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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.
A modified design based estimator for the coefficient with reduced small sample bias is suggested as input for the small area model, while a hierarchical Beta mixed regression model is introduced to combine survey data and auxiliary information.
By using a mixed regression model of repeated measures (AR-1) controlling for age, gender, and HAMD-17 mean sum score at screening, significant differences were found comparing the HAMD-17 mean sum scores of "Week 0" with the corresponding scores at the "Week 3" (t = −2.05, p = 0.045) and "Week 4" visit (t = −2.77, p = 0.008).
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Due to the heterogeneity of the data, meta-analyses and mixed regression modeling were not conducted.
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.
We used linear mixed regression modeling to account for the repeated measures of the LDH assays on successive days for each tissue sample.
Different linear mixed regression models (PROC MIXED, SAS 9.4, SAS Institute Inc).
Mixed regression models for repeated-measures were used.
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