Sentence examples for models were categorised from inspiring English sources

The risk scores obtained from the linear predictors of the four models were categorised into low, medium and high risk groups using the 25% and 75% quantiles and Kaplan Meier survivor curves were plotted.

Alcohol-attributable events considered in the model were categorised based on their pathophysiology of occurrence with regard to level and pattern of alcohol consumption (table 5).

Following the estimation of the high-dimensional propensity score for each patient using a logistic model, patients were categorised into propensity score deciles, which were included as a categorical variable in our final logistic model to estimate the independent effect of PPIs on the risk of HCAP.

Before analysing the addition of the NLR to the Leibovich prognosis model, patients were categorised into risk groups (low, intermediate and high risk) according to Leibovich et al (2003).

We also analysed the ability of the serum NGAL assay to distinguish pancreatic cancer from non-cancer cases by fitting the data to a simple logistic regression model where samples were categorised as 'cancer' or 'no-cancer', and mean NGAL was used as the predictor.

Patient differences between the fitted model and actual data were categorised by stage and treatment for both the Cox and log-normal models.

Articles were categorised according to the specific models that they described.

Causes of MAEs were categorised according to Reason's model of accident causation.

Themes identified in interviews were categorised, and used to build a model explaining dissatisfaction with care.

Delays were categorised according to the 'three phases of delay' model [ 32].

Based on these plots, non-linear risk factors were categorised and the categorised variables were refitted into various Cox models in a stepwise manner to confirm the findings.