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Abstract: Up-to-date monitoring of long-term survival is an important task of population-based cancer registries. Period analysis, a new method of survival analysis introduced a few years ago, has been shown to be particularly useful for that purpose. The “classical” period analysis uses a life-table approach which requires both the year and month of diagnosis for implementation in pertinent software programs. However, an increasing number of cancer registries remove the month of diagnosis from their datasets, mainly to ensure the highest possible protection against re-identification of patients. In this paper, we present modifications of period analysis that allow the application of this technique, while almost completely preserving its advantages, in datasets without the month of diagnosis. The modified techniques are illustrated and evaluated using examples from the Surveillance, Epidemiology, and End Results (SEER) programme of the United States (US) National Cancer Institute (NCI), which also has removed month of diagnosis from its most recently released public use database. [Copyright &y& Elsevier]

Up-to-date monitoring of long-term survival is an important task of population-based and clinical cancer registries. A few years ago, a new method of survival analysis, denoted period analysis, was introduced to provide more up-to-date estimates of long-term survival. However, a prototypical period analysis may not be applicable in situations with delayed recording of incident cases. We introduce herein a hybrid type of analysis that combines elements of both traditional and period analyses which may still be feasible in such settings. The performance of the hybrid type of analysis compared with other design options is empirically evaluated and illustrated for children diagnosed with cancer in the United States. The empirical evaluation indicates that hybrid analysis may be useful to derive more up-to-date estimates of long-term survival compared with traditional design options if there is a strong improvement of survival over time, even in situations with a substantial delay in recording of incident cases. [Copyright &y& Elsevier]