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Aug 18, 2020 at 11:02 answer added BigBendRegion timeline score: 2
Aug 12, 2020 at 0:56 comment added user293790 Compare the percentage alive between 2 groups over time.
Aug 12, 2020 at 0:55 comment added user293790 The response variable is whether a subject is still alive at any time. Don’t suggest coxph because I didn’t measure time until death.
Aug 12, 2020 at 0:54 comment added Dave Then your outcome variable is how many deaths there are, not whether or not a subject lives or dies. What are you trying to model?
Aug 12, 2020 at 0:53 comment added user293790 If I coded the death per ID number as a count, wouldn’t that be a count? Of course anything can only die once.
Aug 12, 2020 at 0:51 review Low quality posts
Aug 12, 2020 at 4:55
Aug 12, 2020 at 0:50 comment added Dave You don’t have zero-inflated data. You have imbalanced classes. Poisson regression does not come into play. Poisson would be when the response is a count. Your response is a category that you happen to code as 0 and 1, but those numbers have no meaning. (You could flip which class corresponds time which number without changing much of anything.)
Aug 12, 2020 at 0:43 history edited user293790
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Aug 12, 2020 at 0:42 comment added user293790 If someone suggests poisson regression, I’ll say no because I’ll have to use quasi poisson with my zero inflated data, or use NBGLMM which I don’t want to do.
Aug 12, 2020 at 0:42 review First posts
Aug 12, 2020 at 2:32
Aug 12, 2020 at 0:40 comment added user293790 Rarely every does death happens
Aug 12, 2020 at 0:39 comment added user293790 There’s a lot of alives and few dead.
Aug 12, 2020 at 0:38 comment added Dave What is zero-inflated in this case, the response variable?
Aug 12, 2020 at 0:34 history asked user293790 CC BY-SA 4.0