Overreliance on a single study: there is no real evidence that applying quality criteria to exposure in asbestos epidemiology affects the estimated risk.

A critical need exists for reliable risk management policies and practices that can effectively mitigate asbestos-related health threats, and such policies and practices need to be based on sound science that adequately distinguishes hazardous situations from those that are not. Toward that end, the disparate means by which study quality has been addressed in recent meta-analyses used to establish potency factors (K ( L ) and K ( M ) values) for asbestos cancer risks were compared by conducting additional sensitivity analyses. Results suggest that, other than placing undue emphasis on the influence of the K ( L ) and K ( M ) values reported from a single study, there appears to be little to no evidence of a systematic effect of study quality on K ( L ) or K ( M ) values; none of the findings warrant excluding studies from current or future meta-analyses. Thus, we argue that it is better to include as much of the available data as possible in these analyses while formally addressing uncertainty as part of the analysis itself, rather than sequentially excluding studies based on one type of limitation or another. Throwing out data without clearly proving some type of bias is never a good idea because it will limit both the power to test various hypotheses and the confidence that can be placed in any findings that are derived from the resulting, truncated data set. We also believe that it is better to identify the factors that contribute to variation between studies included in a meta-analysis and, by adjusting for such factors as part of a model, showing that the disparate values from individual studies can be reconciled. If such factors are biologically reasonable (based on other evidence) and, if such a model can be shown to fit the data from all studies in the meta-analysis, the model is likely to be predictive of the parameters being evaluated and can then be applied to new (unstudied) environments.