Health economics is an important field not just in the UK but globally. Decision-makers can use it to prioritise resources used in healthcare systems to maximise population health whilst ensuring value for money. Since 1999, the National Institute for Health and Care Excellence (NICE) has been responsible for these decisions in the UK. There is a need to provide these decision-makers with the best possible evidence for new treatments to support well-informed decisions.
The vast majority of health economic modelling is still conducted using Markov models to capture disease progression. However, these models rely on restrictive assumptions that may not accurately capture real-world disease progression. This talk will discuss two examples from a recent publication
where regression modelling was used to improve the health economic model. The first example will cover using a multinomial logistic regression to predict the changes in haemoglobin health states over time in patients with chronic kidney disease. Traditional Markov models assume a fixed disease progression rate which fails to account for patient subgroups and time-varying rates. Regression models enable dynamic transitions based on patient characteristics like age, sex, and baseline haemoglobin levels. This leads to better modelling of disease progression and subsequent decision-making.
The second example will cover using a generalised linear mixed model (GLMM) to estimate drug doses for each haemoglobin health state whilst adjusting for subgroup characteristics. This allows for a more granular approach to cost-effectiveness analysis. Unlike Markov models, which often require simplifying assumptions to capture drug doses, a GLMM better reflects the uncertainty in the target-population.
As real-world evidence and individual patient data (IPD) become more accessible, alongside increasing computational power, statisticians must apply and share advanced modelling approaches. By doing so, we can enhance healthcare decision-making, ultimately leading to better policies and improved population health.