title: Response: Reading between the lines of cancer screening trials: using modeling to understand the evidence.
abstract: In our article about limitations of basing screening policy on screening trials, we offered several examples of ways in which modeling, using data from large screening trials and population trends, provided insights that differed somewhat from those based only on empirical trial results. In this editorial, we take a step back and consider the general question of whether randomized screening trials provide the strongest evidence for clinical guidelines concerning population screening programs. We argue that randomized trials provide a process that is designed to protect against certain biases but that this process does not guarantee that inferences based on empirical results from screening trials will be unbiased. Appropriate quantitative methods are key to obtaining unbiased inferences from screening trials. We highlight several studies in the statistical literature demonstrating that conventional survival analyses of screening trials can be misleading and list a number of key questions concerning screening harms and benefits that cannot be answered without modeling. Although we acknowledge the centrality of screening trials in the policy process, we maintain that modeling constitutes a powerful tool for screening trial interpretation and screening policy development.
authors.affiliation: Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109-1024, USA. retzioni@fhcrc.org
authors.forename: Ruth
authors.initials: R
authors.lastname: Etzioni
authors.forename: Roman
authors.initials: R
authors.lastname: Gulati
mesh_terms: D003657:Decision Making; D055088:Early Detection of Cancer / Q000592:standards*; D019317:Evidence-Based Medicine / Q000379:methods*; D006291:Health Policy; D006801:Humans; D015994:Incidence; D008297:Male; D008403:Mass Screening / Q000592:standards; D015233:Models, Statistical; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000401:mortality / Q000517:prevention & control*; D016032:Randomized Controlled Trials as Topic; D018570:Risk Assessment; D014481:United States / Q000453:epidemiology