patients in the example data set is 51.1818 years, so the baseline survival curve shows the predicted

survival for a patient who is exactly 51.1818 years old. But suppose that you want to generate a

survival curve that’s customized for a patient who is a different age — like 55 years old. According to

the PH model, you need to raise the entire baseline curve to some power h. This means you have to

exponentiate the four tabulated points by h.

In general, h depends on two factors:

The value of the predictor variable for that patient. In this example, the value of age is 55.

The values of the corresponding regression coefficients. In this example, in Figure 23-6, you can

see 0.3770 labeled as Coeff. in the regression table.

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

.

This is the value of h. In this example,

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

, or 100 percent

For two-year survival:

, or 99.12 percent

For seven-year survival

, or 92.62 percent

For nine-year survival

, or 81.43 percent

For ten-year survival

, 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!