Survival analysis (Cox proportional hazards model) does not have a stepwise function for automatically selecting covariates ( *1 ).
Therefore, you must manually select the covariates.
There are several proposed methods and selection criteria for covariates, and no clear guidelines for their use. However, we introduce the following simple method as an example for reference.
How to reduce covariates
- Once you have decided on the covariates (effects) to include in the Cox proportional hazards model, launch the Fit Model or Fit Proportional Hazards platform.
- Enter all columns previously defined as covariates into the Model Effects Configuration, set other settings such as Censoring as necessary, and click Run.
- In the report that appears, check the p-value for each factor (effect) in the Effect Summary, select the factor with the largest p-value, and click Remove.
- Repeat step 3 until the p-values of all factors meet the pre-set criteria ( *2 ).
*1 " What types of analyses can be performed using the stepwise method in JMP? "
JMP supports stepwise least squares methods (regression and analysis of variance) and logistic regression. In JMP Pro, you can also use Generalized Regression under Fit Model to perform model selection, including forward selection.
*2 Effect Summary is a feature introduced in JMP 12. In JMP 11, you must perform step 2 above to reduce the effect each time based on the p-value of the Effect Likelihood Ratio Test.
FAQ #3801
