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. 2006 Jan;61(1):39–48. doi: 10.1111/j.1365-2125.2005.02530.x

Table 3.

Effect of covariate analysis on variance of clearance (ω2)

(a) Impact of each covariate alone
Individual covariate alone PPVt2 PPVR2 PPVP2 PPVP2/PPVt2 OBJ
No covariates 0.319 0.319 0 0 2073.0
Allometric scaling 0.3191 0.165 0.154 48.3% 1973.9
Post-conception age 0.3191 0.2 0.119 37.3% 2012.8
NSAID use 0.3191 0.301 0.018 5.6% 2047.6
Sequential nested model PPVt2 PPVR2 PPVP2 PPVP2/PPVt2 OBJ
No covariates 0.319 0.319 0 0 2073.0
Allometric scaling 0.3191 0.165 0.154 48.3% 1973.9
Post-conception age 0.3191 0.119 0.200 62.7% 1919.0
NSAID use 0.3191 0.113 0.206 64.6% 1893.2

(b) Impact of each covariate when added sequentially to the model

1

Assumed from no covariate model estimate.

1

Assumed from no covariate model estimate. PPVt2is the total population parameter variability estimated without covariate analysis, PPVP2is the population parameter variability predictable from covariates, PPVR is the random PPV estimated on a parameter when covariate analysis is included. The ratio of the population parameter variability predictable from covariates (PPVP) to the total population parameter variability obtained without covariate analysis (PPVt2)(i.e.PPVP2/PPVt2) indicates the fraction of the total variability in the parameter that is predictable from covariates. OBJ is the objective function value measuring the goodness of fit.