CHAPTER 23 Survival Regression 345
Finding h
To calculate the h value, do the following for each predictor:
1.
Subtract the average value from the patient’s value.
In this example, you subtract the average age, which is 51.18, from the patient’s
age, which is 55, giving a difference of +3.82.
2.
Multiply the difference by the regression coefficient and call the
product v.
In this example, you multiply 3.82 from Step 1 by the regression coefficient for
age, which is 0.377, giving a product of 1.44 for v.
3.
Calculate the v value for each predictor in the model.
4.
Add all the v values, and call the sum of the individual v values V.
This example has only one predictor variable, which is age, so V equals the v
value you calculate for age in Step 2, which is 1.44.
5.
Calculate eV.
This is the value of h. In this example, e1 44
. gives the value 4.221, which is the h
value for a 55-year-old patient.
6.
Raise each of the baseline survival values to the power of h to get the
survival values for the prognosis curve.
In this example, you have the following prognosis:
• For year-zero survival 1 000
1 000
4 221
.
.
.
, or 100 percent
• For two-year survival: 0 9979
0 9912
4 221
.
.
.
, or 99.12 percent
• For seven-year survival 0 9820
0 9262
4 221
.
.
.
, or 92.62 percent
• For nine-year survival 0 9525
0 8143
4 221
.
.
.
, or 81.43 percent
• For ten-year survival 0 8310
0 4578
4 221
.
.
.
, or 45.78 percent
You then graph these calculated survival values to give a customized survival
curve for this particular patient. And that’s all there is to it!
Here’s a short version of the procedure:
1.
V = sum of [(patient value – average value) * coefficient] summed over all
the predictors
2.
h
eV
3.
Customized survival
baseline survival
h