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

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Many health systems are exploring how to implement low-dose computed tomography (LDCT) screening programs that are effective and patient-centered.


To examine factors that influence when LDCT screening is preference-sensitive.


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:





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.


The quantitative thresholds that were identified may vary depending on the structure of the microsimulation model.


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:


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