Dispersion models and longitudinal data analysis

Stat Med. 1999 Sep 15-30;18(17-18):2257-70. doi: 10.1002/(sici)1097-0258(19990915/30)18:17/18<2257::aid-sim253>3.0.co;2-m.

Abstract

Dispersion models provide a flexible class of non-normal distributions with many potential applications in biostatistics, accommodating a wide range of continuous, discrete and mixed data. Starting with Liang and Zeger's generalized estimating equation method, we review some recent applications of dispersion models in longitudinal data analysis, including state space models based on the Tweedie class of exponential dispersion models. In medical applications the latent process of a state space model may often be interpreted as an unobserved potential morbidity process, which is modelled as a function of time varying covariates. By allowing a multivariate response vector of 'symptoms', the model integrates several response variables of mixed types into a single model. For growth curve models, the latent process reflects the 'true' growth.

Publication types

  • Review

MeSH terms

  • Air Pollutants / adverse effects
  • Asthma / etiology
  • British Columbia
  • Bronchitis / etiology
  • Data Interpretation, Statistical*
  • Ear Diseases / etiology
  • Humans
  • Longitudinal Studies*
  • Models, Biological*
  • Multivariate Analysis*
  • Poliomyelitis / epidemiology
  • Respiration Disorders / etiology
  • Sulfur / adverse effects

Substances

  • Air Pollutants
  • Sulfur