Abstract:
:Recently, Wu and Follmann developed summary measures to adjust for informative drop-out in longitudinal studies where drop-out depends on the underlying true value of the response. In this paper we evaluate these procedures in the common situation where drop-out depends on the observed responses. We also discuss various design and analysis strategies which minimize the bias obtained with this type of drop-out. Of particular interest is the use of multiple measurements of the response at each visit to reduce bias. These strategies are evaluated with a simulation study. The results are highlighted with applications to both a hypertensive and a respiratory disease clinical trial, where multiple measurements of the primary response were made for all participants at each visit.
journal_name
Stat Medjournal_title
Statistics in medicineauthors
Wu MC,Albert PS,Wu BUdoi
10.1002/1097-0258(20010115)20:1<93::aid-sim655>3.0subject
Has Abstractpub_date
2001-01-15 00:00:00pages
93-108issue
1eissn
0277-6715issn
1097-0258pii
10.1002/1097-0258(20010115)20:1<93::AID-SIM655>3.0journal_volume
20pub_type
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