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Abstract Details

Activity Number: 566
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #306804
Title: Recursive Partitioning for Latent Recurrent Events: Application to Multiple Sclerosis Data
Author(s): Shannon Stock*+ and Brian C. Healy and Victor G. DeGruttola
Companies: Harvard University and Massachusetts General Hospital and Harvard School of Public Health
Address: 170 Brookline Ave, Boston, MA, 02215, United States

The progression of autoimmune diseases often involves jumps in disease severity followed by periods of stability. In the case of multiple sclerosis, the Expanded Disability Status Scale is used to track and classify patient disease progression. However, since patients may experience small exacerbations of their disease severity that eventually subside, there is uncertainty whether an observed increase in EDSS is due to an actual jump in the disease process. Therefore, analyses involving such data must accommodate a latent recurrent response. In this paper, we introduce a recursive partitioning method that accommodates latent recurrent events by weighting observed changes in outcome measurements by the probability one or more events occurred since the previous time of measurement. We obtain estimates of these probabilities using the Expectation-Maximization algorithm. This method is evaluated using simulation studies and illustrated using a motivating data set involving multiple sclerosis patients.

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