Distribution-free models for latent mixed population responses in a longitudinal setting with missing data

Hui Zhang*, Li Tang, Yuanyuan Kong, Tian Chen, Xueyan Liu, Zhiwei Zhang, Bo Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Many biomedical and psychosocial studies involve population mixtures, which consist of multiple latent subpopulations. Because group membership cannot be observed, standard methods do not apply when differential treatment effects need to be studied across subgroups. We consider a two-group mixture in which membership of latent subgroups is determined by structural zeroes of a zero-inflated count variable and propose a new approach to model treatment differences between latent subgroups in a longitudinal setting. It has also been incorporated with the inverse probability weighted method to address data missingness. As the approach builds on the distribution-free functional response models, it requires no parametric distribution model and thereby provides a robust inference. We illustrate the approach with both real and simulated data.

Original languageEnglish (US)
Pages (from-to)3273-3285
Number of pages13
JournalStatistical Methods in Medical Research
Issue number10-11
StatePublished - Nov 1 2019


  • Latent population mixture
  • inverse probability weight
  • longitudinal response
  • missing data
  • non-parametric

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management


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