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Assessment of chance, using P-values and confidence intervals (CIs), is still considered an important component of cluster investigations, as evidenced by its continued inclusion in published protocols and reports of clusters. However, true randomness or chance does not exist in epidemiological data. Instead, the concept of chance is introduced to analysis as a pragmatic device to describe the accumulation of many small variations, for which it is not economically feasible or worthwhile to uncover the causes. The theoretical construct of chance (applied to the data) is of no pragmatic value in cluster investigations because P-values and confidence intervals from cluster investigations suffer from an extreme form of the ubiquitous statistical problem of silent multiple comparisons. As a result, they are impossible to interpret, and confuse rather than assist decision-making. We therefore recommend that assessment of chance be removed from protocols for investigating clusters, and that there be an associated shift in emphasis to exposure assessment. We also recommend the framing of investigations of cancer clusters as case-series, rather than as retrospective cohort studies that use routine data from population-based cancer registries.

Assessment of chance should be removed from protocols for investigating cancer clusters