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In logistic regression models, we analysed the associations between hospital mortality and severe hyperoxia (PaO2> 200 mmHg) defined by either identified metric, calculated over the total ICU admission.

In three separate multivariable logistic regression models we analysed associations between a range of explanatory variables and total, patient and health service delay.

Because the interaction effect was not statistically significant for any of the models, we analysed the correlation between general characteristics and total scores for all respondents together.

By the use of multistate models in Cox proportional hazards models, we analysed the HR for re-employment, sickness absence and retirement in models including clinical as well as health-related variables.

In the Cox models, we analysed the risk associated with all possible risk factor combinations for CHADS2 score=1 (four combinations) and CHADS2 seven=2 (seven combinations); we used CHADS2 score=0 as the reference.

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To better determine the maximum practical spatial resolution and the regions well observed by the Swarm gravity field models, we analyse the models in the spatial domain, as well as the two-dimensional (2D) spatial correlation between the Swarm and GRACE models [refer to Ditmar et al. (2012, Section 4.1) for the description of how the 2D spatial correlation is computed].

To illustrate the plots and models we analyse data on smoking status, body mass index (BMI) and physical activity level from a longitudinal study on women's health.

Using this model we analysed core temperature data from an experiment designed to exhibit amplitude dynamics.

To evaluate the sensitivity of the best model, we analysed the absolute sensitivity and sensitivity density distributions.

In order to test the predictions of such modelling we analysed the X-ray time resolved spectra searching for possible spectral breaks within the observed XRT energy band, finding seven GRBs showing such a break.

For each model we analysed its discrimination and calibration.

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