Adjusting for drop-out in clinical trials with repeated measures: design and analysis issues.

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 Med

journal_title

Statistics in medicine

authors

Wu MC,Albert PS,Wu BU

doi

10.1002/1097-0258(20010115)20:1<93::aid-sim655>3.0

subject

Has Abstract

pub_date

2001-01-15 00:00:00

pages

93-108

issue

1

eissn

0277-6715

issn

1097-0258

pii

10.1002/1097-0258(20010115)20:1<93::AID-SIM655>3.0

journal_volume

20

pub_type

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