Intentionally incomplete longitudinal designs: I. Methodology and comparison of some full span designs.

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 Med

journal_title

Statistics in medicine

authors

Helms RW

doi

10.1002/sim.4780111411

subject

Has Abstract

pub_date

1992-10-01 00:00:00

pages

1889-913

issue

14-15

eissn

0277-6715

issn

1097-0258

journal_volume

11

pub_type

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