You are viewing a version of a model that has been updated. To access the latest version, and a more detailed display please go here.

DDMODEL00000060: Troconiz_2009_epilepsy_NegativeBinomial

  public model
Short description:
Model comprised of files: Executable_epilepsy_NB.mod, Command.txt, Simulated_epilepsy_NB.csv, Output_simulated_epilepsy_NB.lst, Output_real_epilepsy_NB.lst
Original code
  • Modelling overdispersion and Markovian features in count data.
  • Trocóniz IF, Plan EL, Miller R, Karlsson MO, Elodie L. Plan, Mats O. Karlsson, Inaki Troconiz
  • Journal of pharmacokinetics and pharmacodynamics, 10/2009, Volume 36, Issue 5, pages: 461-477
  • Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, Pamplona, Spain.
  • The number of counts (events) per unit of time is a discrete response variable that is generally analyzed with the Poisson distribution (PS) model. The PS model makes two assumptions: the mean number of counts (lambda) is assumed equal to the variance, and counts occurring in non-overlapping intervals are assumed independent. However, many counting outcomes show greater variability than predicted by the PS model, a phenomenon called overdispersion. The purpose of this study was to implement and explore, in the population context, different distribution models accounting for overdispersion and Markov patterns in the analysis of count data. Daily seizures count data obtained from 551 subjects during the 12-week screening phase of a double-blind, placebo-controlled, parallel-group multicenter study performed in epileptic patients with medically refractory partial seizures, were used in the current investigation. The following distribution models were fitted to the data: PS, Zero-Inflated PS (ZIP), Negative Binomial (NB), and Zero-Inflated Negative Binomial (ZINB) models. Markovian features were introduced estimating different lambdas and overdispersion parameters depending on whether the previous day was a seizure or a non-seizure day. All analyses were performed with NONMEM VI. All models were successfully implemented and all overdispersed models improved the fit with respect to the PS model. The NB model resulted in the best description of the data. The inclusion of Markovian features in lambda and in the overdispersion parameter improved the fit significantly (P < 0.001). The plot of the variance versus mean daily seizure count profiles, and the number of transitions, are suggested as model performance tools reflecting the capability to handle overdispersion and Markovian features, respectively.
Elodie Plan
Annotations have not been checked.
This model is not certified.
  • Model owner: Elodie Plan
  • Submitted: Dec 13, 2015 8:36:11 PM
  • Last Modified: Dec 14, 2015 10:48:33 AM
Revisions
  • Version: 6 public model Download this version
    • Submitted on: Dec 14, 2015 10:48:33 AM
    • Submitted by: Elodie Plan
    • With comment: Edited model metadata online.
  • Version: 4 public model Download this version
    • Submitted on: Dec 13, 2015 8:36:11 PM
    • Submitted by: Elodie Plan
    • With comment: Edited model metadata online.
 
Help