Sequentially estimated propensity models

We've been estimating response propensity models during data collection for a while. We have at least two reasons for doing this:

We monitor average response probability for active cases.
I uses estimates from these models to determine the next step in experiments.

There is some risk to estimating models in this way. Particularly for the second purpose. The data used to make the estimates is accumulating over time. And those data don't come in randomly -- the easiest cases come in early and the difficult cases tend to come in later.

If I'm interested in the average impact of adding an 8th call to active cases, I might get a different estimate early in the field period than later.

In practice, the impact of this isn't as severe as you might think and there are remedies. Which leads me to the self-promotion part of this post ... I'll be presenting on this topic at AAPOR this year.

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