Abstract:
:Longitudinal designs are important in medical research and in many other disciplines. Complete longitudinal studies, in which each subject is evaluated at each measurement occasion, are often very expensive and motivate a search for more efficient designs. Recently developed statistical methods foster the use of intentionally incomplete longitudinal designs that have the potential to be more efficient than complete designs. Mixed models provide appropriate data analysis tools. Fixed effect hypotheses can be tested via a recently developed test statistic, FH. An accurate approximation of the statistic's small sample non-central distribution makes power computations feasible. After reviewing some longitudinal design terminology and mixed model notation, this paper summarizes the computation of FH and approximate power from its non-central distribution. These methods are applied to obtain a large number of intentionally incomplete full-span designs that are more powerful and/or less costly alternatives to a complete design. The source of the greater efficiency of incomplete designs and potential fragility of incomplete designs to randomly missing data are discussed.
journal_name
Stat Medjournal_title
Statistics in medicineauthors
Helms RWdoi
10.1002/sim.4780111411subject
Has Abstractpub_date
1992-10-01 00:00:00pages
1889-913issue
14-15eissn
0277-6715issn
1097-0258journal_volume
11pub_type
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