Estimands from zero-inflated regression model

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 β€œOne 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.” However, I am afraid that the information in B may be useless due conditioning on the outcome.

Does this estimand make sense to you?

image

Thanks in advance!

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