Cost-effectiveness of using
prognostic information to
select women with breast cancer
for adjuvant systemic therapy
C Williams,1*S Brunskill,2D Altman,3A Briggs,4
H Campbell,5M Clarke,6J Glanville,7A Gray,5
A Harris,8K Johnston9and M Lodge10
1 Bristol Haematology and Oncology Centre, UK
2 National Blood Service, John Radcliffe Hospital, Oxford, UK
3 Centre for Statistics in Medicine, Wolfson College, Oxford, UK
4 Section of Public Health and Health Policy, University of Glasgow, UK
5 Department of Public Health, University of Oxford, UK
6 Clinical Trial Service Unit and Epidemiological Studies Unit, Richard Doll
Building, Oxford, UK
7 Centre for Reviews and Dissemination, University of York, UK
8 Cancer Research UK, Medical Oncology Unit, Churchill Hospital,
9 Economics and Statistics Division, Scottish Executive Environment and
Rural Affairs Department, Edinburgh, UK
10 Cochrane Cancer Network, Wolfson College, Oxford, UK
* Corresponding author
Health Technology Assessment
NHS R&D HTA Programme
Health Technology Assessment 2006; Vol. 10: No. 34
Using prognostic information to select women with
breast cancer for adjuvant systemic therapy
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During the second half of the twentieth century,
researchers came to understand that breast cancer
could spread to other parts of the body at an early
stage in the development of the disease. This led
to a large number of randomised trials testing the
utility of adjuvant hormone and cytotoxic therapy.
These trials have shown that adjuvant therapy
reduces the risk of recurrence and death from
breast cancer, such that combinations of modern
hormonal and cytotoxic therapy might halve the
risk of a woman dying of breast cancer in the first
10 years after diagnosis. However, these
improvements in the outlook for women with
breast cancer have – in the main – been achieved
by research that has required the treatment of all
patients, including those destined not to relapse
and those who relapse despite adjuvant therapy.
Because of this, researchers have sought to find
prognostic and predictive factors which would
allow patients most likely to benefit from adjuvant
therapy to be identified. Using combinations of
factors to develop prognostic models has further
refined their use.
The principal objective of this project was to
investigate the cost-effectiveness of using
prognostic information to identify patients with
breast cancer who should receive adjuvant therapy.
This report systematically reviewed the literature
on the prognostic and predictive factors in breast
cancer. Health economic decision analytic
modelling was then used to draw conclusions on
the most effective and efficient use of these factors
in selecting women with early breast cancer for
adjuvant systemic therapy.
The size of the literature meant that it was not
possible to review systematically all primary
publications in the area. A series of systematic
reviews and a survey were undertaken on the
● quality assessment of prognostic studies (not
● reviews of prognostic information in breast
● prognostic models in breast cancer
● predictive factors in breast cancer
● the clinical use of prognostic information in
breast cancer in the UK (survey)
● quality of life, cost and cost-effectiveness studies
relevant to modelling.
Between six and nine databases were searched by
an information expert. Evidence-based methods
were used to review the abstracts, select those
suitable for inclusion and extract the data using
piloted data extraction forms for each of the
systematic reviews. The quality of each included
paper was assessed using standard assessment
tools reported in the literature or piloted and
developed for this study.
It was not possible to carry out a quantitative
analysis of the data for any of the systematic
reviews. Instead, narrative summaries of the
evidence were prepared with commentaries on the
strengths and weaknesses of the conclusions drawn.
A survey of clinical practice in UK cancer centres
and units was carried out to ensure that conclusions
drawn from the report could be implemented.
These data, along with the information gathered in
the systematic reviews, informed the methodological
approach adopted for the health economic
modelling. Estimation of a definitive model was not
considered feasible based on the current published
literature. Rather, given the obvious benefits to be
gained by establishing prognosis and treatment
effectiveness and cost-effectiveness for individual
patients or groups of patients, a pragmatic decision
was made to develop and report an illustrative
framework for incorporating patient-level
prediction within a health economic decision model.
This framework was applied to a large retrospective
dataset containing data on prognostic factors,
treatments and outcomes for women with early
breast cancer treated in Oxford. The data were used
to estimate directly a parametric regression-based
risk equation, from which a prognostic index was
developed, and prognosis-specific estimates of
the baseline breast cancer hazard could be
Executive summary: Using prognostic information to select women with breast cancer for adjuvant systemic therapy
observed. Published estimates of treatment effects,
health service treatment costs and utilities were used
to construct a decision analytic framework around
this risk equation, thus enabling simulation of the
effectiveness and cost-effectiveness of adjuvant
therapy for all possible combinations of prognostic
factors included in the model. Various ways of using
the outputs from this framework were explored.
