A direct method for measuring discounting and QALYs more easily and reliably.

Time discounting and quality of life are two important factors in evaluations of medical interventions. The measurement of these two factors is complicated because they interact. Existing methods either simply assume one factor given, based on heuristic assumptions, or invoke complicating extraneous factors, such as risk, that generate extra biases.

The metric-frequency measure of similarity for ill-structured data sets, with an application to family therapy.

Similarity measures have been studied extensively in many domains, but usually with well-structured data sets. In many psychological applications, however, such data sets are not available. It often cannot even be predicted how many items will be observed, or what exactly they will entail.

Lessons learned by (from?) an economist working in medical decision making.

This article is a personal account of the author's experiences as an economist working in medical decision making. He discusses the differences between economic decision theory and medical decision making and gives examples of the mutual benefits resulting from interactions. In particular, he discusses the pros and cons of different methods for measuring quality of life (or, as economists would call it, utility), including the standard gamble, the time tradeoff, and the healthy-years equivalent methods.

Explaining the characteristics of the power (CRRA) utility family.

The power family, also known as the family of constant relative risk aversion (CRRA), is the most widely used parametric family for fitting utility functions to data. Its characteristics have, however, been little understood, and have led to numerous misunderstandings. This paper explains these characteristics in a manner accessible to a wide audience.

Correcting biases in standard gamble and time tradeoff utilities.

The standard gamble (SG) method and the time tradeoff (TTO) method are commonly used to measure utilities. However, they are distorted by biases due to loss aversion, scale compatibility, utility curvature for life duration, and probability weighting. This article applies corrections for these biases and provides new data on these biases and their corrections.