Background: All-cause mortality has been suggested as an end-point in cancer screening trials in order to avoid biases in attributing the cause of death. The aim of this study was to investigate which sample size and follow-up is needed to find a significant reduction in all-cause mortality. Methods: A literature review was conducted to identify previous studies that modeled the effect of screening on all-cause mortality. Microsimulation modeling was used to simulate breast cancer, lung cancer, and colorectal cancer screening trials. Model outputs were: cancer-specific deaths, all-cause deaths, and life-years gained per year of follow-up. Results: There were large differences between the evaluated cancers. For lung cancer, when 40 000 high-risk people are randomized to each arm, a significant reduction in all-cause mortality could be expected between 11 and 13 years of follow-up. For breast cancer, a significant reduction could be found between 16 and 26 years of follow-up for a sample size of over 300 000 women in each arm. For colorectal cancer, 600 000 persons in each arm were required to be followed for 15-20 years. Our systematic literature review identified seven papers, which showed highly similar results to our estimates. Conclusion: Cancer screening trials are able to demonstrate a significant reduction in all-cause mortality due to screening, but require very large sample sizes. Depending on the cancer, 40 000-600 000 participants per arm are needed to demonstrate a significant reduction. The reduction in all-cause mortality can only be detected between specific years of follow-up, more limited than the timeframe to detect a reduction in cancer-specific mortality.

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Keywords breast, cancer screening, colorectal, evaluation, lung, mortality reduction, trial
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Journal Cancer Medicine
Heijnsdijk, E.A.M, Csanádi, M. (Marcell), Gini, A. (Andrea), ten Haaf, K, Bendes, R. (Rita), Anttila, A, … de Koning, H.J. (Harry J.). (2019). All-cause mortality versus cancer-specific mortality as outcome in cancer screening trials: A review and modeling study. Cancer Medicine. doi:10.1002/cam4.2476