Abstract
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 language | English |
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Pages (from-to) | 481-500 |
Number of pages | 20 |
Journal | Scandinavian Journal of Statistics |
Volume | 36 |
Issue number | 3 |
DOIs | |
State | Published - Sep 2009 |
Keywords
- Artificial censoring
- Log-rank statistic
- Multiple events data
- Transformation model