Variable selection for distribution-free models for longitudinal zero-inflated count responses

Tian Chen, Pan Wu, Wan Tang, Hui Zhang, Changyong Feng, Jeanne Kowalski, Xin M. Tu

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Zero-inflated count outcomes arise quite often in research and practice. Parametric models such as the zero-inflated Poisson and zero-inflated negative binomial are widely used to model such responses. Like most parametric models, they are quite sensitive to departures from assumed distributions. Recently, new approaches have been proposed to provide distribution-free, or semi-parametric, alternatives. These methods extend the generalized estimating equations to provide robust inference for population mixtures defined by zero-inflated count outcomes. In this paper, we propose methods to extend smoothly clipped absolute deviation (SCAD)-based variable selection methods to these new models. Variable selection has been gaining popularity in modern clinical research studies, as determining differential treatment effects of interventions for different subgroups has become the norm, rather the exception, in the era of patent-centered outcome research. Such moderation analysis in general creates many explanatory variables in regression analysis, and the advantages of SCAD-based methods over their traditional counterparts render them a great choice for addressing this important and timely issues in clinical research. We illustrate the proposed approach with both simulated and real study data.

Original languageEnglish (US)
Pages (from-to)2770-2785
Number of pages16
JournalStatistics in Medicine
Issue number16
StatePublished - Jul 20 2016


  • functional response models
  • one-step SCAD
  • population mixtures
  • zero-inflated negative binomial
  • zero-inflated poisson

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


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