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Updated: May 12, 2026

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

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Published on: January 9, 2016

Deriving time discounting correction factors for TTO tariffs.

Arthur E Attema1, Werner B F Brouwer

  • 1iBMG/iMTA, Erasmus University, Rotterdam, the Netherlands.

Health Economics
|April 9, 2013
PubMed
Summary
This summary is machine-generated.

This study quantifies time discounting for health outcomes, developing correction factors for Time Trade-off (TTO) utilities. Findings show significant upward corrections are needed, especially for severe health states.

Keywords:
QALY modeldiscountingtime trade-offutility measurement

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Last Updated: May 12, 2026

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Area of Science:

  • Health Economics
  • Decision Analysis
  • Psychometrics

Background:

  • The Time Trade-off (TTO) method is widely used for health state utility valuation in economic evaluations.
  • TTO utilities are susceptible to biases, notably the omission of time discounting.
  • Accurate utility values are crucial for reliable cost-effectiveness analyses.

Purpose of the Study:

  • To measure time discounting for health outcomes in a general population sample.
  • To derive correction factors for TTO scores biased by time discounting.
  • To enhance the accuracy of health state utility valuations.

Main Methods:

  • Estimation of TTO scores concurrently with time discounting measures.
  • Development of correction factors based on empirical data.
  • Analysis of discounting behavior across different health state severities.

Main Results:

  • Substantial positive correction factors were identified for TTO scores.
  • Correction factors increase with the severity of the health state.
  • Higher discounting rates were observed for more severe health states.

Conclusions:

  • Time discounting significantly biases TTO utilities, necessitating correction.
  • The derived correction factors can improve the accuracy of health economic evaluations.
  • Further research is needed to refine discount rate elicitation and address other biases like loss aversion.