for an oncology-related study I am looking to find out, whether a certain dichotomous variable (Status X) predicts outcome in a multivariable cox regression model (expressed as hazard ratios). Using Kaplan-Meier methods (so univariable analysis), Status X has already proven significantly predictive. Other covariates would be age, TNM-staging variables, receptor status, etc. Unfortunately, within my time frame of follow-up and a total sample size of 220 patients, only 14 events occurred. Knowing that having 10+ events per covariate is generally recommended for Cox Regression and given the low event count in the study, how can I go about selecting covariates to include in the model?
I've seen similar studies (but with a higher counts of events) using two general approaches:
- Putting all variables in a model and using the stepwise-backward selection.
- Determining covariates to include in the model using univariate analysis.
In univariate analysis of my set of covariates (and depending on the type of survival) only 2-3 covariates affect outcome at a level of p=0.05 anyway. Would it be appropriate to include the 2-3 covariates in the model, given the low count of events?
If not, is there a way i can most ideally use the data at hand with a cox regression model, or is it just a bad idea altogether, given the sample size/count of events?
Side question: Based on the fact that this is a study related to oncology, do I have to conceptually include certain "basic" variables (like age) in the model per se to get an accurate model, regardless of their significance in univariate analysis?
Let me know if you need further information to answer my questions. Your advice is greatly appreciated!