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Mixed models for data from thorough QT studies: part 1. assessment of marginal QT prolongation

Authors

  • Robert Schall,

    Corresponding author
    1. Department of Mathematical Statistics and Actuarial Science, University of the Free State, Bloemfontein, South Africa
    2. Center for Statistics in Drug Development, Quintiles
    • Department of Mathematical Statistics and Actuarial Science (IB 75), University of the Free State, Bloemfontein 9300, South Africa
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  • Arne Ring

    1. Clinical Biostatistics, Boehringer Ingelheim Pharma, Biberach an der Riss, Germany
    2. Institute for Biometry, University of Ulm, Ulm, Germany
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Abstract

We investigate mixed models for repeated measures data from cross-over studies in general, but in particular for data from thorough QT studies. We extend both the conventional random effects model and the saturated covariance model for univariate cross-over data to repeated measures cross-over (RMC) data; the resulting models we call the RMC model and Saturated model, respectively. Furthermore, we consider a random effects model for repeated measures cross-over data previously proposed in the literature. We assess the standard errors of point estimates and the coverage properties of confidence intervals for treatment contrasts under the various models. Our findings suggest: (i) Point estimates of treatment contrasts from all models considered are similar; (ii) Confidence intervals for treatment contrasts under the random effects model previously proposed in the literature do not have adequate coverage properties; the model therefore cannot be recommended for analysis of marginal QT prolongation; (iii) The RMC model and the Saturated model have similar precision and coverage properties; both models are suitable for assessment of marginal QT prolongation; and (iv) The Akaike Information Criterion (AIC) is not a reliable criterion for selecting a covariance model for RMC data in the following sense: the model with the smallest AIC is not necessarily associated with the highest precision for the treatment contrasts, even if the model with the smallest AIC value is also the most parsimonious model. Copyright © 2010 John Wiley & Sons, Ltd.

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