Critique of reanalysis of Casa Pia data on associations of porphyrins and glutathione-S-transferases with dental amalgam exposure

In their 2013 article in Human and Experimental Toxicology, Geier et al. presented reanalyses of data from the ‘‘Casa Pia Study of the Health Effects of Dental Amalgam in Children,’’ in particular, the results described in Woods et al. regarding the association of exposure to dental amalgam with glutathione-S-transferase (GST)-a and GST. In a similar manner, in their 2011 article in ‘‘Biometals,’’ Geier et al. also presented reanalyses of data from the Casa Pia study regarding the association of exposure to dental amalgam with urinary porphyrins, as presented in Woods et al. In both the articles, Geier et al. imply that their analyses, which they claim show significant associations of urinary porphyrins and GSTs with dental amalgam exposure, contradict our published findings on porphyrins and GSTs (which indicate no associations with amalgam exposure) because they used a more sensitive statistical analysis. This letter is in response to those claims. There are two major points we would like to make regarding this issue. The first issue is the major difference and discrepancy in statistical approach. The Casa Pia study was a randomized clinical trial, and porphyrins and GSTs were secondary measures in that clinical trial. Even with secondary measures, the appropriate analytic approach in testing whether the exposure of one of the treatment groups to dental amalgam caused an increase in porphyrins or GSTs compared to the nonexposed (dental composite) group was a comparison of the two randomized treatment groups. By taking advantage of the randomized design and using the treatment assignment that was designated by the randomization, our analyses allowed inferences to be made about the potential cause–effect relationships between amalgam exposure and outcomes. In contrast, analyses that examine associations between outcomes and observed urinary mercury levels or weighted amalgam exposure scores are prone to biases due to confounding. In order to make our analyses as robust and precise as possible we adjusted for covariates that either explained some of the error variance (and therefore increased precision) or that adjusted for group differences that might have occurred by chance despite the random group assignment. Such a procedure in hypothesis testing using a prespecified model and hypothesis offers the most objective evaluation of whether the observed data provide sufficient evidence to conclude that there is an association, while at the same time protecting the overall probability of reaching false positive conclusions. This is the approach advocated in the clinical trial literature. The approach used by Geier et al. is an exploratory method that uses the data to suggest how to best configure the model and the hypothesis so that ‘‘statistical significance’’ is more likely to be declared. This approach is basically the same as in a clinical trial in which intervention and control are compared and declared with no overall difference. Advocates for the intervention, not satisfied with the result, sometimes then delve into the data to try to find subgroups of patients in which a ‘‘statistically significant’’ difference could be declared. This is a wellknown pitfall in clinical trials, which Friedman et al., in their book Fundamentals of Clinical Trials (p. 372), call