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Implementation of a sequential TreeScan algorithm for disease surveillance

Abstract Number:

3270

Submission Type:

Contributed Abstract

Contributed Abstract Type:

Paper

Participants:

Georg Hahn (1)

Institutions:

(1) N/A, N/A

First Author:

Georg Hahn
N/A

Presenting Author:

Georg Hahn
N/A

Abstract Text:

TreeScan is a popular algorithm for hierarchical testing of hypotheses. The algorithm is used in scenarios where the hypotheses under consideration naturally form a hierarchical tree structure, such as in the areas of pharmaceutical drugs or occupations, thereby allowing one to detect unsuspected relationships. Its tree-based scan statistic only assumes a minimum of prior assumptions about the input, and it adjusts for the multiple testing that is inherent in the tree-based testing scenarios. However, the tree structure of the hypotheses is assumed fixed in TreeScan, thus impeding its use in application areas which require dynamic updates, such as time-varying patient enrollment during trials. For this reason, we extend TreeScan to incorporate a sequential testing design which is capable of controlling either the FWER or the FDR criterion by means of appropriate alpha spending. We apply our improved algorithm to EHR and claims databases to study the relationship between health events and various potential risk factors.

Keywords:

Sponsors:

Section on Statistical Computing

Tracks:

Data Science

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