title: Estimating long-term effectiveness of lung cancer screening in the Mayo CT screening study.
abstract: OBJECTIVE
  To use individual-level data provided from the single-arm study of helical computed tomographic (CT) screening at the Mayo Clinic (Rochester, Minn) to estimate the long-term effectiveness of screening in Mayo study participants and to compare estimates from an existing lung cancer simulation model with estimates from a different modeling approach that used the same data.
  
  
  METHODS
  The study was approved by institutional review boards and was HIPAA compliant. Deidentified individual-level data from participants (1520 current or former smokers aged 50-85 years) in the Mayo Clinic helical CT screening study were used to populate the Lung Cancer Policy Model, a comprehensive microsimulation model of lung cancer development, screening findings, treatment results, and long-term outcomes. The model predicted diagnosed cases of lung cancer and deaths per simulated study arm (five annual screening examinations vs no screening). Main outcome measures were predicted changes in lung cancer-specific and all-cause mortality as functions of follow-up time after simulated enrollment and randomization.
  
  
  RESULTS
  At 6-year follow-up, the screening arm had an estimated 37% relative increase in lung cancer detection, compared with the control arm. At 15-year follow-up, five annual screening examinations yielded a 9% relative increase in lung cancer detection. The relative reduction in cumulative lung cancer-specific mortality from five annual screening examinations was 28% at 6-year follow-up (15% at 15 years). The relative reduction in cumulative all-cause mortality from five annual screening examinations was 4% at 6-year follow-up (2% at 15 years).
  
  
  CONCLUSIONS
  Screening may reduce lung cancer-specific mortality but may offer a smaller reduction in overall mortality because of increased competing mortality risks associated with smoking.
authors.affiliation: Institute for Technology Assessment, Massachusetts General Hospital, 101 Merrimac St, 10th Floor, Boston, MA 02114, USA. pamela@mgh-ita.org
authors.forename: Pamela M
authors.initials: PM
authors.lastname: McMahon
authors.forename: Chung Yin
authors.initials: CY
authors.lastname: Kong
authors.forename: Bruce E
authors.initials: BE
authors.lastname: Johnson
authors.forename: Milton C
authors.initials: MC
authors.lastname: Weinstein
authors.forename: Jane C
authors.initials: JC
authors.lastname: Weeks
authors.forename: Karen M
authors.initials: KM
authors.lastname: Kuntz
authors.forename: Jo-Anne O
authors.initials: JA
authors.lastname: Shepard
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: Swensen
authors.forename: G Scott
authors.initials: GS
authors.lastname: Gazelle
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D015331:Cohort Studies; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000453:epidemiology* / Q000517:prevention & control; D008297:Male; D008403:Mass Screening / Q000706:statistics & numerical data*; D008875:Middle Aged; D008910:Minnesota / Q000453:epidemiology; D017063:Outcome Assessment, Health Care; D015995:Prevalence; D011379:Prognosis; D016016:Proportional Hazards Models; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000706:statistics & numerical data*