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- Publisher Website: 10.5705/ss.202018.0319
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Article: Subgroup Analysis in Censored Linear Regression
Title | Subgroup Analysis in Censored Linear Regression |
---|---|
Authors | |
Keywords | Concave penalization Oracle property Subgroup analysis Survival data |
Issue Date | 2020 |
Publisher | Academia Sinica, Institute of Statistical Science. The Journal's web site is located at http://www.stat.sinica.edu.tw/statistica/ |
Citation | Statistica Sinica, 2020, Epub How to Cite? |
Abstract | In the presence of treatment heterogeneity due to unknown grouping information, standard methods assuming homogeneous treatment effects cannot capture the subgroup structure in the
population. To accommodate heterogeneity, we propose a concave fusion approach to identifying the
subgroup structures as well as estimating treatment effects for semiparametric linear regression with
censored data. In particular, the treatment effects are subject-dependent and subgroup-specific, and
our concave fusion penalized method conducts the subgroup analysis without the need to know the
individual subgroup memberships in advance. The proposed estimation procedure can automatically
identify the subgroup structure and simultaneously estimate the subgroup-specific treatment effects.
Our new algorithm proceeds through combining the Buckley–James iterative procedure and the alternating direction method of multipliers. The resulting estimators enjoy the oracle property, and
simulation studies and real data application demonstrate the good performance of the new method. |
Persistent Identifier | http://hdl.handle.net/10722/288182 |
ISSN | 2023 Impact Factor: 1.5 2023 SCImago Journal Rankings: 1.368 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Yan, X | - |
dc.contributor.author | Yin, G | - |
dc.contributor.author | Zhao, X | - |
dc.date.accessioned | 2020-10-05T12:09:04Z | - |
dc.date.available | 2020-10-05T12:09:04Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Statistica Sinica, 2020, Epub | - |
dc.identifier.issn | 1017-0405 | - |
dc.identifier.uri | http://hdl.handle.net/10722/288182 | - |
dc.description.abstract | In the presence of treatment heterogeneity due to unknown grouping information, standard methods assuming homogeneous treatment effects cannot capture the subgroup structure in the population. To accommodate heterogeneity, we propose a concave fusion approach to identifying the subgroup structures as well as estimating treatment effects for semiparametric linear regression with censored data. In particular, the treatment effects are subject-dependent and subgroup-specific, and our concave fusion penalized method conducts the subgroup analysis without the need to know the individual subgroup memberships in advance. The proposed estimation procedure can automatically identify the subgroup structure and simultaneously estimate the subgroup-specific treatment effects. Our new algorithm proceeds through combining the Buckley–James iterative procedure and the alternating direction method of multipliers. The resulting estimators enjoy the oracle property, and simulation studies and real data application demonstrate the good performance of the new method. | - |
dc.language | eng | - |
dc.publisher | Academia Sinica, Institute of Statistical Science. The Journal's web site is located at http://www.stat.sinica.edu.tw/statistica/ | - |
dc.relation.ispartof | Statistica Sinica | - |
dc.subject | Concave penalization | - |
dc.subject | Oracle property | - |
dc.subject | Subgroup analysis | - |
dc.subject | Survival data | - |
dc.title | Subgroup Analysis in Censored Linear Regression | - |
dc.type | Article | - |
dc.identifier.email | Yin, G: gyin@hku.hk | - |
dc.identifier.authority | Yin, G=rp00831 | - |
dc.description.nature | postprint | - |
dc.identifier.doi | 10.5705/ss.202018.0319 | - |
dc.identifier.scopus | eid_2-s2.0-85100217034 | - |
dc.identifier.hkuros | 315667 | - |
dc.identifier.volume | Epub | - |
dc.identifier.isi | WOS:000632441000020 | - |
dc.publisher.place | Taiwan, Republic of China | - |
dc.identifier.issnl | 1017-0405 | - |