Valuation of quality of life for health economics purposes

Why do we need to value quality of life for economic evaluation?

Economic evaluation is used increasingly by health system decision makers to answer questions about how health care resources should be allocated between services and, particularly, which health services should be provided on a subsidised basis.


Economic evaluation can take the form of a cost-benefit analysis, in which costs and outcomes are valued in monetary terms, or a cost-effectiveness analysis, in which interventions are compared in terms of cost per unit of outcome. In the health care setting, cost-effectiveness analysis is most commonly used in evaluation of health interventions. Typically, some measure of health gain is used as the measure of outcome, partly because of practical and other difficulties with placing monetary valuations on health outcomes but also because it is often argued that the relevant objective for the health care system is to maximise health gain from available resources.


The outcomes in cost-effectiveness analysis are generally measured in terms of some natural units, such as cases prevented, lives saved or life years saved.  However, this approach has a major limitation: health interventions impact on both survival and health-related quality of life (HRQoL), and the HRQoL impacts are not captured in a measure such as life years saved.

Cost-utility analysis (CUA) is a more general form of cost effectiveness analysis in which the outcome measure captures both survival and HRQoL. The assumption that underlies the use of CUA to inform decision making is that health care resources should be allocated to maximise health gains in society as measured by a single outcome measure, most commonly the quality adjusted life year (QALY).


QALYs are designed to capture the key aspects, or ‘attributes’, of HRQoL and survival in a measure that is comparable across disease states and health care interventions. This enables comparison across interventions with disparate outcomes, across different health care conditions and across population groups, thus providing information required by the high-level decision makers who are responsible for deciding how health budgets are allocated across disease areas and interventions. In the Australian setting, this would include the Pharmaceutical Benefits Advisory Committee (PBAC).

Quality adjusted life years (QALYs)


What is a QALY?

Quality adjusted life years (QALYs) are calculated by multiplying survival time by a ‘utility’ weight (in this specific case, a QALY weight), to adjust for the HRQoL experienced during that survival time. The QALY weight is anchored at zero (death) and one (full health). The QALY weight assigned is therefore a measure of the relative preference for a year of life in a given health state. Health states worse than death are possible, and these have a negative weight. This weighting system is underpinned by economic theory, and a number of assumptions, in order for the weight to reflect society’s willingness to trade-off between HRQoL and survival.1-3 Because the QALY weight represents an index of strength of preference for different HRQoL outcomes it provides a means of ranking health states in terms of desirability.

How are QALY (or utility) weights determined?

A number of methods have been used to determine QALY weights. In early applications of the approach, the QALY adjustment was often based on a relatively ad hoc judgment by researchers or clinicians, and was not based on the formal elicitation of a representative sample of individuals’ preferences.4, 5 Gerard et al (1999) 6 found that in 30% of the studies in a sample of 100 cost-utility analyses published in 1996, the source of the QALY weights was the researchers’ judgments or guesses. Early analysts advocated the determination of QALY weights through a socio-political process or by a ‘decision maker’ (i.e., representative of a government health department responsible for health resource allocation).7 However, there is now relatively widespread agreement that the preferences of the consumers of health care should be taken into account. There is less agreement about whose values and preferences should be elicited, or how preferences should be best measured.1, 8-11 Many authors argue for general community values (since tax-payers pay for the health care and services that governments provide or subsidise), while others argue for patients’ values (since they have experienced the heath states in question).

The most widely accepted method of deriving QALY weights is through ‘stated preference’ tasks, although this is a broad term covering a wide range of approaches.2, 3, 6, 9, 12-14

There are three related elements that must be decided in deriving preference based QALY weights for a health state: 1) whether the preferences to be measured are those of the general population or those experiencing the health state; 2) whether the health states are to be described through disease specific “vignettes” (or some related disease specific method) or through a generic and standardized HRQoL instrument; and 3) which method will be used to elicit the preferences of the relevant sample.

