I'm quite new to survival analysis, but would appreciate any advice on how to deal with the following finding in my data analysis/ results interpretation. I have been using R and have used a time-dependent Cox proportional hazards model to analyse risk factors for diabetic cats entering remission. I only have 46 cats in the study population and 13 remission events (patient recruitment is difficult in vet medicine.)
In univariate analysis, the following are significant: 1) BMI at enrollment (dichotomous, static covariate): HR 3.3 (95% CI 1.1-10) p= 0.04 2) Weight-loss (kg) (time-dependent covariate): HR 4.6; 95% CI 1.2 – 15.8; p=0.03
However, when entered into a multivariable model, both become non-significant (BMI has p-value 0.24 and weight change has p-value 0.17). I understand that this means neither remains significant in the presence of the other and wondered whether weight loss is the mediator for remission in cats with higher BMIs. I have looked into mediator analysis, but can find little on this when the mediator is a time-varying covariate. Does anyone think this is worth pursuing, or know whether it is possible using R?
Many thanks in advance.