Haplotype-based pharmacogenetic analysis for longitudinal quantitative traits in the presence of dropout

Jung Ying Tzeng, Wenbin Lu, Mark W. Farmen, Youfang Liu, Patrick F. Sullivan

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

We propose a variety of methods based on the generalized estimation equations to address the issues encountered in haplotype-based pharmacogenetic analysis, including analysis of longitudinal data with outcome-dependent dropouts, and evaluation of the high-dimensional haplotype and haplotype-drug interaction effects in an overall manner. We use the inverse probability weights to handle the outcome-dependent dropouts under the missing-at-random assumption, and incorporate the weighted L1 penalty to select important main and interaction effects with high dimensionality. The proposed methods are easy to implement, computationally efficient, and provide an optimal balance between false positives and false negatives in detecting genetic effects.

Original languageEnglish
Pages (from-to)334-350
Number of pages17
JournalJournal of Biopharmaceutical Statistics
Volume20
Issue number2
DOIs
Publication statusPublished - 2010 Mar

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Pharmacology
  • Pharmacology (medical)

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