Daily Propensity Models

We estimate daily propensity models for a number of reasons. A while ago, I started looking at the consistency of the estimates from these models. I worry that the estimates may be biased early in the field period.

I found this example a couple of years ago where the estimates seemed pretty consistent.

I went back recently to see what examples of inconsistent estimates I could find. I have this example where an estimated coefficient (for the number of prior call attempts) in a daily model varies a great deal over the first few months of a study.

It turns out that some of these coefficient estimates are significantly different.

The model from this example was used to classify cases. The estimated propensities were split into tertiles. It turns out that these differences in estimation only made a difference in the classification of about 15% of the cases. But that is 15% that get misclassified at least some of the time.

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