Almost 20 years after its introduction, prostate-specific antigen (PSA) screening remains controversial. Randomized controlled trials are still ongoing in the United States and Europe, and it will be several years before efficacy results become available (1,2). Although prostate cancer mortality rates have declined in some countries with high use of PSA screening, such as the United States, mortality rates are also dropping in other countries with relatively low use of PSA screening, such as the United Kingdom (3). Other factors besides screening may be affecting mortality, including changes in treatment practices and early detection of recurrent disease.

As the debate about the benefits of PSA screening continues, there is growing recognition of its costs. One of the chief drivers of the costs of PSA screening is overdiagnosis—the detection of latent disease that would not have been diagnosed in the patient's lifetime in the absence of screening. Overdiagnosis is a particularly important issue in prostate cancer screening because the latent prevalence of disease, as estimated from autopsy studies, is much higher than its incidence in the absence of screening. Therefore, there is a large pool of silent cancers that could potentially be detected by screening. Because it is not usually clear whether a screen-detected cancer has been overdiagnosed, many overdiagnosed patients receive curative treatment (surgery or radiation therapy), which is associated with substantial costs and morbidity (4).

The frequency of overdiagnosis is associated with the time by which screening advances diagnosis, also called lead time. Because prostate cancer is often a slowly developing disease, PSA screening can be associated with lengthy lead times. The longer the lead time, the greater the likelihood of overdiagnosis. Thus, estimating the lead time is often a critical step in estimating the frequency of overdiagnosis.

Estimates of lead time and overdiagnosis due to PSA screening have been obtained from various sources. Several studies that used stored serum samples found mean lead time estimates ranging from 3 to more than 7 years (5–7); more recently, Tornblom et al. (8) estimated a median lead time of 11 years. Other studies estimated lead times on the basis of a comparison of detection rates in a population-based trial setting with baseline incidence, producing mean lead times between 5 and 12 years (9,10). Further investigations used models to explicitly link PSA screening frequencies with population trends in prostate cancer incidence as reported in the Surveillance, Epidemiology, and End Results (SEER) program (11–13) of the National Cancer Institute. In these studies, mean lead time estimates ranged from 3 to 7 years. Overdiagnosis estimates ranged from 25% to 84% of all screen-detected cancers (10,12–14).

It is clear that published lead time and overdiagnosis estimates vary considerably across studies. There are at least three reasons for this variability: 1) the context of the estimates, including population, epidemiology of the disease, and the way screening is practiced in those populations (eg, PSA level cutoffs and biopsy practices); 2) the definitions of lead time and overdiagnosis used; and 3) the methods used to calculate the estimates. The goal of this article was to explore each of these three factors as we investigate why different studies have yielded different results.

We estimated lead time and overdiagnosis within a specific population setting, namely, the US male population aged 50–84 years in 1985–2000. To investigate the influence of the definition of the lead times on the estimates, we considered three definitions of lead time (non-overdiagnosed, censored, and uncensored, as defined in “Methods”).

The estimates presented were developed using three models that link PSA testing trends with population incidence rates: the model developed at the Fred Hutchinson Cancer Research Center (FHCRC) (11,12), the model developed at the University of Michigan (UMich) (13), and the microsimulation screening analysis (MISCAN) model developed at Erasmus MC in Rotterdam (10,15,16). The use of multiple models allowed us to produce robust results while exploring the influence of estimation methodology.

The FHCRC and UMich models were originally developed to study prostate cancer incidence and mortality in the United States. In contrast, the MISCAN model was originally based on baseline incidence in the Netherlands and results of the Rotterdam section of the European Randomized Study of Screening for Prostate Cancer (ERSPC) (10,15). Thus, to enable comparisons with US data, the MISCAN model was calibrated to SEER incidence data.

This study was carried out in collaboration with the Cancer Intervention and Surveillance Modeling Network (CISNET; http://cisnet.cancer.gov/) of the National Cancer Institute. The primary goal of CISNET is to use modeling to quantify the roles of prevention, screening, and treatment in explaining cancer incidence and mortality trends. A key feature of the CISNET collaboration is that the models are developed independently, but modelers use standardized inputs and share details of model development to understand and explain any differences in model results.

We considered three definitions of lead time that have been used in previous publications and showed that results differ depending on the definition used. The uncensored definition yields the longest estimated lead times and the non-overdiagnosed definition the shortest. We feel strongly that for future studies to be correctly interpreted, analysts should specify the definition used in their publications. Other definitions have also been reported. For example, McGregor et al. (14) defined overdiagnosis as the detection by screening of disease that would not have led to prostate cancer death. Because the majority of prostate cancer patients do not die of the disease (32,33), the estimates of overdiagnosis due to PSA screening reported by McGregor et al. were considerably higher than ours, exceeding 80%.

The definition of lead time may be constrained or even dictated by the study design. In studies that use stored serum samples, for example, mean lead time is estimated empirically as the average time from the first abnormal PSA test result to prostate cancer diagnosis among the cancer patients with serum samples in the repository. Gann et al. (5) used this method to estimate a mean lead time of 5 years that was based on one serum sample per patient, and Pearson et al. (34) estimated a mean lead time of 3 years by use of serial serum samples. Note that the lead times estimated in these studies refer to patients who were clinically diagnosed during the study (excluding overdiagnosed cancers), that is, corresponding to non-overdiagnosed lead time as shown in Figure 1. However, this approach has some deficiencies. First, the estimates could be seriously affected by the limited follow-up time, for example, 10 years in Gann et al. (5). Tornblom et al. (8), for example, studied prostate cancer incidence in Gothenburg (Sweden) in a cohort of men aged 67 years in 1980 and who had a blood sample taken in 1980. They estimated a median lead time of 7.8 years with 12 years of follow-up and 10.7 years with 20 years of follow-up for PSA levels of 3 ng/mL and greater. Second, this approach assumes that cancer would have been identified by biopsy examination at the time of the abnormal PSA test.

