Mapping Short Warwick and Edinburgh Mental Wellbeing Scale (SWEMWBS) to Recovering Quality of Life (ReQoL) to estimate health utilities.

BACKGROUND: The Short Warwick and Edinburgh Mental Wellbeing Scale (SWEMWBS) is a widely used non-preference-based measure of mental health in the UK. The primary aim of this paper is to construct an algorithm to translate the SWEMWBS scores to utilities using the Recovering Quality of Life Utility Index (ReQoL-UI) measure. METHODS: Service users experiencing mental health difficulties were recruited in two separate cross-sectional studies in the UK. The following direct mapping functions were used: Ordinary Least Square, Tobit, Generalised Linear Models. Indirect (response) mapping was performed using seemingly unrelated ordered probit to predict responses to each of the ReQoL-UI items and subsequently to predict using UK tariffs of the ReQoL-UI from SWEMWBS. The performance of all models was assessed by the mean absolute errors, root mean square errors between the predicted and observed utilities and graphical representations across the SWEMWBS score range. RESULTS: Analyses were based on 2573 respondents who had complete data on the ReQoL-UI items, SWEMWBS items, age and sex. The direct mapping methods predicted ReQoL-UI scores across the range of SWEMWBS scores reasonably well. Very little differences were found among the three regression specifications in terms of model fit and visual inspection when comparing modelled and actual utility values across the score range of the SWEMWBS. However, when running simulations to consider uncertainty, it is clear that response mapping is superior. CONCLUSIONS: This study presents mapping algorithms from SWEMWBS to ReQoL as an alternative way to generate utilities from SWEMWBS. The algorithm from the indirect mapping is recommended to predict utilities from the SWEMWBS.

Evaluating the effect of a digital health intervention to enhance physical activity in people with chronic kidney disease (Kidney BEAM): a multicentre, randomised controlled trial in the UK.

BACKGROUND: Remote digital health interventions to enhance physical activity provide a potential solution to improve the sedentary behaviour, physical inactivity, and poor health-related quality of life that are typical of chronic conditions, particularly for people with chronic kidney disease. However, there is a need for high-quality evidence to support implementation in clinical practice. The Kidney BEAM trial evaluated the clinical effect of a 12-week physical activity digital health intervention on health-related quality of life. METHODS: In a single-blind, randomised controlled trial conducted at 11 centres in the UK, adult participants (aged ≥18 years) with chronic kidney disease were recruited and randomly assigned (1:1) to the Kidney BEAM physical activity digital health intervention or a waiting list control group. Randomisation was performed with a web-based system, in randomly permuted blocks of six. Outcome assessors were masked to treatment allocation. The primary outcome was the difference in the Kidney Disease Quality of Life Short Form version 1.3 Mental Component Summary (KDQoL-SF1.3 MCS) between baseline and 12 weeks. The trial was powered to detect a clinically meaningful difference of 3 arbitrary units (AU) in KDQoL-SF1.3 MCS. Outcomes were analysed by an intention-to-treat approach using an analysis of covariance model, with baseline measures and age as covariates. The trial was registered with ClinicalTrials.gov, NCT04872933. FINDINGS: Between May 6, 2021, and Oct 30, 2022, 1102 individuals were assessed for eligibility, of whom 340 participants were enrolled and randomly assigned to the Kidney BEAM intervention group (n=173) or the waiting list control group (n=167). 268 participants completed the trial (112 in the Kidney BEAM group and 156 in the waiting list control group). All 340 randomly assigned participants were included in the intention-to treat population. At 12 weeks, there was a significant improvement in KDQoL-SF.13 MCS score in the Kidney BEAM group (from mean 44·6 AU [SD 10·8] at baseline to 47·0 AU [10·6] at 12 weeks) compared with the waiting list control group (from 46·1 AU [10·5] to 45·0 AU [10·1]; between-group difference of 3·1 AU [95% CI 1·8-4·4]; p<0·0001). INTERPRETATION: The Kidney BEAM physical activity platform is an efficacious digital health intervention to improve mental health-related quality of life in patients with chronic kidney disease. These findings could facilitate the incorporation of remote digital health interventions into clinical practice and offer a potential intervention worthy of investigation in other chronic conditions. FUNDING: Kidney Research UK.

