Evaluation of Uncertainty Parameters Estimated by Different Population PK Software and Methods View Full Text


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Article Info

DATE

2007-01-10

AUTHORS

Céline Dartois, Annabelle Lemenuel-Diot, Christian Laveille, Brigitte Tranchand, Michel Tod, Pascal Girard

ABSTRACT

The uncertainty associated with parameter estimations is essential for population model building, evaluation, and simulation. Summarized by the standard error (SE), its estimation is sometimes questionable. Herein, we evaluate SEs provided by different non linear mixed-effect estimation methods associated with their estimation performances. Methods based on maximum likelihood (FO and FOCE in NONMEMTM, nlme in SplusTM, and SAEM in MONOLIX) and Bayesian theory (WinBUGS) were evaluated on datasets obtained by simulations of a one-compartment PK model using 9 different designs. Bootstrap techniques were applied to FO, FOCE, and nlme. We compared SE estimations, parameter estimations, convergence, and computation time. Regarding SE estimations, methods provided concordant results for fixed effects. On random effects, SAEM and WinBUGS, tended respectively to under or over-estimate them. With sparse data, FO provided biased estimations of SE and discordant results between bootstrapped and original datasets. Regarding parameter estimations, FO showed a systematic bias on fixed and random effects. WinBUGS provided biased estimations, but only with sparse data. SAEM and WinBUGS converged systematically while FOCE failed in half of the cases. Applying bootstrap with FOCE yielded CPU times too large for routine application and bootstrap with nlme resulted in frequent crashes. In conclusion, FO provided bias on parameter estimations and on SE estimations of random effects. Methods like FOCE provided unbiased results but convergence was the biggest issue. Bootstrap did not improve SEs for FOCE methods, except when confidence interval of random effects is needed. WinBUGS gave consistent results but required long computation times. SAEM was in-between, showing few under-estimated SE but unbiased parameter estimations. More... »

PAGES

289-311

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10928-006-9046-9

    DOI

    http://dx.doi.org/10.1007/s10928-006-9046-9

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1045411969

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/17216368


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    82 one-compartment PK model
    83 original dataset
    84 parameter estimation
    85 parameters
    86 performance
    87 random effects
    88 results
    89 routine application
    90 simulations
    91 software
    92 sparse data
    93 standard error
    94 systematic bias
    95 technique
    96 theory
    97 time
    98 unbiased parameter estimation
    99 unbiased results
    100 uncertainty
    101 uncertainty parameters
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