Subgroup analysis in censored linear regression

Xiaodong Yan, Guosheng Yin, Xingqiu Zhao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

6 Citations (Scopus)


In the presence of treatment heterogeneity due to unknown grouping information, standard methods that assume homogeneous treatment effiects cannot capture the subgroup structure in the population. To accommodate such heterogeneity, we propose a concave fusion approach to identifying the subgroup structures and estimating the treatment effiects for a semiparametric linear regression with censored data. In particular, the treatment effiects are subject-dependent and subgroup-specific, and our concave fusion penalized method conducts the subgroup analysis without needing to know the individual subgroup memberships in advance. The proposed estimation procedure automatically identifies the subgroup structure and simultaneously estimates the subgroup-specific treatment effiects. The proposed algorithm combines the Buckley{James iterative procedure and the alternating direction method of multipliers. The resulting estimators enjoy the oracle property, and simulation studies and a real-data application demonstrate the good performance of the proposed method.

Original languageEnglish
Pages (from-to)1027-1054
Number of pages28
JournalStatistica Sinica
Issue number2
Publication statusPublished - Apr 2021


  • Concave penalization
  • Oracle property
  • Subgroup analysis
  • Survival data
  • Treatment heterogeneity

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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