Options (Complex Samples Cox Regression)
Estimation. These controls specify criteria for estimation of regression coefficients.
- Maximum Iterations. The maximum number of iterations the algorithm will execute. Specify a non-negative integer.
- Maximum Step-Halving. At each iteration, the step size is reduced by a factor of 0.5 until the log-likelihood increases or maximum step-halving is reached. Specify a positive integer.
- Limit iterations based on change in parameter estimates. When selected, the algorithm stops after an iteration in which the absolute or relative change in the parameter estimates is less than the value specified, which must be positive.
- Limit iterations based on change in log-likelihood. When selected, the algorithm stops after an iteration in which the absolute or relative change in the log-likelihood function is less than the value specified, which must be positive.
- Display iteration history. Displays the iteration history for the parameter estimates and pseudo log-likelihood and prints the last evaluation of the change in parameter estimates and pseudo log-likelihood. The iteration history table prints every n iterations beginning with the 0th iteration (the initial estimates), where n is the value of the increment. If the iteration history is requested, then the last iteration is always displayed regardless of n.
- Tie breaking method for parameter estimation. When there are tied observed failure times, one of these methods is used to break the ties. The Efron method is more computationally expensive.
Survival Functions. These controls specify criteria for computations involving the survival function.
- Method for estimating baseline survival functions. The Breslow (or Nelson-Aalan or empirical) method estimates the baseline cumulative hazard by a nondecreasing step function with steps at the observed failure times, then computes the baseline survival by the relation survival=exp(−cumulative hazard). The Efron method is more computationally expensive and reduces to the Breslow method when there are no ties. The product limit method estimates the baseline survival by a non-increasing right continuous function; when there are no predictors in the model, this method reduces to Kaplan-Meier estimation.
- Confidence intervals of survival functions. The confidence interval can be calculated in three ways: in original units, via a log transformation, or a log-minus-log transformation. Only the log-minus-log transformation guarantees that the bounds of the confidence interval will lie between 0 and 1, but the log transformation generally seems to perform "best."
User Missing Values. All variables must have valid values for a case to be included in the analysis. These controls allow you to decide whether user-missing values are treated as valid among categorical models (including factors, event, strata, and subpopulation variables) and sampling design variables.
Confidence interval(%). This is the confidence interval level used for coefficient estimates, exponentiated coefficient estimates, survival function estimates, and cumulative hazard function estimates. Specify a value greater than or equal to 0, and less than 100.
How To Specify Options
This feature requires the Complex Samples option.
- From the menus choose:
- Select a plan file. Optionally, select a custom joint probabilities file and then click Continue.
- Click the Options tab.