Data Processing

  • Peter Raggatt
Chapter

Abstract

It is self-evident that computerised processing of immunoassay data is advantageous. What is not self-evident is whether any particular computer method gives the correct result in all circumstances. The objective of this chapter is to provide the information on which a judgement can be formed about the correctness of immunoassay results calculated by computer.

Keywords

Spline Function Influence Function Scatchard Plot Immunometric Assay Assay Response 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References

  1. 1.
    Dudley, R.A., Edwards, P., Ekins, R.P., et al., (1985) Guidelines for immunoassay data processing. Clin. Chem. 31, 1264–71.PubMedGoogle Scholar
  2. 2.
    Rodbard, D. (1974) Statistical quality control and routine data processing for radioimmunoassays and immunoradiometric assays. Clin. Chem. 20, 1255–70.PubMedGoogle Scholar
  3. 3.
    Challand, G.S., Spencer, C.A., Ratcliffe, J.G. (1976) Observations on the automated calculation of radioimmunoassay results. Ann. Clin. Biochem. 13, 354–60.PubMedCrossRefGoogle Scholar
  4. 4.
    Reinsch, C.H. (1967) Smoothing by spline functions. Numer. Math. 10, 177–83.CrossRefGoogle Scholar
  5. 5.
    Reinsch, C.H. (1971) Smoothing by spline functions II. Numer. Math. 16, 451–4.CrossRefGoogle Scholar
  6. 6.
    Marschner, I., Erhardt, F., Scriba, P.C. (1974) Calculation of the radioimmunoassay standard curve by spline function. In Radioimmunoassay and Related Procedures in Medicine. pp 111, International Atomic Energy Agency, Vienna.Google Scholar
  7. 7.
    Marschner, I., Dobry, H., Erhardt, F., et al., (1974) Artzl. Lab 20, 184–91.Google Scholar
  8. 8.
    Malan, P.G., Cox, M.G., Long, E.M.R., Ekins, R.P. (1978) Development in curve fitting procedures for radioimmunoassay data using a multiple binding site model. In Computing in Clinical Laboratories (ed Siemaszko, F. ) p 282–90, Pitman Medical, Tunbridge Wells.Google Scholar
  9. 9.
    Malan, P.G., Cox, M.G., Long, E.M.R., Ekins, R.P. (1978) Curve-fitting to radioimmunoassay standard curves: spline and multiple binding-site models. Ann. Clin. Biochem. 15, 132–4.Google Scholar
  10. 10.
    Hales, C.N. and Randle, P.J. (1963) Immunoassay of insulin with insulin-antibody precipitate. Biochem. J. 88, 137–46.PubMedPubMedCentralCrossRefGoogle Scholar
  11. 11.
    Rodbard, D. and Cooper, J.A. (1970) A model for the prediction of confidence limits in radioimmunoassay and competitive protein binding assays. In In vitro Procedures with Radioisotopes in Medicine. p. 659–73, International Atomic Energy Agency, Vienna.Google Scholar
  12. 12.
    Rodbard, D. and Lewald, J.E. (1970) Computer analysis of radioligand assay and radioimmunoassay data. Acta. Endocrinol. (Kbh) Suppl 147, 7–103.Google Scholar
  13. 13.
    Feldman, H. and Rodbard, D. (1971) Mathematical theory of radioimmunoassay. In Competitive Protein Binding Assays. (eds. Daughaday, W.H. and Odell, W.D).p 158–203, Lippincott, Philadelphia.Google Scholar
  14. 14.
    Berkson, J. (1944) Application of the logistic function in bioassay. J. Amer. Statis. Assoc. 39, 357–65.Google Scholar
  15. 15.
    Berkson, J. (1951) Why I prefer logits to probits. Biometrics. 7, 327–39.CrossRefGoogle Scholar
  16. 16.
    Healy, M.J.R. (1972) Statistical analysis of radioimmunoassay data. Biochem. J. 130, 207–10.PubMedPubMedCentralCrossRefGoogle Scholar
  17. 17.
    Scatchard, G. (1949) The attractions of proteins for small proteins and molecules. Ann. NY. Acad. Sci. 51, 660–72.CrossRefGoogle Scholar
  18. 18.
    Berson,S.A. and Yalow R.S. (1964) The Hormones: Physiology, Chemistry and Applications, vol 4. (eds. Pincus, G., Thiman, K.V. and Astwood, E.B.) p 557–630, Academic Press, New York.CrossRefGoogle Scholar
  19. 19.
