title: A new approach to analyse longitudinal epidemiological data with an excess of zeros.
abstract: BACKGROUND
  Within longitudinal epidemiological research, 'count' outcome variables with an excess of zeros frequently occur. Although these outcomes are frequently analysed with a linear mixed model, or a Poisson mixed model, a two-part mixed model would be better in analysing outcome variables with an excess of zeros. Therefore, objective of this paper was to introduce the relatively 'new' method of two-part joint regression modelling in longitudinal data analysis for outcome variables with an excess of zeros, and to compare the performance of this method to current approaches.
  
  
  METHODS
  Within an observational longitudinal dataset, we compared three techniques; two 'standard' approaches (a linear mixed model, and a Poisson mixed model), and a two-part joint mixed model (a binomial/Poisson mixed distribution model), including random intercepts and random slopes. Model fit indicators, and differences between predicted and observed values were used for comparisons. The analyses were performed with STATA using the GLLAMM procedure.
  
  
  RESULTS
  Regarding the random intercept models, the two-part joint mixed model (binomial/Poisson) performed best. Adding random slopes for time to the models changed the sign of the regression coefficient for both the Poisson mixed model and the two-part joint mixed model (binomial/Poisson) and resulted into a much better fit.
  
  
  CONCLUSIONS
  This paper showed that a two-part joint mixed model is a more appropriate method to analyse longitudinal data with an excess of zeros compared to a linear mixed model and a Poisson mixed model. However, in a model with random slopes for time a Poisson mixed model also performed remarkably well.
authors: Department of Epidemiology and Biostatistics, VU University Medical Center, P.O. Box 7057, Amsterdam 1007 MB, The Netherlands. a.spriensma@vumc.nl
authors: Alette S
authors: AS
authors: Spriensma
authors: Tibor R S
authors: TR
authors: Hajos
authors: Michiel R
authors: MR
authors: de Boer
authors: Martijn W
authors: MW
authors: Heymans
authors: Jos W R
authors: JW
authors: Twisk
mesh_terms: D016010:Binomial Distribution; D003627:Data Interpretation, Statistical; D003924:Diabetes Mellitus, Type 2 / Q000188:drug therapy*; D006801:Humans; D007003:Hypoglycemia; D007004:Hypoglycemic Agents / Q000627:therapeutic use*; D000069036:Insulin Glargine; D049528:Insulin, Long-Acting / Q000627:therapeutic use*; D008137:Longitudinal Studies; D015233:Models, Statistical; D016012:Poisson Distribution
chemical_list: D007004:Hypoglycemic Agents; D049528:Insulin, Long-Acting; D000069036:Insulin Glargine