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Population Mixtures in Biomedical and Psychosocial Research

Call for Papers

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

Before submission authors should carefully read over the journal's Author Guidelines, which are located at http://www.hindawi.com/journals/cmmm/guidelines/. Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System at http://mts.hindawi.com/ according to the following timetable:

Manuscript DueFriday, 30 November 2012
First Round of ReviewsFriday, 22 February 2013
Publication DateFriday, 19 April 2013

Lead Guest Editor

  • Changyong Feng, Department of Biostatistics and Computational Biology University of Rochester Medical Center, Rochester, NY 14642, USA

Guest Editors

  • Xin M. Tu, Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY 14642, USA
  • Gong Tang, Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA