Journal
STATISTICS IN MEDICINE
Volume 21, Issue 2, Pages 277-291Publisher
JOHN WILEY & SONS LTD
DOI: 10.1002/sim.992
Keywords
missing data in longitudinal studies; bias adjustment; pattern of missingness; informative censoring
Categories
Funding
- NCI NIH HHS [R01 CA53786] Funding Source: Medline
- NIDDK NIH HHS [DK36904] Funding Source: Medline
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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 (C) 2002 John Wiley Sons, Ltd.
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