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effect models
The data were analysed unmatched with logistic regression in main effect models.
      
The data were analysed unmatched with logistic regression in main effect models.
      
Examples of the cause-effect models (diagrams) of phenomena of various physical nature are presented, and the application of these models to some specific problems is shown.
      
We extend the iterative weighted least squares algorithm for hierarchical generalized linear models to shared random-effect models.
      
Quantitative effect confirmation is based on joint effect models.
      
Determination of population pharmacokinetic parameters for amikacin in neonates using mixed-effect models
      
We have evaluated both fixed and random effect models, depending on the presence of between-study heterogeneity.
      
However, there is a significant association in Asian descent under both fixed [OR=1.21, 95% CI (1.06-1.39), P=0.005] and random-effect models [OR=1.19, 95% CI (1.01-1.42), P=0.04].
      
Based on our analyses, we have identified several statistically significant models including both main effect models and two-locus, three-locus, and four-locus epistasis models that predict MS disease risk with between ~61% and 85% accuracy.
      
This paper deals with small area indirect estimators under area level random effect models when only area level data are available and the random effects are correlated.
      
The treatment effects were modeled using linear mixed effect models and compared using linear contrast F-tests, by intention-to-treat.
      
Two classes of dose-effect models for cell killing (additive damage and independent effects) are developed under alternative hypotheses about the damage that leads to cell death.
      
We found 126 articles that met our criteria and analyzed the data in linear mixed-effect models.
      
Combined estimates of relative risks (RR) were calculated using fixed and random effect models.
      
These two ingredients of the theory, the reaction potential response function and the specific charge density operator, define unequivocally different solvent effect models.
      
In this paper we briefly review the methodologies of environmental effects with emphasis on continuum solvent effect models and on interphase partitioning.
      
Using mixed effect models, the variables associated with LCI, RFM and dystocia were breed, level of exotic blood and condition score.
      
Prediction of the yield of stochastic effects of low-dose irradiation for nonlinear dose-effect models
      
Estimate of the number of observations required to check the adequacy of dose-effect models
      
Inferences are based upon the hierarchical-likelihood (h-likelihood), which provides a statistically efficient and unified framework for various mixed-effect models.
      
 

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