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Statistical analysis of repeated measurements with informative censoring times
Author(s) -
Sun Jianguo,
Song Peter X.K.
Publication year - 2000
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/1097-0258(20010115)20:1<63::aid-sim656>3.0.co;2-2
Subject(s) - censoring (clinical trials) , parametric statistics , computer science , missing data , statistical hypothesis testing , statistics , regression , econometrics , artificial intelligence , machine learning , mathematics
Incomplete repeated measurement data often arise in medical studies. A problem that has recently drawn much attention in the literature in this situation is that the incompleteness or missingness is informative or related to the underlying variable of interest. In this paper we propose a non‐parametric global test for treatment comparison in the presence of informative incompleteness. A semi‐parametric regression model is also presented for assessing conditional treatment effects given the drop‐out patterns, adopting the idea similar to that behind the pattern‐mixture modelling approach and discussed in Shih and Quan. The proposed methods can be easily implemented and are conceptually simple and similar too, but can be applied to more general cases than those given in Yao et al. They are evaluated by numerical studies and applied to data from a clinical trial of adult schizophrenics. Copyright © 2001 John Wiley & Sons, Ltd.