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Forward and backward stepwise linear regression was run entering all measured co-variates.
Backward stepwise linear regression was used to choose the most significant covariates associated with telomere length.
A backward stepwise linear regression analysis was used to identify factors that significantly relate to post-exercise cTnI-levels.
Principal components analysis followed by backward stepwise linear regression was used to explore relationships between measures of insulin action and metabolic intermediates.
Indeed, in our backward stepwise linear regression models, we found that DM loses its predictivity on AD when BMI was introduced as cofactor.
Variables with a univariate P < 0.10 were then entered into a multivariate backward stepwise linear regression model for each outcome of interest.
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Because data covering all variables for all the patients were not available (as seen in Table 4 with the different n for the three regression analyses), the backward stepwise linear regressions were done manually.
The effect of patient variables on log10-transformed serum concentrations of oxycodone and oxymorphone/oxycodone and noroxycodone/oxycodone ratios were explored by backward stepwise linear regressions, with the criterion for removal of a variable of p > 0.1.
In a second step, stepwise linear regression analyses with backward elimination were performed.
All potential relevant factors were subjected to a stepwise linear regression analysis using a backward technique.
SM and soil clay fraction were the controls for EC from the stepwise linear regression result (Table 2).
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