Includes bibliography. / In 1984 it became legal for pharmacists to offer customers a cheaper generic alternative for a given prescription. The motivation for this was the excessively high cost of brand name drugs. The substitution of a generic alternative for a brand name drug is based on the assumption that drugs with a comparable chemical composition will have a similar therapeutic effect. The fact that this supposition is not always true has been demonstrated by a number of particular drugs, digoxon being perhaps the most vivid example. The objective of this thesis is to review the statistical aspects associated with (i) measuring the bioavailability of a drug (Chapter 2) (ii) establishing the equivalence of a new and standard formulation of a drug (Chapter 3). In the process of reviewing the literature two problems were identified. Firstly, it is commonly assumed that bioavailability parameters follow either the normal or lognormal distribution. This assumption is difficult to defend, hence procedures based on such assumptions became suspect. Secondly, bioavailability is inherently multivariate whereas in practice univariate procedures are employed. Efren's bootstrap method, which does not rest on assumptions about the underlying distribution, is proposed as a tool for assessing bioequivalence. A new measure of bioequivalence, the Index of Concordance, is proposed. This index can be computed with equal ease for univariate or multivariate data using the bootstrap (Chapter 5). The bootstrap idea of resampling the data can also be applied to compartmental modelling of bioavailability data. One result of this is a nonparametric estimate of the underlying distribution of the bioavailability parameters (Chapter 6). The bootstrap is, on its own, a fascinating concept. A review of the bootstrap is given in Chapter 4.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uct/oai:localhost:11427/17004 |
| Date | January 1985 |
| Creators | Fresen, John Lawrence |
| Contributors | Juritz, June M |
| Publisher | University of Cape Town, Faculty of Science, Department of Statistical Sciences |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Master Thesis, Masters, MSc |
| Format | application/pdf |
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