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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.
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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.
When two covariates had a correlation coefficient higher than 0.50, only the covariates explaining most of the variation were included.
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.
Table 4 Which covariates explain most of the urban-rural gap in malnutrition in Yemen?
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).
These main covariates (Table 2), along with other covariates derived from them (Supplementary Table 2), represent the pull and push migration factors, known to influence internal migration59,60,77, that were used to extend the basic gravity model proposed by Zipf64.
Table 2: Summary information about the source datasets and the main covariates tested in the spatial gravity models and used to derive additional covariates (Supplementary Table 2) for improving the predictive power of the models.
We tested for DIF for each item on main covariates.
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