Space–time clustering of case–control data with residential histories: insights into empirical induction periods, age-specific susceptibility, and calendar year-specific effects

Abstract

Our research group recently developed Q-statistics for evaluating space-time clustering in casecontrol studies with residential histories. This technique relies on time-dependent nearest-neighbor relationships to examine clustering at any moment in the life-course of the residential histories of cases relative to that of controls. In addition, in place of the widely used null hypothesis of spatial randomness, each individual's probability of being a case is based instead on his/her risk factors and covariates. In this paper, we extend this approach to illustrate how alternative temporal orientations (e.g., years prior to diagnosis/recruitment, participant's age, and calendar year) influence a spatial clustering pattern. These temporal orientations are valuable for shedding light on the duration of time between clustering and subsequent disease development (known as the empirical induction period), and for revealing age-specific susceptibility windows and calendar year-specific effects. An ongoing population-based bladder cancer case-control study is used to demonstrate this approach. Data collection is currently incomplete and therefore no inferences should be drawn; we analyze these data to demonstrate these novel methods. Maps of space-time clustering of bladder cancer cases are presented using different temporal orientations while accounting for covariates and known risk factors. This systematic approach for evaluating space-time clustering has the potential to generate novel hypotheses about environmental risk factors and provides insights into empirical induction periods, age-specific susceptibility, and calendar year-specific effects.

Performance of cancer cluster Q-statistics for case-control residential histories

“…days, weeks, months, seasons, or years, or temporal orientation, e.g. age, calendar year, or years prior to diagnosis (analogous to age-period-cohort modeling in epidemiology) (Meliker and Jacquez, 2007). Other future research avenues include quantifying the relative importance of cluster size, density, and case mobility in determining characteristics of clusters detectable by Q -statistics, and exploring alternatives for multiple testing adjustments in Q it .…”

Space-time clusters of breast cancer using residential histories: A Danish case–control study

“…The method has been extensively described in previous studies [23,26,27]. Briefly, this novel approach takes all locations over the entire life-course into account in the cluster analysis.…”

Space-Time Clustering of Non-Hodgkin Lymphoma Using Residential Histories in a Danish Case-Control Study

“…Further, the ages as well as number of years prior to date of diagnosis of cases and their matched controls were calculated at the beginning and end of each residence. This enabled us to use different time scales in the spatio-temporal cluster analyses [19].…”