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survival data #26
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Related to r-causal/causal_inference_r_workshop#19 where we need data with loss to follow up |
Could this type of approach be useful here? https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3935334/ |
Some data sources: https://github.com/hfrick/cetaceans (has unknown category, so could be case for loss to followup) |
Time to divorce: https://grodri.github.io/survival/project. number of kids as time-varying exposure? SurvSet (collection of time to event data): https://arxiv.org/pdf/2203.03094.pdf, https://github.com/ErikinBC/SurvSet/tree/main/SurvSet/_datagen/output |
Time to adoption: https://www.kaggle.com/competitions/sliced-s01e10-playoffs-2/data. Spay-neuter as an intervention? Probably a null effect so could be interesting example of confounding |
idea: disney data on time to ride closing. exposure as weather or something? |
Simulating survival data: https://www.jstatsoft.org/article/view/v097i03 |
The touring plans data may not meet the need for censored time-to-event data. Might have an alternate option in this pet adoption data:
https://www.kaggle.com/c/sliced-s01e10-playoffs-2/data
H/T Max Kuhn:
https://topepo.github.io/2021-r-pharma/index.html#3
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