Med Decis Making - Development of an EORTC-8D utility algorithm for Sri Lanka.

Tópicos

{ health(3367) inform(1360) care(1135) }
{ state(1844) use(1261) util(961) }
{ sampl(1606) size(1419) use(1276) }
{ cost(1906) reduc(1198) effect(832) }
{ measur(2081) correl(1212) valu(896) }
{ data(2317) use(1299) case(1017) }
{ survey(1388) particip(1329) question(1065) }
{ spatial(1525) area(1432) region(1030) }
{ estim(2440) model(1874) function(577) }
{ assess(1506) score(1403) qualiti(1306) }
{ clinic(1479) use(1117) guidelin(835) }
{ general(901) number(790) one(736) }
{ process(1125) use(805) approach(778) }
{ treatment(1704) effect(941) patient(846) }
{ error(1145) method(1030) estim(1020) }
{ learn(2355) train(1041) set(1003) }
{ age(1611) year(1155) adult(843) }
{ time(1939) patient(1703) rate(768) }
{ can(981) present(881) function(850) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ activ(1452) weight(1219) physic(1104) }
{ imag(1947) propos(1133) code(1026) }
{ concept(1167) ontolog(924) domain(897) }
{ design(1359) user(1324) use(1319) }
{ howev(809) still(633) remain(590) }
{ studi(1410) differ(1259) use(1210) }
{ model(2341) predict(2261) use(1141) }
{ compound(1573) activ(1297) structur(1058) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ health(1844) social(1437) communiti(874) }
{ model(3404) distribut(989) bayesian(671) }
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{ data(1737) use(1416) pattern(1282) }
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{ motion(1329) object(1292) video(1091) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ framework(1458) process(801) describ(734) }
{ problem(2511) optim(1539) algorithm(950) }
{ chang(1828) time(1643) increas(1301) }
{ algorithm(1844) comput(1787) effici(935) }
{ extract(1171) text(1153) clinic(932) }
{ method(1557) propos(1049) approach(1037) }
{ data(1714) softwar(1251) tool(1186) }
{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ method(984) reconstruct(947) comput(926) }
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{ featur(1941) imag(1645) propos(1176) }
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{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
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{ blood(1257) pressur(1144) flow(957) }
{ record(1888) medic(1808) patient(1693) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ activ(1138) subject(705) human(624) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
{ structur(1116) can(940) graph(676) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ decis(3086) make(1611) patient(1517) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

CKGROUND: Currently there are no reported cancer-specific health state valuations in low- and middle-income countries using a validated preference-based measure. The EORTC-8D, a cancer-specific preference-based measure, has 81,920 health states and is useful for economic evaluations in cancer care. The aim of this study was to develop a utility algorithm to value EORTC-8D health states using preferences derived from a representative population sample in Sri Lanka.METHODS: The time-tradeoff method was used to elicit preferences from a general population sample of 780 in Sri Lanka. A block design of 85 health states, with a time horizon of 10 years, was used for the direct valuation. Data were analyzed using generalized least squares with random effects. All respondents with at least one logical inconsistency were excluded from the analysis.RESULTS: After logical inconsistencies were excluded, 4520 observations were available from 717 respondents for the analysis. The preferred model specified main effects with an interaction term for any level 4 or worse descriptor within a health state. Worsening of physical functioning had a substantially greater utility decrement than any other dimension in this population. Limitations are that the data collection could not include the whole country and that females formed a large part of the sample.CONCLUSIONS: Preference weights for EORTC-8D health states for Sri Lanka have been derived: These will be very useful in economic evaluations of cancer-related interventions in a range of low- and middle-income countries.

Resumo Limpo

ckground current report cancerspecif health state valuat low middleincom countri use valid preferencebas measur eortcd cancerspecif preferencebas measur health state use econom evalu cancer care aim studi develop util algorithm valu eortcd health state use prefer deriv repres popul sampl sri lankamethod timetradeoff method use elicit prefer general popul sampl sri lanka block design health state time horizon year use direct valuat data analyz use general least squar random effect respond least one logic inconsist exclud analysisresult logic inconsist exclud observ avail respond analysi prefer model specifi main effect interact term level wors descriptor within health state worsen physic function substanti greater util decrement dimens popul limit data collect includ whole countri femal form larg part sampleconclus prefer weight eortcd health state sri lanka deriv will use econom evalu cancerrel intervent rang low middleincom countri

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