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Marginal Regression Analysis for Semi‐Competing Risks Data Under Dependent Censoring
Author(s) -
ADAM DING A.,
SHI GUANGKAI,
WANG WEIJING,
HSIEH JINJIAN
Publication year - 2009
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/j.1467-9469.2008.00635.x
Subject(s) - censoring (clinical trials) , identifiability , inference , mathematics , regression analysis , statistical inference , statistics , bone marrow transplantation , regression , econometrics , computer science , artificial intelligence , transplantation , medicine , surgery
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.