Cancer Epidemiology

2005 ◽  
pp. 149-180
Author(s):  
James R. Cerhan
Keyword(s):  
Cancer Epidemiology

Cancer and the environment: Ten topics in environmental cancer epidemiology in Canada

2010 ◽  
Vol 29 (Supplement 1) ◽  
pp. 1-37
Author(s):  
Shirley A. Huchcroft ◽  
Yang Mao ◽  
Robert Semenciw
Keyword(s):  
Cancer Epidemiology

Molecular Epidemiology and Biomarkers in Etiologic Cancer Research: The New in Light of the Old

2020 ◽  
Author(s):  
Lungwani Muungo
Keyword(s):  
Gene Expression ◽  
Cancer Research ◽  
Molecular Epidemiology ◽  
Exposure Assessment ◽  
Large Scale ◽  
First Generation ◽  
Cancer Epidemiology ◽  
Policy Changes ◽  
The Past ◽  
New Generation

The purpose of this review is to evaluate progress inmolecular epidemiology over the past 24 years in canceretiology and prevention to draw lessons for futureresearch incorporating the new generation of biomarkers.Molecular epidemiology was introduced inthe study of cancer in the early 1980s, with theexpectation that it would help overcome some majorlimitations of epidemiology and facilitate cancerprevention. The expectation was that biomarkerswould improve exposure assessment, document earlychanges preceding disease, and identify subgroupsin the population with greater susceptibility to cancer,thereby increasing the ability of epidemiologic studiesto identify causes and elucidate mechanisms incarcinogenesis. The first generation of biomarkers hasindeed contributed to our understanding of riskandsusceptibility related largely to genotoxic carcinogens.Consequently, interventions and policy changes havebeen mounted to reduce riskfrom several importantenvironmental carcinogens. Several new and promisingbiomarkers are now becoming available for epidemiologicstudies, thanks to the development of highthroughputtechnologies and theoretical advances inbiology. These include toxicogenomics, alterations ingene methylation and gene expression, proteomics, andmetabonomics, which allow large-scale studies, includingdiscovery-oriented as well as hypothesis-testinginvestigations. However, most of these newer biomarkershave not been adequately validated, and theirrole in the causal paradigm is not clear. There is a needfor their systematic validation using principles andcriteria established over the past several decades inmolecular cancer epidemiology.

scholarly journals Correction to: Nutritional Metabolomics in Cancer Epidemiology: Current Trends, Challenges, and Future Directions

2019 ◽  
Vol 8 (3) ◽  
pp. 202-202
Author(s):  
Emma E. McGee ◽  
Rama Kiblawi ◽  
Mary C. Playdon ◽  
A. Heather Eliassen
Keyword(s):  
Cancer Epidemiology ◽  
Future Directions ◽  
Current Trends ◽  
Nutritional Metabolomics

scholarly journals Corrigendum to “Cancer epidemiology and trends in North Khorasan Province of Iran” [6, 2 (2018) 51–55]

2021 ◽  
pp. 100732
Author(s):  
Hosein Rafiemanesh ◽  
Atefe Zahedi ◽  
Mojtaba Mehtarpour ◽  
Alireza Zemestani ◽  
Abbas Balouchi ◽  
...  
Keyword(s):  
Cancer Epidemiology

scholarly journals Cancer incidence in the middle region of Libya: Data from the cancer epidemiology study in Misurata

2021 ◽  
Author(s):  
Anas Zarmouh ◽  
Abdulrahman Almalti ◽  
Ahmad Alzedam ◽  
Marwa Hamad ◽  
Hamad Elmughrabi ◽  
...  
Keyword(s):  
Cancer Incidence ◽  
Cancer Epidemiology ◽  
Middle Region ◽  
Epidemiology Study

scholarly journals sparrpowR: a flexible R package to estimate statistical power to identify spatial clustering of two groups and its application

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Ian D. Buller ◽  
Derek W. Brown ◽  
Timothy A. Myers ◽  
Rena R. Jones ◽  
Mitchell J. Machiela
Keyword(s):  
Relative Risk ◽  
Statistical Power ◽  
Risk Function ◽  
R Package ◽  
Cancer Epidemiology ◽  
Spatial Cluster ◽  
Cluster Detection ◽  
Detection Technique ◽  
Power Calculation ◽  
Spatial Clusters

Abstract Background Cancer epidemiology studies require sufficient power to assess spatial relationships between exposures and cancer incidence accurately. However, methods for power calculations of spatial statistics are complicated and underdeveloped, and therefore underutilized by investigators. The spatial relative risk function, a cluster detection technique that detects spatial clusters of point-level data for two groups (e.g., cancer cases and controls, two exposure groups), is a commonly used spatial statistic but does not have a readily available power calculation for study design. Results We developed sparrpowR as an open-source R package to estimate the statistical power of the spatial relative risk function. sparrpowR generates simulated data applying user-defined parameters (e.g., sample size, locations) to detect spatial clusters with high statistical power. We present applications of sparrpowR that perform a power calculation for a study designed to detect a spatial cluster of incident cancer in relation to a point source of numerous environmental emissions. The conducted power calculations demonstrate the functionality and utility of sparrpowR to calculate the local power for spatial cluster detection. Conclusions sparrpowR improves the current capacity of investigators to calculate the statistical power of spatial clusters, which assists in designing more efficient studies. This newly developed R package addresses a critically underdeveloped gap in cancer epidemiology by estimating statistical power for a common spatial cluster detection technique.

scholarly journals Cancer Epidemiology and Control in Taiwan: a Brief Review

2002 ◽  
Vol 32 (suppl 1) ◽  
pp. S66-S81 ◽  
Author(s):  
C.-J. Chen ◽  
S.-L. You ◽  
L.-H. Lin ◽  
W.-L. Hsu ◽  
Y.-W. Yang
Keyword(s):  
Cancer Epidemiology ◽  
And Control
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