Thesis Thursday: Lidia Engel

On the third Thursday of every month, we speak to a recent graduate about their thesis and their studies. This month’s guest is Dr Lidia Engel who graduated with a PhD from Simon Fraser University. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Going beyond health-related quality of life for outcome measurement in economic evaluation
Supervisors
David Whitehurst, Scott Lear, Stirling Bryan
Repository link
https://theses.lib.sfu.ca/thesis/etd10264

Your thesis explores the potential for expanding the ‘evaluative space’ in economic evaluation. Why is this important?

I think there are two answers to this question. Firstly, methods for economic evaluation of health care interventions have existed for a number of years but these evaluations have mainly been applied to more narrowly defined ‘clinical’ interventions, such as drugs. Interventions nowadays are more complex, where benefits cannot be simply measured in terms of health. You can think of areas such as public health, mental health, social care, and end-of-life care, where interventions may result in broader benefits, such as increased control over daily life, independence, or aspects related to the process of health care delivery. Therefore, I believe there is a need to re-think the way we measure and value outcomes when we conduct an economic evaluation. Secondly, ignoring broader outcomes of health care interventions that go beyond the narrow focus of health-related quality of life can potentially lead to misallocation of scarce health care resources. Evidence has shown that the choice of outcome measure (such as a health outcome or a broader measure of wellbeing) can have a significant influence on the conclusions drawn from an economic evaluation.

You use both qualitative and quantitative approaches. Was this key to answering your research questions?

I mainly applied quantitative methods in my thesis research. However, Chapter 3 draws upon some qualitative methodology. To gain a better understanding of ‘benefits beyond health’, I came across a novel approach, called Critical Interpretive Synthesis. It is similar to meta-ethnography (i.e. a synthesis of qualitative research), with the difference that the synthesis is not of qualitative literature but of methodologically diverse literature. It involves an iterative approach, where searching, sampling, and synthesis go hand in hand. It doesn’t only produce a summary of existing literature but enables the development of new interpretations that go beyond those originally offered in the literature. I really liked this approach because it enabled me to synthesise the evidence in a more effective way compared with a conventional systematic review. Defining and applying codes and themes, as it is traditionally done in qualitative research, allowed me to organize the general idea of non-health benefits into a coherent thematic framework, which in the end provided me with a better understanding of the topic overall.

What data did you analyse and what quantitative methods did you use?

I conducted three empirical analyses in my thesis research, which all made use of data from the ICECAP measures (ICECAP-O and ICECAP-A). In my first paper, I used data from the ‘Walk the Talk (WTT)‘ project to investigate the complementarity of the ICECAP-O and the EQ-5D-5L in a public health context using regression analyses. My second paper used exploratory factor analysis to investigate the extent of overlap between the ICECAP-A and five preference-based health-related quality of life measures, using data from the Multi Instrument Comparison (MIC) project. I am currently finalizing submission of my third empirical analysis, which reports findings from a path analysis using cross-sectional data from a web-based survey. The path analysis explores three outcome measurement approaches (health-related quality of life, subjective wellbeing, and capability wellbeing) through direct and mediated pathways in individuals living with spinal cord injury. Each of the three studies addressed different components of the overall research question, which, collectively, demonstrated the added value of broader outcome measures in economic evaluation when compared with existing preference-based health-related quality of life measures.

Thinking about the different measures that you considered in your analyses, were any of your findings surprising or unexpected?

In my first paper, I found that the ICECAP-O is more sensitive to environmental features (i.e. social cohesion and street connectivity) when compared with the EQ-5D-5L. As my second paper has shown, this was not surprising, as the ICECAP-A (a measure for adults rather than older adults) and the EQ-5D-5L measure different constructs and had only limited overlap in their descriptive classification systems. While a similar observation was made when comparing the ICECAP-A with three other preference-based health-related quality of life measures (15D, HUI-3, and SF-6D), a substantial overlap was observed between the ICECAP-A and the AQoL-8D, which suggests that it is possible for broader benefits to be captured by preference-based health-related measures (although some may not consider the AQoL-8D to be exclusively ‘health-related’, despite the label). The findings from the path analysis confirmed the similarities between the ICECAP-A and the AQoL-8D. However, the findings do not imply that the AQoL-8D and ICECAP-A are interchangeable instruments, as a mediation effect was found that requires further research.

How would you like to see your research inform current practice in economic evaluation? Is the QALY still in good health?

