What is the best approach to adopt for identifying the domains for a new measure of health, social care and carer-related quality of life to measure quality-adjusted life years? Application to the development of the EQ-HWB

Economic evaluation combines costs and benefits to support decision-making when assessing new interventions using preference-based measures to measure and value benefits in health or health-related quality of life. These health-focused instruments have limited ability to capture wider impacts on informal carers or outcomes in other sectors such as social care. Sector-specific instruments can be used but this is problematic when the impact of an intervention straddles different sectors.An alternative approach is to develop a generic preference-based measure that is sufficiently broad to capture important cross-sector outcomes. We consider the options for the selection of domains for a cross-sector generic measure including how to identify domains, who should provide information on the domains and how this should be framed. Beyond domain identification, considerations of criteria and stakeholder needs are also identified.This paper sets out the case for an approach that relies on the voice of patients, social care users and informal carers as the main source of domains and describes how the approach was operationalised in the ‘Extending the QALY’ project which developed the new measure, the EQ-HWB (EQ health and wellbeing instrument). We conclude by discussing the strengths and limitations of this approach. The new measure should be sufficiently generic to be used to consistently evaluate health and social care interventions, yet also sensitive enough to pick up important changes in quality of life in patients, social care users and carers

A threshold analysis assessed the credibility of conclusions from network meta-analysis

Objective: To assess the reliability of treatment recommendations based on network meta-analysis (NMA)Study design: We consider evidence in an NMA to be potentially biased. Taking each pair-wise contrast in turn we use a structured series of threshold analyses to ask: (a) “How large would the bias in this evidence-base have to be before it changed our decision?” and (b) “If the decision changed, what is the new recommendation?” We illustrate the method via two NMAs in which a GRADE assessment for NMAs has been implemented: weight-loss and osteoporosis.Results. Four of the weight-loss NMA estimates were assessed as “low” and 6 as “moderate” quality by GRADE; for osteoporosis 6 were “low”, 9 “moderate” and 1 “high”. The threshold analysis suggests plausible bias in 3 of 10 estimates in the weight-loss network could have changed the treatment recommendation. For osteoporosis plausible bias in 6 of 16 estimates could change the recommendation. There was no relation between plausible bias changing a treatment recommendation and the original GRADE assessments.Conclusions. Reliability judgements on individual NMA contrasts do not help decision makers understand whether a treatment recommendation is reliable. Threshold analysis reveals whether the final recommendation is robust against plausible degrees of bias in the data

EVIDENCE INFORMED DECISION MAKING:THE USE OF “COLLOQUIAL EVIDENCE” AT NICE

Objectives: Colloquial evidence (CE) has been described as the informal evidence that helps provide context to other forms of evidence in guidance development. Despite challenges around quality, and the potential biases, the use of CE is becoming increasingly important in assessments where scientific literature is sparse and to also capture the experience of all stakeholders in discussions, including that of experts and patients. We aimed to ascertain how CE was being used at the National Institute for Health and Care Excellence (NICE). Methods: Relevant data corresponding to the use of CE was extracted from all NICE technical and process manuals by two reviewers and quality assured and analyzed by a third reviewer. This was considered in light of the results of a focused literature review and a combined checklist for quality assessment was developed. Results: At NICE, CE is utilised across all guidance producing programmes and at all stages of development. CE could range from information from experts and patient/carers, grey literature (including evidence from websites and policy reports) and testimony from stakeholders through consultation. Six tools for critical appraisal of CE were available from the literature and a combined best practice checklist has been proposed. Conclusions: As decisions often need to be made in areas where there is a lack of published scientific evidence, CE is employed. Therefore to ensure its appropriateness the development of a validated CE data quality check-list to assist decision makers is essential and further research in this area is a priority