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Principal Investigator
Mitchell Gail
M.D., Ph.D.
National Cancer Institute, National Institutes of Health
Position Title
Distinguished Investigator
About this CDAS Project
PLCO (Learn more about this study)
Project ID
Initial CDAS Request Approval
May 5, 2023
Absolute risk from nested case-control data
The purpose of this project is to test new statistical methods to improve the statistical efficiency of estimating absolute risk in the presence of competing risks from nested case-control data. The classical nested case-control study samples time-matched controls from members of the risk set for the cause of interest. To also model competing risks, it is advantageous to sample controls matched to competing events also. Two such designs have been proposed, the supplemental case-control design that gathers covariates on the patients with competing events, and the double nested case-control design that additionally selects controls for patients with competing events. This project compares the efficiency of various sampling designs, develops corresponding asymptotic theory, and develops augmented estimating equations that take advantage of information available on all cohort members to improve efficiency further. We propose using PLCO data on the absolute risk of prostate cancer death following prostate cancer diagnosis to test our methods on real data and to inform our simulation studies to make them more realistic.

• Compare the efficiency of the classical nested case-control design with the supplemental nested case-control design and with the double nested case-control design in PLCO data
• Assess the added efficiency gained by using augmented estimating equations in PLCO data
• Study the operating characteristics such as confidence interval coverage of procedures based on asymptotic theory in simulations motivated by PLCO data


Dr. Minjung Lee is Associate Professor at Kangwon National University: Chuncheon, Gangwon-do, KR. She is currently visiting NCI/DCEG