Assessing performance of Q-statistics for identifying cancer clusters in residential histories using simulated clusters in US and Danish case-control studies

Few cancer clustering investigations have evaluated residential mobility even though exposure to environmental carcinogens may occur decades before a cancer diagnosis. Recently developed Q-statistics can be used to investigate disease clusters based on mobility histories by quantifying space- and time-dependent nearest neighbor relationships. Qik identifies which individuals are centers of spatial clusters through time; and Qikt shows where and when local clustering takes place. These statistics are calculated repeatedly through space and time and therefore face the problem of multiple testing in determining statistical significance. Using case-control residential histories from 2378 participants in a US study and 6594 participants from a Danish study, we created a series of simulated clusters to examine Q-statistic performance and statistical significance. Results suggest the intersection of cases with significant clustering over their life course, Qik, with cases who are constituents of significant local clusters at given times, Qikt, allows us to identify simulated clusters. Using this intersection approach, Q-statistics identified large true positive cluster regions with few false positives; true smaller cluster regions were difficult to differentiate from false positives. Specifically, if three or more significant (Qik, p=0.001, Qikt, p=0.05) cases are detected in the same cluster region then it may be considered a true cluster. Setting k-nearest neighbors equal to 10 or 15 consistently showed strong performance. This approach has potential for identifying clustering in mobile populations but given differences in mobility patterns, future work is required to investigate generalizability of this rule set to other case-control datasets.