Effective population adjustment in evidence synthesis of randomised controlled trials for health technology assessment

To decide which treatments to recommend to patients, we need reliable estimates of how the different treatments compare to each other. However, studies that directly compare all treatments of interest may not be available. Instead, we often have a mixture of studies that compare a selection of different treatments, or in some cases only a single treatment.

Furthermore, there may be differences between the patients in the different studies that change how well the treatments work. To address these issues, a statistical method called "multilevel network meta-regression" (ML-NMR) is available. This method combines evidence from multiple studies, where some studies provide individual-level data on every participant and some only provide published summary estimates, and accounts for differences between patient populations - a process known as "population adjustment".

Importantly, this method can produce estimates that are specific to a relevant population for decision-making (e.g. the UK patient population). This means that decision makers such as the National Institute for Health and Care Excellence (NICE) can make better decisions that are targeted to the relevant population.

However, there are several barriers to the use of ML-NMR in practice which need to be addressed if it is to be used more widely and effectively for decision-making. Firstly, the method requires substantial amounts of data on each treatment, which are not always available. For example, a company making a submission to NICE is likely to have individual-level data from their own trials of their own treatment, but only published summaries from their competitors' trials.

Without enough data, we may instead attempt to simplify the statistical model by making assumptions about how different groups of treatments work, but these assumptions may not be appropriate, which can lead to systematic errors in the results and the wrong conclusions being drawn. Secondly, it is common for clinical trials to encounter issues such as missing data, participants not receiving the treatment they were assigned, or participants being allowed to switch treatments (e.g. if their disease progresses).

Statistical methods are available to account for these issues, since if they are not handled correctly they can lead to systematic errors in the results. However, currently these methods cannot be used together with methods to account for differences between populations like ML-NMR.

This project aims to address these issues to ensure that ML-NMR works well in situations most frequently encountered by decision makers. This will be achieved by: i) developing novel statistical methods for ML-NMR to use additional information available from published trial reports; ii) making recommendations to update guidelines for how clinical trials are reported, to improve the availability of this additional information in published reports; iii) investigating the performance of the statistical methods through real and simulated examples; iv) developing novel statistical methods to combine population adjustment with methods that account for common issues in clinical trials such as missing data or switching treatments; and v) developing accessible software tools and training courses to support the uptake of the methods.

This research will have direct impact for decision makers such as NICE and will lead to better informed treatment decisions. The proposed advances in statistical methods and updated recommendations for reporting clinical trials have the potential to transform healthcare decision-making in wider contexts, even when only published summary data are available, such as the development of NICE clinical guidelines.

Additionally, there are direct applications in personalised medicine, where recommendations are targeted to individuals or smaller groups.