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Raising the Bar for the U.S. Preventive Services Task Force

TO THE EDITOR: We read Bach's editorial (1) with great interest. The U.S. Preventive Services Task Force recommendation on lung cancer screening represents a major synthesis of trial evidence, model-based outcomes, and expert judgment to quantify the tradeoffs of computed tomography screening for the millions of persons at high risk for lung cancer (2, 3). However, Bach states that the Task Force could have been more cautious about relying on modeling for extrapolation well beyond the empirical data to fill in gaps in the evidence (1). Bach questions the net benefit of screening annually over many years. Does he mean that evidence suggests screening 3 times, as was done in NLST (National Lung Screening Trial)? Does he mean that women should receive only 5 breast cancer screenings, because this was the average number in the breast screening trials? Randomized trials are designed to prove the efficacy of an intervention. In translating that evidence to public health, much more is neededespecially to estimate the long-term benefits and harms for the target population. In fact, many would argue that modeling is essential to translate evidence from trials to population guidelines (4), particularly as we face an ever-increasing pace of technology where questions far exceed our ability to do multiple trials. Our model-based analyses (3) required a joint consideration of many factors, including dose response to smoking and age-specific incidence and death from other causes according to smoking behavior and birth cohort. These factors were superimposed onto more than 1000 schedules of screening examinations using NLST as a guide, something too complex to evaluate without the aid of a model. However, Bach states that we were unable to generate models that parallel the natural history of lung cancer and that our models produced inconsistent mortality benefits in reproducing the early years of NLST. That model variability would produce results that differ in the early years when event rates were low and great variation should not be surprising. Even data monitoring committees place low value on the early years. Bach also points out that the models differed in their predictions of the absolute number of cases of lung cancer and lung cancer deaths prevented. Absolute counts naturally vary considerably and are more difficult to estimate accurately. However, in the ranking of competing scenarios, all 5 models place the 27 scenarios consistently. Moreover, the models reproduce the outcomes observed in the trials (5), and we showed the range of absolute effects in the table on harms and benefits of the advantageous scenario. At the end of the editorial, Bach mentions the term sharpshooter. Although this example is dismaying, the analogue is striking. With the models, we indeed draw the target around the greatest cluster of data: On the basis of 200000 persons enrolled in the screening trials, we can best estimate the screen-detectable preclinical period, test sensitivity, and improvements in prognosis made by screening and early treatment according to sex, age, and histologic characteristics. We therefore hope that clinical researchers will engage more closely with modelers and contribute to deliberations about the best use of models with a deeper understanding of the their development and validation processes.