Spat Spatiotemporal Epidemiol - A generalized Poisson-gamma model for spatially overdispersed data.

Tópicos

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{ studi(1119) effect(1106) posit(819) }
{ perform(1367) use(1326) method(1137) }
{ imag(2830) propos(1344) filter(1198) }
{ cancer(2502) breast(956) screen(824) }
{ control(1307) perform(991) simul(935) }
{ assess(1506) score(1403) qualiti(1306) }
{ model(3404) distribut(989) bayesian(671) }
{ problem(2511) optim(1539) algorithm(950) }
{ concept(1167) ontolog(924) domain(897) }
{ data(3963) clinic(1234) research(1004) }
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{ data(2317) use(1299) case(1017) }
{ process(1125) use(805) approach(778) }
{ can(774) often(719) complex(702) }
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{ risk(3053) factor(974) diseas(938) }
{ visual(1396) interact(850) tool(830) }
{ research(1218) medic(880) student(794) }
{ can(981) present(881) function(850) }
{ structur(1116) can(940) graph(676) }
{ use(976) code(926) identifi(902) }
{ implement(1333) system(1263) develop(1122) }
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{ system(1976) rule(880) can(841) }
{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
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{ method(984) reconstruct(947) comput(926) }
{ featur(1941) imag(1645) propos(1176) }
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{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
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{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
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{ model(2656) set(1616) predict(1553) }
{ age(1611) year(1155) adult(843) }
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{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ high(1669) rate(1365) level(1280) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Modern disease mapping commonly uses hierarchical Bayesian methods to model overdispersion and spatial correlation. Classical random-effects based solutions include the Poisson-gamma model, which uses the conjugacy between the Poisson and gamma distributions, but which does not model spatial correlation, on the one hand, and the more advanced CAR model, which also introduces a spatial autocorrelation term but without a closed-form posterior distribution on the other. In this paper, a combined model is proposed: an alternative convolution model accounting for both overdispersion and spatial correlation in the data by combining the Poisson-gamma model with a spatially-structured normal CAR random effect. The Limburg Cancer Registry data on kidney and prostate cancer in Limburg were used to compare the conventional and new models. A simulation study confirmed results and interpretations coming from the real datasets. Relative risk maps showed that the combined model provides an intermediate between the non-patterned negative binomial and the sometimes oversmoothed CAR convolution model.

Resumo Limpo

modern diseas map common use hierarch bayesian method model overdispers spatial correl classic randomeffect base solut includ poissongamma model use conjugaci poisson gamma distribut model spatial correl one hand advanc car model also introduc spatial autocorrel term without closedform posterior distribut paper combin model propos altern convolut model account overdispers spatial correl data combin poissongamma model spatiallystructur normal car random effect limburg cancer registri data kidney prostat cancer limburg use compar convent new model simul studi confirm result interpret come real dataset relat risk map show combin model provid intermedi nonpattern negat binomi sometim oversmooth car convolut model

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