We have the following problem:

We developed what we call a partition roll-up model where subjects are partitioned into subgroups based on hospital diagnostic codes.   We then roll-up these partitions in such a way to combine subgroups that are highly enriched for readmission.

In other words, the hospital has 16% readmitted and our model allows us to identify 20% of all patients with a 50% readmission rate.  This allows us to intervene on a small number of patients and increase our chance of impacting readmission.

PROBLEM:  How do we measure the standard error or some other error around a partition of patients?  We don't want to use Rand since we are bootstrapping several 1000 instances (unless there is a generalized rand index that works with more than 2 partitions).

Any suggestions are welcome.  Feel free to email or call (314-704-8725)

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Thank you

Bill Shannon, PhD, MBA
Professor of Biostatistics in Medicine
Washington University School of Medicine
Director, Biostatistics Center
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