear mixed regression model.

Five imputed datasets were created and the command mim was used to run mixed logistic and mixed regression models with complete datasets similar to the models described above.

In the years following this paper, alternative statistical approaches for genomic selection have been developed, derived from Bayesian models and some other parametric methods including GBLUP and mixed regression models [11] [14].