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Therefore, only a limited number of potential confounders could be included in the models with mortality as outcome variable.
Table 3 shows the results of the second-stage regression models, with mortality and readmission conditional on not dying as dependent variables.
Using the same models with mortality caused by pulmonary diseases as the outcome (Table 4), SpO2 was a significant variable, even when FEV1% predicted was included.
When included in Cox proportional hazard models with mortality as the outcome, the RII can be interpreted as the mortality hazard between the extreme ends of the risk factor distribution.
The models are logistic regression models with mortality as the dependent variable and age, sex, socioeconomic status, severity of main diagnosis, urgency of admission, comorbidities, source of admission, and month of admission as predictor variables.
We used Poisson regression models with mortality as the dependent variable to analyse the relative mortality risk within 5 years of follow-up among diabetics and non-diabetics, for 5-year time periods from 1980 1984 to 2000 2004.
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Table 3 shows the results of the patient level model with mortality instead of complications.
We used a generalized linear model with mortality rate as the dependent and site as the independent categorical variable, to analyze differences in livestock mortality rates among the four sites.
In addition, a bivariate model with mortality (0/1) was used to account for mortality before recording FCS.
We fitted the risk score as an explanatory variable in a logistic regression model with mortality as the outcome.
Third, a second backward-forward logistic regression model with mortality as a dependent variable was carried out.
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