Select the results to display for Fit Cox Model with Fixed Predictors only

Stat > Reliability/Survival > Cox Regression > Fit Cox Model with Fixed Predictors only > Results

Display of results

Simple tables: Displays the simpler version of all tables.
Expanded tables: Displays confidence intervals for the coefficients.

Method

Displays a table that summarizes some of the settings for the analysis.

Categorical predictor information

Displays the table of categorical predictors and their levels.

Censoring information

Displays the table showing the number of uncensored and censored values. An analysis with strata includes a row for each stratum plus a row for totals.

Iteration information

Displays the log-likelihood at each iterative step of the optimization algorithm.

Regression equation

Displays the regression equation. Minitab displays up to 50 equations. If your model contains categorical predictors, the drop-down list is enabled so that you can control how many equations are displayed.

Separate equation for each set of categorical predictor levels: Displays a separate equation for each combination of categorical predictors.
Single equation: Displays one equation that includes all the levels of all the categorical predictors.

Coefficients

Displays the coefficients, standard error of the coefficients, the z-values, and the p-values. If your model contains categorical predictors, the drop-down list is enabled so that you can control how many coefficients for categorical predictors are in the table. If you select Expanded tables for Display of results, the table also displays confidence intervals for the coefficients.

Default coefficients: Displays all the linearly independent coefficients of the categorical predictors.
Full set of coefficients: Displays the coefficients for all levels of the categorical predictors, which includes the final, linearly dependent, level.

Relative risks

Displays the relative risks table. Use the relative risk to determine how the survival time varies between different values of a predictor. Minitab displays a separate table for continuous and categorical predictors.

Model summary

Displays statistics that evaluate the model fit, including log-likelihood, R², the Akaike's Information Criterion (AIC), the corrected Akaike's Information Criterion (AICc), and the Bayesian Information Criterion (BIC).

Goodness-of-fit tests

Displays the likelihood ratio, Wald, and score testing methods. If you perform the analysis based on the Robust variance-covariance of the estimated coefficients, then the likelihood ratio test results are missing values because the method requires independence of observation within clusters.

Test for proportional hazards assumption

Displays the transformation Minitab uses in the proportional hazards test. It is a test of linear association between the scaled Schoenfeld residuals and some function, g(t), of the event times. Use Time transformation to select the function.

Identity function: Use the original units for the tests of proportional hazards. Minitab uses this option if you do not specify a function.
Natural log function: Use the natural log function, g(t) = ln (t), to transform the event times for the tests of proportional hazards.
Rank function: Use the rank function, g(t) = Rank (t), to transform the event times for the tests of proportional hazards. The rank function is based on the ranks of the event times. Tied values are assigned their average ranks.
Kaplan-Meier survival function: Use the Kaplan-Meier's survival function to transform the event times for the tests of proportional hazards. The survival estimates are based on the response time.

Analysis of variance

Displays the ANOVA table that includes the test statistic and the p-value for each predictor.

Survival function table

Displays the event times, survival function values, and confidence intervals for the survival function values. Minitab uses the mean of each predictor for continuous predictors and the reference levels for categorical predictors.

Use log transformation for confidence interval: Calculate the confidence intervals based on a log transformation of the survival function. This is the default setting.
Use log-log transformation for confidence interval: Calculate the confidence intervals based on the complementary log-log transformation of the survival function. Consider this transformation when the confidence intervals are close to 0 and 1 and your application requires more precision.