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The size, and cumulative follow-up available for our cohort allowed us to analyze the independent effect of significant variables in our multivariable models.
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The effects of significant variables were further evaluated by a central composite design under response surface methodology.
The interactive and combined effects of significant variables were evaluated from central composite design and subsequent analysis of variance (ANOVA).
The independent effects of significant variables (P < 0.05) on advanced inflammation, NASH and steatosis were assessed using multiple stepwise regression analysis, with both the backward and forward stepwise selection procedures.
The central composite design (CCD) has also been employed for statistical modeling and analysis of the effects and interactions of significant variables dealing with the Cr VI) uptake process by the developed sorbent.
Variables identified as confounders in the subset analyses were monitored for their effects on the coefficients of significant variables remaining in the comprehensive model.
Given the small number of cases, a bootstrap with 200 replications was run to assess the reliability of the effect of the significant variables.
Plackett Burman and response surface methodology (RS M based on Box Behnken experimental design were applied to evaluate the interactive effect of most significant variables: pH, initial As V) ion concentration and mixing speed (rpm).
Based on the results obtained from the conventional method, a three-level-three factorial central composite design (CCD) was employed to study the effect of three significant variables on COME conversion.
This was tested using multiple logistic regression for the effect of all significant variables (table 1) on LBP1.
Effect of the significant variables was reported as odds ratio (OR) and corresponding 95% confidence intervals following standard methods.
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