Lifetime Data Anal - Bayesian analysis for monotone hazard ratio.

Tópicos

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{ model(2656) set(1616) predict(1553) }
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{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ first(2504) two(1366) second(1323) }
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{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
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{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

We propose a Bayesian approach for estimating the hazard functions under the constraint of a monotone hazard ratio. We construct a model for the monotone hazard ratio utilizing the Cox's proportional hazards model with a monotone time-dependent coefficient. To reduce computational complexity, we use a signed gamma process prior for the time-dependent coefficient and the Bayesian bootstrap prior for the baseline hazard function. We develop an efficient MCMC algorithm and illustrate the proposed method on simulated and real data sets.

Resumo Limpo

propos bayesian approach estim hazard function constraint monoton hazard ratio construct model monoton hazard ratio util cox proport hazard model monoton timedepend coeffici reduc comput complex use sign gamma process prior timedepend coeffici bayesian bootstrap prior baselin hazard function develop effici mcmc algorithm illustr propos method simul real data set

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