I am aware of the limitations of the QALY and although there are increasing concerns that the QALY framework does not capture all benefits of health care interventions, it is important to understand that the evaluative space of the QALY is determined by the dimensions included in preference-based measures. From a theoretical point of view, the QALY can embrace any characteristics that are important for the allocation of health care resources. However, in practice, it seems that QALYs are currently defined by what is measured (e.g. the dimensions and response options of EQ-5D instruments) rather than the conceptual origin. Therefore, although non-health benefits have been largely ignored when estimating QALYs, one should not dismiss the QALY framework but rather develop appropriate instruments that capture such broader benefits. I believe the findings of my thesis have particular relevance for national HTA bodies that set guidelines for the conduct of economic evaluation. While the need to maintain methodological consistency is important, the assessment of the real benefits of some health care interventions would be more accurate if we were less prescriptive in terms of which outcome measure to use when conducting an economic evaluation. As my thesis has shown, some preference-based measures already adopt a broad evaluative space but are less frequently used.

Chris Sampson’s journal round-up for 11th September 2017

Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.

Core items for a standardized resource use measure (ISRUM): expert Delphi consensus survey. Value in Health Published 1st September 2017

Trial-based collection of resource use data, for the purpose of economic evaluation, is wild. Lots of studies use bespoke questionnaires. Some use off-the-shelf measures, but many of these are altered to suit the context. Validity rarely gets a mention. Some of you may already be aware of this research; I’m sure I’m not the only one here who participated. The aim of the study is to establish a core set of resource use items that should be included in all studies to aid comparability, consistency and validity. The researchers identified a long list of 60 candidate items for inclusion, through a review of 59 resource use instruments. An NHS and personal social services perspective was adopted, and any similar items were merged. This list was constructed into a Delphi survey. Members of the HESG mailing list – as well as 111 other identified experts – were invited to complete the survey, for which there were two rounds. The first round asked participants to rate the importance of including each item in the core set, using a scale from 1 (not important) to 9 (very important). Participants were then asked to select their ‘top 10’. Items survived round 1 if they scored at least 7 with more than 50% of respondents, and less than 3 by no more than 15%, either overall or within two or more participant subgroups. In round 2, participants were presented with the results of round 1 and asked to re-rate 34 remaining items. There was a sample of 45 usable responses in round 1 and 42 in round 2. Comments could also be provided, which were subsequently subject to content analysis. After all was said and done, a meeting was held for final item selection based on the findings, to which some survey participants were invited but only one attended (sorry I couldn’t make it). The final 10 items were: i) hospital admissions, ii) length of stay, iii) outpatient appointments, iv) A&E visits, v) A&E admissions, vi) number of appointments in the community, vii) type of appointments in the community, viii) number of home visits, ix) type of home visits and x) name of medication. The measure isn’t ready to use just yet. There is still research to be conducted to identify the ideal wording for each item. But it looks promising. Hopefully, this work will trigger a whole stream of research to develop bolt-ons in specific contexts for a modular system of resource use measurement. I also think that this work should form the basis of alignment between costing and resource use measurement. Resource use is often collected in a way that is very difficult to ‘map’ onto costs or prices. I’m sure the good folk at the PSSRU are paying attention to this work, and I hope they might help us all out by estimating unit costs for each of the core items (as well as any bolt-ons, once they’re developed). There’s some interesting discussion in the paper about the parallels between this work and the development of core outcome sets. Maybe analysis of resource use can be as interesting as the analysis of quality of life outcomes.

A call for open-source cost-effectiveness analysis. Annals of Internal Medicine [PubMed] Published 29th August 2017

Yes, this paper is behind a paywall. Yes, it is worth pointing out this irony over and over again until we all start practising what we preach. We’re all guilty; we all need to keep on keeping on at each other. Now, on to the content. The authors argue in favour of making cost-effectiveness analysis (and model-based economic evaluation in particular) open to scrutiny. The key argument is that there is value in transparency, and analogies are drawn with clinical trial reporting and epidemiological studies. This potential additional value is thought to derive from i) easy updating of models with new data and ii) less duplication of efforts. The main challenges are thought to be the need for new infrastructure – technical and regulatory – and preservation of intellectual property. Recently, I discussed similar issues in a call for a model registry. I’m clearly in favour of cost-effectiveness analyses being ‘open source’. My only gripe is that the authors aren’t the first to suggest this, and should have done some homework before publishing this call. Nevertheless, it is good to see this issue being raised in a journal such as Annals of Internal Medicine, which could be an indication that the tide is turning.

