# Logistic regression - binary to continuous - how to interpret?

Given data with a binary outcome, i.e.: $$0$$ = no event, $$1$$ = event

which can be modeled with logistic regression, how then do we understand the following logic:

1. data-1 (fully observed) $$->$$ estimate of maximum-likelihood coefficients $$\hat{B}$$
2. data-2 - no outcome $$\hat{B}_{data-1}$$ $$->$$ estimate probability of outcome
3. data-3 = data 1 & data 2 $$->$$ update $$\hat{B}$$ in a new logistic model.

What does this mean? Particularly when the dependent variable is changed from binary $$(0, 1)$$ to a continuous probability i.e. $$(0 < P <1)$$.

This logic follows that in a paper I have been reading.

Is this possible to reproduce these steps in SAS?