title: Comparative analysis of 5 lung cancer natural history and screening models that reproduce outcomes of the NLST and PLCO trials.
abstract: BACKGROUND
  The National Lung Screening Trial (NLST) demonstrated that low-dose computed tomography screening is an effective way of reducing lung cancer (LC) mortality. However, optimal screening strategies have not been determined to date and it is uncertain whether lighter smokers than those examined in the NLST may also benefit from screening. To address these questions, it is necessary to first develop LC natural history models that can reproduce NLST outcomes and simulate screening programs at the population level.
  
  
  METHODS
  Five independent LC screening models were developed using common inputs and calibration targets derived from the NLST and the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO). Imputation of missing information regarding smoking, histology, and stage of disease for a small percentage of individuals and diagnosed LCs in both trials was performed. Models were calibrated to LC incidence, mortality, or both outcomes simultaneously.
  
  
  RESULTS
  Initially, all models were calibrated to the NLST and validated against PLCO. Models were found to validate well against individuals in PLCO who would have been eligible for the NLST. However, all models required further calibration to PLCO to adequately capture LC outcomes in PLCO never-smokers and light smokers. Final versions of all models produced incidence and mortality outcomes in the presence and absence of screening that were consistent with both trials.
  
  
  CONCLUSIONS
  The authors developed 5 distinct LC screening simulation models based on the evidence in the NLST and PLCO. The results of their analyses demonstrated that the NLST and PLCO have produced consistent results. The resulting models can be important tools to generate additional evidence to determine the effectiveness of lung cancer screening strategies using low-dose computed tomography.
authors.affiliation: Department of Epidemiology, University of Michigan, Ann Arbor, Michigan.
authors.forename: Rafael
authors.initials: R
authors.lastname: Meza
authors.forename: Kevin
authors.initials: K
authors.lastname: ten Haaf
authors.forename: Chung Yin
authors.initials: CY
authors.lastname: Kong
authors.forename: Ayca
authors.initials: A
authors.lastname: Erdogan
authors.forename: William C
authors.initials: WC
authors.lastname: Black
authors.forename: Martin C
authors.initials: MC
authors.lastname: Tammemagi
authors.forename: Sung Eun
authors.initials: SE
authors.lastname: Choi
authors.forename: Jihyoun
authors.initials: J
authors.lastname: Jeon
authors.forename: Summer S
authors.initials: SS
authors.lastname: Han
authors.forename: Vidit
authors.initials: V
authors.lastname: Munshi
authors.forename: Joost
authors.initials: J
authors.lastname: van Rosmalen
authors.forename: Paul
authors.initials: P
authors.lastname: Pinsky
authors.forename: Pamela M
authors.initials: PM
authors.lastname: McMahon
authors.forename: Harry J
authors.initials: HJ
authors.lastname: de Koning
authors.forename: Eric J
authors.initials: EJ
authors.lastname: Feuer
authors.forename: William D
authors.initials: WD
authors.lastname: Hazelton
authors.forename: Sylvia K
authors.initials: SK
authors.lastname: Plevritis
mesh_terms: D002138:Calibration; D002986:Clinical Trials as Topic; D055088:Early Detection of Cancer / Q000379:methods*; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis*; D008297:Male; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Cancer Intervention and Surveillance Modeling Network (CISNET); cancer natural history models; comparative modeling analyses; low-dose CT screening; lung cancer screening; simulation model; smoking and lung cancer