Abstract and Introduction
Abstract
Background: The time by which prostate-specific antigen (PSA) screening advances prostate cancer diagnosis, called the lead time, has been reported by several studies, but results have varied widely, with mean lead times ranging from 3 to 12 years. A quantity that is closely linked with the lead time is the overdiagnosis frequency, which is the fraction of screen-detected cancers that would not have been diagnosed in the absence of screening. Reported overdiagnosis estimates have also been variable, ranging from 25% to greater than 80% of screen-detected cancers.
Methods: We used three independently developed mathematical models of prostate cancer progression and detection that were calibrated to incidence data from the Surveillance, Epidemiology, and End Results program to estimate lead times and the fraction of overdiagnosed cancers due to PSA screening among US men aged 54-80 years in 1985-2000. Lead times were estimated by use of three definitions. We also compared US and earlier estimates from the Rotterdam section of the European Randomized Study of Screening for Prostate Cancer (ERSPC) that were calculated by use of a microsimulation screening analysis (MISCAN) model.
Results: The models yielded similar estimates for each definition of lead time, but estimates differed across definitions. Among screen-detected cancers that would have been diagnosed in the patients' lifetimes, the estimated mean lead time ranged from 5.4 to 6.9 years across models, and overdiagnosis ranged from 23% to 42% of all screen-detected cancers. The original MISCAN model fitted to ERSPC Rotterdam data predicted a mean lead time of 7.9 years and an overdiagnosis estimate of 66%; in the model that was calibrated to the US data, these were 6.9 years and 42%, respectively.
Conclusion: The precise definition and the population used to estimate lead time and overdiagnosis can be important drivers of study results and should be clearly specified.
Introduction
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. 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. 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.
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; more recently, Tornblom et al. 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. 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 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.
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), the model developed at the University of Michigan (UMich), and the microsimulation screening analysis (MISCAN) model developed at Erasmus MC in Rotterdam. 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). 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.