title: Discrimination between rival dosing histories.
abstract: OBJECTIVE
  In population pharmacokinetic studies, the dosing history is sometimes recorded in more than one way. The purpose of this study was to develop and evaluate a procedure for discriminating between rival dosing histories, i.e., for each individual in a data set, identify the dosing history that is the most plausible.
  
  
  METHODS
  The procedure consists of four steps. In the first step we identify individuals whose dosing histories produce predictions that are consistent. In the second step these individuals are used to build a population pharmacokinetic model which is used, in step three, to select the dosing history for the individuals not identified in step one. In step four the population model is refined using the best available dosing histories for all individuals. The proposed procedure was evaluated using both simulations and a real data set, in which two dosing histories, based on patient diaries and electronic monitoring devices (MEMS) were available.
  
  
  RESULTS
  In the real data set, estimated variabilities were almost always lower when the selected dosing histories were used compared to when no selection procedure was used. The diary dosing histories were selected more often than the MEMS dosing histories. In the simulations, the parameter estimates obtained using the selection procedure were closer to the true parameter values compared to when only one of the dosing histories was used.
  
  
  CONCLUSIONS
  The proposed procedure appears to be robust and should be beneficial in at least two respects: improved parameter estimation of population pharmacokinetic and PK/PD models and objective information by which dosage recording methodologies can be compared and patient dose recording behavior can be assessed.
authors.affiliation: Dept of Pharmacy, Uppsala University, Sweden. niclas.jonsson@biof.uu.se
authors.forename: E N
authors.initials: EN
authors.lastname: Jonsson
authors.forename: J R
authors.initials: JR
authors.lastname: Wade
authors.forename: G
authors.initials: G
authors.lastname: Almqvist
authors.forename: M O
authors.initials: MO
authors.lastname: Karlsson
mesh_terms: D000465:Algorithms; D006801:Humans; D008962:Models, Theoretical; D004364:Pharmaceutical Preparations / Q000008:administration & dosage*; D010599:Pharmacokinetics
chemical_list: D004364:Pharmaceutical Preparations