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We evaluated our time stratified variables by modelling an interaction between health care provider type and therapy type (Table 3).
A log-linear Poisson regression model was fitted to incidence rates of AF, adjusting for a linear trend and modelling an interaction between trend and country.
A logit-linear logistic regression model was fitted to 30-day case fatality risks, adjusting for a linear trend and modelling an interaction between trend and country.
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However, these operators are not able to model an interaction between criteria.
This is not the optimal way of modeling an interaction's start and end point.
In the trend analyses, we additionally modeled an interaction term between trend and individual hospitals.
We modelled an interaction between health care provider type and therapy type to evaluate our findings.
The proportional hazards assumption was verified by modeling an interaction term of time to death by the main exposure.
In this model an interaction between expectations of recovery and posttraumatic stress was found.
In the GEE models, an interaction effect was identified with age.
In these models, an interaction term for diabetes and year of discharge was tested and included if P < 0.05.
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