Advanced Statistics for Evidence Generation
From evidence generation to market access, robust statistical analysis underpins every stage of the evidence journey. We help clients maximise the value of their data and generate insights that stand up to scrutiny.
Our team of statisticians delivers robust statistical analysis that transform clinical trial, observational and real-world data into credible evidence, helping clients answer critical research questions and support successful market access strategies.
Robust analyses. Credible evidence. Confident decisions.
Working closely with health economists, systematic review specialists, and clinical experts, our statistics team develops integrated evidence packages that help clients demonstrate value with confidence.
How we help
Our statistical analyses support:
- Economic modelling and health economic evaluations
- Real-world evidence generation
- Value dossiers and evidence communication
- HTA submissions
- Peer-reviewed publications and conference materials.
Our statistical expertise includes
Evidence synthesis
Generating robust comparative evidence, including when head-to-head data are unavailable.
- Feasibility assessments
- Network meta-analysis
- Population-adjusted indirect treatment comparisons, including matching-adjusted indirect comparison, simulated treatment comparison, and multilevel network meta-regression
- Time-varying indirect treatment comparisons
- Direct meta-analyses
Patient-level data analyses
Unlocking deeper insights from clinical trial data.
- Survival and time-to-event analyses
- Long-term outcome extrapolation
- Utility and quality-of-life analyses
- Subgroup, regression, and post-hoc analyses
Real-world data analysis
Demonstrating treatment effectiveness and patient care in routine clinical practice.
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- Comparative effectiveness research
- Healthcare resource utilisation studies
- Treatment pattern analyses
- Causal inference and confounding adjustment methods (e.g., propensity score matching)
Why work with us?
Experienced specialists
Every project is led by senior statisticians with direct experience of how evidence is used and challenged downstream, in HTA submissions and payer negotiations, so the analysis is built to withstand scrutiny.
Integrated capabilities
Our multidisciplinary approach brings together experts from across the organisation, ensuring statistical outputs are scientifically robust, practical and fit for purpose.
Tailored solutions
We apply the most appropriate analytical methods to each research question, delivering solutions that are robust, transparent and aligned with project objectives.
Client testimonials
“Just a quick personal note to say thank you so much for what you have done over the last year. As partners you have been part of the core team from day 1 and I could not do this without you – you truly are the best team I could work with. Thank you for helping us produce one of the best dossiers and models in terms of content and strategy I’ve ever worked on (this is number 33!)”
“I wanted to take the opportunity to recognise just how well the SOURCE team has worked throughout the project. Their partnership, flexibility, and commitment have been exceptional. Despite demanding timelines and evolving requirements, the team consistently delivered high-quality work, remained collaborative, and helped us navigate challenges with professionalism and a solutions-focused approach. Their support has been instrumental in enabling the project to progress successfully”
Let’s strengthen your evidence base
Talk to us about your evidence synthesis, patient-level data, or real-world evidence needs.
FAQ — Advanced Statistics for Evidence Generation
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What's the difference between a network meta-analysis and a matching-adjusted indirect comparison (MAIC)?
A network meta-analysis compares multiple treatments simultaneously using aggregate data connected through a network of clinical trials. A MAIC is used when important differences exist between trial populations that may violate the assumptions of a network meta-analysis. MAICs use patient data from one study to reweight patients to the published aggregate characteristics of another study before comparing treatment outcomes.
When do you need a time-varying indirect treatment comparison instead of a standard one?
Standard indirect treatment comparisons for survival outcomes typically assume that the relative treatment effect remains constant over time. Where that assumption doesn’t hold, time-varying methods such as fractional polynomials or piecewise exponential models are used to capture how the comparison changes over time instead of flattening it into a single average effect. This is common in oncology, where relative treatment effects often change over the course of follow-up.
What is survival extrapolation, and why does it matter for a cost-effectiveness model?
Survival extrapolation projects long-term survival outcomes beyond the period observed in a clinical trial, since trials rarely run long enough to capture the full impact of an intervention. This projection is often a key driver in cost-effectiveness models, so extrapolation methods need to be robust and defensible under HTA scrutiny.
What is real-world data, and how is it different from clinical trial data?
Real-world data comes from routine clinical practice (such as electronic health records, registries, insurance claims) rather than the controlled setting of a clinical trial. It’s used to generate real-world evidence to understand how a treatment performs, and how patients are treated, outside the more selective population and protocol-driven conditions of a trial.
What is propensity-score matching, and why is it used in real-world evidence studies?
Propensity-score matching is a statistical technique used to reduce bias in observational data by matching patients across treatment groups to have similar characteristics. This helps create more comparable groups and generate more reliable estimates of treatment effectiveness from real-world data.
Do you provide statistical support outside of HTA submissions, such as for earlier-stage decisions?
Yes, statistical analysis is often used well before an HTA submission to support evidence generation and strategic decision-making. This includes subgroup analyses, regression modelling, survival analyses, and post-hoc trial analyses to identify patient populations most likely to benefit from treatment, evaluate treatment outcomes, inform future study design, and support early health economic and real-world evidence strategies.