Hi,

I am working on problem 2 of Chapter 8 in Data Analysis and Graphics Using R and don't know how to approach the second half of the question:

In the data set (an artificial one of 3121 patients, that is similar to a subset of the data analyzed in Stiell et al., 2001) head.injury, obtain a logistic regression model relating clinically.important.brain.injury to other variables. Patients whose risk is sufficiently high will be sent for CT (computed tomography). Using a risk threshold of 0.025 (2.5%), turn the result into a decision rule for use of CT.

This is what I have so far:

> names(head.injury)
 [1] "age.65"                            "amnesia.before"                   
 [3] "basal.skull.fracture"              "GCS.decrease"                     
 [5] "GCS.13"                            "GCS.15.2hours"                    
 [7] "high.risk"                         "loss.of.consciousness"            
 [9] "open.skull.fracture"               "vomiting"                         
[11] "clinically.important.brain.injury"
> attach(head.injury)

> head.glm = glm(clinically.important.brain.injury ~ ., family=binomial, data=head.injury)
> summary(head.glm)

Call:
glm(formula = clinically.important.brain.injury ~ ., family = binomial, 
    data = head.injury)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2774  -0.3511  -0.2095  -0.1489   3.0028  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)            -4.4972     0.1629 -27.611  < 2e-16 ***
age.65                  1.3734     0.1827   7.518 5.56e-14 ***
amnesia.before          0.6893     0.1725   3.996 6.45e-05 ***
basal.skull.fracture    1.9620     0.2064   9.504  < 2e-16 ***
GCS.decrease           -0.2688     0.3680  -0.730 0.465152    
GCS.13                  1.0613     0.2820   3.764 0.000168 ***
GCS.15.2hours           1.9408     0.1663  11.669  < 2e-16 ***
high.risk               1.1115     0.1591   6.984 2.86e-12 ***
loss.of.consciousness   0.9554     0.1959   4.877 1.08e-06 ***
open.skull.fracture     0.6304     0.3151   2.001 0.045424 *  
vomiting                1.2334     0.1961   6.290 3.17e-10 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1741.6  on 3120  degrees of freedom
Residual deviance: 1201.3  on 3110  degrees of freedom
AIC: 1223.3

Number of Fisher Scoring iterations: 6


How do I assess which patients have a high risk level, and how does the risk threshold play into that?

Thanks in advance,

Diana

       
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