With the command linktest, we can assess whether our model is correctly specified. This test uses the linear predicted value (called _hat) and the linear predicted value squared (_hatsq) to rebuild the model. We expect _hat to be statistically significant, and _hatsq to be statistically non-significant. If one or both of these expectations are not met, the model is mis-specified.

However, do not rely too much on this test – remember that you should also use theory and common sense to guide your decisions. It is very seldom relevant to focus on this test if our ambition is to investigate associations (and not to make the best possible prediction of the outcome).

More information help linktest

Practical example

We perform this test for the full model, so let us go back to the example from the multiple regression analysis. The quietly option is included in the beginning of the command to suppress the output.

quietly stcox gpa sex ib1.marstat40 if pop_cox==1, noshow

And then we run the test:

linktest

Since the p-value for the variable _hat is below 0.05 and the p-value for _hatsq is above 0.05, it means that our model is correctly specified.