*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 marginal benefits of healthcare spending in the Netherlands: estimating cost-effectiveness thresholds using a translog production function**. Health Economics [PubMed] *Published 30th August 2019*

The marginal productivity of the healthcare sector or, as commonly known, the supply-side cost-effectiveness threshold, is a hot topic right now. A few years ago, we could only guess at the magnitude of health that was displaced by reimbursing expensive and not-that-beneficial drugs. Since the seminal work by Karl Claxton and colleagues, we have started to have a pretty good idea of what we’re giving up.

This paper by Niek Stadhouders and colleagues adds to this literature by estimating the marginal productivity of hospital care in the Netherlands. Spoiler alert: they estimated that hospital care generates 1 QALY for around €74,000 at the margin, with 95% confidence intervals ranging from €53,000 to €94,000. Remarkably, it’s close to the Dutch upper reference value for the cost-effectiveness threshold at €80,000!

The approach for estimation is quite elaborate because it required building QALYs and costs, and accounting for the effect of mortality on costs. The diagram in Figure 1 is excellent in explaining it. Their approach is different from the Claxton et al method, in that they corrected for the cost due to changes in mortality directly rather than via an instrumental variable analysis. To estimate the marginal effect of spending on health, they use a translog function. The confidence intervals are generated with Monte Carlo simulation and various robustness checks are presented.

This is a fantastic paper, which will be sure to have important policy implications. Analysts conducting cost-effectiveness analysis in the Netherlands, do take note.

**Mixed-effects models for health care longitudinal data with an informative visiting process: a Monte Carlo simulation study**. Statistica Neerlandica *Published 5th September 2019*

Electronic health records are the current big thing in health economics research, but they’re not without challenges. One issue is that the data reflects the clinical management, rather than a trial protocol. This means that doctors may test more severe patients more often. For example, people with higher cholesterol may get more frequent cholesterol tests. The challenge is that traditional methods for longitudinal data assume independence between observation times and disease severity.

Alessandro Gasparini and colleagues set out to solve this problem. They propose using inverse intensity of visit weighting within a mixed-methods model framework. Importantly, they provide a Stata package that includes the method. It’s part of the wide ranging and super-useful merlin package.

It was great to see how the method works with the directed acyclic graph. Essentially, after controlling for confounders, the longitudinal outcome and the observation process are associated through shared random effects. By assuming a distribution for the shared random effects, the model blocks the path between the outcome and the observation process. It makes it sound easy!

The paper goes through the method, compares it with other methods in the literature in a simulation study, and applies to a real case study. It’s a brilliant paper that deserves a close look by all of those using electronic health records.

**Alternative approaches for confounding adjustment in observational studies using weighting based on the propensity score: a primer for practitioners**. BMJ [PubMed] *Published 23rd October 2019*

Would you like to use a propensity score method but don’t know where to start? Look no further! This paper by Rishi Desai and Jessica Franklin provides a practical guide to propensity score methods.

They start by explaining what a propensity score is and how it can be used, from matching to reweighting and regression adjustment. I particularly enjoyed reading about the importance of conceptualising the target of inference, that is, what treatment effect are we trying to estimate. In the medical literature, it is rare to see a paper that is clear on whether it is average treatment effect or average treatment effect among the treated population.

I found the algorithm for method selection really useful. Here, Rishi and Jessica describe the steps in the choice of the propensity score method and recommend their preferred method for each situation. The paper also includes the application of each method to the example of dabigatran versus warfarin for atrial fibrillation. Thanks to the graphs, we can visualise how the distribution of the propensity score changes for each method and depending on the target of inference.

This is an excellent paper to those starting their propensity score analyses, or for those who would like a refresher. It’s a keeper!

**Credits**