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effect models
Meta-analysis was done with both fixed and random effect models; results were shown using Galbraith's radial plot.
      
Meta-analysis was done with both fixed- and random-effect models; results were shown using Galbraith's radial plots.
      
For the purpose of variation root causes diagnosis, the corresponding linear contrasts are defined to represent the possible site variation patterns and the statistical nested effect models are developed accordingly.
      
The article shows that the use of a full factor decomposition model can expedite the determination of the number of nested effect models and the model structure.
      
It is argued that fixed-effect models are suitable specifications for this evaluation study, handling selection bias and the influence of unobservable explanatory variables in a consistent manner.
      
Two particular capture effect models are investigated.
      
Random effect models were used to examine dietary patterns, and compare change by body size and class attendance.
      
Results also suggest that in a data-rich situation nonparametric nonlinear mixed-effect models should be preferred.
      
Non-linear mixed effect models can provide both population and individual estimates of AUC based on sparse sampling protocols; however, appropriate structural models for the description of the pharmacokinetics are required.
      
The method is rapid and efficient for sample size computation in population PK/PD study using nonlinear mixed effect models.
      
The Effect of Collinearity on Parameter Estimates in Nonlinear Mixed Effect Models
      
To evaluate the influence of omission and replacement approaches for data below the limit of quantification (LOQ) on the estimation of pharmacokinetic parameters for two-compartment models when using nonlinear mixed-effect models.
      
Then more specific nonparametric firm effect models are presented.
      
When there are two alternative random-effect models leading to the same marginal model, inferences from one model can be used for the other model.
      
Random effect models have often been used in longitudinal data analysis since they allow for association among repeated measurements due to unobserved heterogeneity.
      
Exact tests and confidence intervals are also established for comparing the random-effects variance components and the sum of random-effects variance components in two independent general random effect models with balanced data.
      
In order to combine results, separate analyses using random effect models were conducted first for controlled and then for both controlled and open studies.
      
A series of mixed-effect models were fitted to the data as described above.
      
All results are from fixed-effect models due to the lack of statistical heterogeneity.
      
An alternative specification would be to retain the time series dimension and use fixed effect models, or use a between estimator.
      
 

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