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bThe multivariable model contains all variables with a P value < 0.1.
Since no imputation methods for missing values were used, the multivariable model contains only patients with full data sets according to the predictive variables.
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OR for non-visual variables were derived from the multivariable model containing AMD as the vision variable.
The best fitting multivariable model containing sex and each of the clinical surrogates was achieved with waist circumference (R = 0.202), in predicting clamp-derived index (Table 2).
A multivariable model containing these four variables exhibited good calibration (Hosmer Lemeshow p=0.38) and discrimination (C-statistic 0.77; 95% CI 0.71 to 0.84).
Moreover, we included length of follow-up in the multivariable model containing CH and IOP and found that it did not significantly alter the relationship between these variables (p>0.05).
> -wrap-foot> aBased on a multivariable model containing all terms shown in the table bThe nulliparous parameter and its interaction with BMI were set to 0, since these variables were linear combinations of other variables We repeated the analyses in the subgroup of women who did not have a personal history of breast cancer, and found similar results (Table 5).
Additionally, our multivariable models contained covariates that were outcomes in the resource utilization and costs analysis.
Table 2 shows the multivariable models containing the covariates that were found to be statistically significantly associated with the four outcomes.
A multivariable regression model containing the former four factors (table 2) was produced after initially entering all factors in figure 1 and removing those accounting for less residual variance than the degrees of freedom or not reaching statistical significance.
The next model contains two NLSs.
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CEO of Professional Science Editing for Scientists @ prosciediting.com