Med Decis Making - Not simply more of the same: distinguishing between patient heterogeneity and parameter uncertainty.

Tópicos

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{ measur(2081) correl(1212) valu(896) }
{ sampl(1606) size(1419) use(1276) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }
{ patient(2315) diseas(1263) diabet(1191) }
{ method(984) reconstruct(947) comput(926) }
{ import(1318) role(1303) understand(862) }
{ cost(1906) reduc(1198) effect(832) }
{ result(1111) use(1088) new(759) }
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{ first(2504) two(1366) second(1323) }
{ motion(1329) object(1292) video(1091) }
{ concept(1167) ontolog(924) domain(897) }
{ model(2220) cell(1177) simul(1124) }
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{ risk(3053) factor(974) diseas(938) }
{ research(1085) discuss(1038) issu(1018) }
{ model(2656) set(1616) predict(1553) }
{ age(1611) year(1155) adult(843) }
{ time(1939) patient(1703) rate(768) }
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{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
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{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
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{ ehr(2073) health(1662) electron(1139) }
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{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ medic(1828) order(1363) alert(1069) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
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{ use(976) code(926) identifi(902) }
{ drug(1928) target(777) effect(648) }
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Resumo

In cost-effectiveness (CE) Markov models, heterogeneity in the patient population is not automatically taken into account. We aimed to compare methods of dealing with heterogeneity on estimates of CE, using a case study in chronic obstructive pulmonary disease (COPD). We first present a probabilistic sensitivity analysis (PSA) in which we sampled only from distributions representing parameter uncertainty. This ignores any heterogeneity. Next, we explored heterogeneity by presenting results for subgroups, using a method that samples parameter uncertainty simultaneously with heterogeneity in a single-loop PSA. Finally, we distinguished parameter uncertainty from heterogeneity in a double-loop PSA by performing a nested simulation within each PSA iteration. Point estimates and uncertainty differed substantially between methods. The incremental CE ratio (ICER) ranged from ? 4900 to ? 13,800. The single-loop PSA led to a substantially different shape of the CE plane and an overestimation of the uncertainty compared with the other 3 methods. The CE plane for the double-loop PSA showed substantially less uncertainty and a stronger negative correlation between the difference in costs and the difference in effects compared with the other methods. This came at the cost of higher calculation times. Not accounting for heterogeneity, subgroup analysis and the double-loop PSA can be viable options, depending on the decision makers' information needs. The single-loop PSA should not be used in CE research. It disregards the fundamental differences between heterogeneity and sampling uncertainty and overestimates uncertainty as a result.

Resumo Limpo

costeffect ce markov model heterogen patient popul automat taken account aim compar method deal heterogen estim ce use case studi chronic obstruct pulmonari diseas copd first present probabilist sensit analysi psa sampl distribut repres paramet uncertainti ignor heterogen next explor heterogen present result subgroup use method sampl paramet uncertainti simultan heterogen singleloop psa final distinguish paramet uncertainti heterogen doubleloop psa perform nest simul within psa iter point estim uncertainti differ substanti method increment ce ratio icer rang singleloop psa led substanti differ shape ce plane overestim uncertainti compar method ce plane doubleloop psa show substanti less uncertainti stronger negat correl differ cost differ effect compar method came cost higher calcul time account heterogen subgroup analysi doubleloop psa can viabl option depend decis maker inform need singleloop psa use ce research disregard fundament differ heterogen sampl uncertainti overestim uncertainti result

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