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.
Value of information methods to design a clinical trial in a small population to optimise a health economic utility function. BMC Medical Research Methodology [PubMed] Published 8th February 2018
Statistical significance – whatever you think of it – and the ‘power’ of clinical trials to detect change, is an important decider in clinical decision-making. Trials are designed to be big enough to detect ‘statistically significant’ differences. But in the context of rare diseases, this can be nigh-on impossible. In theory, the required sample size could exceed the size of the whole population. This paper describes an alternative method for determining sample sizes for trials in this context, couched in a value of information framework. Generally speaking, power calculations ignore the ‘value’ or ‘cost’ associated with errors, while a value of information analysis would take this into account and allow accepted error rates to vary accordingly. The starting point for this study is the notion that sample sizes should take into account the size of the population to which the findings will be applicable. As such, sample sizes can be defined on the basis of maximising the expected (societal) utility associated with the conduct of the trial (whether the intervention is approved or not). The authors describe the basis for hypothesis testing within this framework and specify the utility function to be maximised. Honestly, I didn’t completely follow the stats notation in this paper, but that’s OK – the trial statisticians will get it. A case study application is presented from the context of treating children with severe haemophilia A, which demonstrates the potential to optimise utility according to sample size. The key point is that the power is much smaller than would be required by conventional methods and the sample size accordingly reduced. The authors also demonstrate the tendency for the optimal trial sample size to increase with the size of the population. This Bayesian approach at least partly undermines the frequentist basis on which ‘power’ is usually determined. So one issue is whether regulators will accept this as a basis for defining a trial that will determine clinical practice. But then regulators are increasingly willing to allow for special cases, and it seems that the context of rare diseases could be a way-in for Bayesian trial design of this sort.
This editorial was doing the rounds on Twitter last week. European (and Canadian) health economists love talking about the EQ-5D-5L. The editorial features in the edition of Health Economics that hosts the 5L value set for England, which – 2 years on – has finally satisfied the vagaries of academic publication. The authors provide a summary of what’s ‘new’ with the 5L, and why it matters. But we’ve probably all figured that out by now anyway. More interestingly, the editorial points out some remaining concerns with the use of the EQ-5D-5L in England (even if it is way better than the EQ-5D-3L and its 25-year old value set). For example, there is some clustering in the valuations that might reflect bias or problems with the technique and – even if they’re accurate – present difficulties for analysts. And there are also uncertain implications for decision-making that could systematically favour or disfavour particular treatments or groups of patients. On this basis, the authors support NICE’s decision to ‘pause’ and await independent review. I tend to disagree, for reasons that I can’t fit in this round-up, so come back tomorrow for a follow-up blog post.
Factors influencing health-related quality of life in patients with Type 1 diabetes. Health and Quality of Life Outcomes [PubMed] Published 2nd February 2018
Diabetes and its complications can impact upon almost every aspect of a person’s health. It isn’t clear what aspects of health-related quality of life might be amenable to improvement in people with Type 1 diabetes, or which characteristics should be targeted. This study looks at a cohort of trial participants (n=437) and uses regression analyses to determine which factors explain differences in health-related quality of life at baseline, as measured using the EQ-5D-3L. Age, HbA1c, disease duration and being obese all significantly influenced EQ-VAS values, while self-reported mental illness and unemployment status were negatively associated with EQ-5D index scores. People who were unemployed were more likely to report problems in the mobility, self-care, and pain/discomfort domains. There are some minor misinterpretations in the paper (divining a ‘reduction’ in scores from a cross-section, for example). And the use of standard linear regression models is questionable given the nature of EQ-5D-3L index values. But the findings demonstrate the importance of looking beyond the direct consequences of a disease in order to identify the causes of reduced health-related quality of life. Getting people back to work could be more effective than most health care as a means of improving health-related quality of life.
Financial incentives for chronic disease management: results and limitations of 2 randomized clinical trials with New York Medicaid patients. American Journal of Health Promotion [PubMed] Published 1st February 2018
Chronic diseases require (self-)management, but it isn’t always easy to ensure that patients adhere to the medication or lifestyle changes that could improve health outcomes. This study looks at the effectiveness of financial incentives in the context of diabetes and hypertension. The data are drawn from 2 RCTs (n=1879) which, together, considered 3 types of incentive – process-based, outcome-based, or a combination of the two – compared with no financial incentives. Process-based incentives rewarded participants for attending primary care or endocrinologist appointments and filling their prescriptions, up to a maximum of $250. Outcome-based incentives rewarded up to $250 for achieving target reductions in systolic blood pressure or blood glucose levels. The combined arms could receive both rewards up to the same maximum of $250. In short, none of the financial incentives made any real difference. But generally speaking, at 6-month follow-up, the movement was in the right direction, with average blood pressure and blood glucose levels tending to fall in all arms. It’s not often that authors include the word ‘limitations’ in the title of a paper, but it’s the limitations that are most interesting here. One key difficulty is that most of the participants had relatively acceptable levels of the target outcomes at baseline, meaning that they may already have been managing their disease well and there may not have been much room for improvement. It would be easy to interpret these findings as showing that – generally speaking – financial incentives aren’t effective. But the study is more useful as a way of demonstrating the circumstances in which we can expect financial incentives to be ineffective, and support a better-informed targeting for future programmes.