Brian S. Everitt A Handbook of Statistical Analyses using SPSS

Display 10.8 Declaring a Cox regression model.
The output generated by our Cox regression commands is shown in
Display 10.9. The output starts with a “Case Processing Summary” table
providing summary information about the censoring in the data set. For
example, out of 50 observed times, 26 represented successful task completions.
Then, as in logistic regression, SPSS automatically begins by
fitting a null model, i.e., a model containing only an intercept parameter.
A single table relating to this model, namely the “Omnibus Tests for Model
Coefficients” table is provided in the Output Viewer under the heading “Block
0: beginning block”. The table gives the value of –2 Log Likelihood for
the null model, which is needed to construct Likelihood ratio (LR) tests
for effects of the explanatory variables.
The remainder of the output shown in Display 10.9 is provided in the
Output Viewer under the heading “Block 1: Method = Enter” and provides
details of the requested Cox regression model, here a model containing
the covariates eft, age, and group. The second column of the “Omnibus
Tests of Model Coefficients” table (labeled “Overall (score)”) provides a
score test for simultaneously assessing the effects of the parameters in the
model. We find that our three covariates contribute significantly to explaining
variability in the WISC task completion hazards (Score test: X 2 (3) =
26.3, p < 0.0001). The remaining columns of the table provide an LR test
for comparing the model in the previous output block with this latest
one. The change in –2 Log Likelihood relative to the null model also
indicates a significant improvement in model fit after adding the covariates
(LR test: X 2 (3) = 27, p < 0.001). (As in the logistic regression output, an
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