I have been reading quite a few papers and recently have started analyzing some data on a review that I am doing.
I am trying to examine the effect of two different treatment options on a group of patients. Using Kaplan-Meier analysis, the p-value is statistically significant at 0.03. However I believe that since this is an observational study that various confounding factors may be influencing my survival graphs in each arm. I have done a univariate analysis (using Kaplan-Meier and log rank testing) for three factors age, weight and height. My univariate analysis only identifies a statistically significant difference in survival among my patients in the age group (categorical age >45 or <45).
Now I am not sure what to do.....I believe that I should now fit a Cox proportional hazard model and choose 'age' as a variable to control for and see if this affects my survival curves for treatment modality......is this the right thing to do?
Or should I analyze each of the variables....weight, height, etc., with a different Cox regression model. And if I do, how do I interpret that data? For example, when placing 'age' in the variable column (I am using epi info) my p-value now is no longer significant in my analysis of survival between treatment modalities. Does that mean that age was one of the factors that caused my two groups to have differing survival times?
Is this a multivariate analysis?