Using stated-preferences methods to develop a summary metric to determine successful treatment of children with a surgical condition: a study protocol

Health valuation protocol for dual discrete choice experiment (dual-DCE) surveys to estimate the effects of different scenarios and attributes on main effects

INTRODUCTION

A typical health preference study conducts a single discrete choice experiment (DCE). For example, a health valuation study may elicit preferences on an individual's health-related quality of life along five EQ-5D-5L attributes (Mobility, Self-care, Usual Activities, Pain/Discomfort, Anxiety/Depression). Using this protocol, researchers can conduct a dual-DCE survey (ie, with two different full-block DCEs completed sequentially). To demonstrate this protocol, we will conduct 12 dual-DCE surveys in two waves and estimate the effects of different scenarios and descriptive systems on main effects (ie, incremental differences in value between levels).

METHODS AND ANALYSIS

Each of the two DCEs in a dual-DCE survey equates to a stand-alone health valuation study. To demonstrate this protocol, each is an EQ-5D-5L valuation study, including d-efficient blocks of 15 kaizen tasks and 5 paired comparisons. In wave 1 (six surveys, 1000 US adults each), the two DCEs will differ by scenario (1-year episodes ending in recovery or death or no duration/ending described). In wave 2 (six surveys, 200 US adults each), the two DCEs will include the same 5 EQ-5D-5L attributes but differ by the number of additional attributes related to cognition: none, one composite attribute (memory/concentration) and two component attributes (memory, concentration). For each DCE, we will estimate a conditional logit model and test for differences in value using cluster bootstrap techniques. We hypothesise that the values will differ by scenarios and systems. As secondary analyses, we assess the effects of sampling, scenario/system order and DCE order.

ETHICS AND DISSEMINATION

The independent review board (IRB) at Advarra determined that this research project (Pro00080475; 11 July 2024) is exempt from IRB oversight based on the Department of Health and Human Services regulations found at 45 CFR 46.104(d)(2). Furthermore, the IRB determined that the project is not subject to requirements for continuing review. To disseminate our findings, we will prepare multiple manuscripts for publication in peer-reviewed journals and present highlights at scientific meetings, such as the EuroQol Plenary Meeting, International Academy of Health Preference Research and ISPOR.

The Performance of Kaizen Tasks Across Three Online Discrete Choice Experiment Surveys: An Evidence Synthesis

BACKGROUND

Kaizen is a Japanese term for continuous improvement (kai ~ change, zen ~ good). In a kaizen task, a respondent makes sequential choices to improve an object's profile, revealing a preference path. Including kaizen tasks in a discrete choice experiment has the advantage of collecting greater preference evidence than pick-one tasks, such as paired comparisons. OBJECTIVE AND METHODS: So far, three online discrete choice experiments have included kaizen tasks: the 2020 US COVID-19 vaccination (CVP) study, the 2021 UK Children's Surgery Outcome Reporting (CSOR) study, and the 2023 US EQ-5D-Y-3L valuation (Y-3L) study. In this evidence synthesis, we describe the performance of the kaizen tasks in terms of response behaviors, conditional logit and Zermelo-Bradley-Terry (ZBT) estimates, and their standard errors in each of the surveys.

RESULTS

Comparing the CVP and Y-3L, including hold-outs (i.e., attributes shared by all alternatives) seems to reduce positional behavior by half. The CVP tasks excluded multi-level improvements; therefore, we could not estimate logit main effects directly. In the CSOR, only 12 of the 21 logit estimates are significantly positive (p < 0.05), possibly due to the fixed attribute order. All Y-3L estimates are significantly positive, and their predictions are highly correlated (Pearson: logit 0.802, ZBT 0.882) and strongly agree (Lin: logit 0.744, ZBT 0.852) with the paired-comparison probabilities.

CONCLUSIONS

These discrete choice experiments offer important lessons for future studies: (1) include warm-up tasks, hold-outs, and multi-level improvements; (2) randomize the attribute order (i.e., up-down) at the respondent level; and (3) recruit smaller samples of respondents than traditional discrete choice experiments with only pick-one tasks.

Unravelling Elements of Value of Healthcare and Assessing their Importance Using Evidence from Two Discrete-Choice Experiments in England

BACKGROUND

Health systems are moving towards value-based care, implementing new care models that allegedly aim beyond patient outcomes. Therefore, a policy and academic debate is underway regarding the definition of value in healthcare, the inclusion of costs in value metrics, and the importance of each value element. This study aimed to define healthcare value elements and assess their relative importance (RI) to the public in England.

