[An effective method for the estimation and comparison of the ED50 with small sample sizes].
In ED50 experiments the relationship between dose and probability of response is often modelled by the probit function. Standard statistical analysis estimates the parameters of this function by the maximum likelihood principle and derives the ED50 and its fiducial limits from these parameters. Bayesian analysis is more effective in two respects: It optionally includes prior information and in all but very few instances yields confidence intervals, whereas fiducial intervals often cannot be determined. Bayesian analysis of experiments with one substance has been treated in GRIEVE (1988). In the present article the mathematically interested reader is shown how to compare two substances. The probability of higher ED50 in the one substance as well as estimates of the ratio of the ED50's are obtained. The methods are easily extended to the effective dose for any other reasonable percentage of animals, e.g. ED90 or ED25. Experiments concerning lethal doses can be analysed by these methods as well. Both types of analysis are applied in two examples which compare new batches of vaccines with an established standard. In the first example both substances are nearly equivalent, while in the second example the new batch is considerably more efficient. An interactive FORTRAN program for a personal computer is available (cf. last section of 5.). It computes the maximum likelihood and the Bayesian solution, using approximate formulas in the latter case. Due to these approximations it was possible to develop a Bayesian program which is fast enough to run on a PC. Validation procedures have been performed. The output consists of a print file and, optionally, an ASCII file containing the coordinates of the posterior probability density and distribution functions.
Berres
M
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CIBA-GEIGY AG, Mathematische Applikationen, Basel, Schweiz.
D000818:Animals; D001499:Bayes Theorem; D004305:Dose-Response Relationship, Drug; D008838:Microcomputers; D015203:Reproducibility of Results; D012984:Software