-
Notifications
You must be signed in to change notification settings - Fork 1
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Issue 37 draft: Why it works vignette with analytic solutions #68
Conversation
37e7f18
to
b05b238
Compare
Thanks for this @SamuelBrand1. From a first pass it looks great and having some analytical solutions is very exciting To do before I've read content:
|
Read to dos:
|
…d1/primarycensoreddist into why-it-works-vignette
I can't edit @seabbs check list so I'll C&P here:
|
This is how benchmark results would change (along with a 95% confidence interval in relative change) if 6120812 is merged into main:
|
This is how benchmark results would change (along with a 95% confidence interval in relative change) if 05daf84 is merged into main:
|
My questions towards resolving final check list items:
|
This is how benchmark results would change (along with a 95% confidence interval in relative change) if 9df87c0 is merged into main:
|
The getting started vignette. Can be its own issue.
Given this has taken a while perhaps in its PR.
A new line in |
Interestingly I can tick your boxes but you can't tick mine. Might be about repo status. I will give you some repo permissions |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
This is really clear now. Nice work. I think merge and deal with other changes as their own issues.
This is how benchmark results would change (along with a 95% confidence interval in relative change) if 8059534 is merged into main:
|
Description
This PR closes #37.
This draft PR creates a "why it works" vignette for the mathematics underlying the approach to inference under censoring/truncation used in
primarycensoreddist
.The basic concept is to present the primary censoring window problem in terms of the survival function of the inter-event time after the end of the primary event window. This random variable can be negative therefore instead of the survival function starting at 1, it starts at the probability that the secondary event occurs after the end of the primary censor window.
This survival function can be either the target of numerical quadrature, or it might have analytically accessible solutions.
I've started adding examples of where the survival function has analytic solutions. This is WIP:
Checklist