These datasets simulate specific characteristics of gene expression data, such as heavy tail properties, using three settings: multiplicative models and additive models under signals according to N ±2,0.5) and N ±4,0.5) distributions [ 64].
Parameters (Hb0, PLT0, koutPLT, koutHb, IC50Peg-IFN, IC50RBV) were estimated using longitudinal data analyzed by nonlinear mixed-effect models with the Stochastic Approximation Expectation Minimization (SAEM) algorithm in MONOLIX v. 4.2 (http://www.lixoft.eu), assuming exponential random effects models and additive error models.
BDNF rs6265 (G/A) polymorphism and rs2049046 (A/T) polymorphism were evaluated separately in the allele model, dominant model, recessive model and additive model.
For the main effect of single SNP analysis, we tested three different genetic models: dominant model, recessive model, and additive model.
According to the GRADE approach, the quality of the evidence of CX3CR1 T280M polymorphism was very low in dominant model and allelic model, moderate in recessive model and additive model, and low in codominant model (Table S1).
For the CX3CR1 V249I polymorphism, GRADE suggested that the quality of the evidence was moderate in recessive model, codominant model, and additive model, low in dominant model, and very low in allelic model (Table S1).
Significant heterogeneities were observed in the overall analysis of the association between the rs872071 polymorphism and haematological malignancies risk in four genetic models (homozygote comparison, heterozygote comparison, dominant genetic model and additive model).
Similarly, decreased cancer risk was also found after stratification of the SNP data by cancer type, ethnicity and source of controls in heterozygote model, dominant effects model and additive effects model.
This article shows how to apply generalized additive models and generalized additive mixed models to single-case design data.
The SNP rs12218 was associated with carotid IMT by analyses of a dominate model (P<0.001) and additive model (P = 0.003), and the difference remained significant after multivariate adjustment (P = 0.008, P<0.001, respectively).
This adjustment was performed first on the test statistics obtained by modeling dominant and additive genetic models for each SNP.
