The Role of Patient Preferences in
Cost-Effectiveness Analysis
A Conflict of Values?
John E. Brazier,1 Simon Dixon1 and Julie Ratcliffe2
1 Health Economics and Decision Science, School of Health and Related Research, University of Sheffield,
Sheffield, UK
2 Health Economics and Policy Group, Division of Health Sciences, University of South Australia, Adelaide,
South Australia, Australia
Abstract This paper reviews the role of patient preferences within the framework of
cost-effectiveness analysis (CEA). CEA typically adopts a system-wide per-
spective by focusing upon efficiency across groups in the allocation of scarce
healthcare resources, whereas treatment decisions are made over individuals.
However, patient preferences have been shown to have a direct impact on the
outcome of an intervention via psychological factors or indirectly via patient
adherence/compliance rates. Patient values may also be in conflict with the
results of CEA through the valuation of benefits. CEA relies heavily on the
QALY model to reflect individual preferences, although the healthy year
equivalent offers an alternative measure that may be better at taking in-
dividual preferences into account. However, both measures typically use
mean general population or mean patient values and therefore create conflict
with individual-level preferences.
For CEA to reflect practice, it must take into account the impact of in-
dividual patient preferences even where general population preferences are
used to value the benefits of interventions. Patient preferences have implica-
tions for cost effectiveness through costs and outcomes, and it is important
that cost-effectiveness models incorporate these through its structure
(e.g. allowing for differing compliance rates) and parameter values, including
clinical effectiveness. It will also be necessary to try to predict patient pre-
ferences in order to estimate any impact on cost effectiveness through
analyses of revealed and stated preference data. It is recognized that policy
makers are concerned with making interventions available to patients and not
forcing them to consume healthcare. One way of moving towards this would
be to adopt a two-part decision process: the identification of the most
cost-effective therapy using mean general population values (i.e. the current
rule), then also making available those treatments that are cheaper than the
most cost-effective therapy.
CURRENT OPINION Pharmacoeconomics 2009; 27 (9): 705-7121170-7690/09/0009-0705/$49.95/0
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In addition to information relating to clinical
effectiveness, evidence relating to the cost effective-
ness of new healthcare treatments and techno-
logies is increasingly required by reimbursement
agencies throughout the developed world. Within
the cost-effectiveness framework, effectiveness is
most frequently measured using QALYs. The ra-
tionale for this approach is the observation that
healthcare interventions are capable of improving
the length of life and/or significantly improving the
quality of life of patients. Therefore, to capture the
full benefits of new treatments and technologies
within the context of cost-effectiveness analysis
(CEA), it is important to adopt ameasure of health
outcome, which provides a general indicator of the
patient’s health status over time. In addition, given
that one of the main aims of CEA is to address
policy questions relating to allocative efficiency
within a healthcare budget, it is also important to
include a preference-based measure of quality of
life that allows for the derivation of QALYs.[1]
The system-wide approach, adopted by the pur-
suit of cost effectiveness, stands in sharp contrast to
individual-level treatment decision making. Treat-
ment decisions increasingly take patient preferences
into account,[2] whether through informed decision
making, where the patient alone is assumed tomake
the treatment decision once the clinician has pro-
vided the necessary technical information, or
through shared decisionmaking, where both parties
take steps to build a consensus about the preferred
treatment.[3] The case for incorporating patient pre-
ferences into clinical decision making rests on the
premise that this will lead to improved satisfaction
with the process of care and better health outcomes.
The dual responsibility of the doctor to the
patient in front of them and other patients and
potential patients within a collectively funded
healthcare system may create conflict. This is be-
cause the treatment recommended to an individual
patient to meet their preferences in a shared deci-
sion-making context may differ substantially from
the treatment recommended at a system-wide level
in terms of the cost effectiveness of alternative
treatments. As we show in this paper, patient pre-
ferences also have an impact on cost effectiveness.
This paper describes the nature of the conflict
between the system-wide perspective of CEA,
which is used to inform funding decisions, and
the patient-based perspective, used to make treat-
ment decisions.
1. Cost Effectiveness and Patient
Preferences
CEA compares the costs of alternative ways of
achieving an objective measured along a single di-
mension, traditionally measured in ‘natural’ units
(such as life-years saved or cancer cases detected).
Cost-utility analysis was developed as a form of
CEA, where effectiveness is measured using
QALYs, which combines length of life with health-
related quality of life on a single scale. The number
of QALYs is calculated by multiplying a person’s
life expectancy by the value of the health-related
quality of life in each period, which is measured on
a scale anchored at zero and one, where zero is
dead and one is full health. Being on hospital renal
dialysis, for example, may be assigned a quality-
adjustment value orweight of 0.8. A 20-year period
on renal dialysis is 16 QALYs, and this is assumed
to be equivalent to someone living for 16 years in
full health. For more complex health profiles in-
volving transitions between states of health, the
QALY score is calculated by summing the product
of the time spent in each state and the value
attached to that state.
Healthcare interventions can be compared in
terms of their incremental cost per QALY (i.e. the
extra cost of an intervention over the next-best
alternative divided by the extra QALY gain)
within and between programmes.[1] A collection
of cost-utility analyses can be brought together to
form an ordering of various alternatives, ranked
in terms of effectiveness. Special account can be
taken of dominance, where one intervention is
better in terms of costs or effectiveness and not
worse in the other, or extended dominance, where
a treatment is less effective and has a higher in-
cremental cost effectiveness than an alternative
treatment.[1] The decision about which pro-
gramme to provide requires additional informa-
tion about the threshold cost per QALY that
funders are willing to pay. In the context of the
UK, this threshold has been perceived to be in
the range d20 000–30 000 per additional QALY
706 Brazier et al.
ª 2009 Adis Data Information BV. All rights reserved. Pharmacoeconomics 2009; 27 (9)