Skip to Main Content

An official website of the United States government

Principal Investigator
Ping Hu
Position Title
Mathematical Statistician
About this CDAS Project
NLST (Learn more about this study)
Project ID
Initial CDAS Request Approval
Nov 21, 2016
Statistical models to predict future subject’s lung cancer risk: application to NLST and PLCO data
The National Lung Screening Trial (NLST) compared two ways of detecting lung cancer: low-dose helical computed tomography (CT) and standard chest X-ray. The lung component of the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial was undertaken to determine whether there is a reduction in lung cancer mortality from screening using chest X-ray. Using the data from these two large randomized screening trials with well-defined groups of healthy people, we utilize methods developed by Yong, Wei, etc (2014) to create an optimal stratified prediction procedure to estimate potential lung cancer risk for individuals.

We fitted the NLST data relating to the lung cancer outcome with its baseline covariates. For each fitted model, we create a scoring system for predicting potential lung cancer risk and obtain a corresponding optimal stratification rule. The subpopulation of participants satisfying any given level of risk score can be identified accordingly. Then, all the resulting stratification strategies are evaluated via a conventional cross-validation process.

We illustrate the proposed methods using NLST chest X-ray group as the training and test set, and PLCO lung component as the independent validation set.

The aim of this study is to develop a quantitative stratification procedure for predicting potential cancer risk to identify individuals at higher risk of specific cancers.


SuChun Cheng, ScD, Dana-Farber Cancer Institute
Lu Tian, PhD, Standford University
L.J. Wei, PhD, Harvard University