METHOD

Using data from 26 semi-structured interviews and a literature review, and applying decision-theory axioms, we selected a comprehensive and applicable set of value-based elements. Their RI was determined using two discrete choice experiments (DCEs) based on Bayesian D-efficient DCE designs, with one DCE incorporating healthcare costs expressed as income tax rise. Respondent preferences were analysed using mixed logit models.

RESULTS

Six value elements were identified: additional life-years, health-related quality of life, patient experience, target population size, equity, and cost. The DCE surveys were completed by 402 participants. All utility coefficients had the expected signs and were statistically significant (p < 0.05). Additional life-years (25.3%; 95% confidence interval [CI] 22.5-28.6%) and patient experience (25.2%; 95% CI 21.6-28.9%) received the highest RI, followed by target population size (22.4%; 95% CI 19.1-25.6%) and quality of life (17.6%; 95% CI 15.0-20.3%). Equity had the lowest RI (9.6%; 95% CI 6.4-12.1%), decreasing by 8.8 percentage points with cost inclusion. A similar reduction was observed in the RI of quality of life when cost was included.

CONCLUSION

The public prioritizes value elements not captured by conventional metrics, such as quality-adjusted life-years. Although cost inclusion did not alter the preference ranking, its inclusion in the value metric warrants careful consideration.

Defining treatment success in children with surgical conditions

OBJECTIVES

Develop a score summarising how successfully a child with any surgical condition has been treated, and test the clinical validity of the score.

DESIGN

Discrete choice experiment (DCE), and secondary analysis of data from six UK-wide prospective cohort studies.

PARTICIPANTS

253 people with lived experience of childhood surgical conditions, 114 health professionals caring for children with surgical conditions and 753 members of the general population completed the DCE. Data from 1383 children with surgical conditions were used in the secondary analysis.

MAIN OUTCOME MEASURES

Normalised importance value of attribute (NIVA) for number/type of operations, hospital-treated infections, quality of life and duration of survival (reference attribute).

RESULTS

Quality of life and duration of survival were the most important attributes in deciding whether a child had been successfully treated. Parents, carers and previously treated adults placed equal weight on both attributes (NIVA=0.996; 0.798 to 1.194). Healthcare professionals placed more weight on quality of life (NIVA=1.469; 0.950 to 1.987). The general population placed more weight on survival (NIVA=0.823; 95% CI 0.708 to 0.938). The resulting score (the Children's Surgery Outcome Reporting (CSOR) Treatment Success Score (TSS)) has the best possible value of 1, a value of 0 describes palliation and values less than 0 describe outcomes worse than palliation. CSOR TSSs varied clinically appropriately for infants whose data were included in the UK-wide cohort studies.

CONCLUSIONS

The CSOR TSS summarises how successfully children with surgical conditions have been treated, and can therefore be used to compare hospitals' observed and expected outcomes.

Child health valuation protocol for a discrete choice experiment comparing paired comparison and kaizen tasks and estimating US EQ-5D-Y-3L values on an experience scale

INTRODUCTION

A decade ago, the first national valuation study of the EQ-5D-Y-3L (Y-3L) involved a discrete choice experiment (DCE) that asked 4155 US adult respondents to complete 40 paired comparisons, choosing between two dying children. Instead of choosing between dying children, the respondents in this novel protocol are asked whether 'being in a coma' is better or worse than experiencing 'health problems' (ie, experience scale) and how they would relieve health problems (ie, kaizen tasks). Our aims are to compare the preference evidence of the paired comparison and kaizen tasks and to conduct a DCE for the valuation of Y-3L profiles on an experience scale.

METHODS AND ANALYSIS

Under this protocol, we will conduct an online survey that collects preference evidence from 600 US adult respondents on the health of a 10-year-old child for a week. Across all scenarios, each child will be described as either being 'in a coma' or having 'health problems', namely five three-level attributes (Y-3L). In this DCE, each respondent will be randomly assigned to one of four D-efficient blocks, including five coma comparisons (ie, Y-3L vs coma), 10 paired comparisons (Y-3L vs Y-3L) and 10 kaizen tasks (preference paths). In addition to comparing evidence by task (aim 2), the analysis plan includes the estimation of main-effects conditional logit models to create a Y-3L value set on an 'experience scale' where positive (negative) experiences have positive (negative) values (0 is 'being in a coma' and 1 is full health).

ETHICS AND DISSEMINATION

The institutional review board (IRB) (Advarra) determined that this project (Pro00072276) is exempt from IRB oversight based on DHHS 45 CFR 46.104(d)(2) and is not subject to requirements for continuing review. The results will be prepared for publication in peer-reviewed journals and presented at scientific meetings. The data and code will be made available on reasonable request.