In a multivariable logistic regression model, we entered liver cirrhosis as main covariable and age, sex, OHCA, witnessed CA, time to ROSC, presence of shockable rhythm, cardiac cause of CA, mechanical ventilation, SOFA on admission, initiation of MTH, CCI and cumulative adrenaline dose as other covariates to the model.
In the base model, we entered the dichotomous variable for catchment area.
In the model, we entered "P-in was 0.1 and P-out was 0.05".
As a result, in our final model, we entered the contemporaneous value of cholera.
For each logistic regression model, we entered previously documented population characteristics that contributed to use (e.g., age, gender, marital status; enabling factors such as education, employment).
For the second multiple regression model, we entered in the amount of PA using the PASIPD, after controlling for motor impairment.
In the expanded model, we entered dummy variables representing each interviewer, contrasting our Team Supervisor as the comparison to the other three.
In the second model, we entered an interaction between group and time (group*time) in order to obtain the estimate of the change in the dependent variable along time in each group.
To complete an Internet search for websites containing or linking to a breast cancer risk prediction model, we entered the following search terms: calculate breast cancer risk, breast cancer risk calculator, estimate breast cancer risk, assess breast cancer risk, and breast cancer risk assessment.
Significant variables in the preceding model were retained in proceeding model, thus in model 1, we entered community factors.
