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Evaluation of New Technologies for Cancer Control Based
on Population Trends in Disease Incidence and Mortality
Ruth Etzioni, Isabelle Durand-Zaleski, Iris Lansdorp-Vogelaar
Correspondence to: Ruth Etzioni, PhD, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, M2-B230, PO Box
19024, Seattle, WA 98109-1024 (e-mail: email@example.com).
Cancer interventions often disseminate in the population before evidence of their effectiveness is available. Population disease
trends provide a natural experiment for assessing the characteristics of the disease and the potential impact of the intervention.
We review models for extracting information from population data for use in economic evaluations of cancer screening interven-
tions. We focus particularly on prostate-specific antigen (PSA) screening for prostate cancer and describe approaches that can
be used to project the likely costs and benefits of competing screening policies. Results indicate that the lifetime probability of
biopsy-detectable prostate cancer is 33%, the chance of clinical diagnosis without screening is 13%, and the average time from
onset to clinical diagnosis is 14 years. Less aggressive screening policies that screen less often and use more conservative criteria
(e.g., higher PSA thresholds) for biopsy referral may dramatically reduce PSA screening costs with modest impact on benefit.
J Natl Cancer Inst Monogr 2013;46:117–123
Cancer interventions often disseminate in the population pre-
maturely, before conclusive evidence of their efficacy has been
obtained. For example, prostate-specific antigen (PSA) screening
for prostate cancer became widespread in the United States in the
early 1990s (1), but clinical trials to evaluate screening efficacy were
initiated in 1993 and published results only in 2009 (2,3). Based
largely on these results, the US Preventive Services Task Force
(USPSTF) recently recommended against routine PSA screening,
a reversal which goes against what has become standard practice in
this country (4). However, a great deal of uncertainty still remains
about the harms and benefits of prostate cancer screening.
In this chapter, we examine the conundrum—and the opportu-
nity—represented by the premature adoption of cancer interven-
tions. By premature we mean the adoption and dissemination of
an intervention before conclusive evidence of its efficacy is avail-
able from clinical trials. Premature adoption of an intervention
may have a negative impact—if the harms of the intervention ulti-
mately turn out to outweigh the benefits. The key characteristic of
a premature intervention in the setting of this paper is simply that
conclusive evidence about harm–benefit tradeoffs has not yet been
obtained. Our primary example is the case of PSA screening in the
United States. Although PSA screening began in the late 1980s
and became popular in the early 1990s, large clinical trials first
published results concerning PSA screening benefit only in 2009.
The conundrum is clear—if an intervention is adopted in the
absence of clarity about its benefits, then not only could we end
up squandering money and resources for little benefit, but reve-
lation that benefit is not what was expected could indicate that a
reversal of contemporary standard practice is warranted. However,
the adoption by a population of a novel intervention presents an
opportunity as well, namely to assess the effectiveness and costs of
the intervention in the population setting as opposed to the artifi-
cial setting of a clinical trial.
Because the population represents the ultimate uncontrolled
experiment, great caution has to be exercised in making inferences
about the comparative effectiveness of novel interventions based
solely on population data. Examples of such inferences are provided
by studies conducted by the Cancer Intervention and Surveillance
Modeling Network (CISNET) (www.cisnet.cancer.gov). For
example, CISNET models have been used to quantify the respective
contributions of mammography and adjuvant chemotherapy, two
major fronts of progress in breast cancer control, to declines in
breast cancer mortality (5), and the contribution of colorectal
cancer screening, diet, and treatment to declines in colorectal
cancer mortality (6).
In this chapter, we show how premature adoption of cancer
interventions and their effects on population trends can be used to
help inform economic evaluation and policy decisions. We review
and synthesize a series of modeling studies specifically focused on
extracting the necessary information from population data follow-
ing the dissemination of the intervention. In some cases, the models
we present have been used to make inferences about the contribu-
tions of specific inferences to declines in population mortality; in
other cases, models have been used to estimate disease progression
rates and characteristics of the intervention from population data.
This information is then incorporated in a medical decision-mak-
ing modeling framework that is designed to facilitate inferences
about harm–benefit tradeoffs. We focus specifically on questions
about the benefits, harms, and likely costs of PSA screening for
prostate cancer, but we also discuss how our methods have been
used to learn from trends in colorectal cancer, which are a complex
product of changes in behaviors over time as well as changes in
screening and treatment practices. We show how well-calibrated
models can be of value in determining cost–benefit tradeoffs for
policy development and demonstrate that there is an important role
for modeling to play in determining sound cancer control polices.
