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Estimating the frequency of overdiagnosis due to cancer screening is not a simple task, because overdiagnosis is almost never observable. Since we treat most cancers and their precursors, we rarely get to follow a tumor over its natural course and learn whether that tumor would have persisted as an indolent lesion, spontaneously regressed, or progressed to the point of producing symptoms.
To accurately assess the frequency of overdiagnosis, we need to really understand disease natural history. And this is a hard problem. In fact, one of the greatest controversies in the debate about overdiagnosis is how best to estimate this elusive quantity.
The second camp uses observed quantities as proxies for the unobserved frequency of overdiagnosis. This approach commonly estimates the incidence of overdiagnosis as the “excess incidence” of disease in the presence of screening relative to observed (or extrapolated) incidence in the absence of screening.
As we2 and others have shown, the two approaches typically yield quite different results. Estimates based on the excess-incidence approach routinely exceed those based on the lead-time approach. Therefore, in judging the validity of any overdiagnosis study, it is absolutely critical to know which approach has been used, and to understand its merits.
In their article, Zahl et al.1 champion the excess-incidence approach, rejecting the lead-time approach in all settings. We respectfully disagree. Indeed, the main premise of this Editorial is that, when attempting to estimate an unobservable quantity such as overdiagnosis, an empirical calculation such as that based on excess incidence can be quite misleading.
A growing body of evidence is confirming that the excess-incidence proxy for overdiagnosis is incorrect and generally leads to estimates that are biased upwards.3,4 The reason for this is that, because all cancers are latent for at least some time, the introduction of screening always produces a rapid increase in disease incidence. This increase in incidence is attributable to a combination of overdiagnosed cases and non-overdiagnosed cases whose date of detection has been brought forward in time by the screening test. The excess-incidence approach cannot partition the increase in incidence into the portion that is rightly attributable to overdiagnosis and that due to early detection of disease that would have manifested in the absence of screening.
Recognizing this problem, some practitioners of the excess incidence approach wait until incidence has stabilized and then compute the difference between the observed incidence under screening and observed (or extrapolated) incidence without screening. However, a simulation study4 showed that this method may require waiting for many years. A recent report from the Canadian National Breast Screening study5 waited 10 years after the end of the initial 5-year screening period. However, the accuracy of the resulting estimate of overdiagnosis is unclear, because screening practices among participants on both arms of the trial were not documented during this time. In the absence of a control group, extended extrapolation of the incidence that would be expected without screening is required, and this often amounts to a guessing game with different guesses greatly impacting results. This was an issue in a study of breast cancer incidence in the US,6 which extrapolated the incidence without screening from a baseline in 1976 all the way to 2008.
There are other significant problems2,7 with the excess-incidence approach. Although ad-hoc adjustments have been proposed to address these, they have never been shown to eliminate the resulting bias. Indeed, Biesheuvel et al.,3 referring to their review of many studies using this approach, conclude that “all available estimates of overdetection due to mammography screening are seriously biased.”
What then of the lead-time approach? Or, more generally, any approach that attempts to infer the underlying natural history? Is it doomed as well?
Unfortunately, there is no simple answer to this question. We believe, based on our extensive experience with this approach, that models can be developed that will reliably inform about key features of disease natural history. However, there is more to it than this.
In the most basic case, when the majority of tumors are progressive, the minimal description of disease natural history that enables estimation of overdiagnosis includes a risk of disease onset, a risk of progression from latent to clinical disease in the absence of other-cause death, and the sensitivity of the screening test. So long as good data on disease incidence with and without screening are available and screening patterns in the population are known, lead-time approaches can provide reliable estimates of these quantities. There is a large body of work in the cancer8,9 and HIV10 literatures on such lead-time models.
Zahl et al.’s main objection is that these basic lead-time models (that assume all tumors are progressive) are often used in settings such as breast cancer where, they maintain, many tumors regress. They argue that, by not accommodating the possibility of regression, these models are wrong and consequently that the empirical excess-incidence approach must be preferred.
We have three main responses to this assertion.
First, even if disease does regress in a non-trivial fraction of cases, it has not been demonstrated that the more basic lead-time models always produce incorrect estimates of overdiagnosis. In the case where the interval to regression is short relative to the interval to progression, the presence of regressive cases will not greatly change incidence under screening and will not significantly impact estimates of lead time or overdiagnosis.11 When the interval to regression is longer, the incidence under screening will be impacted and this will generate a higher estimate of the average lead time than in the absence of regression and, consequently, a greater projected frequency of overdiagnosis. More thorough investigation is needed to establish how biased these basic lead-time models are likely to be when some tumors regress. As the oft-cited adage by George Box goes, “all models are wrong, but some are useful.”
Second, there are versions of lead-time models that do address a more complex biology11, 12, 13 and do not simply assume that all tumors are progressive. Such models generally need to incorporate external information on parts of the disease progression and detection process, because typical screening studies do not provide enough information to identify all of the risks describing a complex disease natural history. Thus, for example, a recent cervical cancer model11 allowed for regression, persistence, and progression of CIN 2/3 lesions, but incorporated external information on the risk of CIN 2/3 onset based on the age-specific prevalence of HPV lesions in the population. In the case where appropriate external information is available and a coherent statistical method is used to integrate this information with data on disease incidence with and without screening, the lead-time modeling approach can provide important insights into disease natural history and overdiagnosis.
Third, we would challenge Zahl et al.’s insistence that a significant number of breast tumors must regress. This conclusion is based on two studies14,15 that empirically compared two overlapping age-matched groups of women. The first group was screened several times and the second was screened only once, at the end of a comparable-length interval. The incidence of detected disease was higher in the first group than in the second, and Zahl et al. attributed the excess to detection of tumors that would have regressed in the absence of screening. However, since mammography is not perfectly sensitive, particularly in detecting smaller tumors, the increased incidence in the group with multiple screens is to be expected even if tumors do not regress. Indeed, the observed excess could be partly (or even largely) due to increased intensity of screening and consequently better program sensitivity to detect small, latent tumors in this group. That the difference between the two groups persists with further follow-up is also to be expected because, owing to the way in which the comparison groups were constructed there will always be more intensive screening in the first group than in the second group. Thus, there may be plausible alternative explanations for the empirical observation of increased detection in the group with multiple screening tests.
In general, we agree with Zahl et al. that when disease can regress, application of lead-time models may represent a simplification of disease biology and may produce biased results, or at least results that are contingent on other inputs to the analysis. But this does not mean that the empirical excess-incidence approach, with all of its known deficiencies, should be used instead. In this endeavor to estimate the unobservable, we stand by a well-known quote attributed to Albert Einstein: “Everything should be made as simple as possible, but not simpler.”
This work was supported by the National Cancer Institute at the National Institutes of Health (U01CA157224) as part of the Cancer Intervention and Surveillance Modeling Network (CISNET). The contents of this study are solely the responsibility of the authors and do not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health.
Conflict of Interest
The authors declare that they do not have a conflict of interest.