A new study published in The European Journal of Health Economics by Arthur E. Attema and colleagues sheds light on the significant variability in the application of the Time Trade-Off (TTO) methodology, leading to incomparable health state values crucial for healthcare decision-making. The paper, titled “Time trade-off: one methodology, different methods,” underscores the critical need for harmonization and presents a comprehensive checklist to guide future TTO studies.
The Time Trade-Off (TTO) method is a cornerstone in economic evaluations within healthcare, used to elicit preferences for health states by having individuals imagine living a defined number of years in an imperfect health state and then indicating how many fewer years in full health they would accept to avoid that impaired health state. The goal is to quantify a trade-off between length of life and quality of life. These valuations contribute to the Quality-Adjusted Life Year (QALY), a key measure for comparing treatment effectiveness across different conditions.
However, despite its widespread use, there is no scientific consensus on the optimal specification for the TTO task. This lack of standardization has resulted in substantial differences in elicited health state values, even when researchers use the same technique for the same health states. This “pluriformity” of specifications, while advancing methodological understanding, ultimately hinders the comparability of health state values, which is highly desired in contexts such as health technology assessments (HTA).
The research systematically identifies and discusses several alternative specifications of TTO found in the literature, classifying their defining elements as either methodological, procedural, or analytical. The authors indicate how these various elements can influence health state values, causing them to shift either upward or downward.
Key areas of variation and their impact highlighted in the study include:
- Value Range Spanned and Worse Than Dead (WTD) States: The handling of health states considered “worse than dead” presents a major challenge. Different approaches, such as the MVH protocol, lead-time TTO, and lag-time TTO, are used, with lead-time TTO being susceptible to framing effects that can lower values.
- Time Frame: The total time frame, including disease duration and any lead/lag time, can affect TTO values, though empirical studies show mixed results regarding a systematic effect.
- Iteration Procedure: The method used to determine the indifference point, such as bisection or titration, influences values. Titration, for example, can result in 0.10 to 0.15 higher health state valuations compared to a ping-pong approach. Starting points in the iteration can also influence values due to anchoring bias.
- Response Scale: While most studies use duration in full health as the response variable, fixing the duration in full health and asking for duration in the disease state can significantly lower TTO values.
- Mode of Administration: The method of data collection (e.g., face-to-face interviews, group interviews, self-administered questionnaires, Internet experiments) can influence results, with personal interviews generally promoting better data quality despite higher costs.
- Visual Aids: The use and type of visual aids (e.g., TTO boards, computer-assisted graphical illustrations) can impact results, as respondents often find these easier to understand than numerical descriptions.
- Context Effects: Learning effects and the order in which health states are presented to respondents can influence valuations, often addressed through warm-up tasks and randomization.
- Sampling Frame: The type of population from which values are elicited (e.g., general public, patients, healthcare providers) significantly impacts health state values, with policymakers often preferring values from a fully informed representative sample of the public.
- Exclusion Criteria: Different criteria for excluding respondents (e.g., non-traders, logical errors) can introduce selection bias and affect results, with “non-trading” behavior being a direct result of valuing life years.
- Time Preference: TTO values are affected by time preferences, and neglecting these can lead to underestimation of TTO scores. Adjustments can be made, but practical challenges remain in eliciting time preferences.
According to the authors, the proliferation of different TTO specifications, while valuable for methodological development, poses a significant problem from a policy perspective, especially in Health Technology Assessments (HTA), where comparability is essential for resource allocation decisions. They note that while standards exist for cost studies, there’s little guidance for health state valuation studies, with only NICE having issued specific guidance based on the UK EQ-5D valuation studies protocol.
As a central contribution, the study provides a checklist for TTO studies (summarized in Tables 1, 2, and 3 of the paper), which incorporates a list of choices researchers must make when performing a TTO task. This checklist is intended to enable researchers to align methodologies and thereby enhance the comparability of health state values, ultimately supporting the harmonization efforts needed in health economics.
The authors conclude that for most TTO characteristics, unambiguous “best practices” cannot yet be defined, highlighting an agenda for future methodological research. Their work aims to increase awareness and understanding of how different TTO factors affect results, emphasizing the urgent need for standardization in the field.
About the Authors: Arthur E. Attema and Matthijs M. Versteegh are affiliated with iBMG/iMTA, Erasmus University, Rotterdam, The Netherlands. Yvette Edelaar-Peeters is from the Department of Medical Decision Making, Leiden University Medical Centre, Leiden, The Netherlands. Elly A. Stolk is also affiliated with iBMG/iMTA, Erasmus University, Rotterdam, The Netherlands. Elly Stolk and Matthijs Versteegh are members of the EuroQol Group, a not-for-profit group that develops and distributes instruments to assess and value health.
APA Reference for the Article: Attema, A. E., Edelaar-Peeters, Y., Versteegh, M. M., & Stolk, E. A. (2013). Time trade-off: one methodology, different methods. The European Journal of Health Economics, 14(Suppl 1), S53–S64. https://doi.org/10.1007/s10198-013-0508-x
