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The Effect of Drop‐Out on the Efficiency of Longitudinal Experiments

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  • Geert Verbeke
  • Emmanuel Lesaffre

Abstract

It is shown that drop‐out often reduces the efficiency of longitudinal experiments considerably. In the framework of linear mixed models, a general, computationally simple method is provided, for designing longitudinal studies when drop‐out is to be expected, such that there is little risk of large losses of efficiency due to the missing data. All the results are extensively illustrated using data from a randomized experiment with rats.

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  • Geert Verbeke & Emmanuel Lesaffre, 1999. "The Effect of Drop‐Out on the Efficiency of Longitudinal Experiments," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 48(3), pages 363-375.
    • Handle: RePEc:bla:jorssc:v:48:y:1999:i:3:p:363-375
    DOI: 10.1111/1467-9876.00158
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    Cited by:

    1. Andrew Gelman & Iven Van Mechelen & Geert Verbeke & Daniel F. Heitjan & Michel Meulders, 2005. "Multiple Imputation for Model Checking: Completed-Data Plots with Missing and Latent Data," Biometrics, The International Biometric Society, vol. 61(1), pages 74-85, March.
    2. Geert Verbeke & Geert Molenberghs, 2003. "The Use of Score Tests for Inference on Variance Components," Biometrics, The International Biometric Society, vol. 59(2), pages 254-262, June.

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