Cargando…

Spatial heterogeneity of type I error for local cluster detection tests

BACKGROUND: 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...

Descripción completa

Detalles Bibliográficos
Autores principales:	Guttmann, Aline, Li, Xinran, Gaudart, Jean, Gérard, Yan, Demongeot, Jacques, Boire, Jean-Yves, Ouchchane, Lemlih
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BioMed Central 2014
Materias:	Methodology
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4040115/ https://www.ncbi.nlm.nih.gov/pubmed/24885343 http://dx.doi.org/10.1186/1476-072X-13-15

Ejemplares similares

Performance map of a cluster detection test using extended power
por: Guttmann, Aline, et al.
Publicado: (2013)

Cluster Detection Tests in Spatial Epidemiology: A Global Indicator for Performance Assessment
por: Guttmann, Aline, et al.
Publicado: (2015)

General Practitioners’ Barriers to Prescribe Physical Activity: The Dark Side of the Cluster Effects on the Physical Activity of Their Type 2 Diabetes Patients
por: Lanhers, Charlotte, et al.
Publicado: (2015)

Can the SCD test and terminal uridine nick-end labeling by flow cytometry technique (TUNEL/FCM) be used interchangeably to measure sperm DNA damage in routine laboratory practice?
por: Grèze, Cécile, et al.
Publicado: (2019)

General Practitioners’ Barriers to Physical Activity Negatively Influence Type 2 Diabetic Patients’ Involvement in Regular Physical Activity
por: Duclos, Martine, et al.
Publicado: (2011)

Machine learning analysis plans for randomised controlled trials: detecting treatment effect heterogeneity with strict control of type I error
por: Watson, James A., et al.
Publicado: (2020)

Factors associated with the spatial heterogeneity of the first wave of COVID-19 in France: a nationwide geo-epidemiological study
por: Gaudart, Jean, et al.
Publicado: (2021)

Border analysis for spatial clusters
por: Oliveira, Fernando L. P., et al.
Publicado: (2018)

Type I Error Control for Tree Classification
por: Jung, Sin-Ho, et al.
Publicado: (2014)

Is R(2)* a New MRI Biomarker for the Progression of Parkinson’s Disease? A Longitudinal Follow-Up
por: Ulla, Miguel, et al.
Publicado: (2013)

Maximin design of cluster randomized trials with heterogeneous costs and variances
por: van Breukelen, Gerard J. P., et al.
Publicado: (2021)

Nonparametric intensity bounds for the delineation of spatial clusters
por: Oliveira, Fernando LP, et al.
Publicado: (2011)

The mixed model for repeated measures for cluster randomized trials: a simulation study investigating bias and type I error with missing continuous data
por: Bell, Melanie L., et al.
Publicado: (2020)

Comparison of spatial scan statistic and spatial filtering in estimating low birth weight clusters
por: Ozdenerol, Esra, et al.
Publicado: (2005)

Modelling recurrent events: comparison of statistical models with continuous and discontinuous risk intervals on recurrent malaria episodes data
por: Sagara, Issaka, et al.
Publicado: (2014)

Modelling malaria incidence with environmental dependency in a locality of Sudanese savannah area, Mali
por: Gaudart, Jean, et al.
Publicado: (2009)

Quantitative prediction error analysis to investigate predictive performance under predictor measurement heterogeneity at model implementation
por: Luijken, Kim, et al.
Publicado: (2022)

Effect of spatial resolution on cluster detection: a simulation study
por: Ozonoff, Al, et al.
Publicado: (2007)

A flexibly shaped spatial scan statistic for detecting clusters
por: Tango, Toshiro, et al.
Publicado: (2005)

Spatial event cluster detection using an approximate normal distribution
por: Torabi, Mahmoud, et al.
Publicado: (2008)

Power for tests of interaction: effect of raising the Type I error rate
por: Marshall, Stephen W
Publicado: (2007)

Quantifying spatial heterogeneity of chlorophyll fluorescence during plant growth and in response to water stress
por: Bresson, Justine, et al.
Publicado: (2015)

The approaches to measuring the potential spatial access to urban health services revisited: distance types and aggregation-error issues
por: Apparicio, Philippe, et al.
Publicado: (2017)

Evaluation of sliding baseline methods for spatial estimation for cluster detection in the biosurveillance system
por: Xing, Jian, et al.
Publicado: (2009)

Detecting multiple spatial disease clusters: information criterion and scan statistic approach
por: Takahashi, Kunihiko, et al.
Publicado: (2020)

Optimizing the maximum reported cluster size in the spatial scan statistic for survival data
por: Lee, Sujee, et al.
Publicado: (2021)

Optimizing the maximum reported cluster size for the multinomial-based spatial scan statistic
por: Moon, Jisu, et al.
Publicado: (2023)

Using Gini coefficient to determining optimal cluster reporting sizes for spatial scan statistics
por: Han, Junhee, et al.
Publicado: (2016)

Impact of genotyping errors on the type I error rate and the power of haplotype-based association methods
por: Marquard, Vivien, et al.
Publicado: (2009)

The choice of statistical methods for comparisons of dosimetric data in radiotherapy
por: Chaikh, Abdulhamid, et al.
Publicado: (2014)

Global, local and focused geographic clustering for case-control data with residential histories
por: Jacquez, Geoffrey M, et al.
Publicado: (2005)

Local spatial analysis: an easy-to-use adaptive spatial EEG filter
por: Bufacchi, R. J., et al.
Publicado: (2021)

Spatial transcriptomics tools allow for regional exploration of heterogeneous muscle pathology in the pre-clinical rabbit model of rotator cuff tear
por: Ruoss, Severin, et al.
Publicado: (2022)

Type I error rates of multi-arm multi-stage clinical trials: strong control and impact of intermediate outcomes
por: Bratton, Daniel J., et al.
Publicado: (2016)

Accounting for regional background and population size in the detection of spatial clusters and outliers using geostatistical filtering and spatial neutral models: the case of lung cancer in Long Island, New York
por: Goovaerts, Pierre, et al.
Publicado: (2004)

Random error units, extension of a novel method to express random error in epidemiological studies
por: Janszky, Imre, et al.
Publicado: (2019)

Design, rationale, and baseline demographics of SEARCH I: a prospective cluster-randomized study
por: Albers, Frank, et al.
Publicado: (2012)

Cluster identification, selection, and description in cluster randomized crossover trials: the PREP-IT trials
por: Sprague, Sheila, et al.
Publicado: (2020)

sparrpowR: a flexible R package to estimate statistical power to identify spatial clustering of two groups and its application
por: Buller, Ian D., et al.
Publicado: (2021)

A simulation study for geographic cluster detection analysis on population-based health survey data using spatial scan statistics
por: Moon, Jisu, et al.
Publicado: (2022)

Cannot write session to /tmp/vufind_sessions/sess_jdhd6u2ej5408ful27c3e55pfl