Apologies if this is a bit basic question but examples and other questions I have seen so far do not address my problem. Here it is:
I'm trying to understand multivariate analysis for survival data and I've got the following problem. I've got a small sample size study with a main variable for which I want to do a survival analysis. I did some fisher's/chi-square tests to see whether there were associations between my main variable and other variables present on the data. There were none.
Now, I did a survival analysis (Kaplan-Meier and log-rank test) using my main variable and I got significance. I fitted also a Cox proportional hazards model and I got significance. Someone suggested to adjust my model for one of the other variables and that resulted on a significant effect. However, I've got quite small sample size in two of the subgroups (N=4 for two of the subgroups) and given that there was no association between these two variables using the fisher's exact test, my opinion would be to not to take this second variable to the model since there was no association and I've got very small sample size.
Can I do that? Should I still include that second variable? If so, how can I interpret the results all together (with the no-association found)? Can anybody suggest any book/paper/blog/link where they address this issue?
Thank you so much
Edits following the comments:
Number of total events: 28
As I mentioned: Initially I just wanted to look one variable and I was suggested to adjust for another variable, so in total, my model would have two variables.
All patients are dead at the end of the analysis. No censored data. No recurrent events.