Differential item functioning in quality of life measurement: an analysis using anchoring vignettes. Social Science & Medicine [PubMed] [RePEc] Published 26th August 2017

Differential item functioning (DIF) occurs when different groups of people have different interpretations of response categories. For example, in response to an EQ-5D questionnaire, the way that two groups of people understand ‘slight problems in walking about’ might not be the same. If that were the case, the groups wouldn’t be truly comparable. That’s a big problem for resource allocation decisions, which rely on trade-offs between different groups of people. This study uses anchoring vignettes to test for DIF, whereby respondents are asked to rate their own health alongside some health descriptions for hypothetical individuals. The researchers conducted 2 online surveys, which together recruited a representative sample of 4,300 Australians. Respondents completed the EQ-5D-5L, some vignettes, some other health outcome measures and a bunch of sociodemographic questions. The analysis uses an ordered probit model to predict responses to the EQ-5D dimensions, with the vignettes used to identify the model’s thresholds. This is estimated for each dimension of the EQ-5D-5L, in the hope that the model can produce coefficients that facilitate ‘correction’ for DIF. But this isn’t a guaranteed approach to identifying the effect of DIF. Two important assumptions are inherent; first, that individuals rate the hypothetical vignette states on the same latent scale as they rate their own health (AKA response consistency) and, second, that everyone values the vignettes on an equivalent latent scale (AKA vignette equivalence). Only if these assumptions hold can anchoring vignettes be used to adjust for DIF and make different groups comparable. The researchers dedicate a lot of effort to testing these assumptions. To test response consistency, separate (condition-specific) measures are used to assess each domain of the EQ-5D. The findings suggest that responses are consistent. Vignette equivalence is assessed by the significance of individual characteristics in determining vignette values. In this study, the vignette equivalence assumption didn’t hold, which prevents the authors from making generalisable conclusions. However, the researchers looked at whether the assumptions were satisfied in particular age groups. For 55-65 year olds (n=914), they did, for all dimensions except anxiety/depression. That might be because older people are better at understanding health problems, having had more experience of them. So the authors can tell us about DIF in this older group. Having corrected for DIF, the mean health state value in this group increases from 0.729 to 0.806. Various characteristics explain the heterogeneous response behaviour. After correcting for DIF, the difference in EQ-5D index values between high and low education groups increased from 0.049 to 0.095. The difference between employed and unemployed respondents increased from 0.077 to 0.256. In some cases, the rankings changed. The difference between those divorced or widowed and those never married increased from -0.028 to 0.060. The findings hint at a trade-off between giving personalised vignettes to facilitate response consistency and generalisable vignettes to facilitate vignette equivalence. It may be that DIF can only be assessed within particular groups (such as the older sample in this study). But then, if that’s the case, what hope is there for correcting DIF in high-level resource allocation decisions? Clearly, DIF in the EQ-5D could be a big problem. Accounting for it could flip resource allocation decisions. But this study shows that there isn’t an easy answer.

How to design the cost-effectiveness appraisal process of new healthcare technologies to maximise population health: a conceptual framework. Health Economics [PubMed] Published 22nd August 2017

The starting point for this paper is that, when it comes to reimbursement decisions, the more time and money spent on the appraisal process, the more precise the cost-effectiveness estimates are likely to be. So the question is, how much should be committed to the appraisal process in the way of resources? The authors set up a framework in which to consider a variety of alternatively defined appraisal processes, how these might maximise population health and which factors are key drivers in this. The appraisal process is conceptualised as a diagnostic tool to identify which technologies are cost-effective (true positives) and which aren’t (true negatives). The framework builds on the fact that manufacturers can present a claimed ICER that makes their technology more attractive, but that the true ICER can never be known with certainty. As a diagnostic test, there are four possible outcomes: true positive, false positive, true negative, or false negative. Each outcome is associated with an expected payoff in terms of population health and producer surplus. Payoffs depend on the accuracy of the appraisal process (sensitivity and specificity), incremental net benefit per patient, disease incidence, time of relevance for an approval, the cost of the process and the price of the technology. The accuracy of the process can be affected by altering the time and resources dedicated to it or by adjusting the definition of cost-effectiveness in terms of the acceptable level of uncertainty around the ICER. So, what determines an optimal level of accuracy in the appraisal process, assuming that producers’ price setting is exogenous? Generally, the process should have greater sensitivity (at the expense of specificity) when there is more to gain: when a greater proportion of technologies are cost-effective or when the population or time of relevance is greater. There is no fixed optimum for all situations. If we relax the assumption of exogenous pricing decisions, and allow pricing to be partly determined by the appraisal process, we can see that a more accurate process incentivises cost-effective price setting. The authors also consider the possibility of there being multiple stages of appraisal, with appeals, re-submissions and price agreements. The take-home message is that the appraisal process should be re-defined over time and with respect to the range of technologies being assessed, or even an individualised process for each technology in each setting. At least, it seems clear that technologies with exceptional characteristics (with respect to their potential impact on population health), should be given a bespoke appraisal. NICE is already onto these ideas – they recently introduced a fast track process for technologies with a claimed ICER below £10,000 and now give extra attention to technologies with major budget impact.