118 Journal of the National Cancer Institute Monographs, No. 46, 2013
PSA Screening Patterns and Prostate
Cancer Trends in the United States
The PSA screening era in the United States began in 1986 when
the test was approved for monitoring prostate cancer progression
but disseminated rapidly for early detection purposes. Different
areas of the United States adopted PSA screening at slightly dif-
ferent times (7), but the period of most significant dissemination
was the early 1990s when prostate cancer incidence more than
doubled relative to historic trends (8). The peak in incidence was
followed by a rapid decline as screening use stabilized, and it was
at this point that prostate cancer deaths began to fall. The drop
in disease-specific mortality has been sustained and impressive;
prostate cancer deaths have declined by 44% since their peak in
1991 (9). Among men aged 50–84, the primary group targeted by
screening, the fall has been even more substantial, reaching 49%
The harms and benefits of PSA screening have been hotly
debated, with speculation that PSA explains the mortality declines
counterbalanced by skepticism. Until 2009, when results of the
two large screening trials were published (2,3), the population
data represented the best available evidence about screening ben-
efit. However, interpreting population mortality trends is com-
plex because the population constitutes the ultimate uncontrolled
experiment. In the case of PSA and prostate cancer, there have
been multiple other changes in disease control and management
that have occurred concurrently with the spread of PSA screen-
ing. These include changes in primary treatment, with historical
treatment trends showing dramatic increase in radical prostatec-
tomy rates during the 1980s (2,3) and similar increases in the use of
adjuvant hormone therapy for localized disease during the mid to
late 1990s (10). There have also been changes in the detection and
treatment of recurrent disease, primarily due to PSA monitoring
following primary treatment.
Can we use population prostate cancer trends to learn about the
benefits and harms of PSA screening despite these challenges? This
has been the mission of the CISNET prostate group, which has
used modeling of prostate cancer in the population as its primary
Surveillance Modeling: Learning About Disease
Progression From Population Cancer Trends
Surveillance modeling is an approach designed to learn about the
process of disease progression from trends in population incidence
and mortality. The central idea is that although the events in dis-
ease progression are not all observable, they produce an observ-
able process, namely disease incidence trends, that can be used
to inform about the underlying natural history. Disease incidence
trends that have been recorded before and after the advent of
screening in a population are particularly informative, so long as
information is available about screening and biopsy referral prac-
tice patterns. In the case of prostate cancer, PSA screening became
adopted in the late 1980s, so we have used prostate cancer inci-
dence trends, together with retrospectively ascertained screening
patterns in the United States, to make inferences about rates of
disease onset, metastasis, and clinical detection in the absence of
A Model of Prostate Cancer Progression: Parameter
Estimation Using Population Incidence Data
Figure 1 summarizes our model, which includes two main com-
ponents. The first describes how PSA grows in healthy men and
cancer cases, and how this growth varies across the population. The
second links PSA with disease progression and describes how the
risks of disease spread and generation of clinical symptoms change
as PSA grows after disease onset. We assume that the risk of disease
onset increases with age and that the risks of disease spread and
symptoms are proportional to the level of PSA at any given time.
This assumption is a mathematical representation of a mechanism
that generates the known correlation between the level of PSA and
stage of disease at diagnosis, and was found to be most consistent
of several models (12,13) with observed data on PSA growth and
disease stage from a retrospective series (14). The natural history
parameters are, therefore, the PSA growth rates and risks of disease
onset, metastasis, and clinical symptoms.
Estimation of the natural history parameters proceeds as follows.
PSA growth and its variation are based on serial PSA data from the
Prostate Cancer Prevention Trial (PCPT), which screened 18 882
men for up to 7 years (15). Of these, 9459 were in the control group
and were used for our analysis. We use the results to simulate a
population of men aged 50–84 beginning in 1975 and ending in
2000, of whom a fixed percentage experienced disease onset at a
rate proportional to their age. After onset, PSA growth is reset
based on the PCPT results, and the events of disease metastasis and
clinical diagnosis are set to occur at rates that grow proportionally
with the PSA level. We superimpose screening, according to US
screening patterns (1), on this simulated population and project the
corresponding trends in age- and stage-specific incidence. We then
vary the rates of onset, metastasis, and clinical diagnosis so that
the projected trends best match the observed trends in incidence.
We use a simulated likelihood-based framework (11) to quantify
the extent of the mismatch and optimize the simulated likelihood
to obtain the best-fitting natural history parameters conditional
on the PCPT-based PSA growth curves. Details of our methods
and results are provided elsewhere (11,16); we note here that the
projected stage-specific incidence curves under the fitted natural
history parameters capture both the dramatic peak in local-regional
incidence observed in the early 1990s and the steady decline in
distant-stage incidence observed after this time. The fitted model
suggests that the lifetime probability of biopsy-detectable prostate
cancer is 33%, whereas the chance of a clinical diagnosis in the
absence of screening is 13% and the average time from onset to
clinical diagnosis is 14 years on average (17).