Methodological quality of prognostic
There was a lack of empirical evidence to support
the importance of particular study features
affecting the reliability of findings and the
avoidance of bias. However, there is much
evidence that prognostic research in cancer tends
to be of poor quality, contributing to the fact that
prognostic markers often remain under
investigation for years without good evidence that
they are useful. Multiple small, separate,
uncoordinated and often unvalidated studies often
delay the process of defining the role of particular
prognostic markers. Cooperation between research
groups could lead to clear results emerging more
rapidly, especially if such efforts are put into
prospective studies or retrospective studies based
on individual data from carefully assembled
databases and/or tissue banks.
Systematic review of studies of
There is a plethora of evidence relating to possible
prognostic factors for breast cancer. It was only
possible to review those reviews that appeared to
use systematic methods. There is a lack of high-
quality, well-reported evidence in areas where it is
taken for granted that factors have prognostic
value, such as node status and age, and we have
not reviewed these, accepting the commonly
assumed value of such factors. A small number of
eligible reviews (from 1 to –6 per factor) were
found for each of 18 different factors. The lack of
good-quality systematic reviews and well-
conducted studies of prognostic factors in breast
cancer was striking. In only five instances was the
evidence strong enough to conclude that there is
clear evidence of a relationship between the factor
and survival (tumour size, proliferation indices,
p53, cathepsin D and urokinase and its receptors).
Although many prognostic models for breast
cancer have been published, remarkably few have
been re-examined by independent groups in
independent settings. The few validation studies
have been carried out on ill-defined samples,
sometimes of smaller size and short follow-up, and
sometimes using different patient outcomes when
validating a model.
The evidence from the validation studies shows
support for the prognostic value of the
Nottingham Prognostic Index (NPI). No new
prognostic factors have been shown to add
substantially to those identified in the 1980s.
Improvement of this index depends on finding
factors that are as important as, but independent
of, lymph node, stage and pathological grade. The
NPI remains a useful clinical tool, although
additional factors may enhance its use.
We accepted that hormone receptor status (ER) for
hormonal therapy such as tamoxifen and
prediction of response to trastuzumab by HER2
did not require systematic review, as the
mechanism of action of these drugs requires intact
receptors. There was no clear evidence that other
factors were useful predictors of response and
Survey of UK practice when selecting
women for adjuvant therapy
The survey confirmed pathological nodal status,
tumour grade, tumour size and ER status as the
most clinically important factors for consideration
when selecting women with early breast cancer for
adjuvant systemic therapy in the UK. The
protocols revealed that although UK cancer
centres appear to be using the same prognostic
and predictive factors when selecting women to
receive adjuvant therapy, much variation in clinical
practice exists. Some centres use protocols based
upon the NPI whereas others do not use a single
index score. Within NPI and non-NPI users,
between-centre variability exists in guidelines for
women for whom the benefits are uncertain.
Consensus amongst units appears to be greatest
when selecting women for adjuvant hormone
therapy with the decision based primarily upon
ER or progesterone receptor (PR) status rather
than combinations of a number of factors.
Guidelines as to who should receive adjuvant
chemotherapy, however, were found to be much
Cost-effectiveness of prognostic models
Searches of the literature revealed only five
published papers that had previously examined
the cost-effectiveness of using prognostic
information for clinical decision-making.
Health Technology Assessment 2006; Vol. 10: No. 34 (Executive summary)
These studies were of varying quality and
highlight the fact that economic evaluation in this
area appears still to be in its infancy.
By combining methodologies used in determining
prognosis with those used in health economic
evaluation, it was possible to illustrate an approach
for simulating the effectiveness (survival and
quality-adjusted survival) and the cost-effectiveness
associated with the decision to treat individual
women or groups of women with different
The model showed that effectiveness and cost-
effectiveness of adjuvant systemic therapy have the
potential to vary substantially depending upon
prognosis. For some women therapy may prove
very effective and cost-effective, whereas for others
it may actually prove detrimental (i.e. the
reductions in health-related quality of life
outweigh any survival benefit).
Conclusions and further research
Outputs from the framework constructed using the
methods described here have the potential to be
useful for clinicians, attempting to determine
whether net benefits can be obtained from
administering adjuvant therapy for any presenting
woman; and also for policy makers, who must be
able to determine the total costs and outcomes
associated with different prognosis-based
treatment protocols as compared with more
conventional treat all or treat none policies. A risk
table format enabling clinicians to look up a
patient’s prognostic factors to determine the likely
benefits (survival and quality-adjusted survival)
from administering therapy may be helpful. For
policy makers, it was demonstrated that the
model’s output could be used to evaluate
the cost-effectiveness of different treatment
protocols based upon prognostic information. The
framework should also be valuable in evaluating
the likely impact and cost-effectiveness of new
potential prognostic factors and adjuvant
Williams C, Brunskill S, Altman D, Briggs A,
Campbell H, Clarke M, et al. Cost-effectiveness of
using prognostic information to select women with
breast cancer for adjuvant systemic therapy. Health
Technol Assess 2006;10(34).
Executive summary: Using prognostic information to select women with breast cancer for adjuvant systemic therapy
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