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A short review of model selection techniques for radiation epidemiology

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Abstract

A common type of statistical challenge, widespread across many areas of research, involves the selection of a preferred model to describe the main features and trends in a particular data set. The objective of model selection is to balance the quality of fit to data against the complexity and predictive ability of the model achieving that fit. Several model selection techniques, including two information criteria, which aim to determine which set of model parameters the data best support, are reviewed here. The techniques rely on computing the probabilities of the different models, given the data, rather than considering the allowed values of the fitted parameters. Such information criteria have only been applied to the field of radiation epidemiology recently, even though they have longer traditions of application in other areas of research. The purpose of this review is to make two information criteria more accessible by fully detailing how to calculate them in a practical way and how to interpret the resulting values. This aim is supported with the aid of some examples involving the computation of risk models for radiation-induced solid cancer mortality fitted to the epidemiological data from the Japanese A-bomb survivors. These examples illustrate that the Bayesian information criterion is particularly useful in concluding that the weight of evidence is in favour of excess relative risk models that depend on age-at-exposure and excess relative risk models that depend on age-attained.

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Notes

  1. However, this should not give the impression that the standard model selection approach involving maximum likelihoods pays no attention to the number of fit parameters, which, in fact, determines the number of degrees of freedom, as explained below.

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Acknowledgments

The author would like to thank Dr. W. Rühm and Dr. J. R. Walsh for critically reading the manuscript, Prof. D. Pierce and Dr. P. Jacob for useful discussions and two anonymous reviewers for many valuable comments which lead to an improvement of the original manuscript. This work makes use of the data obtained from the Radiation Effects Research Foundation (RERF) in Hiroshima, Japan. RERF is a private foundation funded equally by the Japanese Ministry of Health and Welfare and the US Department of Energy through the US National Academy of Sciences. The conclusions in this work are those of the author and do not necessarily reflect the scientific judgement of RERF or its funding agencies.

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  1. Institute of Radiation Protection, GSF National Center for Environment and Health, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany

    Linda Walsh

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  1. Linda Walsh
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Correspondence to Linda Walsh.

Appendix

Appendix

Table 6

Table 6 Fit parameters [with standard errors (SE)] for the four preferred models in Table 5 as defined by Eq. 6–10
Full size table

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Walsh, L. A short review of model selection techniques for radiation epidemiology. Radiat Environ Biophys 46, 205–213 (2007). https://doi.org/10.1007/s00411-007-0109-0

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