Lifetime Data Anal - Bayesian semiparametric modeling for stochastic precedence, with applications in epidemiology and survival analysis.

Tópicos

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{ framework(1458) process(801) describ(734) }
{ perform(999) metric(946) measur(919) }
{ data(1737) use(1416) pattern(1282) }
{ treatment(1704) effect(941) patient(846) }
{ problem(2511) optim(1539) algorithm(950) }
{ model(2220) cell(1177) simul(1124) }
{ method(984) reconstruct(947) comput(926) }
{ risk(3053) factor(974) diseas(938) }
{ use(976) code(926) identifi(902) }
{ estim(2440) model(1874) function(577) }
{ sequenc(1873) structur(1644) protein(1328) }
{ method(1219) similar(1157) match(930) }
{ error(1145) method(1030) estim(1020) }
{ general(901) number(790) one(736) }
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{ medic(1828) order(1363) alert(1069) }
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{ patient(2837) hospit(1953) medic(668) }
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{ sampl(1606) size(1419) use(1276) }
{ 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) }
{ can(981) present(881) function(850) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
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{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

We propose a prior probability model for two distributions that are ordered according to a stochastic precedence constraint, a weaker restriction than the more commonly utilized stochastic order constraint. The modeling approach is based on structured Dirichlet process mixtures of normal distributions. Full inference for functionals of the stochastic precedence constrained mixture distributions is obtained through a Markov chain Monte Carlo posterior simulation method. A motivating application involves study of the discriminatory ability of continuous diagnostic tests in epidemiologic research. Here, stochastic precedence provides a natural restriction for the distributions of test scores corresponding to the non-infected and infected groups. Inference under the model is illustrated with data from a diagnostic test for Johne's disease in dairy cattle. We also apply the methodology to the comparison of survival distributions associated with two distinct conditions, and illustrate with analysis of data on survival time after bone marrow transplantation for treatment of leukemia.

Resumo Limpo

propos prior probabl model two distribut order accord stochast preced constraint weaker restrict common util stochast order constraint model approach base structur dirichlet process mixtur normal distribut full infer function stochast preced constrain mixtur distribut obtain markov chain mont carlo posterior simul method motiv applic involv studi discriminatori abil continu diagnost test epidemiolog research stochast preced provid natur restrict distribut test score correspond noninfect infect group infer model illustr data diagnost test john diseas dairi cattl also appli methodolog comparison surviv distribut associ two distinct condit illustr analysi data surviv time bone marrow transplant treatment leukemia

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