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SUMMARY:Cross-sectional versus longitudinal design: does it matter? - Schw
 abe\, R (Otto-von-Guericke-Universitt Magdeburg)
DTSTART:20150710T084500Z
DTEND:20150710T093000Z
UID:TALK60110@talks.cam.ac.uk
CONTACT:42080
DESCRIPTION:Mixed models play an important role in describing observations
  in healthcare. To keep things simple we only consider linear mixed models
 \, and we are only interested in the typical response\, i.e. in the popula
 tion location parameters. When discussing the corresponding design issues 
 one is often faced with the widespread belief that standard designs which 
 are optimal\, when the random effects are neglected\, retain their optimal
 ity in the mixed model. This seems to be obviously true\, if there are onl
 y random block effects related to a unit specific level (e.g. random inter
 cept).  However\, if there are also random effect sizes which vary substan
 tially between units\, then these standard designs may lose their optimali
 ty property. This phenomenon occurs in the situation of a cross-sectional 
 design\, where the experimental setting is fixed for each unit and varies 
 between units\, as well as in the situation of a longitudinal design\, whe
 re the experimental setting varies within units and coincides across units
 . We will compare the resulting optimal solutions and check their relative
  efficiencies.\n
LOCATION:Seminar Room 1\, Newton Institute
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