T
Takeshi Emura
Researcher at Chang Gung University
Publications - 100
Citations - 1213
Takeshi Emura is an academic researcher from Chang Gung University. The author has contributed to research in topics: Estimator & Copula (probability theory). The author has an hindex of 17, co-authored 85 publications receiving 844 citations. Previous affiliations of Takeshi Emura include Academia Sinica & National Chiao Tung University.
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A joint frailty-copula model between tumour progression and death for meta-analysis
TL;DR: Copulas are utilizes to generalize the joint frailty model by introducing additional source of dependence arising from intra-subject association between tumour progression and death and are applied to a meta-analysis for assessing a recently suggested biomarker CXCL12 for survival in ovarian cancer patients.
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compound.Cox: Univariate feature selection and compound covariate for predicting survival
TL;DR: This work develops the compound.Cox R package that implements univariate significance tests (via the Wald tests or score tests) for feature selection and provides three algorithms for constructing a multigene predictor, which are tailored to the subset of genes obtained from univariate feature selection.
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Gene selection for survival data under dependent censoring: A copula-based approach
Takeshi Emura,Yi-Hau Chen +1 more
TL;DR: In this paper, a copula-based dependence model was used to investigate the bias caused by dependent censoring on gene selection and then, an alternative gene selection procedure was developed for non-small-cell lung cancer data.
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Personalized dynamic prediction of death according to tumour progression and high-dimensional genetic factors: Meta-analysis with a joint model.
TL;DR: This work extends the existing joint frailty-copula model to a model allowing for high-dimensional genetic factors and proposes a dynamic prediction formula to predict death given tumour progression events possibly occurring after treatment or surgery.
Posted Content
Gene selection for survival data under dependent censoring: a copula-based approach
Takeshi Emura,Yi-Hau Chen +1 more
TL;DR: Simulations show that the proposed procedure adjusts for the effect ofdependent censoring and thus outperforms the existing method when dependent censoring is indeed present, and implemented in an R “compound.Cox” package.