Computational and Mathematical Methods in Medicine

Population Mixtures in Biomedical and Psychosocial Research

Publishing date
19 Apr 2013
Submission deadline
30 Nov 2012

Lead Editor
Guest Editors

1Department of Biostatistics and Computational Biology University of Rochester Medical Center, Rochester, NY 14642, USA

2Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA

This issue is now closed for submissions.

Population Mixtures in Biomedical and Psychosocial Research

This issue is now closed for submissions.


Many studies in biomedical, psychosocial, and related services research involve population mixtures. A proper acknowledgment of the existence of mixtures in a study population has quite important implications for modeling and evaluating intervention strategies in research and clinical studies. For example, if we treat everyone in a study population as being at risk for suicide attempts and apply standard survival analysis models such as the Cox proportional hazards regression, we assume that everyone is at risk for the rare event, although allowing individual characteristics to modify the risk for such an event. This standard approach lacks specificity and fails to identify the at-risk subgroup for which the intervention is targeted and most efficacious.

Recent years have witnessed a significant increase in the number of publications on models and their applications to population mixtures. For example, zero-inflated Poisson and zero-inflated negative binomial have been widely used to model count responses with structure zeros, a concept to distinguish a nonrisk subpopulation from the rest at-risk group. In the front of survival analysis, cure models have been increasingly employed to account for the presence of a subgroup with no risk or almost no risk for failure.

In response to this trend in the literature, we like to dedicate this special issue to new statistical models and novel applications of existing models for population mixtures in biomedical and psychosocial research. Both frequentist and Bayesian statistical methods are welcome. Potential topics include, but are not limited to:

  • Cure model in recurrent events data
  • Model of zero-inflated count data in longitudinal studies
  • Finite mixture models in biomedical research
  • Pattern-mixture models for analysis of missing data
  • Causal inference with time-dependent covariates
  • Clinical applications of subtyping of diseases

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