Lifetime Data Anal - Bayesian inference of the fully specified subdistribution model for survival data with competing risks.

Tópicos

{ model(2656) set(1616) predict(1553) }
{ risk(3053) factor(974) diseas(938) }
{ estim(2440) model(1874) function(577) }
{ model(3404) distribut(989) bayesian(671) }
{ framework(1458) process(801) describ(734) }
{ process(1125) use(805) approach(778) }
{ can(774) often(719) complex(702) }
{ treatment(1704) effect(941) patient(846) }
{ extract(1171) text(1153) clinic(932) }
{ control(1307) perform(991) simul(935) }
{ age(1611) year(1155) adult(843) }
{ cancer(2502) breast(956) screen(824) }
{ error(1145) method(1030) estim(1020) }
{ record(1888) medic(1808) patient(1693) }
{ state(1844) use(1261) util(961) }
{ sampl(1606) size(1419) use(1276) }
{ structur(1116) can(940) graph(676) }
{ activ(1452) weight(1219) physic(1104) }
{ system(1976) rule(880) can(841) }
{ method(1219) similar(1157) match(930) }
{ featur(3375) classif(2383) classifi(1994) }
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{ data(1714) softwar(1251) tool(1186) }
{ case(1353) use(1143) diagnosi(1136) }
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{ gene(2352) biolog(1181) express(1162) }
{ high(1669) rate(1365) level(1280) }
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{ studi(1410) differ(1259) use(1210) }
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{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ health(3367) inform(1360) care(1135) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ research(1218) medic(880) student(794) }
{ data(2317) use(1299) case(1017) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ 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) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ use(976) code(926) identifi(902) }
{ drug(1928) target(777) effect(648) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Competing risks data are routinely encountered in various medical applications due to the fact that patients may die from different causes. Recently, several models have been proposed for fitting such survival data. In this paper, we develop a fully specified subdistribution model for survival data in the presence of competing risks via a subdistribution model for the primary cause of death and conditional distributions for other causes of death. Various properties of this fully specified subdistribution model have been examined. An efficient Gibbs sampling algorithm via latent variables is developed to carry out posterior computations. Deviance information criterion (DIC) and logarithm of the pseudomarginal likelihood (LPML) are used for model comparison. An extensive simulation study is carried out to examine the performance of DIC and LPML in comparing the cause-specific hazards model, the mixture model, and the fully specified subdistribution model. The proposed methodology is applied to analyze a real dataset from a prostate cancer study in detail.

Resumo Limpo

compet risk data routin encount various medic applic due fact patient may die differ caus recent sever model propos fit surviv data paper develop fulli specifi subdistribut model surviv data presenc compet risk via subdistribut model primari caus death condit distribut caus death various properti fulli specifi subdistribut model examin effici gibb sampl algorithm via latent variabl develop carri posterior comput devianc inform criterion dic logarithm pseudomargin likelihood lpml use model comparison extens simul studi carri examin perform dic lpml compar causespecif hazard model mixtur model fulli specifi subdistribut model propos methodolog appli analyz real dataset prostat cancer studi detail

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