Cox Proportional Hazards Regression - Options Tab

Select the Options tab of the Cox Proportional Hazards dialog box to access the options described here:

Element Name Description
Estimation Use the options in this group box to specify which likelihood (Breslow, Efron, or Discrete) to use in the presence of tied survival times
Coding of factors Use the options in this group box to specify how Statistica codes categorical variables in the design matrix. For more information, see
Set reference category Click this button to specify the reference category of each categorical factor
Model Building Use the options in this group box to specify how effects are entered into the model
All effects Select this option button to enter all effects specified in the current design
Forward Select this option button to specify the forward selection method. Effects are entered into the model one at a time beginning with no effects and ending when either there are no additional effects to enter or the effect associated with the largest adjusted score statistic does not meet the significance criterion defined by the p to Enter value
Backward Select this option button to specify the backward selection method. Effects are removed from the model one at a time beginning with the full model and ending when either there are no additional effects to remove or the effect associated with the least significant Wald statistic is less than the p to Remove value
Stepwise Statistica performs a stepwise selection by starting with the best fitting 1 variable model, and then sequentially adds variables according to the best adjusted chi -square score statistic if the p -value of the chi -square score statistic is less than the user specified p to Enter value. Unlike the forward selection method, an effect that has been entered into the model can be removed if the p -value of the Wald statistic is greater than the user specified p to Remove value
Best subsets Statistica performs a best subset search using the Branch and Bound algorithm of Furnival and Wilson (1974). The best subset for each number of predictors is found; that is, the best 1 predictor model is found, 2 predictor model, and so forth. Note that this option is only available for models that contain only covariates and no categorical factors
p to Enter Specify the criterion for an effect to enter in the model during a forward step. This field is available only when Forward or Stepwise option is selected
p to Remove Specify the criterion for an effect to be removed from the model during a backward step. This field is available only when Backward or Stepwise option is selected
Effects to Force This value enables you to force selected effects into the model (i.e., they will be part of every model that is considered); specifically, when the Effects to Force value k is greater than 0 (zero), the first k effects in the design will be forced into all models that are evaluated
Subject This option can be used in conjunction with the selection of the robust covariance matrix, but it is not necessary to do so. In the counting process style of input, it might be common to have multiple rows or cases in the spreadsheet that pertain to a single subject. In order to aggregate results over each individual subject, a subject variable in the spreadsheet that identifies each subject can be specified
Robust variance estimator Select this check box to use the robust sandwich estimate of the covariance matrix