1. A random sample of 200 patients admitted to an adult intensive care unit (ICU) was collected to examine factors associated with death during hospital stay for ICU patients. Data was also collected on patient's age (in years), race, whether the patient had an infection at the time of ICU admission, and whether the patient had CPR administered prior to the hospital admission. Of specific interest is whether or not infection at the time of admission is associated with increased probability of death during hospital stay. Logistic regression was employed to help answer the substantive question. Below find the estimated coefficients for infection status at time of admission from 4 different logistic regression models all relating the probability of death in the ICU to patient characteristics.
f. How do the resulting adjusted odds ratio estimates and confidence intervals from part (d) compare across the 3 sets of logistic regression models with additional predictors?
g. How could you use these results to assess whether the relationship between death in ICU patients and infection at the time of admission is confounded by other patient characteristics?
h. What type of study design is this? Would it be possible to use the results from the 4th logistic regression model (with infection status, age, CPR, and race as predictors) estimate the probability (risk) of death for various groups of patients based on the reported patient characteristics?