Select the options for Fit Cox Model with Fixed Predictors only

Specify the method Minitab uses to handle ties. Usually the Efron method provides better estimates than the Breslow method when there are many ties in the response data. The two methods yield the same estimates when there are no ties in the response data.

Enter the level of confidence for the confidence intervals for the coefficients, relative risks, and survival function.

Usually, a confidence level of 95% works well. A 95% confidence level indicates that, if you took 100 random samples from the population, the confidence intervals for approximately 95 of the samples would contain the mean response. For a given set of data, a lower confidence level produces a narrower interval, and a higher confidence level produces a wider interval.

You can select a two-sided interval or a one-sided bound. For the same confidence level, a bound is closer to the point estimate than the interval. The upper bound does not provide a likely lower value. The lower bound does not provide a likely upper value.

From the drop-down list, select Robust variance-covariance to perform the analysis using the robust covariance matrix¹ for the parameter estimates. When you select this option, all the tests and confidence intervals in the analysis use the robust covariance matrix.

You can specify a column in Cluster identification for robust covariance matrix (optional) to identify groups of correlated observations due to the study design. Rows with the same value are clustered observations. For example, in recurrent event models where each subject can experience the event multiple times, the observations within the same subjects are correlated. If you specify a column, Minitab calculates the robust covariance to account for the presence of clustered observations. If you do not specify a column, the effect is the same as if you use a column with a different value in every row.

The input column can be numeric, text or date/time. Minitab includes missing values when it calculates the robust variance-covariance and groups them together in the analysis.

Specify the test Minitab uses for the ANOVA table. Empirical studies have shown that the convergence rates of the Likelihood ratio test and Wald test are similar. The Score test converges less rapidly to the limiting chi-squared distribution.

When you select Robust variance-covariance in the Variance-covariance matrix for analysis drop-down list, the ANOVA table always displays the Wald test because the Likelihood ratio test and Score test assume that the observations within clusters are independent.