survival analysis: goodness of fit with c-index? I have a survival analysis (cox-ph and cox-ph cause specific) that uses a lot of observations (excess of 2million), with many fewer events (around 200,000 in one configuration and 10k in another).
I read online that the c-index is (a) the most popular goodness of fit test of survival analyses, and (b) is optimistic with many censored results. My questions are:

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*If I do use the c-index, is there a generally acceptable threshold for what is a good value (besides just over .5)?


*If I don't use a c-index, what should I use?

If you are testing a hypothesis, the c-index may not help much. For hypothesis testing, you should select and use the confounders that you believe are most important based on your content knowledge. Then, test whether the covariate of interest is associated with time-to-event in the presence of confounders.
Perhaps you want to compare two or more nested models that contain different confounders and/or confounder specifications? For these comparisons, you can compare with values of the c-index but the Akaike Information Criterion is probably better for comparing nested models.
See: What is the difference in what AIC and c-statistic (AUC) actually measure for model fit?
