To identify factors affecting birth weight, we used linear mixed regression models to estimate associations between both monitor and modeled Ta during different time windows and term birth weight, and logistic mixed regression to estimate associations with preterm birth (< 37 weeks) and LBW (< 2,500 g) (Kloog et al. 2012b; Zeka et al. 2008).
As an example of future application, we applied mixed regression models with daily random slopes to correlate Landsat BT data with monitored air temperature (Tair) measurements using 14 images for 1989 2014.
In the absence of disease epidemics, increased human disturbance or predation, we applied multilevel, mixed, regression models to assess the hypothesis that the collapse from high to low breeding success was due primarily to changes in prey availability.
Proportional hazards models of time to all cause discontinuation and mixed regression models for continuous measures of symptoms, quality of life and side effects were used to test for interactions between randomly assigned drug and number of capsules prescribed per visit.
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].
Main treatment effects were analyzed using linear mixed regression models.
Mixed regression models for repeated-measures were used.
Repeated measurements were analyzed with mixed regression models.
Data were analyzed in confounder-adjusted mixed regression models.
Associations were determined in potential confounder and mediator adjusted mixed regression models.
