Detection and estimation of the increasing trend of cancer incidence in relatively small populations

Publication date: October 2017
Source:Cancer Epidemiology, Volume 50, Part B
Author(s): Rina Chen, Enrique Y. Bitchatchi
BackgroundDetection and estimation of trends in cancer incidence rates are commonly achieved by fitting standardized rates to a joinpoint log-linear regression. The efficiency of this approach is inadequate when applied to a relatively low levels of incidence. We compared that approach with the Cuscore test with respect to detecting a log-linear increasing trend of chronic myelomonocytic leukemia (CMML) in datasets simulated to match a province of about 700,000 inhabitants.MethodsFor better efficiency, we replaced the standardized rate as the dependent variable with a continuous statistic that reflects the inverse of the standardized incidence ratio (SIR). Both procedures were applied to datasets simulated to match published results in the Girona Province of Spain. We also present the use of the q-interval in displaying the temporal pattern of the events. This approach is demonstrated by analyses of CMML diagnoses in Girona County (1994–2008).ResultsThe Cuscore was clearly more efficient than regression in detecting the simulated trend. The relative efficiency of the Cuscore is likely to be maintained in even higher levels of incidence. The use of graphical displays in providing clues regarding interpretation of the results is demonstrated.ConclusionsThe Cuscore test coupled with visual inspection of the temporal pattern of the events seems to be more efficient than regression analysis in detecting and interpreting data suspected to be at elevated risk. A confirmatory analysis is expected to weed out 75% of the superfluous significant results.



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