Do we need full compliance data for population pharmacokinetic analysis?

  • Pascal Girard
  • Lewis B. Sheiner
  • Helen Kastrissios
  • Terrence F. Blaschke


For population pharmacokinetic analysis of multiple oral doses one of the key issues is knowing as precisely as possible the dose inputs in order to fit a model to the input-output (dose-concentration) relationship. Recently developed electronic monitoring devices, placed on pill containers, permit precise records to be obtained over months, of the time/date opening of the container. Such records are reported to be the most reliable measurement of drug taking behavior for ambulatory patients. To investigate strategies for using and summarizing this new abundant information, a Markov chain process model was developed, that simulates compliance data from real data from electronically monitored patients, and data simulations and analyses were conducted. Results indicate that traditional population pharmacokinetic analysis methods that ignore actual dosing information tend to estimate biased clearance and volume and markedly overestimate random interindividual variability. The best dosing information summarization strategies consist of initially estimating population pharmacokinetic parameters, using no covariates and only a limited number of dose records, the latter chosen based on an a priori estimate of the half-life of the drug in the compartment of interest; then resummarizing the dose records using either population or individual posterior Bayes parameter estimates from the first population fit; and finally reestimating the population parameters using the newly summarized dose records. Such summarization strategies yield the same parameter estimates as using full dosing information records while reducing by at least 75% the CPU time needed for a population pharmacokinetic analysis.

