A comparative simulation study for estimating accelerated failure time models
DC Field | Value | Language |
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dc.contributor.author | 최상범 | - |
dc.date.accessioned | 2021-09-02T19:07:18Z | - |
dc.date.available | 2021-09-02T19:07:18Z | - |
dc.date.created | 2021-06-17 | - |
dc.date.issued | 2018 | - |
dc.identifier.issn | 1598-9402 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/79674 | - |
dc.description.abstract | Semiparametric accelerated failure time (AFT) models directly relate the predicted failure times to covariates and are a useful alternative to Cox's proportional hazards models that work on the hazard function or the survival function. In this paper, we briey review di_erent approaches to estimate the AFT model and evaluate their performance with _nite samples via extensive simulation studies. Speci_cally, we compared (i) inverse probability of censoring weighted (IPCW) least squares, (ii) log-rank estimator, (iii) Gehan-type log-rank estimator, (iv) Buckley-James estimator, and (v) nonparametric maximum likelihood estimator (NPMLE). Overall, rank-based estimators and Buckley-James estimator are e_cient and relatively more robust to distributions of residual and censoring variables, whereas the IPCW estimator is very sensitive to distribution and amount of censoring. The NPMLE is asymptotically e_cient and useful as it allows for hazard-based formulation, and thus can be used to analyze more structured survival data. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | 한국데이터정보과학회 | - |
dc.title | A comparative simulation study for estimating accelerated failure time models | - |
dc.title.alternative | A comparative simulation study for estimating accelerated failure time models | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | 최상범 | - |
dc.identifier.doi | 10.7465/jkdi.2018.29.6.1457 | - |
dc.identifier.bibliographicCitation | 한국데이터정보과학회지, v.29, no.6, pp.1457 - 1468 | - |
dc.relation.isPartOf | 한국데이터정보과학회지 | - |
dc.citation.title | 한국데이터정보과학회지 | - |
dc.citation.volume | 29 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 1457 | - |
dc.citation.endPage | 1468 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART002405466 | - |
dc.description.journalClass | 2 | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | Linear model | - |
dc.subject.keywordAuthor | rank regression | - |
dc.subject.keywordAuthor | relative efficiency | - |
dc.subject.keywordAuthor | survival analysis | - |
dc.subject.keywordAuthor | Linear model | - |
dc.subject.keywordAuthor | rank regression | - |
dc.subject.keywordAuthor | relative efficiency | - |
dc.subject.keywordAuthor | survival analysis. | - |
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