Real World Evidence for Health Technology Assessment (HTA)

We have expertise in a range of methodologies for the analysis of real-world data (RWD) to generate real-world evidence (RWE) to inform health technology assessment (HTA) processes. Using our world-class knowledge and expertise we can plan and conduct RWD studies, and report RWE, all in line with the National Institute for Health and Care Excellence's (NICE's) RWE framework (www.nice.org.uk/corporate/ecd9)

We provide advice and technical input into how to produce high-quality real-world evidence which meets the requirements outlined by the NICE RWE framework, with relevance to other reimbursement agencies globally.

We use a multidisciplinary team which covers data architects, quantitative methodologists, statisticians, econometricians, health economists, and clinicians.

We specialise in RWE for HTA, including for NICE in England & Wales. Our condition expertise includes cancer, mental health, and emergency care.

We understand the complexities in accessing, cleaning, and structuring RWD, alongside structural and analytical methods for controlling bias, how to assess the robustness of results, and adhering to appropriate reporting standards for RWE.

Our Team

Our team has a wealth of experience in Real World Evidence and Health Technology Assessments. Click each team member below to find out more about their expertise

Staff

Postgraduate Research Students

Data Access and Suitability 

The analysis of RWD begins with accessing data and assessing it's suitability, for instance using the NICE Data Suitability Assessment Tool (DataSAT) . Whether the data is electronic health records (EHRs), disease registries, or another existing dataset, we can advise on and undertake the following:

Structural Methods for Reducing Bias 

Suitable RWD can be structured for analysis in order to reduce the potential for biased estimates. The application of the following methods can help inform high quality Statistical and Health Economics Analysis Plans:

Analytical Methods for Reducing Bias  

Suitably structured data then needs to be analysed with methods sympathetic to the complexity of your question. Whether confounding factors exist at baseline, evolve over time, or may be missing, we can advise on and undertake analyses employing a range of methods appropriate for HTA:

Assessing Robustness of Results

In addition to earlier stages informing the analysis plan, the robustness of the study results can be assessed through a range of methods appropriate for HTA, including:

Overall Analyses and Approaches

The analysis of RWD to generate RWE can be informed by, or go on to inform, a broad range of HTA activities. We have expertise in applying each of these approaches in the setting of statistical, survival, economic, and decision-analytical modelling analyses.

Case Studies