This study represents a substantial change in clinical practice in the perioperative setting. Same-day surgical patients given short-acting anesthetic agents and who are awake, alert, and mobile requiring no parenteral pain medications and with no bleeding or nausea at the end of an operative procedure can safely bypass the PACU.

In a simulation study of inference on population pharmacokinetic parameters, two methods of performing tests of hypotheses comparing two populations using NONMEM were evaluated. These two methods are the test based upon 95% confidence intervals and the likelihood ratio test. Data were simulated according to a monoexponential model and, in that context, power curves for each test were generated for (i) the ratio of mean clearance and (ii) the ratio of the population standard deviations of clearance. To generate the power curves, a range of these parameters was employed; other pharmacokinetic parameters were selected to reflect the variability typically present in a Phase II clinical trial. For tests comparing the means, the confidence interval tests had approximately the same power as the likelihood ratio tests and were consistently more faithful to the nominal level of significance. For comparison of the standard deviations, and when the volume of information available was relatively small, however, the likelihood ratio test was more able to detect differences between the two groups. These results were then compared to results on parameter estimation in order to gain insight into the question of power. As an example, the nonnormality of estimates of the ratio of standard deviations plays an important role in explaining the low power for the confidence interval tests. We conclude that, except for the situation of modeling standard deviations with only sparse information, NONMEM produces tests of significance that are effective at detecting clinically significant differences between two populations.

In a simulation study of the estimation of population pharmacokinetic parameters, including fixed and random effects, the estimates and confidence intervals produced by NONMEM were evaluated. Data were simulated according to a monoexponential model with a wide range of design and statistical parameters, under both steady state (SS) and non-SS conditions. Within the range of values for population parameters commonly encountered in research and clinical settings, NONMEM produced parameter estimates for CL, V, sigma CL, and sigma epsilon which exhibit relatively small biases. As the range of variability increases, these biases became larger and more variable. An important exception was bias in the estimate for sigma V which was large even when the underlying variability was small. NONMEM standard error estimates are appropriate as estimates of standard deviation when the underlying variability is small. Except in the case of CL, standard error estimates tend to deteriorate as underlying variability increases. An examination of confidence interval coverage indicates that caution should be exercised when the usual 95% confidence intervals are used for hypothesis testing. Finally, simulation-based corrections of point and interval estimates are possible but corrections must be performed on a case-by-case basis.

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Patients should be followed for as long as four weeks after discharge to determine the true incidence and risk of ABT-associated PI.