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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probability of dosing changes based on test results, and tacrolimus exposure (i.e., in vs out of therapeutic range) from published studies, and costs from the Medicare Fee Schedule. We modeled rates of clinical outcomes [i.e., delayed graft function (DGF), acute rejection (AR), and mortality] for patients in vs out of therapeutic range. We evaluated the impact of the rates of clinical outcomes on total cost between PGxguided vs non-PGx guided tacrolimus therapy. Results: CYP3A5 PGx-guided therapy yielded a higher total mean cost per patient of $516 USD, with no added benefit on clinical outcomes compared to non-PGx guided therapy. Differences in modeled inputs between patients in vs out of therapeutic range did not have an impact on total cost. However, cost savings occurred when PGx-guided therapy vs non-PGxguided therapy resulted in relative incidences of DGF and AR below 0.7 and 0.88, respectively. Conclusions: In this conceptual model, the relative incidences of DGF and AR after PGx-guided tacrolimus therapy vs non-PGx-guided therapy may be major drivers of the value of PGx testing. This framework will be used to guide realworld evidence generation for the economic evaluation of CYP3A5 PGx testing in the transplant setting.

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