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Body weight (BW) was the main covariate explaining the between subject variability of clearance (CL) and volume of distribution (V. Parameter estimates were CLi = CLTYP* BW 70 3 4 + Vi = VTYP* BW 70 1 + where typical clearance (CLTYP) and typical volume of distribution (VTYP) were 2.42 L h−1 and 5.1 L, respectively.
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Body weight prior the surgery (BWPREOP) and the related postoperative variation (BW t)) were the main covariates explaining CL and V between subject variabilities.
When two covariates had a correlation coefficient higher than 0.50, only the covariates explaining most of the variation were included.
Speed was included as the main covariate in each model.
BW was the main covariate influencing CL and q0 (P <0.001).
Remission during three years was the dependent variable with time-to-remission as the main covariate.
Table 4 Which covariates explain most of the urban-rural gap in malnutrition in Yemen?
The comparison of adjusted VPCs between the intercept only and the fitted models with higher level covariates explained the portion of heterogeneity in the data that was accounted for by the covariates.
None of the covariates explained these associations.
The covariates explain 28% of the variation in hospital costs.
The covariates explained 12.5% of the variability in ABI alone while the top SNP-covariate interactions explained an additional 2.25% (adjusted R2 = 15.04).
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