In a two-arm clinical trial with approximately 100 participants per arm and remission rates around 50% (RR significantly different both in a direct comparison and 95% CI crossing a pre-defined non-inferiority limit) I have compared the risk of relapsing depending on the treatment received. The risk is very similar irrespective of treatment group.

Comparing those that do relapse within a three-month period (group 1: 19/43; Group 2: 25/55) participants in the first treatment arm have significantly fewer days to remission when running an independent samples t-test. The mean time, in days, to relapse is 23 and 41 days.

While the study was not powered for this comparison and given that the numbers are small, an almost three week difference in time to relapse (the patients have received antidepressant treatment, and the mechanisms and explanations underlying relapsing are complex), the time difference in days - if true - is clinically relevant.

However, the proper thing to do is of course to do a Kapler-Meier analysis. Such an analysis unequivocally supports a null hypothesis of similar survival rates.

Not being a statistician, merely a humble researcher, I would appreciate some input on the issue.

I understand that in some circumstances the exclusion of right-censored (hope I got the lingo right there) cases is by necessity an exclusion of data that (eventually will) have a specific value. Everyone I assume will eventually die. In my current analysis it is not so - likely more individuals will relapse but in theory it is possible that all remaining remitters will remain well ad infinitum.

**Does this have any bearing?**

By mistake I at first ran the analysis without censored cases, i.e. the data set contained only those remitters that relapsed. Depending on analysis method (which as I gather put different emphasis on different parts of the survival curve?), the survival rates are border-significant with the p-values range from between 0.02-0.07 or thereabout.

To me it is

**not clear**why taking the censored individuals into consideration, which the analysis obviously does, is relevant. What follows is that if fewer of our patients remitted - say only those that later relapsed - we would claim that the relapse time did differ and was in fact shorter in patients receiving treatment X. Why should the the size of the non-relapsing proportion (which might even be identical between groups) influence the comparison of values in the group of relapsers?

Personally, I lean towards the t-test asking "among those remitters that do relapse, is there a difference in the number of days they stay in remission?"

I will report both results from the survival analysis and the t-test, so readers can draw their own conclusions. Many readers are I guess even less proficient than myself in interpreting statistical outcomes and I would greatly appreciate any explanation as to why the use of a t-test here is fundamentally wrong (if that is the case) or why a Kapler-Meier survival analysis (or perhaps some other related test is better) is self-evident here?

I hope I have been able to make my points clear and look forward to some feedback.