Bayesian versus frequentist statistical inference for investigating a one-off cancer cluster reported to a health department

Background. The problem of silent multiple comparisons is one of the most difficult statistical problems faced by scientists. It is a particular problem for investigating a one-off cancer cluster reported to a health department because any one of hundreds, or possibly thousands, of neighbourhoods, schools, or workplaces could have reported a cluster, which could have been for any one of several types of cancer or any one of several time periods. Methods. This paper contrasts the frequentist approach with a Bayesian approach for dealing with silent multiple comparisons in the context of a one-off cluster reported to a health department. Two published cluster investigations were re-analysed using the Dunn-Sidak method to adjust frequentist p-values and confidence intervals for silent multiple comparisons. Bayesian methods were based on the Gamma distribution. Results. Bayesian analysis with non-informative priors produced results similar to the frequentist analysis, and suggested that both clusters represented a statistical excess. In the frequentist framework, the statistical significance of both clusters was extremely sensitive to the number of silent multiple comparisons, which can only ever be a subjective "guesstimate". The Bayesian approach is also subjective: whether there is an apparent statistical excess depends on the specified prior. Conclusion. In cluster investigations, the frequentist approach is just as subjective as the Bayesian approach, but the Bayesian approach is less ambitious in that it treats the analysis as a synthesis of data and personal judgements (possibly poor ones), rather than objective reality. Bayesian analysis is (arguably) a useful tool to support complicated decision-making, because it makes the uncertainty associated with silent multiple comparisons explicit

Syrian hamster dermal cell immortalization is not enhanced by power line frequency electromagnetic field exposure

Several epidemiological studies have suggested associations between exposure to residential power line frequency electromagnetic fields and childhood leukaemia, and between occupational exposure and adult leukaemia. A variety of in vitro studies have provided limited supporting evidence for the role of such exposures in cancer induction in the form of acknowledged cellular end points, such as enhanced mutation rate and cell proliferation, though the former is seen only with extremely high flux density exposure or with co-exposure to ionizing radiation. However, in vitro experiments on a scale large enough to detect rare cancer-initiating events, such as primary cell immortalization following residential level exposures, have not thus far been reported. In this study, large cultures of primary Syrian hamster dermal cells were continuously exposed to power line frequency electromagnetic fields of 10 100 and 1000 μT for 60 h, with and without prior exposure to a threshold (1.5 Gy), or sub-threshold (0.5 Gy), immortalizing dose of ionizing radiation. Electromagnetic field exposure alone did not immortalize these cells at a detectable frequency (≥ 1 × 10−7); furthermore, such exposure did not enhance the frequency of ionizing radiation-induced immortalization. © 1999 Cancer Research Campaig

Spatial analysis of lung, colorectal, and breast cancer on Cape Cod: An application of generalized additive models to case-control data

BACKGROUND: The availability of geographic information from cancer and birth defect registries has increased public demands for investigation of perceived disease clusters. Many neighborhood-level cluster investigations are methodologically problematic, while maps made from registry data often ignore latency and many known risk factors. Population-based case-control and cohort studies provide a stronger foundation for spatial epidemiology because potential confounders and disease latency can be addressed. METHODS: We investigated the association between residence and colorectal, lung, and breast cancer on upper Cape Cod, Massachusetts (USA) using extensive data on covariates and residential history from two case-control studies for 1983–1993. We generated maps using generalized additive models, smoothing on longitude and latitude while adjusting for covariates. The resulting continuous surface estimates disease rates relative to the whole study area. We used permutation tests to examine the overall importance of location in the model and identify areas of increased and decreased risk. RESULTS: Maps of colorectal cancer were relatively flat. Assuming 15 years of latency, lung cancer was significantly elevated just northeast of the Massachusetts Military Reservation, although the result did not hold when we restricted to residences of longest duration. Earlier non-spatial epidemiology had found a weak association between lung cancer and proximity to gun and mortar positions on the reservation. Breast cancer hot spots tended to increase in magnitude as we increased latency and adjusted for covariates, indicating that confounders were partly hiding these areas. Significant breast cancer hot spots were located near known groundwater plumes and the Massachusetts Military Reservation. DISCUSSION: Spatial epidemiology of population-based case-control studies addresses many methodological criticisms of cluster studies and generates new exposure hypotheses. Our results provide evidence for spatial clustering of breast cancer on upper Cape Cod. The analysis suggests further investigation of the potential association between breast cancer and pollution plumes based on detailed exposure modeling

Statistical inference is overemphasised in cluster investigations: The case of the cluster of breast cancers at the Australian Broadcasting Corporation studios in Brisbane, Australia

The aim of statistical analyses in cluster investigations is to estimate the probability that the aggregation of cases could be due to chance. As a result of several statistical problems – including the post-hoc nature of the analysis and the subjective nature of implied multiple comparisons – this cannot be carried out with any certainty. In cluster investigations, expert opinion should carry much more weight than P-values, which are exceedingly difficult to interpret