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In the quantile regressions, we modelled age at triage in years as a predictor of pulse and respiratory rates.
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We also modelled age as a cubic spline with four knots.
In a sensitivity analysis, we also modelled age as a linear variable instead of a dichotomous one.
Modelled ages obtained by Bayesian analysis of deposition are thoroughly consistent with actual ages, with maximum offset of ±1700 years.
Modelled age-specific diabetes prevalence is shown in Fig. 1.
Incidence was estimated from modelled age-specific prevalence.
For multivariate models, ER status was modelled as a dichotomous variable and age at diagnosis was modelled as a categorical variable (<40, 40 49, 50 59 and 60+).
Competing mortality was modelled separately and adjusted for age at diagnosis.
Adjusted odds ratios were calculated from a multivariable logistic regression model that included Charlson comorbidity score (modelled as an integer-valued quantitative variable), age at diagnosis (expressed in years), and race (white vs non-white).
It was approximately normally distributed and was modelled using linear regression in relation to covariates including age at study entry, occupational position, region of residence and partner's smoking habit.
When waning of vaccine-induced immunity was modelled, cost-effectiveness of HZ vaccination was highly dependent on the age at vaccination.
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