Standard Gamble (SG)

The Standard Gamble (SG) method is widely regarded as one of the most appropriate techniques for eliciting utility values, especially when decisions involve risk. This method is based on the expected utility theory proposed by von Neumann and Morgenstern in 1944, which is a normative model for decision-making under uncertainty.

Key aspects of the Standard Gamble method include:

– Principle: The SG method involves presenting a respondent with two options:

  1. Certain Option: The respondent remains in a specific health state (Health State A) for a given period (X years).
  2. Risky Option: The respondent faces a probability of either living in full health for the same period (X years) or dying immediately.

– Determining Utility: The probability of immediate death in the risky option is adjusted until the respondent reaches a point of indifference, where they value both options equally. For example, if the point of indifference occurs at a 75% probability of survival, it implies that the individual values Health State A as 75% of full health.

– Calculation: At the point of indifference, both options provide the same expected value of QALYs. If ‘X’ is 10 years, and the utility of Health State A is 0.75, the expected value of QALYs is 7.5 for both options (0.75 utility × 10 years).

– Advantages:

– Theoretical Basis: The SG method aligns with the axioms of expected utility theory, providing a robust theoretical foundation for decision-making under uncertainty.

– Normative Validity: It is considered the most theoretically sound method for utility elicitation when risk is involved.

– Limitations:

– Complexity: Respondents may struggle to understand and accurately interpret the complex probabilities involved in the SG method.

– Bias Towards Certainty: The method assumes that respondents do not have inherent biases towards certain outcomes over risky ones, which may not always hold true.

The SG method is instrumental in health economics for deriving utility values that reflect individuals’ preferences for different health states, particularly when these involve trade-offs between risk and certainty. Despite its limitations, it remains a valuable tool for informing quality-adjusted life year (QALY) calculations and other decision-making processes in healthcare.

For an interactive demonstration of this method, please [click here](#).