A Bayesian hierarchical modeling approach for studying the factors affecting the stage at diagnosis of prostate cancer

Zhou, Huafeng; Lawson, Andrew; Hébert, James R.; Slate, Elizabeth H.; Hill, Elizabeth G.

doi:10.1002/sim.3024

Cited by 17 publications

(18 citation statements)

References 32 publications

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“…The importance of creating maps that accurately describe disease spatial distribution patterns appeared to be a consensual issue (Kulldorff et al, 2006) though the method used to achieve this was not consensual. Some articles intended to define the best method for some type of analysis for some particular datasets by comparing the results of the application of different spatial analysis methods (Bailony et al, 2011;Biggeri et al, 2009;Chen et al, 2008a;Colonna, 2004;Dasgupta et al, 2014;Goovaerts, 2005Goovaerts, , 2006aHegarty et al, 2010;Huang et al, 2008;Kaldor and Clayton, 1989;Kulldorff et al, 2006;Meliker et al, 2009;Sherman et al, 2014;Sloan et al, 2012;Zhou et al, 2008b). Table 5 shows a classification of some of most common spatial issues covered by research papers, as well as methods used to answer them.…”

Section: Applied Methods In Data Analysismentioning

confidence: 99%

Spatial epidemiology of cancer: a review of data sources, methods and risk factors

Roquette¹,

Paìnho²,

Nunes

2017

Geospat Health

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Cancer is a major concern among chronic diseases today. Spatial epidemiology plays a relevant role in this matter and we present here a review of this subject, including a discussion of the literature in terms of the level of geographic data aggregation, risk factors and methods used to analyse the spatial distribution of patterns and spatial clusters. For this purpose, we performed a websearch in the Pubmed and Web of Science databases including studies published between 1979 and 2015. We found 180 papers from 63 journals and noted that spatial epidemiology of cancer has been addressed with more emphasis during the last decade with research based on data mostly extracted from cancer registries and official mortality statistics. In general, the research questions present in the reviewed papers can be classified into three different sets: i) analysis of spatial distribution of cancer and/or its temporal evolution; ii) risk factors; iii) development of data analysis methods and/or evaluation of results obtained from application of existing methods. This review is expected to help promote research in this area through the identification of relevant knowledge gaps. Cancer's spatial epidemiology represents an important concern, mainly for public health policies design aimed to minimise the impact of chronic disease in specific populations. IntroductionGiven the relevance of spatial epidemiology in health research and the emphasis of cancer among chronic diseases, as well as the growing amount of studies in this area, it is important to know what the literature says about spatial epidemiology of cancer as well as provide its structured description. According to the World Health Organization (WHO), cancer is a leading cause of death in the world (WHO, 2015). It is also the cause of various morbidities and co-morbidities and can be responsible for loss of years of life years as well as loss of years without disability. Considering the aging population, it is predicted that the number of new cases of cancer will increase by more than 12% over the next decade in the European Union (EU) (DGS, 2013). The fight against cancer is a major challenge in public health. This challenge is due in part to the inequalities in terms of incidence, mortality, and survival. Therefore, a multidisciplinary approach is needed (Bastos et al., 2010). Among the various fields that can contribute to the development of knowledge about this disease, spatial epidemiology plays an important role. It can promote the understanding of spatial and temporal distribution patterns, helping to better identify the risk factors that influence them.Three types of approach can be established in spatial epidemiology: i) mapping; ii) geographic correlation; and iii) clustering (Elliott and Wartenberg, 2004). Mapping or map design regarding health and disease situations is the most often mentioned and used of these three approaches. Further, geographic correlation studies have the goal to spatially compare the health with several types of factors such as e...

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Section: Applied Methods In Data Analysismentioning

confidence: 99%

Spatial epidemiology of cancer: a review of data sources, methods and risk factors

Roquette¹,

Paìnho²,

Nunes

2017

Geospat Health

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“…The spatially correlated heterogeneity accounts for any unmeasured risk to caries progression that is common for adjacent/proximal tooth surfaces [25]. We now define the neighborhood in our spatial setup.…”

Section: Statistical Modelmentioning

confidence: 99%

Bayesian modeling of multivariate spatial binary data with applications to dental caries

Bandyopadhyay

Reich

Slate

2009

Statistics in Medicine

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SUMMARYDental research gives rise to data with potentially complex correlation structure. Assessments of dental caries yields a binary outcome indicating the presence or absence of caries experience for each surface of each tooth in a subject's mouth. In addition to this nesting, caries outcome exhibit spatial structure among neighboring teeth. We develop a Bayesian multivariate model for spatial binary data using random effects autologistic regression that controls for the correlation within tooth surfaces and spatial correlation among neighboring teeth. Using a sample from a clinical study conducted at the Medical University of South Carolina, we compare this autologistic model with covariates to alternative models to demonstrate the improvement in predictions and also to assess the effects of covariates on caries experience.

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“…In this study, the total random variation could also be decomposed into both spatially correlated as well as uncorrelated heterogeneities, often referred to as the ‘convolution prior’45, i.e. μ i ( s ) = F ( β 0 + X 1 is T β + U 1 ( s ) + E i ).…”

Section: Discussionmentioning

confidence: 99%

A spatial beta-binomial model for clustered count data on dental caries

Bandyopadhyay

Reich

Slate

2010

Stat Methods Med Res

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One of the most important indicators of dental caries prevalence is the total count of decayed, missing or filled (DMF) surfaces in a tooth. These count data are often clustered in nature (several count responses clustered within a subject), over-dispersed, as well as spatially referenced (a diseased tooth might be positively influencing the decay process of a set of neighboring teeth). In this paper, we develop a multivariate spatial Beta-Binomial (BB) model for these data that accommodates both over-dispersion as well as latent spatial associations. Using a Bayesian paradigm, the re-parameterized marginal mean (as well as variance) under the BB framework are modeled using a regression on subject/tooth-specific co-variables and a conditionally autoregressive (CAR) prior that models the latent spatial process. The necessity of exploiting spatial associations to model count data arising in dental caries research is demonstrated using a small simulation study. Real data confirms that our spatial BB model provides a superior estimation and model fit as compared to other sub-models that do not consider modeling spatial associations.

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A Bayesian hierarchical modeling approach for studying the factors affecting the stage at diagnosis of prostate cancer

Cited by 17 publications

References 32 publications

Spatial epidemiology of cancer: a review of data sources, methods and risk factors

Spatial epidemiology of cancer: a review of data sources, methods and risk factors

Bayesian modeling of multivariate spatial binary data with applications to dental caries

A spatial beta-binomial model for clustered count data on dental caries

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