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Variables with P <.1 in univariable analyses were tested by stepwise selection in multivariable models.
Interactions between variables in the multivariate analyses were tested by the addition of all possible pairwise interaction terms.
Next, the predictive values obtained by univariate analyses were tested by bootstrap resampling, in which a logistic regression model with a backward elimination procedure included 1000 repetitions.
Linearity assumptions for all Cox regression analyses were tested by log-minus-log survival plots and partial (Schönfeld) residuals versus survival time plots and found valid.
Blood gas analyses were tested by Nova Biomedical Stat Profile, and the oxygenation index (PaO2/FiO2) was calculated and abnormality was defined as values ≤300.
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What's more, the 3-factor construction obtained from the exploratory factor analyses was tested by confirmatory factor analyses and it was seen that the tested model was confirmed with high fit indices.
The model fit of the multivariable analyses was tested by visual examination of diagnostic plots [ 17].
Each of these linear regression analyses was tested by leave-one-out cross validation.
Moreover, the validity of the reference analyses was tested by carrying out repeatability, reproducibility, and linearity studies; results are shown in the section below.
The model fit of the multivariable analyses was tested by visual examination of diagnostic plots according to Dohoo et al. [ 27].
Parameters found to be statistically significant in univariate analyse, were tested by logistic regression to predict the risk of fatality (Table 3).
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