Even though all patients have died, it's still a good idea to use a Cox regression model for this type of data. That way, you model whether the risk of death over time is associated with the predictor (
Tumor Percentage). You don't have to make any assumptions about the distributions of actual times (
Survival Days), like you would for a standard regression model. Only the order of the deaths in time matters.
Furthermore, a Cox model can allow you to include patients who received the therapy but haven't yet died. In fact, if there are such patients, it would be incorrect to omit information about them. Otherwise your results will be biased.
Using cutoffs of continuous predictor values like
Tumor Percentage is not a good idea. Keep them continuous. In a Cox model, you then examine the association between the log of the hazard of death and the value of the predictor.
It's unlikely that there is a strict linear association between
Tumor Percentage and the log-hazard of death, however. A regression spline can let the data tell you the form of the association while you test its significance. You can specify the flexibility of the association by specifying the number of "knots" in the
Tumor Percentage that separate the regions that are fit internally with smooth curves. An alternative is what's called a "penalized spline," available with a
pspline() term in the R
coxph() function. This page explains the difference, with links for further study.
In principle, you could do a multiple regression involving both
Tumor Percentage and
Ki67 as predictors in the Cox model. The danger in this situation is that you only have 27 cases, while you typically need about 15 times as many cases with events as the number of "degrees of freedom" you are using up in the model (the effective number of coefficients that you are fitting). I understand that
Ki67 is a continuous measure, and thus should be modeled flexibly like I recommend for
Tumor Percentage. The problem is that the extra flexibility provided by splines for continuous predictors involves using up more degrees of freedom, so a simple flexible fit to
Tumor Percentage by itself might be as far as you can push the data that you have.