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Principal Investigator
Olli Saarela
University of Toronto
Position Title
Assistant Professor
About this CDAS Project
NLST (Learn more about this study)
Project ID
Initial CDAS Request Approval
Sep 10, 2015
Time-specific measures of benefits due to screening
The proportional reduction in cancer-specific mortality has been recognized as the definitive measure of benefits due to cancer screening, and this is also how the results from randomized screening trials are commonly reported. However, the cumulative mortality reduction measure is not entirely unproblematic as the object of inference, since it depends on the number and schedule of the screening examinations used in a particular trial, and the length of the follow-up period. Also, it has been argued that the mortality impact in a randomized screening trial is time-dependent; it appears after some delay from the initiation of the screening and gradually wanes after the screening has been discontinued. However, detection of such a delay, if indeed present, is complicated by the small number of deaths early on in the trial, where the participants are asymptomatic at the outset. Instead of mortality reduction, an alternative approach to model the screening impact would be to focus on the gained lifetime. In this work we explore time-specific measures for the impact of screening in randomized screening trials, and statistical methods for estimating and visualizing these. In particular, we focus on statistical modeling of the gained lifetime due to screening. We apply the methods to the NLST data to investigate the time-specific effect of screening on lung cancer mortality.

- Explore statistical methods to estimate time-specific effects in randomized screening trials.
- Explore statistical modeling of the gained lifetime due to screening.
- Apply the methods to NLST data, with the focus on the timing of the mortality impact after initiation and discontinuation of the screening.


Amy Liu, Cancer Care Ontario and University of Toronto

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