Spatial Cluster Detection for Censored Outcome Data
While numerous methods have been proposed to test for spatial cluster detection, in particular for discrete outcome data (e.g., disease incidence), few have been available for continuous data that are subject to censoring. This article provides an extension of the spatial scan statistic ( Kulldorff, 1997, Communications in Statistics26, 1481–1496) for censored outcome data and further proposes a simple spatial cluster detection method by utilizing cumulative martingale residuals within the framework of the Cox's proportional hazards models. Simulations have indicated good performance of the proposed methods, with the practical applicability illustrated by an ongoing epidemiology study which investigates the relationship of environmental exposures to asthma, allergic rhinitis/hayfever, and eczema.
Document Type: Research Article
Affiliations: 1: The Channing Laboratory, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, U.S.A. 2: Department of Biostatistics, Harvard School of Public Health and the Dana Farber Cancer Institute, Boston, Massachusetts 02115, U.S.A.
Publication date: June 1, 2007