QALYs measure health benefit by combining quantity of life (years lived) with quality of life (health-related quality of life or utility). QALY = years × utility weight, where utility reflects individual or population preferences for health states on a 0-1 scale (0 = death, 1 = perfect health). QALYs enable cost-effectiveness analysis by quantifying willingness-to-pay trade-offs. Different methods elicit utility weights (time trade-off, visual analog scale, preference-based instruments like EQ-5D), which substantially affect QALY calculations and cost-effectiveness conclusions.
When a health system or insurer must decide whether to fund a new treatment, it faces a fundamental comparison problem: how do you weigh a treatment that adds two years of life in perfect health against one that adds five years with significant disability? A simple "years of life" metric can't answer this. Quality-Adjusted Life Years (QALYs) address it by attaching a utility weight to each year lived — a number between 0 (equivalent to death) and 1 (perfect health) — and multiplying: QALY = years × utility. A person who lives 10 years with a utility of 0.6 (say, moderate chronic pain limiting activity) accumulates 6 QALYs. A treatment that raises that utility to 0.8 while extending life by 2 years would generate (12 × 0.8) − (10 × 0.6) = 9.6 − 6 = 3.6 incremental QALYs.
The most contested part of QALY calculation is how utility weights are derived. Three main methods are used. The visual analog scale (VAS) asks patients to mark their current health state on a line from 0 to 100 — quick but considered less reliable because it doesn't require trade-offs. The time trade-off (TTO) method asks: "How many years in your current health state would you trade for X years in perfect health?" If someone would trade 8 years in their current state for 6 years in perfect health, their utility is 6/8 = 0.75. The standard gamble method offers a choice between certain life in the current state versus a gamble with probability p of perfect health and (1−p) of immediate death; the utility equals the probability p at which the person is indifferent. TTO and standard gamble are preference-based and grounded in expected utility theory; VAS is not. Standardized instruments like the EQ-5D convert responses to pre-measured utility tariffs from population surveys, allowing consistent comparison across studies.
The QALY framework becomes powerful when combined with cost data in cost-effectiveness analysis. The key metric is the incremental cost-effectiveness ratio (ICER): ICER = ΔCost / ΔQALY. If a new cancer drug costs $200,000 more than standard care and generates 2 more QALYs, its ICER is $100,000/QALY. Decision-makers then compare this to a willingness-to-pay threshold — in the UK's NICE, roughly £20,000–30,000/QALY; in the US, commonly cited as $50,000–$150,000/QALY. Treatments below the threshold are considered cost-effective; those above require special justification or negotiation.
Despite their utility, QALYs carry important limitations. Utility weights vary across populations: a patient who has adapted to disability may rate their utility higher than a healthy person imagining that state (disability paradox). QALYs also treat all years equally regardless of age, which raises equity concerns — a QALY gained by a child effectively counts the same as one gained at age 80. And measurement method matters: TTO, standard gamble, and EQ-5D tariffs for the same health state can differ substantially, producing different ICERs and different funding decisions. Understanding these limitations is essential for critically interpreting cost-effectiveness analyses in health policy discussions.