Hi all

I am using a **zero inflated beta binomial regression model** for predicting an outcome that assumes integer values ranging from 0 to 10, where **0 = absence** of the finding and **1-10 = extension** of the finding when present.

I used the formula provided here (amazing guidance by the way) to get P(Y = 0) and to estimate Y given the value of a predictor whose model I fitted using restricted cubic splines with knots at 1, 2 and 5. Hope setting knots to successive scores is not problematic - most patients are concentrated in these scores.

Then, I divided this estimate by (1 - P(Y = 0)) to get the estimated outcome among patients who do have the outcome to some extent.

It seems interesting for me to say something like βOne has a lower chance of being outcome-free with higher predictor values (**Graph A**). However, if one has the outcome, it is expected to behave similarly with predictor values between zero and three (**Graph B**). When considering all patients together, the estimated mean outcome values are shown in **Graph C**.β However, I am afraid that the information in B may be useless due conditioning on the outcome.

Does this estimand make sense to you?

Thanks in advance!