Since the advent of PSA screening, there has been uncertainty about screening benefit and concern about screening harms. The recent USPSTF recommendation against PSA screening for prostate cancer has raised awareness of the harms of existing screening strategies. In response, we sought to identify smarter screening strategies using microsimulation modeling.
The use of modeling in policy development is becoming more accepted (25
). The USPSTF relied on modeling to determine strategies for breast (27
) and colorectal cancer screening (28
). And numerous models have been developed to study prostate cancer screening (29
). Indeed, a recent publication considered six different strategies for prostate cancer screening (24
). However, like other existing prostate screening models, it did not conceptualize the disease process in a way that permits comprehensive evaluation of all screening strategy parameters. Our model is unique in that it not only represents individual PSA over time but also explicitly links PSA growth with disease progression, which is linked with mortality. As a consequence, we can explore outcomes due to varying PSA thresholds for biopsy referral as well as variations in screening ages and intervals, which may change dynamically depending on PSA levels. By quantifying the likelihood of a false positive test, overdiagnosis, or life saved associated with a broad range of screening strategies, we can identify strategies that reduce harms but preserve the impact of early detection on prostate cancer mortality.
Our results yield several important conclusions. First, we find that aggressive screening strategies, particularly those that lower the PSA threshold for biopsy, do reduce prostate cancer mortality relative to the reference strategy. However, the harms of unnecessary biopsies, diagnoses, and treatments may be unacceptable. Quantifying the magnitude of these harms relative to potential gains in lives saved is critical for determining whether the projected harms are acceptable.
Second, we find substantial improvements in the harm-benefit tradeoff of PSA screening with less frequent testing and more conservative criteria for biopsy referral in older men. These approaches preserve the majority of the survival impact and markedly reduce screening harms compared with the reference strategy. In particular, using age-specific PSA thresholds for biopsy referral (Strategy 20) reduces false positive tests by a relative 25% and overdiagnoses by 30% while preserving 87% of lives saved under the reference strategy. Alternatively, using longer inter-screen intervals for men with low PSA levels (Strategy 22) reduces false positive tests by a relative 50% and overdiagnoses by 27% while preserving 83% of lives saved under the reference strategy. These adaptive, personalized strategies represent prototypes for a smarter approach to screening.
When smarter screening strategies achieve similar absolute probabilities of life saved, the choice between them depends on relative weighting of overdiagnosis and other harms. Using these two prototype strategies as an example, Strategy 22 reduces total tests by a relative 59% and false positive tests by 33% but increases overdiagnoses by 5% relative to Strategy 20. In general, the relative weighting of harms, like the relative weighting of benefits and harms, may depend on whether one adopts an individual or societal perspective. If an individual perspective is adopted, preferences may be variable across the population.
Other investigators have recommended personalized strategies for PSA screening as a means to reduce harms while preserving benefit. Carter et al. (14
) suggested that the inter-screening interval should be lengthened in men with low PSA. The risk calculator from the Prostate Cancer Prevention Trial produces a personalized prediction of the risk of occult disease based on PSA, age, race, and family history (33
). In principle we could compare an approach based on this calculator with other personalized strategies, but this would require adding race and family history to the model, recalibrating the model accordingly, and determining a reasonable risk threshold for biopsy referral. This is possible in principle but beyond the scope of the present study.
We recognize that every model is necessarily a simplification of reality and is limited by its assumptions. Our model is no exception. We allow the likelihood of developing high-grade disease to vary with age but do not model grade progression. Due to limitations in the SEER data used to calibrate the model, we are limited to two stages (SEER local-regional or distant stage) and two grades (Gleason 2–7 or 8–10). We model survival benefit via a stage-shift mechanism which is likely also a simplification. Yet, a close match between our calibrated model and observed incidence and absolute and relative mortality reductions in a simulated ERSPC give us confidence that we are producing a valid representation of the likely tradeoffs involved in screening for a complex heterogeneous disease. Our model also does not incorporate utilities and does not produce quality-adjusted estimates of the impact of screening on survival. However, existing data on utilities associated with prostate cancer screening and post-diagnosis health states are extremely limited (34
) and we do not feel that they are sufficiently reliable for modeling at this time. Further versions of the model will include other elements that are missing in the present version, including utilities once adequate data become available, costs, and race-specific disease progression.
In his recent editorial (7
), Welch concludes that “In the case of the prostate, for the past two decades we’ve been looking too damn hard. That’s what’s led to so many biopsies and so much overdiagnosis.” By screening smarter, we look less hard, particularly in older men at the highest risk of overdiagnosis. As demonstrated in the PLCO trial and supported by our model results across a broad range of alternative strategies, there are diminishing returns to intensive screening. If we recognize that realistic screening strategies must achieve an acceptable balance of benefits and harms as opposed to unconditionally maximizing benefits, we can improve on the effectiveness of existing PSA-based screening for prostate cancer.