Regulatory false positives: true, false, or uncertain?

Hansen et al. (2007) recently assessed the historical performance of the precautionary principle in 88 specific cases, concluding that "applying our definition of a regulatory false positive, we were able to identify only four cases that fit the definition of a false positive." Empirically evaluating how prone the precautionary principle is to classify nonproblems as problems ("false positives") is an excellent idea. Yet, Hansen et al.'s implementation of this idea applies a diverse set of questionable criteria to label many highly uncertain risks as "real" even when no real or potential harm has actually been demonstrated. Examples include treating each of the following as reasons to categorize risks as "real": considering that a company's actions contaminated its own product; lack of a known exposure threshold for health effects; occurrence of a threat; treating deliberately conservative (upper-bound) regulatory assumptions as if they were true values; treating assumed exposures of children to contaminated soils (by ingestion) as evidence that feared dioxin risks are real; and treating claimed (sometimes ambiguous) epidemiological associations as if they were known to be true causal relations. Such criteria can classify even nonexistent and unknown risks as "real," providing an alternative possible explanation for why the authors failed to find more false positives, even if they exist.

Estimating the relative recurrence risk ratio using a global cross-ratio model

The relative recurrence risk ratio lambdaR (and particularly the sibling recurrence risk ratio, lambdaS) is often of interest to those wanting to quantify the genetic contribution towards risk of disease or to discriminate between different genetic models. However, estimating lambdaR for complex diseases for which genetic and environmental risk factors are both involved is not straightforward. Ignoring environmental factors may lead to inflated estimates of lambdaR. We present a marginal model which uses a copula function to model the association in cumulative incidence rates between pairs of relatives. This model is applicable to present-state data and allows estimation of risk of disease in a pair of relatives (and hence lambdaR), given measured environmental covariates. We apply the model to leprosy among sibling pairs from the Karonga district, Malawi. If risk factors are ignored, the apparent lambdaS in this population is over 3. Accounting for known nongenetic risk factors reduces it to just under 2.