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Overdiagnosis in lung cancer screening: why modelling is essential
  1. Kevin ten Haaf,
  2. Harry J de Koning
  1. Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
  1. Correspondence to Kevin ten Haaf, Department of Public Health, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, Rotterdam 3000 CA, The Netherlands; k.tenhaaf{at}erasmusmc.nl

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Introduction

Screening for diseases is an important part of healthcare, as the detection of disease at an early stage may improve the chance of successful treatment of the disease. Although high-quality screening programmes may provide substantial benefits, one of the major harms associated with screening is overdiagnosis. Overdiagnosis refers to the event that a disease is diagnosed that would not have been clinically detected; for example, screening may detect a slowly progressing tumour that would not have caused symptoms during the patient's life-time. Overdiagnosis may lead to serious consequences, such as unnecessary treatments. Furthermore, overdiagnosis will lead to biased survival outcomes for screen-detected cases.1

Notable harms of CT lung cancer screening include the high proportion of false-positive results and radiation exposure.2 The decision of the USPSTF to recommend lung cancer screening with CT for persons up to age 80 has also raised concerns on the magnitude of overdiagnosis.2 The Medicare Evidence Development and Coverage Advisory Committee commented that the USPSTF's decision to extend the upper age of screening from 74 to 80 years ‘was based upon modelling only, with no empirical data’.3

The opinion that modelling cannot provide additional information beyond that of a clinical trial is a common misconception. Models are often criticised for depending on assumptions with regard to the processes of carcinogenesis and progression of cancer.4 However, the same could be said of statistical tests used to evaluate the results of clinical trials, many of which are also based on implicit assumptions. Similarly to other statistical methods, if the underlying assumptions are properly stated, models can be valuable tools in bridging the gaps between the evidence provided by clinical trials and the evidence needed for the development of clinical guidelines.5 In fact, modelling is essential to derive detailed estimates of the benefits and …

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