Incorporation of patient and public involvement in statistical methodology research: development of an animation.

BACKGROUND: Patient and Public Involvement and Engagement (PPIE) is important to all aspects of health research. However, there are few examples of successful PPIE in statistical methodology research. One of the reasons for this relates to challenges in the identification of individuals interested in statistical methodology research projects, and ambiguities over the importance of PPIE to these projects. METHODS: This project was conducted between August 2022 and August 2023. The aim is to report the process of the development of an accessible animation to describe what statistical methodology is and the importance of PPIE in statistical methodology research projects. For this, we combined storyboarding and scriptwriting with feedback from PPIE members and researchers. RESULTS: After three stages that incorporated feedback from the relevant stakeholders, we produced a final animation about PPIE in statistical methodology. The resulting animation used minimal text, simple animation techniques and was of short duration (< 3 min) to optimise the communication of the key messages clearly and effectively. CONCLUSIONS: The resulting animation provides a starting point for members of the public to learn about PPIE in statistical methodology research and for methodologists who wish to conduct PPIE. We recommend further work to explore ways in which members of the public can be more meaningfully involved in methodology research.

Patient and public involvement and engagement (PPIE) is when members of the public are directly involved in carrying out research projects. This is important because we as researchers want to make sure we are focusing on what matters most to patients, so that the research has as large an impact as possible. PPIE has typically been used in more applied research projects, such as clinical trials, but is equally as important in statistical methodology research, where we focus on making sure the statistical tools that we use in the applied projects are as good as possible. The aim of this project was to create a short animation that helps to explain the importance of PPIE in statistical methodology research projects. Researchers sometimes incorrectly assume that PPIE is less important in these projects as this type of research has a less obvious benefit to patients. The animation helps to further explain these concepts. It describes what statistical methodology research is and why involving members of the public is still important. This paper explains the process of developing the animation, including receiving feedback from members of the public to make sure the animation is accessible to as many people as possible. The result is a short, 3-min animation that is free to view on the NIHR website. This can be used by other researchers to help them when recruiting members of the public to their research projects.

Incorporation of patient and public involvement in statistical methodology research: a survey assessing current practices and attitudes of researchers.

BACKGROUND: Patient and public involvement (PPI) ensures that research is designed and conducted in a manner that is most beneficial to the individuals whom it will impact. It has an undisputed place in applied research and is required by many funding bodies. However, PPI in statistical methodology research is more challenging and work is needed to identify where and how patients and the public can meaningfully input in this area. METHODS: A descriptive cross-sectional research study was conducted using an online questionnaire, which asked statistical methodologists about themselves and their experience conducting PPI, either to inform a grant application or during a funded statistical methodology project. The survey included both closed-text responses, which were reported using summary statistics, and open-ended questions for which common themes were identified. RESULTS: 119 complete responses were recorded. Individuals who completed the survey displayed an even range of ages, career lengths and positions, with the majority working in academia. 40.3% of participants reported undertaking PPI to inform a grant application and the majority reported that the inclusion of PPI was received positively by the funder. Only 21.0% of participants reported undertaking PPI during a methodological project. 31.0% of individuals thought that PPI was "very" or "extremely" relevant to statistical methodology research, with 45.5% responding "somewhat" and 24.4% answering "not at all" or "not very". Arguments for including PPI were that it can provide the motivation for research and shape the research question. Negative opinions included that it is too technical for the public to understand, so they cannot have a meaningful impact. CONCLUSIONS: This survey found that the views of statistical methodologists on the inclusion of PPI in their research are varied, with some individuals having particularly strong opinions, both positive and negative. Whilst this is clearly a divisive topic, one commonly identified theme was that many researchers are willing to try and incorporate meaningful PPI into their research but would feel more confident if they had access to resources such as specialised training, guidelines, and case studies.

