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Activity Number: 420 - Contributed Poster Presentations: Health Policy Statistics Section
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 2:00 PM to 3:50 PM
Sponsor: Health Policy Statistics Section
Abstract #306386
Title: Estimating Time to Intermediate Endpoints Using Population-Level Survival Data and Deconvolution Methods, with Application to Cancer Progression and Recurrence
Author(s): Marlena Bannick*
Companies: University of Washington
Keywords: time-to-event data; survival analysis; deconvolution; cancer; epidemiology; health policy

Individuals diagnosed with cancer progress through disease stages with rates that are often unobserved but potentially estimable. We use deconvolution as a method to partition population survival data into two components: time from diagnosis to an intermediate endpoint, and time from the intermediate endpoint to death. Using overall survival data from diagnosis and from the intermediate endpoint to death we propose a novel deconvolution method to estimate the distribution of the time from diagnosis to the intermediate endpoint. The method allows for an individual frailty to influence the correlation between time to the intermediate endpoint and time to death.

We apply the deconvolution method to SEER data. First, we estimate time to progress to a later stage of cancer after diagnosis to simulate the benefit that could be induced by early detection for several cancers. Second, we estimate time to metastatic recurrence of breast cancer and melanoma. Finally, we validate the deconvolution method for individuals with prostate cancer using clinical trial data on both time from diagnosis to prostate cancer death and time from diagnosis to the intermediate endpoint of metastasis.

Authors who are presenting talks have a * after their name.

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