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Bias adjustment in analysing longitudinal data with informative missingness
Author(s) -
Park Soomin,
Palta Mari,
Shao Jun,
Shen Lei
Publication year - 2001
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/sim.992
Subject(s) - missing data , censoring (clinical trials) , estimator , computer science , statistics , longitudinal data , econometrics , data mining , mathematics
The recent biostatistical literature contains a number of methods for handling the bias caused by ‘informative censoring’, which refers to drop‐out from a longitudinal study after a number of visits scheduled at predetermined intervals. The same or related methods can be extended to situations where the missing pattern is intermittent. The pattern of missingness is often assumed to be related to the outcome through random effects which represent unmeasured individual characteristics such as health awareness. To date there is only limited experience with applying the methods for informative censoring in practice, mostly because of complicated modelling and difficult computations. In this paper, we propose an estimation method based on grouping the data. The proposed estimator is asymptotically unbiased in various situations under informative missingness. Several existing methods are reviewed and compared in simulation studies. We apply the methods to data from the Wisconsin Diabetes Registry Project, a longitudinal study tracking glycaemic control and acute and chronic complications from the diagnosis of type I diabetes. Copyright © 2002 John Wiley & Sons, Ltd.