Accounting for Overdiagnosis in Estimating Components of Survival Time in Randomized Cancer Screening Trials
Commonwealth Professor and Chair, Department of Statistics, University of Virginia
Date: Friday, October 29, 2021
Location: JC Gold Room
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Cancer screening is assumed to be beneficial, in terms of reduced mortality and extended survival. Survival is often measured as the time between clinical detection of disease and endpoint (cure or death). When the disease is screen-detected, survival has two additional components: lead time (time by which the screening test advances the time of clinical diagnosis) and benefit time (extended survival time if the screen detection is beneficial). All three components are affected by two effects: length biased sampling (slow-growing cases are more likely to be screen-detected than fast-growing ones) and overdiagnosis (cases that are screen-detected but would never have surfaced clinically in the absence of screening). We quantify both effects in this talk and illustrate their non-trivial impacts on the results from actual randomized cancer screening trials.
(This work is performed in collaboration with Dr. Philip C. Prorok, former Chief of the Biometry Research Group, National Cancer Institute.)