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Jane J. Kim, Eduardo L. Franco, Natasha K. Stout, Sue J. Goldie, THE AUTHORS REPLY, American Journal of Epidemiology, Volume 166, Issue 8, 15 October 2007, Pages 983–984, https://doi.org/10.1093/aje/kwm241
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We thank Drs. Basu and Galvani for their observations (1) on our paper (2). However, we disagree with several of their comments. We did not assert that our multiparameter likelihood-based approach was superior to alternative calibration techniques, including Bayesian approaches. Rather, we emphasized the lack of consensus on an ideal approach, credited the “theoretical appeal” of Bayesian methods, and discussed the limitations of our own methods. We suggested that our approach would “appeal to epidemiologists” because of the empirical value of using the full complement of data from a credible cohort study to demonstrate the validity of the model. Van de Velde et al. (3) recently published an elegant analysis of parameter uncertainty in a model of vaccine effectiveness, based on similar principles.
We noted in our paper (2) that Bayesian techniques require “meaningful” prior distributions (not “informative” priors, as Basu and Galvani mistakenly claim), to highlight the uncontested view that priors are a critical component of Bayesian methods (4). Indeed, uninformed priors may be “meaningful,” since they can reflect the uncertainty of knowledge at baseline. Basu and Galvani state that our approach “is not capable of discriminating among ‘good-fitting’ parameter sets” (1, p. 983). While we agree that a Bayesian approach can offer “specific criteria… for distinguishing among alternative model structures,” we believe that in view of the biologic complexity of the natural history of human papillomavirus (HPV) infection and cervical cancer, capturing the uncertainty, as we did, is more important than using statistical constructs to discriminate among alternative model specifications.
