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The proportionality of hazards was tested in the regression model, both for individual variables and in the final models.
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With models for both years, Wald test statistics for individual variables within these groups were then examined, as were possible interactions between them.
In the first phase, univariate sensitivity analyses were conducted for individual variables, representing both costs and effects, over a plausible range of values.
Statistical models were produced by binary logistic regression for individual variables in both datasets against ICU mortality, with model selection based on analysis of variance (ANOVA) and Akaike information criterion (AIC) values.
The associations revealed by both multi variate PCA and expression/concentration profiles for individual variables are noteworthy relative to what is known about gonadotropins and their receptors in fish.
Analysis of these variables pooled both EOT and FUP outcomes given the paucity of homogenous data for individual variables.
Descriptive statistics were computed for individual variables, subgroups defined by BMI classifications, and both sexes.
Methods like Adam and RMSProp tune learning rates for individual variables and make life easier.
Finally, it generates populations of virtual users rather than summary statistics for individual variables.
We performed one-way sensitivity analyses for individual variables over their plausible ranges and propagated uncertainty in our model inputs using probabilistic sensitivity analysis with 10,000 Monte Carlo simulations.
In this paper two classification algorithms for determining local and global observability and redundancy for individual variables and measurements are presented.
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