Marginal regression analysis for semi-competing risks data under dependent censoring

A. Adam Ding*, Guangkai Shi, Weijing Wang, Jin Jian Hsieh

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


Multiple events data are commonly seen in medical applications. There are two types of events, namely terminal and non-terminal. Statistical analysis for non-terminal events is complicated due to dependent censoring. Consequently, joint modelling and inference are often needed to avoid the problem of non-identifiability. This article considers regression analysis for multiple events data with major interest in a non-terminal event such as disease progression. We generalize the technique of artificial censoring, which is a popular way to handle dependent censoring, under flexible model assumptions on the two types of events. The proposed method is applied to analyse a data set of bone marrow transplantation.

Original languageEnglish
Pages (from-to)481-500
Number of pages20
JournalScandinavian Journal of Statistics
Issue number3
StatePublished - 1 Sep 2009


  • Artificial censoring
  • Log-rank statistic
  • Multiple events data
  • Transformation model


Dive into the research topics of 'Marginal regression analysis for semi-competing risks data under dependent censoring'. Together they form a unique fingerprint.

Cite this