Evidence map›Paper›PMID 24885343›Full record

ArticleInternational journal of health geographics2014

Spatial heterogeneity of type I error for local cluster detection tests.

Aline Guttmann, Xinran Li, Jean Gaudart, Yan Gérard, Jacques Demongeot, Jean-Yves Boire, Lemlih Ouchchane

Abstract read
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Article in International journal of health geographics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Spatio-Temporal Variability of Malaria Incidence in the Health District of Kati, Mali, 2015-2019.International journal of environmental research and public health · 2022
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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Aline GuttmannDepartment of Biostatistics, Medical Informatics and Communication Technologies, Clermont University Hospital, Clermont-Ferrand F-63000, France. aline.guttmann@udamail.fr.
Xinran Li
Jean Gaudart
Yan Gérard
Jacques Demongeot
Jean-Yves Boire
Lemlih Ouchchane

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundJust 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.

methodsA 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.

resultsThe 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.

conclusionsIn 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.

Indexed as

Cluster AnalysisResearch DesignSpatial AnalysisCongenital AbnormalitiesDatabases, FactualFranceHumansRisk Factors

Identifiers

PMID24885343
PMCPMC4040115

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.