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Parallel, AA/BB, AB/BA and Balaam's design: efficient and maximin choices when testing the treatment effect in a mixed effects linear regression
Author(s) -
Candel Math J. J. M.
Publication year - 2012
Publication title -
pharmaceutical statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 38
eISSN - 1539-1612
pISSN - 1539-1604
DOI - 10.1002/pst.502
Subject(s) - carry (investment) , minimax , estimator , optimal design , dropout (neural networks) , mathematics , function (biology) , statistics , mathematical optimization , linear regression , econometrics , computer science , economics , machine learning , finance , evolutionary biology , biology
When examining the effect of treatment A versus B , there may be a choice between a parallel group design, an AA/BB design, an AB/BA cross‐over and Balaam's design. In case of a linear mixed effects regression, it is examined, starting from a flexible function of the costs involved and allowing for subject dropout, which design is most efficient in estimating this effect. For no carry‐over, the AB/BA cross‐over design is most efficient as long as the dropout rate at the second measurement does not exceed 2ρ /(1 + ρ ), ρ being the intraclass correlation. For steady‐state carry‐over, depending on the costs involved, the dropout rate and ρ , either a parallel design or an AA/BB design is most efficient. For types of carry‐over that allow for self carry‐over, interest is in the direct treatment effect plus the self carry‐over effect, with either an AA/BB or Balaam's design being most efficient. In case of insufficient knowledge on the dropout rate or ρ , a maximin strategy is devised: choose the design that minimizes the maximum variance of the treatment estimator. Such maximin designs are derived for each type of carry‐over. Copyright © 2012 John Wiley & Sons, Ltd.