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This was done with the aim of deepening our understanding of the probable causalities and explanatory powers of predictors in multivariate models, but not with the aim of generating a predictive equation [69].
However, the associations weakened in multivariate models, but high waist circumference remained its significance for OIs.
Rate of weight gain was analyzed as a continuous variable in multivariate models but is presented in categories here.
The baseline levels of both steroid hormones were elevated among the heaviest smokers in multivariate models, but not statistically significantly: 22% for E1C and nearly 40% for PdG (Table 3).
All three potential explanatory variables (age, productive type and number of herd tests performed in the herd) were tested in the multivariate models but only significant (p < 0.05) covariates were retained in the final model.
To improve comparability of the 2 vaccine groups, we controlled for the propensity score in multivariate models, but first we excluded all outlier observations ("trimming"), defined as those below the lower 2.5% of the tail of the TIV observations (3,355 observations) and above the upper 2.5% tail of the ATIV observations (2,894 observations).
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Admission PRISM score was forced into the multivariate model, but it was not statistically associated with anemia at PICU discharge (p = 0.62).
Cells with two dashes indicate that the variable was included in the multivariate model but excluded after the multivariate analysis.
Alcohol consumption was included in the final multivariate model but it was subsequently excluded because it was not statistically significant.
We used validated measures of key factors and multivariate modeling, but we could not correct for all possibly relevant confounders.
Other variables were added to the multivariate model, but were found not to be significant at the p < 0.2 level.
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Justyna Jupowicz-Kozak
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