Modelling a Spontaneously Reported Side Effect by Use of a Markov Mixed-Effects Model

Aims: To present a method for analyzing side-effect data where change in severity is spontaneously reported during the experiment. Methods: A clinical study in 12 healthy volunteers aimed to investigate the concentration-response characteristics of a CNS-specific side-effect was conducted. After an open session where the subjects experienced the side-effect and where the individual pharmacokinetic parameters were evaluated they were randomized to a sequence of three different infusion rates of the drug in a double-blinded crossover way. The infusion rates were individualized to achieve the same target concentration in all subjects and different drug input rates were selected to mimic absorption profiles from different formulations. The occurrence of the specific side-effect and any subsequent change in severity was self-reported by the subjects. Severity was recorded as 0 = no side-effect, 1 = mild side-effect and 2 = moderate or severe side-effect. Results: The side-effect data were analyzed using a mixed-effects model for ordered categorical data with and without Markov elements. The former model estimated the probability of having a certain side-effect score conditioned on the preceding observation and drug exposure. The observed numbers of transitions between scores were from 0 −> 1: 24, from 0− > 2: 11, from 1 − >, 2: 23, from 2− > 1: 1, from 2− > 0: 32 and from 1 − >0: 2. The side-effect model consisted of an effect-compartment model with a tolerance compartment. The predictive performance of the Markov model was investigated by a posterior predictive check (PPC), where 100 datasets were simulated from the final model. Average number of the different transitions from the PPC was from 0 − > 1: 26, from 0 − > 2: 11, from 1 − > 2: 25, from 2 − >1: 1, from 2 − >0: 35 and from 1 − > 0: 1. A similar PPC for the model without Markov elements was at considerable disparity with the data. Conclusion: This approach of incorporating Markov elements in an analysis of spontaneously reported categorical side-effect data could adequately predict the observed side-effect time course and could be considered in analyses of categorical data where dependence between observations is an issue.

[1]  E N Jonsson,et al.  Xpose--an S-PLUS based population pharmacokinetic/pharmacodynamic model building aid for NONMEM. , 1999, Computer methods and programs in biomedicine.

[2]  Lewis B. Sheiner,et al.  Simultaneous modeling of pharmacokinetics and pharmacodynamics: Application to d‐tubocurarine , 1979 .

[3]  Mats O Karlsson,et al.  Population pharmacokinetics of clomethiazole and its effect on the natural course of sedation in acute stroke patients. , 2003, British journal of clinical pharmacology.

[4]  L B Sheiner,et al.  Simultaneous modeling of pharmacokinetics and pharmacodynamics: application to d-tubocurarine. , 1980, Clinical pharmacology and therapeutics.

[5]  L. Sheiner,et al.  Evaluating Pharmacokinetic/Pharmacodynamic Models Using the Posterior Predictive Check , 2001, Journal of Pharmacokinetics and Pharmacodynamics.

[6]  Mats O. Karlsson,et al.  Interchangeability and Predictive Performance of Empirical Tolerance Models , 1999, Clinical pharmacokinetics.

[7]  L B Sheiner,et al.  A Markov mixed effect regression model for drug compliance. , 1998, Statistics in medicine.

[8]  Lewis B. Sheiner,et al.  A Population Pharmacokinetic–Pharmacodynamic Analysis of Repeated Measures Time-to-Event Pharmacodynamic Responses: The Antiemetic Effect of Ondansetron , 1999, Journal of Pharmacokinetics and Biopharmaceutics.

[9]  L B Sheiner,et al.  Pharmacodynamic model of tolerance: application to nicotine. , 1988, The Journal of pharmacology and experimental therapeutics.

[10]  L B Sheiner,et al.  A new approach to the analysis of analgesic drug trials, illustrated with bromfenac data , 1994, Clinical pharmacology and therapeutics.

[11]  Meindert Danhof,et al.  A pharmacodynamic Markov mixed‐effect model for the effect of temazepam on sleep , 2000, Clinical pharmacology and therapeutics.

[12]  J. DeJongh,et al.  Population pharmacokinetic and pharmacodynamic modeling of propofol for long‐term sedation in critically ill patients: A comparison between propofol 6% and propofol 1% , 2002, Clinical pharmacology and therapeutics.

[13]  D R Stanski,et al.  Population pharmacodynamic model for ketorolac analgesia , 1996, Clinical pharmacology and therapeutics.

[14]  Mats O. Karlsson,et al.  Three new residual error models for population PK/PD analyses , 1995, Journal of Pharmacokinetics and Biopharmaceutics.

[15]  Mats O Karlsson,et al.  Pharmacokinetic/pharmacodynamic models for the depletion of Vbeta5.2/5.3 T cells by the monoclonal antibody ATM-027 in patients with multiple sclerosis, as measured by FACS. , 2004, British journal of clinical pharmacology.