Assessment of the spatial occurrence of childhood leukaemia mortality using standardized rate ratios with a simple linear Poisson model

Trichloroethylene cancer epidemiology: a consideration of select issues

A large body of epidemiologic evidence exists for exploring causal associations between cancer and trichloroethylene (TCE) exposure. The U.S. Environmental Protection Agency 2001 draft TCE health risk assessment concluded that epidemiologic studies, on the whole, support associations between TCE exposure and excess risk of kidney cancer, liver cancer, and lymphomas, and, to a lesser extent, cervical cancer and prostate cancer. As part of a mini-monograph on key issues in the health risk assessment of TCE, this article reviews recently published scientific literature examining cancer and TCE exposure and identifies four issues that are key to interpreting the larger body of epidemiologic evidence: a) relative sensitivity of cancer incidence and mortality data ; b) different classifications of lymphomas, including non-Hodgkin lymphoma ; c) differences in data and methods for assigning TCE exposure status ; and d) different methods employed for causal inferences, including statistical or meta-analysis approaches. The recent epidemiologic studies substantially expand the epidemiologic database, with seven new studies available on kidney cancer and somewhat fewer studies available that examine possible associations at other sites. Overall, recently published studies appear to provide further support for the kidney, liver, and lymphatic systems as targets of TCE toxicity, suggesting, as do previous studies, modestly elevated (typically 1.5-2.0) site-specific relative risks, given exposure conditions in these studies. However, a number of challenging issues need to be considered before drawing causal conclusions about TCE exposure and cancer from these data.

Spatial analysis of childhood leukemia in a case/control study

A simple and direct analysis of the spatial distribution of childhood leukemia was performed using geographic data from a large case/control study. The data consist of cases of childhood leukemia and their corresponding birth cohort controls located in seven San Francisco Bay Area counties. Both parametric and randomization analyses show no evidence of a non-random spatial pattern of childhood leukemia among six of these counties. The data from San Francisco County, however, produce a moderately small significance probability (0.08) arising from a distance analysis and a significant p-value (0.01) arising from a frequency analysis of concordant case pairs. Although these p-values accurately reflect the probability of the observed spatial pattern occurring by chance alone, these results are based on only four cases of leukemia.

Bayes without priors

BACKGROUND AND OBJECTIVES

Classical statistical inference has attained a dominant position in the expression and interpretation of empirical results in biomedicine. Although there have been critics of the methods of hypothesis testing, significance testing (P-values), and confidence intervals, these methods are used to the exclusion of all others.

METHODS

An alternative metaphor and inferential computation based on credibility is offered here.

RESULTS

It is illustrated in three datasets involving incidence rates, and its advantages over both classical frequentist inference and Bayesian inference, are detailed.

CONCLUSION

The message is that for those who are unsatisfied with classical methods but cannot make the transition to Bayesianism, there is an alternative path.