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Leveraging screening data to better estimate survival rates via data fusion techniques

Principal Investigator
Name
Sijia Li
Degrees
Ph.D.
Institution
University of California, Los Angeles
Position Title
Assistant Professor
Email
About this CDAS Project
Study
PLCO (Learn more about this study)
Project ID
PLCO-1970
Initial CDAS Request Approval
Aug 12, 2025
Title
Leveraging screening data to better estimate survival rates via data fusion techniques
Summary
Right-censored data and current status (screening) data may arise side-by-side within the same research context. The statistical properties of estimators constructed from right-censored and current status data differ substantially. Despite their differences, both data types are often available to address the same problem of estimating the survival probability at a given time point. This raises a natural and important question: how can we harness the information from both right-censored and current status data to achieve better inference than using either alone? In this work, we develop a principled approach to integrate right-censored and current status data for estimating marginal survival probability at a given time point of interest. We aim to illustrate our method on cancer incidence data.
Aims

Many cancer studies have screening arms (where individuals are periodically checked and only status is known at screening) and follow-up arms (where individuals are followed for survival or time-to-event). Fusing these datasets provides better estimation of incidence or survival distributions.

We aim to estimate arm-specific survival rate leveraging both follow-up data and screening data. Specifically, we will treat screening data as current status data, and
---1. augment follow-up data with screening data to estimate the survival rate of patients in the placebo arm.
-- 2. augment screening data with follow-up data to estimate the survival rate of patients in the treatment arm.

Overall, we wish to demonstrate the benefits and limitations of data fusion of right-censored data and current status data.

Collaborators

Sijia Li University of California, Los Angeles
Xiudi Li University of California, Berkeley