LAM strip test is always shown while only significant co-variates in each multivariate regression are shown *n = number included in each of the multivariate models depending on outcome and sub-group considered bLAM strip results are considered as a binary variable (positive/negative) using the grade 2 cut-point.
Sample size required for a descriptive multivariate regression model depends on the probabilities of Type I and II errors, the effect size and its variation, the number of independent variables and correlation coefficients [ 46] and other factors.
The predictive performance of a multivariate model largely depends on the number of independent informative genes included in the model, the magnitude of differential expression of the informative genes and the complexity of the background.
Under the time-series design with four pollutants, the multivariate vector X t (t=1,…,400) corresponding to daily exposure measurements on a period of 400 days was generated by an autoregressive model depending on previous 10 days.
Performance of multivariate models will depend on the genetic architecture of traits and this must be considered [ 28].
It is important to highlight that, in this multivariate model, each gene may depend not only on its own past values, but, also, on the past values of the other genes.
It is known that one or another variable miss the significance of the prediction, depending on the respectively applied multivariate model.
A multivariate model (Additional file 1: Table S4) demonstrates how survival centiles differ depending on covariate patterns.> -wrap-foot> acuteacute kidney Injury; MRR, mortality rate ratio.
The variables found to be significant in case-control study depend on what is included in a multivariate model.
To identify a final multivariate model for all of the PK parameters simultaneously, we used selection methods that depend on changes in the objective function value.
