Suppose we conduct a study to determine the predictors of mortality among adults in an intensive care unit (ICU).
We collect the vital status (STA) of 200 individuals; STA is coded as 0 if the individual "Lived" and 1 if the individual "Died". Data on potential predictors were also collected including age (in years), presence of cancer (CAN, coded as 0=No and 1=Yes), CPR prior to ICU admission (CPR, coded as 0=No and 1=Yes), probable infection at ICU admission (INF, coded as 0=N0 and 1=Yes). We analyze the data using SAS's LOGISTIC procedure and observe the results shown in Table 2 .
1) Identify the dependent variable and the independent variables in this study. Also, state the Omnibus Null and Alternative hypotheses.
2) Report the test statistic and P-value that should be used to test the Omnibus Null hypothesis (i.e. "Global Null" per SAS). What is your conclusion about the Omnibus Null hypothesis?
3) Report the odds ratios and 95% confidence intervals for all four independent variables. Based on those confidence intervals, which of the independent variables are significant predictors of vital status in the ICU and which ones are not significant predictors? Be sure to include the reasoning for your decisions.
4) Report p-values of all four independent variables using "Analysis of Maximum Likelihood Estimates" table in the SAS output. Based on the p-values, which of the independent variables are significant predictors for the virtual status in the ICU and which ones are not significant predictors? Be sure to include the reasoning for your decisions. Compare your answer with part 2, question 3.
5) Discuss what these findings mean from public health perspective.
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