Recursive partitioning for monotone missing at random longitudinal markers

Authors

  • Shannon Stock,

    Corresponding author
    1. Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, U.S.A.
    • Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, U.S.A.
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  • Victor DeGruttola

    1. Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, U.S.A.
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Correspondence to: Shannon Stock, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, CLS11007, 450 Brookline Avenue, Boston, MA 02215, U.S.A.

E-mail: sstock@jimmy.harvard.edu

Abstract

The development of HIV resistance mutations reduces the efficacy of specific antiretroviral drugs used to treat HIV infection and cross-resistance within classes of drugs is common. Recursive partitioning has been extensively used to identify resistance mutations associated with a reduced virologic response measured at a single time point; here we describe a statistical method that accommodates a large set of genetic or other covariates and a longitudinal response. This recursive partitioning approach for continuous longitudinal data uses the kernel of a U-statistic as the splitting criterion and avoids the need for parametric assumptions regarding the relationship between observed response trajectories and covariates. We propose an extension of this approach that allows longitudinal measurements to be monotone missing at random by making use of inverse probability weights. We assess the performance of our method using extensive simulation studies and apply them to data collected by the Forum for Collaborative HIV Research as part of an investigation of the viral genetic mutations associated with reduced clinical efficacy of the drug abacavir. Copyright © 2012 John Wiley & Sons, Ltd.

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