We use real-world data to answer the questions that emerge beyond the trial, combining fit-for-purpose data with pre-specified methods to support clinical, regulatory and post-approval decisions.
We design and analyse prospective and retrospective studies using data from routine clinical care. These studies help answer questions around treatment use, safety and effectiveness without changing how patients are treated.
We design and support long-term patient registries, covering study set-up, data strategy, governance, statistical analysis and reporting as evidence builds over time.
We design and analyse PASS studies around specific post-market safety questions, with protocols, analysis and reporting aligned to regulatory requirements.
We design PAES studies to assess how an approved treatment performs in routine clinical practice and address effectiveness questions that remain after authorisation.
We compare treatments or care pathways using real-world data and appropriate causal-inference methods, helping create fairer and more meaningful comparisons between patient groups.
We build real-world comparator groups for single-arm trials, using carefully selected data and statistical methods to create the strongest possible external control.
We design studies using claims data, electronic health records and registries, helping choose the right data source and statistical approach for the question being asked.
We design and analyse surveys, investigator-initiated studies and other real-world data projects with the same statistical discipline applied across clinical development.
It needs the same discipline as any other evidence strategy: a clear research question, the right data and a statistical approach defined before the analysis starts.
We design RWE to support external controls, PASS and post-marketing effectiveness studies within EMA and FDA frameworks, not as an afterthought to clinical development.
Confounding, selection bias and time-related bias are considered upfront, with appropriate adjustment and sensitivity analyses built into the study plan.
We pre-specify the statistical approach in the SAP, using methods such as propensity score matching, IPW and g-methods where they fit the research question and available data.
Studies are reported in line with recognised frameworks including ENCePP, STROBE, RECORD and GRACE, keeping methods, assumptions and results transparent and traceable.
