Predict disease-specific mortality for lung cancer in NLST sub-groups
Principal Investigator
Name
ping hu
Degrees
ScD, SM
Institution
NCI
Position Title
Mathematical Statistician
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-6
Initial CDAS Request Approval
Dec 14, 2012
Title
Predict disease-specific mortality for lung cancer in NLST sub-groups
Summary
The National Lung Screening Trial (NLST) compared two ways of detecting lung cancer: low-dose helical computed tomography (CT) and standard chest X-ray. Through approximately six years of follow-up, there was a 20% reduction in lung cancer mortality in the low-dose CT arm. In this study, we used methods developed by Zhao, Wei , etc (2012) to investigate whether low-dose CT is effective for reducing lung cancer mortality in comparing with standard chest X-ray for sub-group analysis, such as gender, age, smoking and prior lung disease.
Using the NLST data, we first create a parametric scoring system as a function of multiple baseline covariates to estimate mortality difference in comparing two screening tests in sub-groups. Based on this scoring system, we specify a desired level of this difference and obtain a subgroup of patients, defined as those whose estimated scores exceed this threshold. An empirically calibrated threshold-specific mortality difference curve across a range of score values is constructed. The subpopulation of patients satisfying any given level of screening benefit can then be identified accordingly. To avoid bias due to over-optimism, we utilize a cross-training-evaluation method for implementing the above two-step procedure. We then show how to select the best scoring system among all competing models. Furthermore, for cases in which only a single pre-specified working model is involved, inference procedures are proposed for the average mortality difference over a range of score values using the entire data set, and are justified theoretically and numerically. Note that the new procedure created by Zhao, Wei, etc (2012) can be quite useful for the management of future participants in screening trials, so that screening may be targeted towards those who would receive nontrivial benefits to compensate for the risk or cost of the new screening.
Using the NLST data, we first create a parametric scoring system as a function of multiple baseline covariates to estimate mortality difference in comparing two screening tests in sub-groups. Based on this scoring system, we specify a desired level of this difference and obtain a subgroup of patients, defined as those whose estimated scores exceed this threshold. An empirically calibrated threshold-specific mortality difference curve across a range of score values is constructed. The subpopulation of patients satisfying any given level of screening benefit can then be identified accordingly. To avoid bias due to over-optimism, we utilize a cross-training-evaluation method for implementing the above two-step procedure. We then show how to select the best scoring system among all competing models. Furthermore, for cases in which only a single pre-specified working model is involved, inference procedures are proposed for the average mortality difference over a range of score values using the entire data set, and are justified theoretically and numerically. Note that the new procedure created by Zhao, Wei, etc (2012) can be quite useful for the management of future participants in screening trials, so that screening may be targeted towards those who would receive nontrivial benefits to compensate for the risk or cost of the new screening.
Aims
The aims of this study is to investigate whether low-dose CT is effective for reducing lung cancer mortality in comparing with standard chest X-ray for sub-group analysis, such as gender, age, smoking and prior lung disease; and did lung cancer specific mortality vary by these co-variates?
Collaborators
Christine Berg, MD, NLST project officer
LJ Wei, PhD, Harvard University
Lihui Zhao, PhD, Northwestern University