Using the Model to Explain Prostate
Cancer Mortality Trends
We used our model to investigate the likely role of PSA screening
versus changes in prostate cancer treatment in explaining the dra-
matic and sustained decline in prostate cancer deaths in the United
States through the year 2005. To do so, we first needed to project
what mortality rates would have been in the absence of screen-
ing. We assumed that in the absence of screening or treatment,
stage-specific incidence of prostate cancer would have remained
constant at levels observed in 1987, just prior to the PSA era, and
Journal of the National Cancer Institute Monographs, No. 46, 2013 119
disease-specific survival would have been similar to survival among
cases in the Surveillance, Epidemiology, and End Results (SEER)
database diagnosed from 1983 to 1986 who did not receive curative
primary therapy. We then used information on treatment trends
for localized prostate cancer and results from studies comparing
primary treatments with each other and with observation (18,19) to
project how changes in treatment might have impacted the num-
ber of cases dying from prostate cancer. We found that treatment
changes explained about one-third of the drop in prostate cancer
mortality by 2005 (20). This left two-thirds to be explained by
other factors, chief among them being PSA screening.
Adding PSA screening to the model and projecting disease-
specific survival under the resulting model-projected stage distri-
bution produced further declines in disease-specific deaths; screen-
ing and treatment together accounted for two-thirds of the drop
in prostate cancer mortality by 2005 (Figure 2). We concluded
that treatment alone could not explain prostate cancer mortality
decline in the United States; screening has likely played an impor-
tant role and could account for as many as 10 000 lives saved per
year by 2005.
Estimating Harms of Prostate Cancer
It has become clear that screening for cancer can confer harm as
well as benefits. Imperfect diagnostic tests can lead to false posi-
tive results, generating anxiety along with unnecessary biopsies.
Overdiagnosis, or detection by screening of cancers that would
never have presented clinically during a patients’ lifetime, can lead
to unnecessary treatment with all of its consequences. Screening
itself is a costly endeavor because of the sheer number of tests that
must be conducted to screen a healthy population.
Overdiagnosis is a particular concern in prostate cancer screen-
ing. Because prostate cancer is known to have high latent preva-
lence relative to its clinical incidence, particularly in older men,
there is enormous potential for overdiagnosis and overtreatment.
The likelihood of overdiagnosis is closely linked with the lead
time, which is the time by which screening advances diagnosis.
Lead time, in turn, can be estimated from patterns of disease inci-
dence following the dissemination of a new screening test, so long
as information is available on screening patterns in the population.
In particular, the height and width of the peak in disease incidence
after the introduction of a novel screening test are informative
about lead time (21). This is because when a sensitive screening
test is adopted in a previously unscreened population where latent
disease is prevalent, many cases are identified by the test and their
date of diagnosis is correspondingly advanced by the lead time. In
later years, these cases are no longer present and there is a conse-
quent drop in disease incidence. The lead time determines when
the later incidence drop takes place relative to the initial incidence
gain. When the lead time is longer, the incidence drop takes place
later and the initial incidence gains are sustained, producing a more
pronounced incidence peak.
The likelihood of overdiagnosis can be estimated once the
distribution of lead time is known, because overdiagnosis occurs
when other-cause death takes place after screen detection but
Figure 1. A model of prostate cancer (PCA) natural history, diagnosis, and survival in the absence and presence of screening. Following disease
onset, PSA is assumed to grow exponentially. The risks of metastasis and clinical diagnosis (dx) increase proportionally with the PSA level. Without
screening, the cancer is diagnosed in distant stage, but with screening, detection occurs while disease is still localized. The figure shows how over-
diagnosis depends on the date of other-cause (OC) death relative to the lead time, which is the time from screen diagnosis to clinical diagnosis.
120 Journal of the National Cancer Institute Monographs, No. 46, 2013
before the end of the lead time. Thus, given lead time, the
chance of overdiagnosis can be calculated from population life
In the case of PSA screening, the premature dissemination
and rapid uptake of the test during the late 1980s and early 1990s
have provided an excellent opportunity to estimate the lead time
and corresponding overdiagnosis frequency associated with PSA
screening. Indeed, our simulated likelihood-based framework for
estimating our model parameters produces a virtual population of
men in which the times of screen detection and clinical diagno-
sis in the absence of screening are known. We can use these data
to produce empirical estimates of lead time and, given dates of
other-cause death, overdiagnosis. We have developed several other
algorithms that use data on PSA testing patterns and prostate can-
cer incidence to estimate lead time and overdiagnosis (17,22–24).
