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Complex Samples Cox Regression
Sample design information. Displays summary information a bout the sample, including the
unweighted count and the population size.
Eventand censoring summary. Displays summary information about the number and percentage of
censored cases.
Riskset at event times. Displays number of events and number at risk for each event time in
each baseline stratum.
Parameters. This group allows you to control the display of statistics relatedto the model
parameters.
Estimate. Displays estimates of the coefficients.
Exponentiatedestimate. Displays the base of the natural logarithm raised to the power of the
estimates of the coefficients. While the estimate has nice properties for statistical testing, the
exponentiated estimate, or exp(B), is easier to interpret.
Standarderror. Displays the standard error for each coefficient estimate.
Confidence interval. Displays a confidence interval for each coefficient estimate. The
confidence level for the interval is set in the Options dialog box.
t-test. Displays a ttest of each coefficientestimate. The null hypothesis for each test is that the
value of the coefficient is 0.
Covariances of parameter estimates. Displays an estimate of the covariance matrix for the
model coefficients.
Correlationsof parameter estimates. Displays an estimate of the correlation matrix for the
model coefficients.
Designeffect. The ratio of the variance of the estimate to the variance obtained by assuming
that the sample is a simple randomsample. This is a measure of the effect of specifying a
complex design, where values further from 1 indicate greater effects.
Squareroot of design effect. This is a measure of the effect of specifying a complex design,
where values further from 1 indicate greater effects.
Model Assumptions. This group allows you to produce a test of the proportional hazards
assumption. The test compares the fittedmodeltoanalternative model that includes
time-dependentpredictorsx*_TFfor each predictor x,where_TF is the specified time function.
TimeFunction.Specifies the form of _TF for the alternative model. For the identity function,
_TF=T_.Forthelog function, _TF=log(T_). For Kaplan-Meier,_TF=1−SKM(T_), where
SKM(.) is the Kaplan-Meier estimate of the survival function. Fo r rank,_TF isthe rank-order
of T_ among the observed end times.
Parameterestimates for alternative model. Displaysthe estimate, standard error, and confidence
interval for each parameter in the alternativemodel.
Covariancematrix for alternative model. Displays the matrix of estimated covariances between
parameters in the alternative model.
Baselinesurvival and cumulative hazard functions. Displays the baseline survival function and
baseline cumulative hazardsfunction along with their standard errors.
Note: If time-dependent predictors defined on the Predictors tab are included in the model, this
option is not available.