Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-27T13:25:14.776Z Has data issue: false hasContentIssue false

Methods of estimating the LD 50 in quantal response data

Published online by Cambridge University Press:  15 May 2009

P. Armitage
Affiliation:
From the Medical Research Council Statistical Research Unit, London School of Hygiene and Tropical Medicine
Irene Allen
Affiliation:
From the Medical Research Council Statistical Research Unit, London School of Hygiene and Tropical Medicine
Rights & Permissions [Opens in a new window]

Extract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

A quantal response is one in which a certain event either happens or does not happen. If, in an animal experiment, we record merely whether or not the animal dies, we are measuring a quantal response. The type of data with which we are concerned here is familiar to all workers in biological assay, and occurs constantly in bacteriological and immunological experiments. A number of animals is divided randomly into several groups, and all the animals in each group are treated with the same dose of a certain substance. The doses differ from group to group, and are frequently arranged so that successive doses differ by a common dilution factor. At each dose the numbers of animals which respond positively and negatively are recorded. The potency of the substance may be measured by that dose which would in the long run produce a positive response in exactly 50 % of the animals, and the main statistical problem is how to estimate this dose (the LD 50) from the available data. It is assumed that any inaccuracies in measuring the doses are negligible in comparison with the sampling errors due to the inevitable differences between experimental animals.

Type
Research Article
Copyright
Copyright © Cambridge University Press 1950

References

REFERENCES

Berkson, J. (1944). Application of the logistic function to bio-assay. J. Amer. statist. Ass. 39, 357.Google Scholar
Berkson, J. (1949). Minimum X2 and maximum likelihood solution in terms of a linear transform, with particular reference to bio-assay. J. Amer. statist. Ass. 44, 273.Google ScholarPubMed
Bliss, C. I. (1935). The calculation of the dosage-mortality curve. Ann. appl. Biol. 22, 134.CrossRefGoogle Scholar
Bliss, C. I. (1938). The determination of the dosage-mortality curve from small numbers. Quart. J. Pharm. 11, 192.Google Scholar
Chen, K. K., Anderson, R. C. & Robbins, E. B. (1938). Gelsemicine, aconitine, and pseudaconitine: which is the most toxic alkaloid? Quart. J. Pharm. 11, 84.Google Scholar
Cornfield, J. & Mantel, N. (1948). Simplified calculation of the dosage-response curve. Division of Public Health Methods, Public Health Service, Bethesda, Maryland. (Unpublished communication.)Google Scholar
Cramér, H. (1946). Mathematical Methods of Statistics. Princeton University Press.Google Scholar
Epstein, B. & Churchman, C. W. (1944). On the statistics of sensitivity data. Ann. math. Statist. 15, 90.CrossRefGoogle Scholar
Fieller, E. C. (1944). A fundamental formula in the statistics of biological assay, and some applications. Quart. J. Pharm. 17, 117.Google Scholar
Finney, D. J. (1947a). Probit Analysis. Cambridge University Press.Google Scholar
Finney, D. J. (1947b). The principles of biological assay. J.R. statist. Soc, Suppl., 9, 46.CrossRefGoogle Scholar
Fisher, R. A. & Yates, F. (1948). Statistical Tables for Biological, Agricultural and Medical Research, 3rd ed.London: Oliver and Boyd.Google Scholar
Gaddum, J. H. (1933). Reports on biological standards. III. Methods of biological assay depending on a quantal response. Spec. Rep. Ser. med. Res. Council, Lond., no. 183.Google Scholar
Garwood, F. (1941). The application of maximum likelihood to dosage-mortality curves. Biometrika, 32, 46.CrossRefGoogle Scholar
Irwin, J. O. (1937). Statistical method applied to biological assays. J.R. statist. Soc, Suppl., 4, 1.CrossRefGoogle Scholar
Ibwin, J. O. & Cheeseman, E. A. (1939 a). On an approximate method of determining the median effective dose and its error, in the case of a quantal response. J. Hyg., Camb., 39, 574.Google Scholar
Irwin, J. O. & Cheeseman, E. A. (1939 b). On the maximum-likelihood method of determining dosage-response curves and approximations to the median-effective dose, in cases of a quantal response. J.R. statist. Soc, Suppl., 6, 174.CrossRefGoogle Scholar
Knudsen, L. F. & Curtis, J. M. (1947). The use of the angular transformation in biological assays. J. Amer. statist. Ass. 42, 282.CrossRefGoogle ScholarPubMed
Murray, C. A. (1938). Dosage-mortality in the Peet-Grady method. Soap, 14 (2), 99.Google Scholar
Reed, L. J. & Muench, H. (1938). A simple method of estimating fifty per cent endpoints. Amer. J. Hyg. 27, 493.Google Scholar
Smith, W. (1932). The titration of antipneumococcus serum. J. Path. Bad. 35, 509.CrossRefGoogle Scholar
Snedecor, G. W. (1946). Statistical Methods, 4th ed.Iowa State College Press.Google ScholarPubMed
Spearman, C. (1908). The method of ‘right and wrong cases’ (‘constant stimuli’) without Gauss's formulae. Brit. J. Psychol. 2, 227.Google Scholar
Strand, A. L. (1930). Measuring the toxicity of insect fumigants. Industr. Engng Chem. (Anal, ed.), 2, 4.Google Scholar
Thompson, W. R. (1947). Use of moving averages and interpolation to estimate median-effective dose. I. Fundamental formulas, estimation of error, and relation to other methods. Bact. Rev. 11, 115.CrossRefGoogle ScholarPubMed
Woodard, G., Lange, S. W., Nelson, K. W. & Calvery, H. O. (1941). The acute oral toxicity of acetic, chloracetic, dichloracetic and trichloracetic acids. J. industr. Hyg. Toxicology, 23, 78.Google Scholar