RE: "MULTIPARAMETER CALIBRATION OF A NATURAL HISTORY MODEL OF CERVICAL CANCER"

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Abstract
In a recent article, Kim et al. (1) addressed the important question of how best to estimate parameters in complex models of human papillomavirus. Unfortunately, in their paper they make a number of incorrect claims about the advantages of their method and the disadvantages of more established methods. First, they wrongly claim that Bayesian techniques for identifying model parameters require informative prior distributions (2). They also claim that their technique of identifying multiple discrete parameter sets to fit curves is superior to the approach of finding a joint probability density of the parameters in a model, partly because the former uses common measures of effect produced in cross-sectional, case-control, and cohort studies as part of the approach to model calibration. In fact, more rigorous Bayesian inference procedures regularly incorporate meta-analysis with these common measures, projecting the results of this analysis directly from prior data onto the inference used to construct posterior parameter distributions (3). The software and techniques for implementing these analyses have been made simple, free, and open-source (4).

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