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    Determination of cell dose-survival relationships from endpoint dilution assays.

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    Authors
    Roberts, Stephen A
    Affiliation
    Cancer Research Campaign Biomathematics and Computing Unit, Paterson Institute for Cancer Research, Christie Hospital, Withington, Manchester, UK.
    Issue Date
    1993-08
    
    Metadata
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    Abstract
    Methods for fitting radiation survival curves to data obtained from endpoint-dilution assays are described. It is shown that for functional forms such as the linear-quadratic model the problem can be recast as a generalized linear model (GLM) and the data fitted using standard software. For functional forms which are not capable of being linearized, such as the multitarget model, the direct maximum likelihood (DML) techniques of Thames et al. (1986) can be used. Both these techniques produce exact maximum likelihood parameter estimates. Compared with the weighted least-squares (WLS) approach traditionally employed, these approaches avoid the need to approximate the binomial distribution of the number of negative wells by a normal distribution, and avoid the biases introduced by the need for arbitrary treatment of data points with 0 or 100% negative wells. The results of fittings using the novel GLM and DML approaches are compared with those obtained using the WLS method on a large series of datasets. For most datasets the WLS method performs well, compared with the exact method, but in a small number of cases the WLS predicted parameter estimates can be in error by as much as their estimated standard errors. A method for the use of a concurrent control to correct for interexperimental variation is outlined. The methods have been implemented in a Fortran computer program using the NAG subroutine library.
    Citation
    Determination of cell dose-survival relationships from endpoint dilution assays. 1993, 64 (2):251-5 Int. J. Radiat. Biol.
    Journal
    International Journal of Radiation Biology
    URI
    http://hdl.handle.net/10541/100383
    DOI
    10.1080/09553009314551371
    PubMed ID
    8103551
    Type
    Article
    Language
    en
    ISSN
    0955-3002
    ae974a485f413a2113503eed53cd6c53
    10.1080/09553009314551371
    Scopus Count
    Collections
    All Paterson Institute for Cancer Research

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