Our results consistently point to a frequency of overdiagnosis
during the1990s that amounts to approximately one out of every
four screen-detected cases in men over age 50. Our results are
consistent with another model developed using US data, but are
lower than estimates from a model developed partially using data
from the European Randomized Study of Screening for Prostate
Economic Evaluation of Prostate Cancer
The economic implications of cancer screening tests are vast and
rest on the drivers of costs that we have already mentioned: the tests
themselves, false positive results, and overdiagnosis. Estimation of
the costs of prostate cancer screening, therefore, requires an assess-
ment of the costs of testing as well as the costs of prostate biop-
sies and treatments, including the harms associated with treatment
like impotence and incontinence. Given these costs, differences
between screening strategies will be determined by how the cost
drivers vary across the strategies.
The calibrated model provides a representation for how disease
progresses in the absence of screening and, in particular, yields
a distribution of age and stage at disease diagnosis without PSA
testing. Superimposing a specified screening protocol produces a
change in the timing of diagnosis and, consequently, a change in
age and stage of disease in the presence of screening. Using stage-
specific curves for prostate cancer survival (8), we are able to proj-
ect the consequences of this earlier detection for disease-specific
The universe of potential PSA screening strategies is enormous
and includes strategies that vary in terms of their starting and
stopping ages, interscreening intervals, and criteria for biopsy
referral. Each of these screening strategy parameters has been the
topic of a great deal of debate and controversy. In the case of criteria
for biopsy referral, for example, there is disagreement about the
threshold for declaring a test to be abnormal and about whether
to base biopsy referral decisions on PSA velocity in addition to
absolute PSA (25).
Using our calibrated model, we considered a range of potential
strategies and projected a large set of relevant outcomes, including
the aforementioned drivers of cost and several measures of benefit.
Figure 3 illustrates the results of varying the ages to start and stop
screening, the interscreening intervals, and the criteria for biopsy.
The results show clearly that less intensive strategies can materi-
ally reduce key drivers of cost although only modestly impacting
Modification of Natural History Models for
Other Settings and Health Systems
Some aspects of natural history models are dependent on local pop-
ulation practice patterns. An example is the risk of clinical detection
in the absence of screening. This depends on the intensity of pros-
tate cancer diagnosis due to other means, and this can differ greatly
across population settings. When the same model was calibrated
Figure 2. Modeled impact of changes in primary treatment and changes
in primary treatment combined with screening on age-adjusted pros-
tate cancer mortality in the United States. The figure shows mortality
among men diagnosed after 1975 as observed and then as modeled
given changes in treatment and screening. For comparison, the figure
also shows total mortality due to prostate cancer in the United States.
By 2005, treatment changes account for about one-third of the drop in
disease-specific mortality (20), whereas the combination of screening
and treatment changes accounts for about two-thirds of the drop in
Journal of the National Cancer Institute Monographs, No. 46, 2013 121
to prostate cancer incidence patterns in the Rotterdam section of
the European Randomized Trial of Screening for Prostate Cancer
(ERSPC) and then again to data on prostate cancer incidence in
the US population after the advent of PSA screening, the clinical
incidence hazard rate was higher in the model fit to the US data
than in the model fit to the Rotterdam data (24). This example
indicates that one important criterion to be applied when selecting
data sources as inputs for population-based modeling is that the
data should match the setting for which policy is eventually going
to be developed. In developing policies for prostate cancer screen-
ing in the United States, it will not be appropriate to use models
calibrated to ERSPC data.
In this chapter, we have shown how dissemination of cancer inter-
ventions at the population level can be used to inform about harm
and benefit, key inputs for the development of sound public health
policies. We have also demonstrated how a well-calibrated model
can be adapted and used for economic evaluation of candidate
policies that go beyond historic population practices. Our results
focused on specific drivers of cost rather than the economic costs
themselves, because we were interested in differentiating between
harms like false positive tests and overdiagnosis. Unlike costs,
which vary across clinical and geographical settings, these mea-
sures of harm have consistent absolute interpretations. However,
the translations of these measures into economic costs of care will
be necessary for cost-effectiveness comparisons. Information on
the costs of care is available from a wide variety of sources. For
example, Ekwueme et al. (26) reviewed 28 studies (15 US and 13
international) of publicly available data on the resource costs of
prostate cancer screening, diagnosing, and staging. They were able
to quantify and pool both direct costs—resources used, physician
costs, medical supplies, and facility costs—and indirect costs, such
as loss of income from time off work, transportation costs, and
travel time. Once the costs of different aspects of care have been
quantified, they can be incorporated into the models as multipli-
ers of the numbers of corresponding procedures (eg, for screening
tests or biopsies) or cases (eg, for treatment costs).
