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.
The effect of health care expenditure on patient outcomes: evidence from English neonatal care. Health Economics [PubMed] Published 12th May 2017
Recently, people have started trying to identify opportunity cost in the NHS, by assessing the health gains associated with current spending. Studies have thrown up a wide range of values in different clinical areas, including in neonatal care. This study uses individual-level data for infants treated in 32 neonatal intensive care units from 2009-2013, along with the NHS Reference Cost for an intensive care cot day. A model is constructed to assess the impact of changes in expenditure, controlling for a variety of variables available in the National Neonatal Research Database. Two outcomes are considered: the in-hospital mortality rate and morbidity-free survival. The main finding is that a £100 increase in the cost per cot day is associated with a reduction in the mortality rate of 0.36 percentage points. This translates into a marginal cost per infant life saved of around £420,000. Assuming an average life expectancy of 81 years, this equates to a present value cost per life year gained of £15,200. Reductions in the mortality rate are associated with similar increases in morbidity. The estimated cost contradicts a much higher estimate presented in the Claxton et al modern classic on searching for the threshold.
A comparison of four software programs for implementing decision analytic cost-effectiveness models. PharmacoEconomics [PubMed] Published 9th May 2017
Markov models: TreeAge vs Excel vs R vs MATLAB. This paper compares the alternative programs in terms of transparency and validation, the associated learning curve, capability, processing speed and cost. A benchmarking assessment is conducted using a previously published model (originally developed in TreeAge). Excel is rightly identified as the ‘ubiquitous workhorse’ of cost-effectiveness modelling. It’s transparent in theory, but in practice can include cell relations that are difficult to disentangle. TreeAge, on the other hand, includes valuable features to aid model transparency and validation, though the workings of the software itself are not always clear. Being based on programming languages, MATLAB and R may be entirely transparent but challenging to validate. The authors assert that TreeAge is the easiest to learn due to its graphical nature and the availability of training options. Save for complex VBA, Excel is also simple to learn. R and MATLAB are equivalently more difficult to learn, but clearly worth the time saving for anybody expecting to work on multiple complex modelling studies. R and MATLAB both come top in terms of capability, with Excel falling behind due to having fewer statistical facilities. TreeAge has clearly defined capabilities limited to the features that the company chooses to support. MATLAB and R were both able to complete 10,000 simulations in a matter of seconds, while Excel took 15 minutes and TreeAge took over 4 hours. For a value of information analysis requiring 1000 runs, this could translate into 6 months for TreeAge! MATLAB has some advantage over R in processing time that might make its cost ($500 for academics) worthwhile to some. Excel and TreeAge are both identified as particularly useful as educational tools for people getting to grips with the concepts of decision modelling. Though the take-home message for me is that I really need to learn R.
Economic evaluation of factorial randomised controlled trials: challenges, methods and recommendations. Statistics in Medicine [PubMed] Published 3rd May 2017
Factorial trials randomise participants to at least 2 alternative levels (for example, different doses) of at least 2 alternative treatments (possibly in combination). Very little has been written about how economic evaluations ought to be conducted alongside such trials. This study starts by outlining some key challenges for economic evaluation in this context. First, there may be interactions between combined therapies, which might exist for costs and QALYs even if not for the primary clinical endpoint. Second, transformation of the data may not be straightforward, for example, it may not be possible to disaggregate a net benefit estimation with its components using alternative transformations. Third, regression analysis of factorial trials may be tricky for the purpose of constructing CEACs and conducting value of information analysis. Finally, defining the study question may not be simple. The authors simulate a 2×2 factorial trial (0 vs A vs B vs A+B) to demonstrate these challenges. The first analysis compares A and B against placebo separately in what’s known as an ‘at-the-margins’ approach. Both A and B are shown to be cost-effective, with the implication that A+B should be provided. The next analysis uses regression, with interaction terms demonstrating the unlikelihood of being statistically significant for costs or net benefit. ‘Inside-the-table’ analysis is used to separately evaluate the 4 alternative treatments, with an associated loss in statistical power. The findings of this analysis contradict the findings of the at-the-margins analysis. A variety of regression-based analyses is presented, with the discussion focussed on the variability in the estimated standard errors and the implications of this for value of information analysis. The authors then go on to present their conception of the ‘opportunity cost of ignoring interactions’ as a new basis for value of information analysis. A set of 14 recommendations is provided for people conducting economic evaluations alongside factorial trials, which could be used as a bolt-on to CHEERS and CONSORT guidelines.