    Ekins, R.P., Newman G.B., O’Riordan, J.L.H. (1968) In Radioisotopes in Medicine (eds. Hayes R.L., Goswitz, F.A. and Murphy B.E.P.) p 59, US Atomic Energy Commission, Oak Ridge, Tennessee.Google Scholar
  20. 20.
    Ekins, R.P. and Newman, G.B. (1970) Theoretical aspects of saturation analysis. Acta. Endocrinol. (Kbh) Suppl 147, 11–36.Google Scholar
  21. 21.
    Wilkins, T.A., Chadney D.C., Bryant, J., et al., (1978) Non-linear least-squares curve fitting of a simple theoretical model using a mini-computer. In Automated calculation of radioimmunoassay results (ed Challand, G.S.) Ann. Clin. Biochem. 15, 123–35.CrossRefGoogle Scholar
  22. 22.
    Wilkins, T.A., Chadney, D.C., Bryant, J., et al., (1978) Non-linear least-squares curve fitting of a simple statistical model to radioimmunoassay dose-response data using a mini-computer. In Radioimmunoassay and Related Procedures in Medicine. p 399–423, International Atomic Energy Agency, Vienna.Google Scholar
  23. 23.
    Hampel, F.R. (1974) The influence curve and its role in robust estimation. J. Amer. Statist. Assoc. 69, 383–93.CrossRefGoogle Scholar
  24. 24.
    Barnett, V. and Lewis, T. (1984) Outliers in statistical data. ( 2nd edition ). John Wiley and Sons., Chichester.Google Scholar
  25. 25.
    Healy, M.J.R. (1979) Outliers in clinical chemistry and quality control schemes. Clin. Chem. 25, 675–7.PubMedGoogle Scholar
  26. 26.
    Burnett, R.W. (1975) Accurate estimation of standard deviations for quantitative methods used in clinical chemistry. Clin. Chem. 21, 1935–8.PubMedGoogle Scholar
  27. 27.
    Rodbard, D. (1971) Statistical aspects of immunoassays. In Competitive Protein Binding Assays. (eds. Daughaday, W.H. and Odell W.D. ) p 204–59, Lippincott, Philadelphia.Google Scholar
  28. 28.
    Nicol, R., Smith, P., Raggatt, P.R. (1985) A robust microcomputer routine for the identification of outlying and influential points in radioimmunoassay standard curves. Comput. Biomed. Res. 18, 334–46.PubMedCrossRefGoogle Scholar
  29. 29.
    Hawker, F. and Challand, G.S. (1981) Effects of outlying standard points on curve-fitting in radioimmunoassay. Clin. Chem. 27, 14–17.PubMedGoogle Scholar
  30. 30.
    Nicol, R., Smith, P., Raggatt, P.R. (1986) The use of the simplex method for the optimisation of non-linear functions on a laboratory microcomputer. Comput. Biol. Med. 16, 145–52.PubMedCrossRefGoogle Scholar
  31. 31.
    World Association of Societies of Pathology. (1979) Am. J. Clin. Pathol. 71, 624–30.Google Scholar
  32. 32.
    Rodbard, D., Rayford, P.L., Cooper, J.A., Ross, G.T. (1968) Statistical quality control of radioimmunoassays. J. Clin. Endocrinol. 28, 1412–18.CrossRefGoogle Scholar
  33. 33.
    Healy, M.J.R. (1972) Statistical analysis of radioimmunoassay. Biochem. J. 130, 207–10.PubMedPubMedCentralCrossRefGoogle Scholar
  34. 34.
    Ekins, R.P. (1983) The precision profile: its use in assay design, assessment and quality control. In: Immunoassays in Clinical Medicine. (eds. Hunter, W.M. and Corne J.E.T. ) p 76–105, Churchill-Livingstone, Edinburgh,.Google Scholar
  35. 35.
    Raggatt, P.R. (1989) Duplicates or singletons? — An analysis of the need for replication in immunoassay and a computer program to calculate the distribution of outliers and the precision profile from assay duplicates. Ann. Clin. Biochem. 26, 26–37.PubMedCrossRefGoogle Scholar
  36. 36.
    Sadler, W.A. and Smith, M.H. (1990) Use and abuse of precision profiles: some pitfalls illustrated by computing and plotting confidence intervals. Clin. Chem. 36, 1346–50.PubMedGoogle Scholar

Copyright information

© Palgrave Macmillan, a division of Macmillan Publishers Limited 1991

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  • Peter Raggatt

There are no affiliations available

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