Table 2: Output for Part Two The LOGISTIC Procedure Model Information Data Set WORK.ICU Response Variable STA Testing Global Null Hypothesis: BETA-0...
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Question

# 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.

Table 2: Output for Part Two
The LOGISTIC Procedure
Model Information
Data Set
WORK.ICU
Response Variable
STA
Testing Global Null Hypothesis: BETA-0
Number of Response Levels 2
Test
Chi-Square OF Pr &gt; ChiSq
Model
binary logit
Likelihood Ratio
16.0796
4
0.0029
Optimization Technique
Fisher's scoring
Score
15.9143
4
0.0031
Wald
14 0527
4
0.0071
Number of Observations Used 200
Analysis of Maximum Likelihood Estimates
Response Profile
Standard
Wald
Ordered
Parameter OF Estimate
Error Chi-Square Pr &gt; ChiSq
Value STA Frequency
Intercept
3 3407
0.7470
20.0024
0001
160
AGE
1
-0 0235
0.0110
4 5742
0 032
21
CAN
1
0.2155
0.6075
0.1259
0.7227
Probability modeled is STA-0.
CPR
1
-0 9591
0.5219
3.3767
0.0561
INF
1
-0.7803
0.3725
13869
0.0362
Model Convergence Status
Convergence criterion (GCONV= 15-8) satisfied
Odds Ratio Estimates
95% Wald
Model Fit Statistics
Effect Point Estimate Confidence Limits
Intercept and
AGE
0.977
0.956
0.998
Criterion Intercept Only
Covariates
302 151
194.081
CAN
0.806
0.245
2.651
AIC
SC
205 459
210.573
CPR
0 383
0. 138
1.066
-2 Log L
200.151
184.081
INF
0.458
0.221
0.951

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