๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Nonparametric estimation of potency ratio for dilution assays

โœ Scribed by Mohamed A.A. Moussa


Publisher
Elsevier Science
Year
1993
Tongue
English
Weight
448 KB
Volume
23
Category
Article
ISSN
0010-4825

No coin nor oath required. For personal study only.

โœฆ Synopsis


The paper considers the estimation of the potency of an unknown test preparation in relation to a standard in a dilution assay. The method utilizes the Mantel-Haenszel one degree of freedom chi-square approach to test consistency of the observed potency ratio with a range of alternative potency ratios. The method is nonparametric as it does not require any distributional assumptions. However, it requires the doses employed to be equally spaced logarithmically. The dose-response curve is also required to be monotone over the dose range used.


๐Ÿ“œ SIMILAR VOLUMES


Robust Estimation of Relative Potency in
โœ N. J. Hill; A. R. Padmanabhan ๐Ÿ“‚ Article ๐Ÿ“… 2007 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 292 KB ๐Ÿ‘ 2 views

For inference regarding relative potency in dilution assays with possible latent effects, a robust procedure is proposed. Monte Carlo studies show that this procedure compares favourably with h o r n procedures. The theory is illustrated by an application to the tolerances of c a b for tinctures of

Improved estimation of potency in slope
โœ V. K. Srivastava; B. N. Bhattacharya; K. Kumar ๐Ÿ“‚ Article ๐Ÿ“… 1980 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 241 KB ๐Ÿ‘ 2 views

## Abstract Regression coefficients are regarded as stochastic variables in order to account for this kind of variability. This is needed, because differences in responses at successive doses vary generally and thus the assumption of constancy of regression coefficients is violated.

Alternative criteria for optimal designs
โœ J. N. S. Matthews ๐Ÿ“‚ Article ๐Ÿ“… 1998 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 133 KB ๐Ÿ‘ 1 views

It is well known that criteria for optimal non-linear designs usually depend on the unknown value of parameters. An approximate Bayesian approach imposes a prior on these values and optimizes the expectation of the criterion over this distribution. While this method produces designs that perform wel