Cargando…

Spatially varying effects of measured confounding variables on disease risk

BACKGROUND: The presence of considerable spatial variability in incidence intensity suggests that risk factors are unevenly distributed in space and influence the geographical disease incidence distribution and pattern. As most human common diseases that challenge investigators are complex traits an...

Descripción completa

Detalles Bibliográficos
Autores principales:	Wu, Chih-Chieh, Chu, Yun-Hsuan, Shete, Sanjay, Chen, Chien-Hsiun
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BioMed Central 2021
Materias:	Methodology
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8582111/ https://www.ncbi.nlm.nih.gov/pubmed/34763707 http://dx.doi.org/10.1186/s12942-021-00298-6

Ejemplares similares

Differentiating anomalous disease intensity with confounding variables in space
por: Wu, Chih-Chieh, et al.
Publicado: (2020)

Assessing current temporal and space-time anomalies of disease incidence
por: Wu, Chih-Chieh, et al.
Publicado: (2017)

Spatial+: A novel approach to spatial confounding
por: Dupont, Emiko, et al.
Publicado: (2022)

Recognizing spatial and temporal clustering patterns of dengue outbreaks in Taiwan
por: Lai, Wei-Ting, et al.
Publicado: (2018)

Adjusting for time‐varying confounders in survival analysis using structural nested cumulative survival time models
por: Seaman, Shaun, et al.
Publicado: (2019)

Rank correlation under categorical confounding
por: Plante, Jean-François
Publicado: (2017)

Longitudinal effects of lipid indices on incident cardiovascular diseases adjusting for time-varying confounding using marginal structural models: 25 years follow-up of two US cohort studies
por: Koohi, Fatemeh, et al.
Publicado: (2022)

Control of confounding in the analysis phase – an overview for clinicians
por: Kahlert, Johnny, et al.
Publicado: (2017)

Quantitative gene set analysis generalized for repeated measures, confounder adjustment, and continuous covariates
por: Turner, Jacob A., et al.
Publicado: (2015)

A Proximity-Based Method to Identify Genomic Regions Correlated with a Continuously Varying Environmental Variable
por: Di Gaetano, Cornelia, et al.
Publicado: (2013)

Risk of confounding variables in multivariate analysis
por: Gagnon, Bruno, et al.
Publicado: (2022)

Confounding in observational studies based on large health care databases: problems and potential solutions – a primer for the clinician
por: Nørgaard, Mette, et al.
Publicado: (2017)

Analysis of genes for alcoholism using two-disease-locus models
por: Wu, Chih-Chieh, et al.
Publicado: (2005)

A methodological framework for tackling confounding by indication when assessing the treatment effects of Chinese herbal injections in the real world
por: Tan, Jing, et al.
Publicado: (2022)

Dealing with confounding in observational studies
por: Fumo-dos-Santos, Cristiane, et al.
Publicado: (2023)

Annotation depth confounds direct comparison of gene expression across species
por: Oziolor, Elias, et al.
Publicado: (2021)

Semiquantitative RT-PCR measurement of gene expression in rat tissues including a correction for varying cell size and number
por: Romero, Maria del Mar, et al.
Publicado: (2007)

A multi-modal relative spatial access assessment approach to measure spatial accessibility to primary care providers
por: Lin, Yan, et al.
Publicado: (2018)

Exploratory disease mapping: kriging the spatial risk function from regional count data
por: Berke, Olaf
Publicado: (2004)

Calculation of exact p-values when SNPs are tested using multiple genetic models
por: Talluri, Rajesh, et al.
Publicado: (2014)

Feature selection and causal analysis for microbiome studies in the presence of confounding using standardization
por: Goren, Emily, et al.
Publicado: (2021)

Within-person variability in calculated risk factors: Comparing the aetiological association of adiposity ratios with risk of coronary heart disease
por: Wormser, David, et al.
Publicado: (2013)

Survival analysis for AdVerse events with VarYing follow-up times (SAVVY)—estimation of adverse event risks
por: Stegherr, Regina, et al.
Publicado: (2021)

Empirical estimation of sequencing error rates using smoothing splines
por: Zhu, Xuan, et al.
Publicado: (2016)

Mediation analysis of time‐to‐event endpoints accounting for repeatedly measured mediators subject to time‐varying confounding
por: Vansteelandt, Stijn, et al.
Publicado: (2019)

Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits
por: Oury, V., et al.
Publicado: (2022)

A ubiquitous method for street scale spatial data collection and analysis in challenging urban environments: mapping health risks using spatial video in Haiti
por: Curtis, Andrew, et al.
Publicado: (2013)

Geostatistical analysis of disease data: visualization and propagation of spatial uncertainty in cancer mortality risk using Poisson kriging and p-field simulation
por: Goovaerts, Pierre
Publicado: (2006)

Investigation of Confounding Factors in Measuring Tissue Saturation with NIRS Spatially Resolved Spectroscopy
por: Kovacsova, Z., et al.
Publicado: (2018)

Time-varying SMART design and data analysis methods for evaluating adaptive intervention effects
por: Dai, Tianjiao, et al.
Publicado: (2016)

Host variables confound gut microbiota studies of human disease
por: Vujkovic-Cvijin, Ivan, et al.
Publicado: (2020)

Sigma-RF: prediction of the variability of spatial restraints in template-based modeling by random forest
por: Lee, Juyong, et al.
Publicado: (2015)

From spatial ecology to spatial epidemiology: modeling spatial distributions of different cancer types with principal coordinates of neighbor matrices
por: Voutilainen, Ari, et al.
Publicado: (2014)

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)

Differences in pulse rate variability with measurement site
por: Yuda, Emi, et al.
Publicado: (2020)

Adjusting for sampling variability in sparse data: geostatistical approaches to disease mapping
por: Hampton, Kristen H, et al.
Publicado: (2011)

Geostatistical analysis of disease data: accounting for spatial support and population density in the isopleth mapping of cancer mortality risk using area-to-point Poisson kriging
por: Goovaerts, Pierre
Publicado: (2006)

Application of a spatially-weighted Relief algorithm for ranking genetic predictors of disease
por: Stokes, Matthew E, et al.
Publicado: (2012)

Detecting multiple spatial disease clusters: information criterion and scan statistic approach
por: Takahashi, Kunihiko, 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)

Cannot write session to /tmp/vufind_sessions/sess_m9pqgn4j3ejh89ju0dgnp188n3