Comput Methods Programs Biomed - The mstate package for estimation and prediction in non- and semi-parametric multi-state and competing risks models.

Tópicos

{ estim(2440) model(1874) function(577) }
{ model(2656) set(1616) predict(1553) }
{ data(1714) softwar(1251) tool(1186) }
{ framework(1458) process(801) describ(734) }
{ process(1125) use(805) approach(778) }
{ analysi(2126) use(1163) compon(1037) }
{ use(976) code(926) identifi(902) }
{ measur(2081) correl(1212) valu(896) }
{ howev(809) still(633) remain(590) }
{ error(1145) method(1030) estim(1020) }
{ state(1844) use(1261) util(961) }
{ sequenc(1873) structur(1644) protein(1328) }
{ imag(2830) propos(1344) filter(1198) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ import(1318) role(1303) understand(862) }
{ data(2317) use(1299) case(1017) }
{ result(1111) use(1088) new(759) }
{ method(2212) result(1239) propos(1039) }
{ system(1976) rule(880) can(841) }
{ network(2748) neural(1063) input(814) }
{ clinic(1479) use(1117) guidelin(835) }
{ extract(1171) text(1153) clinic(932) }
{ risk(3053) factor(974) diseas(938) }
{ research(1085) discuss(1038) issu(1018) }
{ studi(1119) effect(1106) posit(819) }
{ model(3480) simul(1196) paramet(876) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ first(2504) two(1366) second(1323) }
{ drug(1928) target(777) effect(648) }
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{ treatment(1704) effect(941) patient(846) }
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{ featur(1941) imag(1645) propos(1176) }
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{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ 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) }
{ use(1733) differ(960) four(931) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

In recent years, multi-state models have been studied widely in survival analysis. Despite their clear advantages, their use in biomedical and other applications has been rather limited so far. An important reason for this is the lack of flexible and user-friendly software for multi-state models. This paper introduces a package in R, called 'mstate', for each of the steps of the analysis of multi-state models. It can be applied to non- and semi-parametric models. The package contains functions to facilitate data preparation and flexible estimation of different types of covariate effects in the context of Cox regression models, functions to estimate patient-specific transition intensities, dynamic prediction probabilities and their associated standard errors (both Greenwood and Aalen-type). Competing risks models can also be analyzed by means of mstate, as they are a special type of multi-state models. The package is available from the R homepage http://cran.r-project.org. We give a self-contained account of the underlying mathematical theory, including a new asymptotic result for the cumulative hazard function and new recursive formulas for the calculation of the estimated standard errors of the estimated transition probabilities, and we illustrate the use of the key functions of the mstate package by the analysis of a reversible multi-state model describing survival of liver cirrhosis patients.

Resumo Limpo

recent year multist model studi wide surviv analysi despit clear advantag use biomed applic rather limit far import reason lack flexibl userfriend softwar multist model paper introduc packag r call mstate step analysi multist model can appli non semiparametr model packag contain function facilit data prepar flexibl estim differ type covari effect context cox regress model function estim patientspecif transit intens dynam predict probabl associ standard error greenwood aalentyp compet risk model can also analyz mean mstate special type multist model packag avail r homepag httpcranrprojectorg give selfcontain account under mathemat theori includ new asymptot result cumul hazard function new recurs formula calcul estim standard error estim transit probabl illustr use key function mstate packag analysi revers multist model describ surviv liver cirrhosi patient

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