Computational methods in medical decision making: to screen or not to screen?
Screening for a disease such as cancer is often regarded as a beneficial and successful strategy for reducing mortality. However, as with any clinical treatment or intervention, benefit cannot be assumed, and screening can entail both costs and harms, so the screening as a 'treatment' must undergo evaluation. An evaluation requires a definition of the treatment 'benefit', design of studies to measure that benefit with as little bias and variance as possible, and the development of methods for estimating the potential benefit. In screening studies, the factors most central to the evaluation are unobservable (e.g. earliest point in time at which disease becomes detectable, or 'preclinical'; time at which disease might have been detected in the absence of screening; test sensitivity). Thus, screening programs should be evaluated on scenarios in which these factors are varied, to ensure the robustness of the estimated benefit under a variety of circumstances. This article describes the importance of computational methods and simulations to assess the benefit of screening programs, particularly for cancer, based on randomized screening trials, with special attention to benefit time, lead time, and bias due to length-biased sampling.