Evidence map›Paper›PMID 29809244›Full record

ArticleAnnals of internal medicine2018

Identifying Patients for Whom Lung Cancer Screening Is Preference-Sensitive: A Microsimulation Study.

Tanner J Caverly, Pianpian Cao, Rodney A Hayward, Rafael Meza

Registry-linked trialAbstract read
In one paragraph

Article in Annals of internal medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07073898 (Population Health Management Approaches to Increase Lung Cancer Screening in Community Health Centers - UG3 Pilot Clinical Trial), which is not on this map. Cited by 69 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
69citing papers in PubMed, 3 pooled it
7.3field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT07073898 nacompletednot on this mapstarted 2025, after this paper: background citation

Population Health Management Approaches to Increase Lung Cancer Screening in Community Health Centers - UG3 Pilot Clinical Trial

TypeinterventionalSponsorUniversity of UtahRan2025 to 2026Enrolled65ConditionsLung CancerArmsRepeated Text Messages (TM+), Conversational Agent (CA), Educational Video, Proactive Patient Navigation (PPN), Reactive Patient Navigation (RPN)
3 · Its place in the literature

Who cites it

69 citing papers in PubMed, 3 syntheses or guidelines pooled it, 92 citations in OpenAlex.

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  18. Natural history models for lung Cancer: A scoping review.Lung cancer (Amsterdam, Netherlands) · 2025
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  19. Impact of Comorbidities on the Mortality Benefits of Lung Cancer Screening: A Post-Hoc Analysis of the PLCO and NLST Trials.Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2025
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9 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Tanner J CaverlyVA Center for Clinical Management Research and University of Michigan Medical School, Ann Arbor, Michigan (T.J.C., R.A.H.).
Pianpian CaoUniversity of Michigan, Ann Arbor, Michigan (P.C., R.M.).
Rodney A HaywardVA Center for Clinical Management Research and University of Michigan Medical School, Ann Arbor, Michigan (T.J.C., R.A.H.).
Rafael MezaUniversity of Michigan, Ann Arbor, Michigan (P.C., R.M.).
University of Michigan–Ann Arbor · US

Funding

Comparative Modeling of Lung Cancer Prevention and Control PoliciesU01CA199284 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DE KONING, HARRY J, HOLFORD, THEODORE R · 2015 to 2019
$8.4M
NCI NIH HHS U01 CA199284
6 · The paper itself

Abstract

Background: Many health systems are exploring how to implement low-dose computed tomography (LDCT) screening programs that are effective and patient-centered. Objective: To examine factors that influence when LDCT screening is preference-sensitive. Design: State-transition microsimulation model. Data Sources: Two large randomized trials, published decision analyses, and the SEER (Surveillance, Epidemiology, and End Results) cancer registry. Target Population: U.S.-representative sample of simulated patients meeting current U.S. Preventive Services Task Force criteria for screening eligibility. Time Horizon: Lifetime. Perspective: Individual. Intervention: LDCT screening annually for 3 years. Outcome Measures: Lifetime quality-adjusted life-year gains and reduction in lung cancer mortality. To examine the effect of preferences on net benefit, disutilities (the "degree of dislike") quantifying the burden of screening and follow-up were varied across a likely range. The effect of varying the rate of false-positive screening results and overdiagnosis associated with screening was also examined. Results of Base-Case Analysis: Moderate differences in preferences about the downsides of LDCT screening influenced whether screening was appropriate for eligible persons with annual lung cancer risk less than 0.3% or life expectancy less than 10.5 years. For higher-risk eligible persons with longer life expectancy (roughly 50% of the study population), the benefits of LDCT screening overcame even highly negative views about screening and its downsides. Results of Sensitivity Analysis: Rates of false-positive findings and overdiagnosed lung cancer were not highly influential. Limitation: The quantitative thresholds that were identified may vary depending on the structure of the microsimulation model. Conclusion: Identifying circumstances in which LDCT screening is more versus less preference-sensitive may help clinicians personalize their screening discussions, tailoring to both preferences and clinical benefit. Primary Funding Source: None.

Indexed as

AgedAged, 80 and overComputer SimulationEarly Detection of CancerFemaleHumansLungLung NeoplasmsMaleMarkov ChainsMiddle AgedOutcome and Process Assessment, Health CareQuality-Adjusted Life YearsRisk AssessmentRisk FactorsSEER Program

Identifiers

PMID29809244
PMCPMC6033668
OpenAlexW2807693864

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.