Key Words

compliance MEMS population pharmacokinetics Markov chain model 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    M. Davidian and A. R. Gallant. Smooth nonparameteric maximum likehood estimation for population pharmacokinetics, with application to quindine.J. Pharmacokin. Biopharm. 20:529–556 (1992).CrossRefGoogle Scholar
  2. 2.
    L. Aarons. The estimation of population pharmacokinetic parameters using an EM algorithm.Comput. Meth. Prog. Biomed. 41:9–16 (1993).CrossRefGoogle Scholar
  3. 3.
    M. O. Karlsson and L. B. Sheiner. Estimating biovailability when clearnace varies with time.Clin. Pharmacol. Ther. 55:623–637 (1994).PubMedCrossRefGoogle Scholar
  4. 4.
    L. P. Balant, M. Rowland, L. Aarons, F. Mentré, P. L. Morselli, J. L. Steimer, and S. Vozeh. New strategies in drug development and clincial evaluation: The population approach.Eur. J. Clin. Pharmacol. 45:93–94 (1993).PubMedCrossRefGoogle Scholar
  5. 5.
    New Strategies in Drug Development and Clinical Evaluation The Population Approach, M. Rowland and L. Aarons (eds.). Brussels, Commission of the European Communities, 1992.Google Scholar
  6. 6.
    L. Aarons. Practical issues in possible implementation of population pharmacokinetics in drug development discussion. In M. Rowland and L. Aarons. (eds.),New Strategies in Drug Development and Clinical Evaluation. The Population Approach, Commission of the European Communities, Brussels, 1992.Google Scholar
  7. 7.
    M. K. Al-Banna, A. W. Kelman, and B. Whiting. Experimental design and efficient parameter estimation in population pharmacokinetics.J. Pharmacokin. Biopharm. 18:347–360 (1990).CrossRefGoogle Scholar
  8. 8.
    Y. Merlé, F. Mentré, A. Mallet, and A. H. Aurengo. Designing an optimal experiment for bayesian estimation: application to the kinetics of iodine thyroid uptake.Stat. Med. 13:185–196 (1994).PubMedCrossRefGoogle Scholar
  9. 9.
    J. Urquhart. Role of patient compliance in clinical pharmacokinetics.Clin. Pharmacokin. 27:202–215 (1994).CrossRefGoogle Scholar
  10. 10.
    Van der Stichele. Measurement of patient compliance and the interpretation of randomized clinical trials.Eur. J. Clin. Pharmacol. 41:27–35 (1991).CrossRefGoogle Scholar
  11. 11.
    D. M. Waterhouse, K. A. Calzone, C. Mele, and D. E. Brenner. Adherence to oral tamoxifen: A comparison of patient self-report, pill counts, and microelectronic monitoring [see Comments].J. Clin. Oncol. 11:1189–1197 (1993).PubMedGoogle Scholar
  12. 12.
    S. L. Beal and L. B. Sheiner.NONMEM User's Guide, Part I.NONMEM Project Group (ed.), San Francisco, University of California at San Francisco, 1992.Google Scholar
  13. 13.
    M. J. Lindstrom and D. M. Bates. Nonlinear mixed effects models for repeated measures data.Biometrics 46:673–687 (1990).PubMedCrossRefGoogle Scholar
  14. 14.
    S. L. Beal and L. B. Sheiner.NONMEM User's Guide, Part VII.NONMEM Project Group (ed.), San Francisco, University of California at San Francisco, 1992.Google Scholar
  15. 15.
    S. M. Ross.Introduction to Probability Models. In Z. W. Birnbaum and C. Lukacs (eds.), Academic Press, New York, 1980.Google Scholar
  16. 16.
    H. Kastrissios, J. Suarez, B. S. Flowers, and T. F. Blaschke. Could decreased compliance in an AIDS clinical trial affect analysis of outcomes?Clin. Pharmacol. Ther. 57:190 (1995).Google Scholar
  17. 17.
    Statistical Sciences.S-PLUS Programmer's Manual, Version 3.2, StatSci, a division of MathSoft, Seattle, 1993.Google Scholar
  18. 18.
    The Coronary Drug Project Research Group. Influence of adherence to treatment and response of cholesterol on mortality in the coronary drug project.N. Engl. J. Med. 303:1038–1041 (1980).CrossRefGoogle Scholar
  19. 19.
    J. A. Cramer, R. H. Mattson, M. L. Prevey, R. D. Scheyer, and V. L. Quellette. How often is medication taken as prescribed? A novel assessment technique.J. Am. Med. Assoc. 261:3273–3277 (1989).CrossRefGoogle Scholar
  20. 20.
    B. Efron and D. Feldman. Compliance as an explanatory variable in clinical trials.J. Am. Stat. Assoc. 86:9–26 (1991).CrossRefGoogle Scholar
  21. 21.
    D. Gillings. The application of the principle of intention to treat to the analysis of clinical trials.Drug Inform. J. 25:411–424 (1991).Google Scholar
  22. 22.
    D. B. Rubin. Response to “Compliance as an explanatory variable in clinical trials”.J. Am. Stat. Assoc. 86:36–38 (1991).CrossRefGoogle Scholar
  23. 23.
    G. Levy. A pharmacokinetic perspective on medicament noncompliance.Clin. Pharmacol. Ther. 54:242–243 (1993).PubMedCrossRefGoogle Scholar
  24. 24.
    A. Rubio, C. Cox, and M. Weintraub. Prediction of diltiazem plasma concentration curves from limited measurements using compliance data.Clin. Pharmacokin. 22:238–246 (1992).CrossRefGoogle Scholar
  25. 25.
    T. H. Grasela, E. J. Antal, R. J. Townsend, and R. B. Smith. An evaluation of population pharmacokinetics in therapeutic trials. Part I. Comparison of methodologies.Clin. Pharmacol. Ther. 39:605–612 (1986).PubMedCrossRefGoogle Scholar
  26. 26.
    E. J. Antal, T. H. Grasela, and R. B. Smith. An evaluation of population pharmacokinetics in therapeutic trials. Part III. Prospective data collection versus retrospective data assembly.Clin. Pharmacol. Ther. 46:552–559 (1989).PubMedCrossRefGoogle Scholar

Copyright information

© Plenum Publishing Corporation 1996

Authors and Affiliations

  • Pascal Girard
    • 1
    • 2
  • Lewis B. Sheiner
    • 1
    • 3
  • Helen Kastrissios
    • 4
  • Terrence F. Blaschke
    • 4
  1. 1.Department of Pharmacy, School of PharmacyUniversity of CaliforniaSan Francisco
  2. 2.Service de Pharmacologie CliniqueHopital CardiologiqueLyon Cedex 03France
  3. 3.Department of Laboratory Medicine and Medicine, School of MedicineUniversity of CaliforniaSan Francisco
  4. 4.Division of Clinical PharmacologyStanford UniversityPalo Alto

Personalised recommendations