Finding the right distribution for highly skewed zero-inflated clinical data

Resmi Gupta*, Bradley S. Marino, James F. Cnota, Richard F. Ittenbach

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Discrete, highly skewed distributions with excess numbers of zeros often result in biased estimates and misleading inferences if the zeros are not properly addressed. A clinical example of children with electrophysiologic disorders in which many of the children are treated without surgery is provided. The purpose of the current study was to identify the optimal modeling strategy for highly skewed, zeroinflated data often observed in the clinical setting by: (a) simulating skewed, zero-inflated count data; (b) fitting simulated data with Poisson, Negative Binomial, Zero-Inflated Poisson (ZIP) and Zero-inflated Negative Binomial (ZINB) models; and, (c) applying the aforementioned models to actual, highly skewed, clinical data of children with an EP disorder. The ZIP model was observed to be the optimal model based on traditional fit statistics as well as estimates of bias, mean-squared error, and coverage.

Original languageEnglish (US)
JournalEpidemiology Biostatistics and Public Health
Issue number1
StatePublished - Jul 25 2013


  • Generalized linear model
  • Skewed data
  • Zero-inflated poisson

ASJC Scopus subject areas

  • Epidemiology
  • Health Policy
  • Public Health, Environmental and Occupational Health
  • Community and Home Care


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