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BMC Medical Research Methodology

, 16:136

Data analysis, statistics and modelling

Abstract

BackgroundThe reliability of spatial statistics is often put into question because real spatial variations may not be found, especially in heterogeneous areas. Our objective was to compare empirically different cluster detection methods. We assessed their ability to find spatial clusters of cancer cases and evaluated the impact of the socioeconomic status e.g., the Townsend index on cancer incidence.

MethodsMoran’s I, the empirical Bayes index EBI, and Potthoff-Whittinghill test were used to investigate the general clustering. The local cluster detection methods were: i the spatial oblique decision tree SpODT; ii the spatial scan statistic of Kulldorff SaTScan; and, iii the hierarchical Bayesian spatial modeling HBSM in a univariate and multivariate setting. These methods were used with and without introducing the Townsend index of socioeconomic deprivation known to be related to the distribution of cancer incidence. Incidence data stemmed from the Cancer Registry of Isère and were limited to prostate, lung, colon-rectum, and bladder cancers diagnosed between 1999 and 2007 in men only.

ResultsThe study found a spatial heterogeneity p < 0.01 and an autocorrelation for prostate EBI = 0.02; p = 0.001, lung EBI = 0.01; p = 0.019 and bladder EBI = 0.007; p = 0.05 cancers. After introduction of the Townsend index, SaTScan failed in finding cancers clusters. This introduction changed the results obtained with the other methods. SpODT identified five spatial classes p < 0.05: four in the Western and one in the Northern parts of the study area standardized incidence ratios: 1.68, 1.39, 1.14, 1.12, and 1.16, respectively. In the univariate setting, the Bayesian smoothing method found the same clusters as the two other methods RR >1.2. The multivariate HBSM found a spatial correlation between lung and bladder cancers r = 0.6.

ConclusionsIn spatial analysis of cancer incidence, SpODT and HBSM may be used not only for cluster detection but also for searching for confounding or etiological factors in small areas. Moreover, the multivariate HBSM offers a flexible and meaningful modeling of spatial variations; it shows plausible previously unknown associations between various cancers.

KeywordsSpatial analysis Cluster detection Cancer Oblique decision tree AbbreviationsBYMBesag-York-Mollié

CARConditional autoregressive

CARTClassification and regression tree

DICDeviance information criterion

EBIEmpirical Bayes Index

GAMGeographical analysis machine

HBSMHierarchical Bayesian spatial modeling

MCMCMarkov-chain Monte-Carlo

RRRelative risk

SaTScanSpatial scan statistic of Kulldorff

SIRStandardized incidence ratio

SpODTSpatial oblique decision Tree

Electronic supplementary materialThe online version of this article doi:10.1186-s12874-016-0228-x contains supplementary material, which is available to authorized users.

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Autor: Juste Aristide Goungounga - Jean Gaudart - Marc Colonna - Roch Giorgi

Fuente: https://link.springer.com/







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