The health state descriptors used to define the health states being valued may be ‘generic’ (i.e., composed of attributes relevant to any health condition),7, 15 or ‘disease specific’ (i.e., composed of attributes that are targeted at a particular disease, such as cancer).16 A sample of individuals experiencing a particular disease and treatment may be asked to value their own current health states using one of the methods (which they may also be asked to describe/rate using an existing HRQoL instrument). But, more commonly, individuals from a randomly selected community sample are asked to rate a number of hypothetical health states, described in a vignette or scenario or by a set of descriptors from a generic multi-attribute HRQoL instrument (see below). 

The three main stated preference tasks for deriving QALY weights are standard gamble, time trade-off and rating/visual analogue scale.

Visual Analogue Scale

The visual analogue scale (VAS) is the most conceptually straightforward task and is relatively easy to administer. Respondents are asked to indicate on a scale, with anchor points of death and full health, how they rate the given health state, hk. There may be a time dimension specified, which will be held constant across the given health state and full health. The scale is normalized such that death is zero, (hd = 0) and full health is unity (hf = 1), and the QALY weight is therefore measured as the ratio of distance along the scale from death to the total length of the scale. In some cases, the end points are set at “worst imaginable health state” and “best imaginable health state”, to allow for the possibility of health states worse than death.9

Standard Gamble (SG)

In this technique,1, 2 respondents are given the choice between a specified period of time (e.g., 1 year) at the given health state or a lottery in which they might either live for the same time period in full health at probability p or be dead at probability 1 – p. The probability p is varied until the respondent reports themselves unable to choose between the options given. The more desirable the health state is, the higher the probability of full health will need to be for the respondent to find it difficult to choose.

Time tradeoff (TTO)

Here,1, 2, 17 the respondent is given a choice between a specified period of time in perfect health or a specified period in the given health state. The period of time in perfect health is then varied until the respondent is unable to choose between a longer period in the given health state (t) or a shorter period in full health (x).

What is a multi-attribute utility instrument?

A multi-attribute utility instrument (MAUI) has two components:

1)      A descriptive HRQoL instrument of the kind referred to in the rest of the FAQs on this website. This comprises a set of items or statements with multiple response categories that cover various domains of HRQoL. These effectively describe a series of health states in terms of various combinations of domains and levels of HRQoL.

2)      A set of utility weights. The utility weight for each health state described by the MAUI is a single number that reflects the value, or the ‘strength of preference’ (usually of society, but sometimes of patients) for that health state. This set of weights is determined once only in a direct valuation study (usually using VAS, SG or TTO as above), then standardly applied every time the MAU instrument is subsequently used in a clinical trial.


MAUIs are particularly useful for ‘piggyback’ economic evaluations that are run simultaneously with randomised controlled trials and utilise patient-level data about effectiveness and resource use. The descriptive HRQoL component of the instrument is administered to participants in the clinical trial, allowing the self-reported HRQOL of patients to be incorporated into the economic evaluation in terms of the health states described by the MAU instrument. An algorithm is then used to calculate the utility of the sample in each arm of the trial.

The most commonly used MAU instruments to date have been generic, that is, not specific to a particular health condition such as cancer or heart disease. These include the EQ-5D (,18, 19 the SF-6D (devised from the SF-36) (,20 the Health Utilities Index (HUI-II, HUI-III) ( 21 and the Assessment of Quality of Life (AQoL)( The EQ-5D is historically the most widely used, and cancer is one of its most frequent disease-specific applications.23 PoCoG’s Quality of Life Office is currently involved in an international collaboration to develop MAUIs from the two most widely used cancer-specific descriptive HRQoL questionnaires, the EORTC QLQ-C30 and FACT-G.

Suggested reading

Feeny D. Preference-based measures: utility and quality-adjusted life years. In: Fayers P, Hays R, eds. Assessing quality of life in clinical trials: Methods and practice. 2nd ed. Oxford: Oxford University Press; 2005:405-430.

Hall J and Tattersall M. Economic considerations for cancer clinicians. In: Souhami RL, Tannock I, Hohenberger P, Horiot J-C, eds. Oxford textbook of oncology. 2nd ed. Oxford: Oxford University Press; 2002: 1161 - 1172.


Useful links

Cancer Research Economics Support Team - for members of the Australian Cancer Clinical Trials Groups

Centre for Health Economics Research and Evaluation (CHERE)


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