Int J Health Geogr - Spatial heterogeneity of type I error for local cluster detection tests.

Tópicos

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Resumo

CKGROUND: Just as power, type I error of cluster detection tests (CDTs) should be spatially assessed. Indeed, CDTs' type I error and power have both a spatial component as CDTs both detect and locate clusters. In the case of type I error, the spatial distribution of wrongly detected clusters (WDCs) can be particularly affected by edge effect. This simulation study aims to describe the spatial distribution of WDCs and to confirm and quantify the presence of edge effect.METHODS: A simulation of 40 000 datasets has been performed under the null hypothesis of risk homogeneity. The simulation design used realistic parameters from survey data on birth defects, and in particular, two baseline risks. The simulated datasets were analyzed using the Kulldorff's spatial scan as a commonly used test whose behavior is otherwise well known. To describe the spatial distribution of type I error, we defined the participation rate for each spatial unit of the region. We used this indicator in a new statistical test proposed to confirm, as well as quantify, the edge effect.RESULTS: The predefined type I error of 5% was respected for both baseline risks. Results showed strong edge effect in participation rates, with a descending gradient from center to edge, and WDCs more often centrally situated.CONCLUSIONS: In routine analysis of real data, clusters on the edge of the region should be carefully considered as they rarely occur when there is no cluster. Further work is needed to combine results from power studies with this work in order to optimize CDTs performance.

Resumo Limpo

ckground just power type error cluster detect test cdts spatial assess inde cdts type error power spatial compon cdts detect locat cluster case type error spatial distribut wrong detect cluster wdcs can particular affect edg effect simul studi aim describ spatial distribut wdcs confirm quantifi presenc edg effectmethod simul dataset perform null hypothesi risk homogen simul design use realist paramet survey data birth defect particular two baselin risk simul dataset analyz use kulldorff spatial scan common use test whose behavior otherwis well known describ spatial distribut type error defin particip rate spatial unit region use indic new statist test propos confirm well quantifi edg effectresult predefin type error respect baselin risk result show strong edg effect particip rate descend gradient center edg wdcs often central situatedconclus routin analysi real data cluster edg region care consid rare occur cluster work need combin result power studi work order optim cdts perform

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