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I'm somewhat new to R, so I'm guessing this might be a basic question. In any case, I have some interval-censored data that I'm trying to fit a parametric model to. I've looked at the complementary log-log plot of the survival curves and have ruled out a Weibull model; the only other model my class has discussed — at least insofar as assessing the appropriateness of the model — is the log-logistic, so I figure I should try that as well. However, I'm not sure how to plot the log of the survival odds against $\log (t)$. Could anyone offer any advice on how to do this?

Beyond that, I've already run survreg on my data and hit a bit of a bump: When I tried to create the survival object using type interval2, I received a message saying that I had "invalid survival times for this distribution". When I changed the type to "interval", it worked fine. What are the reasons this might happen?

Thank you!

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I'm 90% sure your problems with survreg is that it does not allow you declare having the left side of your interval to be $\leq 0$ (emphasis on equal) or the right side of your interval to be $\infty$.

If you're not married to the AFT model fit by survreg, I may biased-ly point you toward the package icenReg. This fits both semi-parametric and non-parametric Cox-PH and proportional odds models. And it allows you to have the left side of your interval be equal 0 and the right equal $\infty$.

In fact, for your problem of comparing fits, you can use the function diag_baseline. It will fit several choices of parametric baseline survival functions against the equivalent semi-parametric model of choice (i.e. proportional odds or proportional hazards).

But it does not fit a AFT model at this time.

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