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dincerti avatar dincerti commented on July 24, 2024

I would echo Max and suggest using survfit() to get survival predictions. It does a lot under the hood and can handle stratification, start/stop data, etc. There is also a summary.survfit() method can can interpolate for arbitrary time points.

type = "cumulative_hazard" would be a good idea and would work for most/all models. Perhaps type = "hazard" could be restricted to models where it is straightforward to compute (i.e., for parametric models)?

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topepo avatar topepo commented on July 24, 2024

Do people ever want (instantaneous) hazard estimates for this model?

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hfrick avatar hfrick commented on July 24, 2024

update: survival probabilities for coxph models now use survfit()

leaving this open for the (instantaneous) hazard discussion

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dincerti avatar dincerti commented on July 24, 2024

I think hazards can be useful as a diagnostic (e.g., for understanding whether a parametric model seems reasonable, exploring non-proportional hazards). But I don't think they are nearly as useful as other summaries of the survival distribution, particularly in a prediction context.

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hfrick avatar hfrick commented on July 24, 2024

thanks for the feedback @dincerti !

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github-actions avatar github-actions commented on July 24, 2024

This issue has been automatically locked. If you believe you have found a related problem, please file a new issue (with a reprex: https://reprex.tidyverse.org) and link to this issue.

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