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Final multivariate models were designed by using a saturated model and manual backwards elimination.
Final multivariable models were designed by using a saturated model including all potential risk factors and manual backwards elimination.
Phenotypic correlations, twin and twin-sibling correlations, and within person and cross-person cross-trait correlations were estimated using a saturated model in OpenMx (Boker et al. 2011).
The model was fitted by firstly using a saturated model in which all variables to the left of a variable could cause all variables to the right of the variable, through the BETA matrix in LSIREL.
The model was parameterized using a "saturated" model with parameters for each of the health states and study covariates such as age, gender and own health measured using the EQ-5D total score.
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†Final multivariable models were designed that used a saturated model including all potential risk factors (see Methods) and manual backwards elimination.
Feeding was performed using a saturated cotton-wool pad.
Triplet states were quenched in all three systems using a saturated Trolox solution.
The peptide mixture was analyzed using a saturated solution of CHCA in 50%acetonitrile/0.1%% TFA.
It is therefore important first to examine the overall amount of variation explained by the exposure, perhaps using the R value in a saturated model or extreme end of the elbow plot, and second to examine the correlation structure among the exposure measurements.
One such approach uses an F test to compare a saturated model with hypothesized models concerning the association between binary exposure variables, measured over the life course, and an outcome.
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