We have focused on the example of PSA screening for pros-
tate cancer, adopted in the United States even before the initiation
of the US trial of prostate cancer screening, which began enroll-
ment in 1993. There are many other cases where interventions
have been adopted prematurely and, with the subsequent release
Figure 3. Three outcomes of harm (false positive and overdiagnosis) and
benefit (years of life saved) corresponding to six candidate PSA screen-
ing policies, varying ages to start and stop screening, and interscreen-
ing intervals as well as the criterion or threshold for biopsy referral.
Outcomes are numbers of false positives, overdiagnoses, and lives
saved per 1 million men screened. The ages to start and stop screening
are specified below the figure; upper and lower bounds are provided
and the interscreening interval is given in parentheses. As an example,
the policy 40, 45, 50, (2), 75 indicates that screens take place at ages 40,
45, 50, and thereafter every 2 years until stopping at age 75. The figure
shows that less intensive screening strategies can yield dramatic reduc-
tions in screening harms with very modest differences in benefit.
122 Journal of the National Cancer Institute Monographs, No. 46, 2013
of data indicating adverse impact, have been dropped on a wide
scale. A classic example is that of female hormone replacement
therapy, which was broadly adopted in the United States until
publication of results from the Women’s Health Initiative in
2002 showed that it adversely impacted cardiovascular and breast
cancer risks (27). Examples in cancer chemotherapy abound. In
France, for instance, between 2004 and 2010, 31 new cancer drugs
obtained market approval, the majority of which were targeted
therapies (usually monoclonal antibodies). Although the actual
medical benefit from targeted therapies was seldom challenged,
the Transparency Commission expressed reservations regarding
the survival advantage over existing treatments. In 2009 and 2010,
eight targeted drugs were reviewed and received market approval
with no improvement in actual benefit and only a few were rated
as providing a minor improvement in actual benefit. In the United
States, the US Food and Drug Administration actually revoked its
accelerated approval of the drug Avastin for advanced breast can-
cer, noting that the drug “used for metastatic breast cancer has not
been shown to provide a benefit, in terms of delay in the growth
of tumors, that would justify its serious and potentially life-threat-
We have demonstrated how the surveillance modeling approach
allows us to separate the contributions of PSA screening and changes
in primary treatment to the declines in prostate cancer mortality.
This approach has been similarly used in breast cancer, to separate
the contributions of screening and changes in chemotherapy (5),
and in colorectal cancer (28), where changes in disease-impacting
behaviors over time must also be considered. The MISCAN-colon
micro-simulation model used four waves of data from the National
Health and Nutrition Examination Survey (NHANES) to estimate
the prevalence over time of risk factors, such as physical activity; fruit
and vegetable consumption; and use of folate, aspirin, and female
hormone replacement therapy. Incorporating estimates of the
effects of these risk factors on colorectal cancer incidence from the
epidemiological case-control studies allowed the model to separately
project the contributions of these factors and the contributions of
screening and treatment to mortality (6). The MISCAN-colon
model has also been harnessed to compare different potential
screening policies, and their results have been used by the
USPSTF in determining their most recent recommendations (29).
This case of the use of modeling within the policy development
process is still unfortunately the exception rather than the rule.
The USPSTF has used modeling in defining policy for both
breast (30) and colorectal cancer screening (29), but not for
prostate cancer screening. And most professional societies do not
use models to quantify harm–benefit tradeoffs, but rather rely
on literature review and consensus decision making on the basis
of observed results. These may not even reflect the likely long-
term population costs and benefits of the policies that are being
considered. Certainly, economic evaluation on the basis of disease
modeling may produce results that are unpopular, particularly
if they project that costs of new promising interventions are
excessive relative to benefits. However, this type of analysis, on the
basis of well-calibrated models, is likely to be a critically important
weapon in our battle to manage health-care costs while advancing
cancer control in the future.
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This work was made possible by Award Numbers U01-CA-157224 and
U01-CA-152959 from the National Cancer Institute.
The content is solely the responsibility of the authors and does not necessarily
represent the official views of the National Cancer Institute, the National
Institutes of Health, or the Centers for Disease Control and Prevention.
Affiliations of authors: Fred Hutchinson Cancer Research Center, Seattle,
WA (RE); Santé Publique URCEco APHP , Hôpital Henri Mondor, Créteil,
France (ID-Z); Department of Public Health, Erasmus Medical Center,
Rotterdam, the Netherlands (IL-V).