IBM SPSS COMPLEX SAMPLES 19 manual Complex Samples Cox Regression

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Complex Samples Cox Regression

Sample design information. Displays summary information about the sample, including the unweighted count and the population size.

Event and censoring summary. Displays summary information about the number and percentage of censored cases.

Risk set 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 related to the model parameters.

„Estimate. Displays estimates of the coefficients.

„Exponentiated estimate. 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.

„Standard error. 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 t test of each coefficient estimate. 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.

„Correlations of parameter estimates. Displays an estimate of the correlation matrix for the model coefficients.

„Design effect. The ratio of the variance of the estimate to the variance obtained by assuming that the sample is a simple random sample. This is a measure of the effect of specifying a complex design, where values further from 1 indicate greater effects.

„Square root 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 fitted model to an alternative model that includes time-dependent predictors x*_TF for each predictor x, where _TF is the specified time function.

„Time Function. Specifies the form of _TF for the alternative model. For the identity function,

_TF=T_. For the log function, _TF=log(T_). For Kaplan-Meier, _TF=1−SKM(T_), where

SKM(.) is the Kaplan-Meier estimate of the survival function. For rank, _TF is the rank-order of T_ among the observed end times.

„Parameter estimates for alternative model. Displays the estimate, standard error, and confidence interval for each parameter in the alternative model.

„Covariance matrix for alternative model. Displays the matrix of estimated covariances between parameters in the alternative model.

Baseline survival and cumulative hazard functions. Displays the baseline survival function and baseline cumulative hazards function 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.