Spatio-temporal interaction with disease mapping

Sun, Dongchu; Tsutakawa, Robert K.; Kim, Hoon; He, Zhuoqiong

doi:10.1002/1097-0258(20000815)19:15<2015::aid-sim422>3.0.co;2-e

Cited by 107 publications

(90 citation statements)

References 33 publications

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“…Work included identifying geographical areas with excess incidence of leukaemia [325], mapping of lung cancer mortality in Ohio with errors in covariates [326], and combining longitudinal and spatial data [327] built on previous work on mapping spatial smoothing of survival from breast cancer and malignant melanoma [328]. Other applications included spatiotemporal analysis of lung cancer rates in Missouri [329]. Conditionally autoregressive models, which allow each site to 'borrow strength' from it neighbours were used for lip cancer in Scotland [330], and asthma mortality in Taiwan [331].…”

Section: Spatial Analysismentioning

confidence: 99%

Bayesian statistics in medicine: a 25 year review

2006

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SUMMARYThis review examines the state of Bayesian thinking as Statistics in Medicine was launched in 1982, reflecting particularly on its applicability and uses in medical research. It then looks at each subsequent five-year epoch, with a focus on papers appearing in Statistics in Medicine, putting these in the context of major developments in Bayesian thinking and computation with reference to important books, landmark meetings and seminal papers. It charts the growth of Bayesian statistics as it is applied to medicine and makes predictions for the future. From sparse beginnings, where Bayesian statistics was barely mentioned, Bayesian statistics has now permeated all the major areas of medical statistics, including clinical trials, epidemiology, meta-analyses and evidence synthesis, spatial modelling, longitudinal modelling, survival modelling, molecular genetics and decision-making in respect of new technologies.

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Section: Spatial Analysismentioning

confidence: 99%

Bayesian statistics in medicine: a 25 year review

2006

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“…For rare outcomes, the binomial distribution may be approximated by a Poisson distribution for the D ax (e.g. Sun et al, 2000Sun et al, , p. 2108)…”

Section: Figure 1: the London Boroughsmentioning

confidence: 99%

“…time as well as age and area) a common assump-tion is that age effects are independent of area (e.g. McNab and Dean, 2001), though changes over time in the age profile of mortality may be included (Sun et al, 2000). The present analysis uses age and area classifiers only and considers either total deaths (males and females combined) or deaths for one sex only.…”

Section: Figure 1: the London Boroughsmentioning

confidence: 99%

“…Sun et al (2000) treat them as fixed effects; McNab and Dean (2001) and Nandram et al (1999) use spline models; Ibrahim et al (2001) suggest random walks, while demographic applications (Anson, 1991) may use polynomials in age. Here the first two models use a random effects approach combining a structured random walk prior with unstructured age effects.…”

Section: A Model Based On Proportionalitymentioning

confidence: 99%

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A model for geographical variation in health and total life expectancy

Congdon¹

2006

DemRes

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“…A spline represents a smooth curve of piecewise polynomial. It is often used to model non-linear patterns such as growth curves (Brumback and Rice, 1998) and non-linear effects such as time and age (Sun et al, 2000;Pickle, 2000;Dean, 2001, 2002;MacNab, 2003b). Spline and regression spline methodologies are discussed quite extensively in Brumback and Rice (1998); Hastie and Tibshirani (1990); Green and Silverman (2000); a relatively non-technical discussion can be found in MacNab (2003b).…”

Section: Introductionmentioning

confidence: 99%