Modified spatial scan statistics using a restricted likelihood ratio for ordinal outcome data

Myeonggyun Lee and Inkyung Jung

Computational Statistics & Data Analysis, 2019, vol. 133, issue C, 28-39

Abstract: Spatial scan statistics are widely used as a technique to detect geographical disease clusters for different types of data. It has been pointed out that the Poisson-based spatial scan statistic tends to detect rather larger clusters by absorbing insignificant neighbors with non-elevated risks. We suspect that the spatial scan statistic for ordinal data may also have similar undesirable phenomena. In this paper, we propose to apply a restricted likelihood ratio to spatial scan statistics for ordinal outcome data to circumvent such a phenomenon. Through a simulation study, we demonstrated not only that original spatial scan statistics have the over-detection phenomenon but also that our proposed methods have reasonable or better performance compared with the original methods. We illustrated the proposed methods using a real data set from the 2014 Health Screening Program of Korea with the diagnosis results of normal, caution, suspected disease, and diagnosed with disease as an ordinal outcome.

Keywords: Cluster detection; Likelihood ratio test; Ordinal data; Spatial scan statistic (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:133:y:2019:i:c:p:28-39

DOI: 10.1016/j.csda.2018.09.005

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