Then including a covariate might make the model much more accurate. If this were a typical linear regression, the R^2 for including time would be 0. Then I would say time itself has *no* explanatory power, as the hazard is constant. It's just 2% failure rate at every interval, and the covariate increases this to 4%. Hazard rate is actually constant and independent of time. What I want to know is whether the explanatory power of the covariate is larger than the baseline contribution of time to event itself. I can compare the contributions of each covariate using the log-likelihood ratio, and covariate 2 has a large contribution to the log likelihood and is very significant. I include covariates 1, 2, and 3 in the model. Let's say between conditions A and B they have different lifespans (survival times). I'm running a survival analysis with standard cox regression on multiple covariates. This is a rather nuanced question but hopefully an expert out there can help me! R-bloggers - blog aggregator with statistics articles generally done with R software. Kaggle Self posts with throwaway accounts will be deleted by AutoModerator Memes and image macros are not acceptable forms of content. Just because it has a statistic in it doesn't make it statistics. Please try to keep submissions on topic and of high quality. They will be swiftly removed, so don't waste your time! Please kindly post those over at: r/homeworkhelp. This is not a subreddit for homework questions.
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