Academic literature on the topic 'Nonparametric Pharmacokinetics'

Keller Frieder, Erdmann Klaus, Giehl Michael, and Czock David. "PHARMNEPH: COMPUTER-SYSTEM FOR DRUG DOSAGE ADJUSTMENT IN RENAL FAILURE." In Studies in Health Technology and Informatics. IOS Press, 1993. https://doi.org/10.3233/978-1-60750-854-0-164.

"strate IBE the upper bound of a 90% confidence interval for the above aggregate metric must fall below 2.49. The required upper bound can be calculated in at least three different ways: (1) method-of-moments estimation with a Cornish-Fisher approx-imation (Hyslop et al., 2000; FDA Guidance, 2001), (2) bootstrapping (FDA Guidance, 1997), and (3) by asymptotic approximations to the mean and variance of ν and ν (Patterson, 2003; Patterson and Jones, 2002b,c). Method (1) derives from theory that assumes the inde-pendence of chi-squared variables and is more appropriate to the analysis of a parallel group design. Hence it does not fully account for the within-subject correlation that is present in data obtained from cross-over tri-als. Moreover, the approach is potentially sensitive to bias introduced by missing data and imbalance in the study data (Patterson and Jones, 2002c). Method (2), which uses the nonparametric percentile bootstrap method (Efron and Tibshirani, 1993), was the earliest suggested method of calculating the upper bound (FDA Guidance, 1997), but it has sev-eral disadvantages. Among these are that it is computationally intensive and it introduces randomness into the final calculated upper bound. Re-cent modifications to ensure consistency of the bootstrap (Shao et al., 2000) do not appear to protect the Type I error rate (Patterson and Jones, 2002c) around the mixed-scaling cut-off (0.04) unless calibration (Efron and Tibshirani, 1993) is used. Use of such a calibration technique is questionable if one is making a regulatory submission. Hence, we pre-fer to use method (3) and will illustrate its use shortly. We note that this method appears to protect against inflation of the Type I error rate in IBE and PBE testing, and the use of REML ensures unbiased esti-mates (Patterson and Jones, 2002c) in data sets with missing data and imbalance, a common occurrence in cross-over designs, (Patterson and Jones, 2002a,b). In general (Patterson and Jones, 2002a), cross-over tri-als that have been used to test for IBE and PBE have used sample sizes in excess of 20 to 30 subjects, so asymptotic testing is not unreasonable, and there is a precedent for the use of such procedures in the study of pharmacokinetics (Machado et al., 1999). We present findings here based on asymptotic normal theory using REML and not taking into account shrinkage (Patterson and Jones, 2002b,c). It is possible to account for this factor using the approach of Harville and Jeske (1992); see also Ken-ward and Roger (1997). However, this approach is not considered here in the interests of space and as the approach described below appears to control the Type I error rate for sample sizes as low as 16 (Patterson and Jones, 2002c). In a 2 × 2 cross-over trial it is not possible to estimate separately the within-and between-subject variances and hence a replicate design, where subjects receiving each formulation more than once is required." In Design and Analysis of Cross-Over Trials. Chapman and Hall/CRC, 2003. http://dx.doi.org/10.1201/9781420036091-19.