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Alastair Canaway’s journal round-up for 28th August 2017

Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.

Valuing health-related quality of life: an EQ-5D-5L value set for England. Health Economics [PubMed] Published 22nd August 2017

With much anticipation, the new EQ-5D-5L value set was officially published. For over 18 months we’ve had access to values via the OHE’s discussion paper but the formal peer-reviewed paper has (I imagine) been in publication purgatory. This paper presents the results of the value-set for the new (ish) EQ-5D-5L measure. The study used the internationally agreed hybrid model combining TTO and DCE data to generate the values for the 3125 health states. It’s worth noting that the official values are marginally different to those in the discussion paper, although in practice this is likely to have little impact on results. Important results of the new value set include fewer health states worse than death (5.1% vs over 33%), and a higher minimum value (-0.285 vs -0.594). I’d always been a bit suspect of the values for worse than death states for the 3L measure, so this if anything is encouraging. This does, however, have important implications, primarily for interventions seeking to improve those in the worst health, where potential gains may be reduced. Many of us are actively using the EQ-5D-5L within trials and have been eagerly awaiting this value set. Perhaps naively, I always anticipated that with more levels and an improved algorithm it would naturally supersede the 3L and the outdated 3L value set upon publication. Unfortunately, to mark the release of the new value set, NICE released a ‘position statement’ [PDF] regarding the choice of measure and value sets for the NICE reference case. NICE specifies that i) the 5L value set is not recommended for use, ii) the EQ-5D-3L with the original UK TTO value set is recommended and if both measures are included then the 3L should be preferred, iii) if the 5L measure is included, then scores should be mapped to the EQ-5D-3L using the van Hout et al algorithm, iv) NICE supports the use of the EQ-5D-5L generally to collect data on quality of life, and v) NICE will review this decision in August 2018 in light of future evidence. So, unfortunately, for the next year at least, we will be either sticking to the original 3L measure or mapping from the 5L. I suspect NICE is buying some time as transitioning to the 5L is going to raise lots of interesting issues e.g. if a measure is cost-effective according to the 3L, but not the 5L, or vice-versa, and comparability of 5L results to old 3L results. Interesting times lie ahead. As a final note, it’s worth reading the OHE blog post outlining the position statement and OHE’s plans to satisfy NICE.

Long-term QALY-weights among spouses of dependent and independent midlife stroke survivors. Quality of Life Research [PubMed] Published 29th June 2017

For many years, spillover impacts were largely being ignored within economic evaluation. There is increased interest in capturing wider impacts, indeed, the NICE reference case recommends including carer impacts where relevant, whilst the US Panel on Cost-Effectiveness in Health and Medicine now advocates the inclusion of other affected parties. This study sought to examine whether the dependency of midlife stroke survivors impacted on their spouses’ HRQL as measured using the SF-6D. An OLS approach was used whilst controlling for covariates (age, sex and education, amongst others). Spouses of dependent stroke survivors had a lower utility (0.69) than those whose spouses were independent (0.77). This has interesting implications for economic evaluation. For example, if a treatment were to prevent dependence, then there could potentially be large QALY gains to spouses. Spillover impacts are clearly important. If we are to broaden the evaluative scope as suggested by NICE and the US Panel to include spillover impacts, then work is vital in terms of identifying relevant contexts, measuring spillover impacts, and understanding the implications of spillover impacts within economic evaluation. This remains an important area for future research.

Conducting a discrete choice experiment study following recommendations for good research practices: an application for eliciting patient preferences for diabetes treatments. Value in Health Published 7th August 2017

To finish this week’s round-up I thought it’d be helpful to signpost this article on conducting DCEs, which I feel may be helpful for researchers embarking on their first DCE. The article hasn’t done anything particularly radical or made ground-breaking discoveries. What it does however do is give you a practical guide to walk you through each step of the DCE process following the ISPOR guidelines/checklist. Furthermore, it expands upon the ISPOR checklist to provide researchers with a further resource to consider when conducting DCEs. The case study used relates to measuring patient preferences for type 2 diabetes mellitus medications. For every item on the ISPOR checklist, it explains how they made the choices that they did, and what influenced them. The paper goes through the entire process from identifying the research question all the way through to presenting results and discussion (for those interested in diabetes – it turns out people have a preference for immediate consequences and have a high discount rate for future benefits). For people who are keen to conduct a DCE and find a worked example easier to follow, this paper alongside the ISPOR guidelines is definitely one to add to your reference manager.

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