COVERAGE AND PRECISION OF CONFIDENCE-INTERVALS FOR AREA-UNDER-THE-CURVE USING PARAMETRIC AND NONPARAMETRIC METHODS IN A TOXICOKINETIC EXPERIMENTAL-DESIGN

Purpose. The coverage and precision of parametric Bailer-type confiden ce intervals (CIs) for area under the curve (AUG) was compared to nonp arametric bootstrap confidence intervals. Methods. Concentration-time data was simulated using Monte Carlo simulation under a toxicokinetic paradigm with sparse (SSC) and dense sampling (DSC) conditions. AUC wa s calculated using the trapezoidal rule and 95% CIs were computed usin g various parametric and nonparametric methods. Results. Under SSC, th e various parametric CIs contained the true population AUC with covera ge probabilities ranging from 0.77 to 0.95 with low inter-subject vari ation (coefficient of variation (CV) = 15%) and from 0.82 to 0.95 with high inter-subject variation (CV = 50%). The nominal value should be close to 0.95. DSC tended to increase coverage by about 0.05. Bailer's method always produced the lowest coverage of all parametric CIs exam ined. Under SSC, bootstrap CIs had coverage probabilities ranging from 0.62 (CV = 15%) to 0.68 (CV = 50%). DSC increased coverage to 0.77. P arametric CIs were wider than their nonparametric counterparts, often giving lower CI estimates less than zero. Bailer's method and Bailer's method using the jackknife estimate of the standard error were the wo rst in this respect. Bootstrap CIs never had lower CI estimates less t han zero. However, SSC tends to produce bootstrap distributions that a re not continuous which, if used, may produce biased CI estimates. Con clusions. Bootstrap CI estimates were judged to be the ''best''. Howev er, the limitations of the bootstrap should be clearly recognized and it should not be used indiscriminately. Examination of the bootstrap d istribution for its degree of discrete-ness must be part of the statis tical process.