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We made novel use of parametric time-to-event analysis to model age at initial use of assistive device for ambulation.
With a multivariate logistic regression model, age at onset and overall duration of illness remained the only clinical criteria identified as predictors of response to antipsychotic treatment at D42. Clinical variables do not clearly appear to be good predictors of treatment efficacy.
We model age at migration as both a continuous and dichotomous explanatory variable.
Therefore, results were presented separately by attained follow-up duration (≤5 vs >5 years), and adjusted for sex (as covariate in the regression model), age at entry, and area of residence (as stratification variables).
Under this model, age at first exposure was inversely related with mesothelioma risk.
We used four proportional hazards methods to model age at diagnosis [ 36– 36].
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In this model, ages at first and last reproduction were assigned to have a direct effect on fecundity, which, in turn, have a direct positive effect on fitness [4] [5].
In the development of the prognostic models, age at diagnosis (number of years), radiological stage (stage IA-IV) and treatment (curative vs palliative) were included because these are strong predictors of survival [26].
The following set of variables were included in all models: age at enrollment, race, gender, age at first rubella vaccination, age at second rubella vaccination, and cohort status (first versus second).
In the quantile regressions, we modelled age at triage in years as a predictor of pulse and respiratory rates.
In the final models, age at diagnosis was treated as a continuous variable and adjustments were included for stage, decade of diagnosis, and geographic region.
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