There are also different definitions of overdiagnosis. From an epidemiological or public health perspective, the standard definition is the one that we used in this analysis, namely, the event of other-cause death before the date of clinical diagnosis. However, the clinical literature has suggested an alternative definition, namely, the detection of “clinically insignificant” disease—tumors smaller than 0.2 cm³, organ confined, and with Gleason score less than 7 (35). By this definition, the frequency of overdiagnosis is substantially lower than that reported in the present article (36). However, autopsy studies have shown that tumors that are clinically significant in this sense have a considerable chance of going undiagnosed during a lifetime, as recently reviewed (37). Therefore, we argue that this alternative definition of overdiagnosis, although potentially useful in the future, is likely premature now.

Regarding the issue of context, comparing the results from the MISCAN ERSPC and MISCAN SEER models is revealing. Lead time and overdiagnosis estimates from the original model that was based on the Rotterdam data were comparable with those published for PSA screening in the Netherlands (10). Clearly, prostate cancer and PSA screening in the US population seem to be different from the trial setting in Rotterdam (see also Figure 5). Two sets of parameters were changed: In the SEER model, the sensitivity of the screening test was lower than that in the ERSPC model, and the hazard of clinical diagnosis higher, implying an earlier diagnosis in the absence of PSA screening. The lower sensitivity is justified by the lower PSA cutoff at 3 ng/mL in Rotterdam vs 4 ng/mL in the 1990s in the United States, and probably more important, by the higher biopsy compliance rate (90%) in the ERSPC Rotterdam study than in the Prostate, Lung, Colorectal, and Ovarian (PLCO) cancer screening trial (approximately 40%) (29), which is supposedly representative of US practice. Partially counterweighting these differences may be adherence to the less sensitive sextant biopsy scheme in ERSPC Rotterdam, whereas US biopsy practices gradually adopted extended-core schemes. For the assumed earlier diagnosis in the absence of PSA screening, there is less evidence, but it allowed a higher predicted incidence rate in 1985–1987 without raising incidence over the entire study period. Because lead time and overdiagnosis are defined relative to clinical diagnosis, this assumption also resulted in lower estimates, consistent with the other models. This exercise shows that baseline clinical incidence and the intensity of screening follow-up, both of which may differ across populations, may be important drivers of reported estimates of lead time and overdiagnosis in different studies.

Another source of variation could be caused by model parameterization. In the multiparameter MISCAN model, it is likely that different combinations of parameter values might fit the data equally well, which might impact on lead time and overdiagnosis estimates. By contrast, in the more parsimonious UMich model, parameters are well identified and have narrow confidence intervals (13). However, the impact of this source of variation is likely to be much smaller than that of model structure and assumptions. In this respect, the UMich model differs from both the MISCAN and FHCRC models in that its parameter estimates are based on SEER incidence only, whereas in the other models, data from other sources were also used for parameter estimation.

Finally, we discuss the role of the methods used to estimate lead times and overdiagnosis. In the present investigation, the specific model used plays a relatively minor role. The models yielded lead time and overdiagnosis estimates that were fairly consistent. It is important to note that these estimates depend on a common assumption in all three models—the dissemination of PSA screening is assumed to be the main causal factor of incidence trends since 1985. Although the models do reproduce overall incidence trends, the fit is not perfect. For example, the observed reduction of distant disease incidence is only partially reproduced by the models, replicating results of Etzioni et al. (38), who, using a different model (not calibrated to stage-specific incidence), also found that the model-projected decline in distant-stage incidence was less extreme than that observed in SEER. Also, the estimates of the mean uncensored lead time and overdiagnosis frequency are higher than those reported by Telesca et al. (39). Assuming observed incidence to be the sum of a smooth incidence trend in the absence of screening and an excess incidence that is a function of screening patterns and exponentially distributed lead times, they obtained estimates of mean uncensored lead times of 6.34 years for whites and 7.67 years for blacks. Telesca et al. (39) also showed that their estimates, which were based on population incidence, are sensitive to assumptions about background incidence. Thus, the specific modeling approach used can be influential, although our experience suggests that context and lead time definition are probably more important in explaining the heterogeneity of published lead time and overdiagnosis estimates across studies.

This study has several limitations. The estimates depend on the following assumptions: 1) All incidence trends since 1985 are due to PSA screening, which amounts to assuming an unobserved flat incidence rate in the absence of screening. This assumption may be reasonable, but we do not have independent evidence to support it. 2) We assumed that Mariotto's model of PSA testing practice (19), which we used, is about screening tests. In the construction of her model, all follow-up PSA tests taken after diagnosis were eliminated as well as PSA tests occurring within 3 months of a previous PSA test. A fraction of the remaining tests might be diagnostic tests that were used to confirm a suspicion for prostate cancer. The size of this fraction is unknown, but it would imply that the screening rate is lower than we assumed. Finally, it is clear that these models were not perfect in predicting observed incidence. Incidence as predicted by the models show a lag of 1 or 2 years with respect to observed incidence, and the models fail to explain fully the decline in distant disease. Consequently, the estimates of mean lead time and overdiagnosis rate will not be perfect either, although it is not clear in what direction they might be biased.