Real World Evidence for Health Technology Assessment (HTA)

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

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:

• Navigating Metadata

• Information Governance (IG) processes

• Developing Data Sharing Agreements (DSAs)

• Data Cleaning and Structuring Considerations

• Assessing Data Suitability

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:

• Target Trial framework

• Directed Acyclic Graphs (DAGs)

• Estimands Framework for Clinical and Patient-Reported Outcomes

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:

• Multivariable Regression

• Propensity Score Methods (including weighting and matching)

• G-methods: inverse probability weights (IPWs) within marginal structural models (MSMs), g-estimation of a structural nested model, and the g-formula

• Regression Discontinuity Design (RDD) and Interrupted Time Series (ITS)

• Difference-in-Differences (DiD)

• Instrumental Variables (IVs)

• Methods to account for missing and censored data (e.g. multiple Imputation and censoring-related IPWs)

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:

• Robust deterministic and probabilistic sensitivity analyses

• Methods to understand the implication of unaccounted for bias, such as e-values, and more sophisticated Quantitative Bias analysis techniques

• Application of reporting standards (e.g. ROBINS-I risk-of-bias tool)

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.

• Statistical Methods of Clinical, Patient-Reported, and Survival Outcomes

• Economic Analyses Including Economic Evaluation

• Patient-Reported Outcome and Utility Expertise

• Decision-Analytic Modelling

• Systematic Review and Meta-Analyses

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