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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.

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(This is more of a comment than an answer, but I can't comment because I do not have enough reputation points).

Have you seen my question here? I have a similar situation in which I want to test for mediation using Cox proportional hazards models. My situation is slightly different because my mediator is not a time-varying covariate.

I do not believe weight loss could be a mediator, because weight loss would need to remain significant even in the presence of BMI. This is step 3 in Baron and Kenny's procedure for testing mediation. It seems more likely that BMI and weight loss are correlated and you are getting the observed effects because of multicollinearity issues.

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  • $\begingroup$ Thanks for replying and directing me to Baron and Kenny's procedure! Having spent more time reading and looking at my data, I think it probably is the case that obesity and weight loss are multicollinear. I suspect you might have already seen it, but the following paper's additional files gives a detailed description of how the authors carried out mediation analysis on their Cox regression model: ncbi.nlm.nih.gov/pmc/articles/PMC3917547 $\endgroup$
    – cat.vet
    May 8 '16 at 14:53
  • $\begingroup$ I had not seen that paper. Thanks that is helpful as well! $\endgroup$
    – bigdataumd
    May 9 '16 at 19:34

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