Grant Details
Grant Number: |
5R01CA277756-02 Interpret this number |
Primary Investigator: |
Lund, Jennifer |
Organization: |
Univ Of North Carolina Chapel Hill |
Project Title: |
Applying Causal Inference Methods to Improve Estimation of the Real-World Benefits and Harms of Lung Cancer Screening |
Fiscal Year: |
2024 |
Abstract
ABSTRACT
Randomized controlled trials have demonstrated that low-dose computed tomography can substantially reduce
lung cancer mortality, albeit at the cost of relatively high rates of false positives and complications from
downstream procedures. However, systematic differences between trial and general populations eligible for
lung cancer screening raise concerns about the relevance of trial findings for guiding the development and
dissemination of lung cancer screening programs in clinical practice. Despite clear recommendations from the
United States Preventative Services Task Force, lung cancer screening uptake and adherence remain low.
Several studies have documented dramatic and selective attrition across the screening continuum – where
about 10-20% of eligible individuals undergo lung cancer screening and of those, only about 40-60% are up-to-
date with their annual screening at 15 months. When the benefits and harms of an intervention vary across
subgroups and there is selective attrition, the balance of population-level benefits and harms is expected to
change. As such, there is an urgent need to better characterize the effectiveness of lung cancer screening with
low-dose computed tomography when applied to individuals outside of clinical trial settings. The primary
objective of this proposal is to generate real-world evidence of the benefits and harms of lung cancer screening
with low-dose computed tomography that explicitly considers the characteristics of populations at each step of
the screening continuum, from the screening-eligible, to the screened, to the adherent. To address this
objective, we will use cutting-edge causal inference methods, including trial transport and target trial emulation
using real-world data, which can avoid the potential for time-related biases. To carry-out our proposed
analyses, we will draw upon individual-level data from the randomized National Lung Screening Trial, as well
as four real-world datasets including the National Health and Interview Survey, the Behavioral Risk Factors
Surveillance Survey (Lung Cancer Screening Module), a 20% nationwide sample of Medicare claims, and the
North Carolina Lung Screening Registry. Findings from this study will generate information about the
effectiveness of lung cancer screening in real-world settings that can be used by patients, providers, and
policymakers. This work will enhance the evidence base used by policymakers to update screening
recommendations and refine decision aids to support communication with patients about screening net-
benefits during shared decision-making. Ultimately, this work will support efforts to improve the delivery of lung
cancer screening at the population level.
Publications
Effect of SARS-CoV-2 Infection on Incident Diabetes by Viral Variant: Findings From the National COVID Cohort Collaborative (N3C).
Authors: Wong R.
, Hall M.A.
, Wiggen T.
, Johnson S.G.
, Huling J.D.
, Turner L.E.
, Wilkins K.J.
, Yeh H.C.
, Stürmer T.
, Bramante C.T.
, et al.
.
Source: Diabetes Care, 2024-10-01 00:00:00.0; 47(10), p. 1846-1854.
PMID: 39207804
Related Citations
Estimating the Effects of Cancer Screening in Clinical Practice Settings: The Role of Selective Uptake and Suboptimal Adherence along the Cancer Screening Continuum.
Authors: Lund J.L.
, Rivera M.P.
, Su I.H.
, Long J.M.
, Chen X.
, Pak J.
, Hudgens M.G.
, Stürmer T.
, Reuland D.S.
, Henderson L.M.
.
Source: Cancer Epidemiology, Biomarkers & Prevention : A Publication Of The American Association For Cancer Research, Cosponsored By The American Society Of Preventive Oncology, 2024-08-01 00:00:00.0; 33(8), p. 984-988.
PMID: 39012954
Related Citations
Prevalence of Lung Cancer Screening in the US, 2022.
Authors: Henderson L.M.
, Su I.H.
, Rivera M.P.
, Pak J.
, Chen X.
, Reuland D.S.
, Lund J.L.
.
Source: Jama Network Open, 2024-03-04 00:00:00.0; 7(3), p. e243190.
EPub date: 2024-03-04 00:00:00.0.
PMID: 38512257
Related Citations