Michał Jakubczyk

Objectives: This study qualitatively explored how bolt-ons affect the perception of EQ-5D-5L core dimensions in a valuation context. Methods: Sixty Indonesian adults (aged 20–67 y, 50% female) each valued 10 health states using composite time tradeoff (cTTO). States were presented in either forward (EQ-5D-5L, then with 1 bolt-on, then 2) or backward (reversed sequence) order. Participants were assigned to 1 of 3 bolt-on dyads: vision and tiredness, cognition and social relationships, or skin irritation and self-confidence. In semistructured qualitative interviews, respondents described how adding or removing bolt-ons changed the perceived importance of the 5 core dimensions. We classified these changes as related either to measurement or to valuation, with the latter further categorized as a relative or absolute shift in importance. Results: Cognition and vision generated the most shifts in perceived importance in the forward and backward groups, respectively. Regardless of ordering group, most shifts occurred between the EQ-5D-5L alone and the version with 1 bolt-on (first presented in the dyad), with significantly fewer shifts observed between the 1-bolt-on and 2-bolt-on states. In the forward group, most shifts were classified as measurement (56%) or relative preference (29%), while the reverse was true in the backward group: relative preference (53%) or measurement (31%). Absolute preference was least common across both groups. Conclusions: This is the first study to explore how individuals reason when valuing EQ-5D-5L+bolt-on health states. Our findings suggest that interactions between dimensions are complex and may be influenced by presentation order. Further qualitative research should directly investigate absolute preferential reasoning. Highlights: No studies have qualitatively explored how individuals value EQ-5D health states with bolt-ons. Understanding how bolt-ons influence reasoning and interact with core dimensions is crucial for informing valuation methods and modeling strategies. Our findings show that bolt-ons can alter how participants perceive the importance of EQ-5D-5L dimensions, although changes are mostly not preference driven. Participants often rely on accessible reasoning, such as conceptual associations between dimensions. Effects vary by the bolt-on used and presentation order. Interactions between bolt-ons and core dimensions complicate efforts to develop robust valuation approaches. Future qualitative studies should aim to capture preference-based reasoning, while quantitative work is needed to disentangle preferential from nonpreferential effects.

Objectives: In 2013, the EQ-5D-Y-3L valuation study conducted by Craig and colleagues (ie, the original study) of child health-related quality of life (HRQoL) revealed that U.S. respondents often found it burdensome and guilt-inducing to choose between hypothetical health problems of children. This study introduces an alternative approach where respondents sequentially relieve the health problems of a 10-year-old child for 1 week. Methods: We conducted a discrete choice experiment (DCE) survey (N=631) with paired comparisons and kaizen tasks. Each kaizen task displayed a single profile of a child’s HRQoL using the EQ-5D-Y-3L descriptive system and asked respondents to select first, second, and third improvements for the child’s problems. Combining the preference evidence, a conditional logit model was estimated to produce EQ-5D-Y-3L values on an “experience” scale, where positive values signify experiences better than “being in a coma” and negative values worse. Results: All 10 main effects were statistically significant (P<0.01), with the highest value placed on alleviating pain and discomfort. The worst-case scenario (33333) had a value of −0.337 on the experience scale, indicating it is worse than a coma. These new estimates highly correlate with the original U.S. EQ-5D-Y-3L values (Pearson correlation=0.726; Spearman correlation=0.794). Conclusion: This innovative approach to child health valuation replaces paired comparisons with Kaizen tasks, reducing respondent burden and study costs. Its use of experience scaling, instead of QALYs, aligns with U.S. guidelines (eg, the Inflation Reduction Act of 2022) and summarizes child HRQoL gains for health technology assessment.

Background: Clinical trials handle a huge amount of data which can be used during the trial to improve the ongoing study conduct. It is suggested by regulators to implement the remote approach to evaluate clinical trials by analysing collected data. Central statistical monitoring helps to achieve that by employing quantitative methods, the results of which are a basis for decision-making on quality issues. Methods: This article presents a scoping review which is based on a systematic and iterative approach to identify and synthesise literature on central statistical monitoring methodology. In particular, we investigated the decision-making processes (with emphasis on quality issues) of central statistical monitoring methodology and its place in the clinical trial workflow. We reviewed papers published over the last 10 years in two databases (Scopus and Web of Science) with a focus on data mining algorithms of central statistical monitoring and its benefit to the quality of trials. Results: As a result, 24 scientific papers were selected for this review, and they consider central statistical monitoring at two levels. First, the perspective of the central statistical monitoring process and its location in the study conduct in terms of quality issues. Second, central statistical monitoring methods categorised into practices applied in the industry, and innovative methods in development. The established methods are discussed through the prism of categories of their usage. In turn, the innovations refer to either research on new methods or extensions to existing ones. Discussion: Our review suggests directions for further research into central statistical monitoring methodology – including increased application of multivariate analysis and using advanced distance metrics – and guidance on how central statistical monitoring operates in response to regulators’ requirements.

The present study describes the data sets produced in Warsaw, Poland with the aim of developing tools and methods for the implementation of human-centred and data-driven solutions to the enhancement of sustainable mobility transition. This study focuses on school commutes and alternatives to private cars for children drop off and pick up from primary schools. The dataset enables the complex analysis of interactions between determinants of transport mode choice, revealed choices, and air quality impact. We draw on four data collection methods, namely, (i) air quality and noise sensors’ measurements, (ii) in-person observations of transport behaviours, (iii) travel diaries, and (iv) social surveys. Moreover, all trip data from travel diaries are complemented with the calculated attributes of alternative travel modes. The data produced in the project can be also combined with publicly available information on air quality, public transport schedules, and traffic flows. The present data sets help to open new venues for interdisciplinary analyses of sustainable mobility transition effectiveness and efficiency.