Multiple cancer sites incidence rates estimation using a multivariate Bayesian model

Assunção, Renato M.; Castro, Mônica Silva Monteiro de

doi:10.1093/ije/dyh040

Cited by 20 publications

(20 citation statements)

References 24 publications

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“…However the amount of information that is “borrowed” across diseases depends on the amount of correlation. In addition to our own work, only very few other examples of estimated joint spatial correlation exist in the epidemiologic literature (Assuncao and Castro, 2004; Feltbower et al, 2005; Thompson et al, 2007). A study of acute lymphoblastic leukemia and type 1 diabetes found some suggestion of spatial correlation (r=0.33, 95% credible interval -0.20, 0.74) in Yorkshire (Feltbower et al, 2005).…”

Section: Discussionmentioning

confidence: 87%

Evaluating geographic variation in type 1 and type 2 diabetes mellitus incidence in youth in four US regions

et al. 2010

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We evaluated geographic variation of Type 1 and Type 2 diabetes mellitus (T1DM, T2DM) in four regions of the United States. Data on 807 incident T1DM cases diabetes and 313 T2DM cases occurring in 2002-03 in South Carolina (SC) and Colorado (CO), 5 counties in Washington (WA), and an 8 county region around Cincinnati, Ohio (OH) among youth aged 10 through 19 years were obtained from the SEARCH for Diabetes in Youth Study. Geographic patterns were evaluated in a Bayesian framework. Incidence rates differed between the study regions, even within race/ethnic groups. Significant small area variation within study region was observed for T1DM and for T2DM. Evidence for joint spatial correlation between T1DM and T2DM was present at the county level for SC (rSC= 0.31) and CO non-Hispanic whites (rCO= 0.40) and CO Hispanics (rCO= 0.72). At the tract level no evidence for meaningful joint spatial correlation was observed (rSC= -0.02; rCO= -0.02; rOH= 0.03; rWA= 0.09). Our study provides evidence for the presence of both regional and small-area, localized variation in type 1 and type 2 incidence among youth aged 10-19 years in the United States.

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Section: Discussionmentioning

confidence: 87%

Evaluating geographic variation in type 1 and type 2 diabetes mellitus incidence in youth in four US regions

et al. 2010

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“…Researchers and practitioners in systems engineering and risk analysis are commonly plagued by the data scarceness problem, which can be prevalent in modeling complex systems of systems. On the other hand, it is well known that when estimating the parameters of a model by traditional statistical methods using relatively small datasets, those methods generate “unstable” results with large estimation variance [Farrell, MacGibbon, and Tomberlin, 1997; Assuncao and Castro 2004]. Consequently, important model parameters cannot be estimated and tested within an acceptable level of significance.…”

Section: Phantom System Models (Psm) and Meta‐modelingmentioning

confidence: 99%

Modeling complex systems of systems with Phantom System Models

Haimes

2012

Systems Engineering

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Complex systems are commonly composed of interconnected and inter‐ and intradependent subsystems, which in their essence constitute systems of systems with multiple functions, operations, and stakeholders. Phantom System Models (PSM) is a modeling methodology inspired by philosophical and conceptual thinking from the arts, and is driven and supported by systems engineering theory, methodology, and practice. The PSM is designed to model inter‐ and intradependencies between and among the subsystems of a complex system of systems by exploiting vital knowledge and information embedded in the intrinsic and extrinsic common and uncommon state variables among the subsystems. Among the several systems engineering theories and methodologies, the PSM in particular builds on the centrality of the states of the system in modeling and in risk analysis; fundamentals in system identification (the inverse problem); hierarchical holographic modeling; coordinated hierarchical Bayesian model; and hierarchical decomposition and higher‐level coordination. An example problem of a PSM‐based modeling of a prototype system of systems is presented. © 2012 Wiley Periodicals, Inc. Syst Eng

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“…Let Y ij , the observed number of cases in the i-th We assumed that for each area log(θ i ) be independently drawn from a multivariate normal distribution with vector mean µ i = (µ i1 ,…,µ ij ,…,µ iJ ) and JxJ covariance matrix Σ (Assunção and Castro, 2004).…”

Section: Discussionmentioning

confidence: 99%

“…genetic composition and susceptibility, and environmental characteristics. Assunção and Castro (2004) used a multivariate model to make inference on the correlation among responses and investigate on the existence of shared risk factors for some diseases. We do not address this point, while stressing area-specific disease profile as the basic object to be ranked.…”

Section: Discussionmentioning

confidence: 99%

A statistical approach to rank multiple priorities in Environmental Epidemiology: an example from high-risk areas in Sardinia, Italy

Catelan

Biggeri

2008

Geospat Health

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Abstract. In Environmental Epidemiology, long lists of relative risk estimates from exposed populations are compared to a reference to scrutinize the dataset for extremes. Here, inference on disease profiles for given areas, or for fixed disease population signatures, are of interest and summaries can be obtained averaging over areas or diseases. We have developed a multivariate hierarchical Bayesian approach to estimate posterior rank distributions and we show how to produce league tables of ranks with credibility intervals useful to address the above mentioned inferential problems. Applying the procedure to a real dataset from the report "Environment and Health in Sardinia (Italy)" we selected 18 areas characterized by high environmental pressure for industrial, mining or military activities investigated for 29 causes of deaths among male residents. Ranking diseases highlighted the increased burdens of neoplastic (cancerous), and non-neoplastic respiratory diseases in the heavily polluted area of Portoscuso. The averaged ranks by disease over areas showed lung cancer among the three highest positions.

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Multiple cancer sites incidence rates estimation using a multivariate Bayesian model

Abstract: More precise age-standardized cancer incidence rates can be calculated using data from other cancers. The method is conceptually simple, easy to perform, has low cost, and can improve substantially the estimation of cancer incidence and other vital rates.

Cited by 20 publications

References 24 publications

Evaluating geographic variation in type 1 and type 2 diabetes mellitus incidence in youth in four US regions

Evaluating geographic variation in type 1 and type 2 diabetes mellitus incidence in youth in four US regions

Modeling complex systems of systems with Phantom System Models

A statistical approach to rank multiple priorities in Environmental Epidemiology: an example from high-risk areas in Sardinia, Italy

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