Patient and public involvement (or PPI) means researchers working in partnership with patients and the public in any part of research. It can include helping decide what the research question is, how to pass on results to the public, and telling researchers what areas are most important to patients and the public. Statistical methods are the tools we use to analyse data. Statistical methodology research involves making sure these tools use our healthcare data in the best way. PPI is essential in health research and is becoming more common in statistical methodology research. But it can be hard to know how to include patients and the public in statistical methodology research. It may seem complex and not directly related to patients. This paper describes the results from a survey we did about the experiences of researchers who have carried out PPI for statistical methodology research. We asked them what they think about it, and how it affects their research. We also asked if they feel confident including PPI in their research, and whether they are given enough help. Researchers had different views about PPI for statistical methodology research. Some people thought PPI was very important in their research, but others weren't sure. Many people said that they would like more help such as training and guidelines to help them do better PPI in the future.

Measuring quality of life in trials including patients on haemodialysis: methodological issues surrounding the use of the Kidney Disease Quality of Life Questionnaire.

BACKGROUND: Haemodialysis (HD) treatment causes a significant decrease in quality of life (QoL). When enrolled in a clinical trial, some patients are lost prior to follow-up because they die or they receive a kidney transplant. It is unclear how these patients are dealt with in the analysis of QoL data. There are questions surrounding the consistency of how QoL measures are used, reported and analysed. METHODS: A systematic search of electronic databases for trials measuring QoL in HD patients using any variation of the Kidney Disease Quality of Life (KDQoL) Questionnaire was conducted. The review was conducted in Covidence version 2. Quantitative analysis was conducted in Stata version 16. RESULTS: We included 61 trials in the review, of which 82% reported dropouts. The methods to account for missing data due to dropouts include imputation (7%) and complete case analysis (72%). Few trials (7%) conducted a sensitivity analysis to assess the impact of missing data on the study results. Single imputation techniques were used, but are only valid under strong assumptions regarding the type and pattern of missingness. There was inconsistency in the reporting of the KDQoL, with many articles (70%) amending the validated questionnaires or reporting only statistically significant results. CONCLUSIONS: Missing data are not dealt with according to the missing data mechanism, which may lead to biased results. Inconsistency in the use of patient-reported outcome measures raises questions about the validity of these trials. Methodological issues in nephrology trials could be a contributing factor to why there are limited effective interventions to improve QoL in this patient group. PROSPERO REGISTRATION: CRD42020223869.

Measuring quality of life in trials including patients on dialysis: how are transplants and mortality incorporated into the analysis? A systematic review protocol.

INTRODUCTION: It is estimated that 25 000 people in the UK receive dialysis. Dialysis is an intrusive and time-consuming intervention that causes significant reductions in quality of life. When enrolled in a clinical trial, often some patients drop out of the study either because they die, receive a kidney transplant or are lost to follow-up for other reasons. It is unclear how these events are dealt with when analysing quality of life measures within clinical trials. This review will assess current practice for dealing with loss to follow-up in trials including patients on haemodialysis. The methods currently used will be analysed in terms of their adequacy and will form the basis of future work assessing the most appropriate methods to employ under these circumstances. The results of this review will feed into recommendations for future nephrology trials. METHODS AND ANALYSIS: A systematic search of electronic databases including MEDLINE and the Cochrane Library will be conducted to find clinical trials enrolling patients on haemodialysis that measure quality of life using either the kidney disease quality of life (KDQoL) or the short form 36 health survey (SF-36) (or any variation of these two measures). Ongoing trials will be identified through a search of trial registers. Articles will be screened against inclusion/exclusion criteria and data will be extracted using a predetermined data extraction form. General information such as the title, location, trial design will be extracted along with more specific information on how the study dealt with patients that died or received a transplant before the end of the follow-up period. Two independent reviewers will perform screening and extraction. Disagreements will be resolved by discussion or by a third independent reviewer. Data synthesis will be performed as a narrative summary. ETHICS AND DISSEMINATION: Ethics approval is not required. Dissemination will be by publication in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42020223869.