Advanced statistical analysis using R — from descriptive analytics to predictive modelling.

Key capabilities

Advanced statistical analysis using R, tailored to your domain.

We help organisations across health, government, research, and industry transform their data into actionable insights. Our expertise spans from foundational descriptive analytics through to advanced Bayesian modelling and machine learning.

Every analysis is reproducible, documented, and designed to support evidence-based decision-making. We combine deep statistical expertise with practical business understanding to deliver insights that are both rigorous and actionable.

Why It Matters for Data-Driven Businesses

Data is abundant, but insight is scarce. Many organisations collect extensive datasets yet lack the statistical rigour needed to extract meaningful conclusions. Without proper analytical methods, patterns are missed, biases go undetected, and decisions rest on intuition rather than evidence.

The cost of unanalysed or poorly analysed data is substantial. Programmes may continue despite lacking effectiveness, resources are allocated without understanding need or demand, and risks remain hidden beneath surface-level summaries. In health, government, and regulated industries, the stakes are even higher — flawed analysis can mislead policy, delay interventions, or produce findings that don’t withstand peer review or audit.

Statistical modelling bridges the gap between raw data and confident decisions. It provides a structured framework for understanding relationships, quantifying uncertainty, and testing hypotheses against observed evidence. The right model, applied to the right question, can transform a sea of numbers into a clear basis for action — turning uncertainty into measured risk and intuition into tested evidence.

Our Capabilities

Statistical modelling turns raw data into structured evidence. Whether you’re running programme evaluations, forecasting trends, or building predictive tools, we provide the analytical rigour your decisions need.

We work across the full spectrum of statistical methods, choosing the right approach for your data, your constraints, and the decisions you need to support.

Our modelling toolkit includes generalised linear models (GLMs) for relating outcomes to multiple predictors, generalised additive models (GAMs) for capturing non-linear relationships without rigid parametric assumptions, survival analysis for time-to-event questions in clinical and operational settings, and Bayesian methods (via brms and the Stan ecosystem) where incorporating prior knowledge or quantifying parameter uncertainty is essential. For predictive tasks, we use the tidymodels framework to build, tune, and validate classification and regression models within a consistent R-native workflow.

We handle complex data structures that standard tools struggle with — clustered or hierarchical data through mixed-effects models, longitudinal observations through repeated-measures approaches, and spatial or geographic data through specialised modelling frameworks.

What differentiates our work is the emphasis on communication. We don’t just deliver coefficients and p-values. Every model is accompanied by clear interpretation, visual summaries of key findings, and practical recommendations grounded in the evidence. Our deliverables typically include executable analysis code, a written report summarising methodology and findings, and visualisations that make the results accessible to non-technical stakeholders.

Driving Decision-Making

A model is only valuable if it informs action. We design every analysis with the downstream decision in mind, ensuring that outputs translate directly into programme evaluation, resource allocation, risk management, or strategic planning.

Our approach to decision-focused analysis includes building decision frameworks that map model outputs to specific business choices. For example, a predictive model for patient admission rates doesn’t just produce a forecast — it identifies which regions, time periods, and patient groups drive the most uncertainty, enabling targeted resource planning rather than blanket responses.

We specialise in communicating statistical results to audiences who didn’t train in statistics. This means translating confidence intervals into ranges of plausible outcomes, expressing model predictions in terms familiar to the domain (e.g., “an estimated 200 additional cases per quarter” rather than “a beta coefficient of 0.32”), and providing clear guidance on what the analysis does and doesn’t tell us.

Where organisations need ongoing decision support, we build analytical tools that can be re-run as new data arrives — turning a one-off analysis into a living decision-support resource.

Influence and Engagement

Rigorous analysis gains traction when it reaches the right people in the right format. We work closely with clients to ensure that statistical evidence is presented in ways that resonate with decision-makers, satisfy peer reviewers, and inform strategic discussions.

Our engagements often influence policy or programme direction by providing evidence that clarifies trade-offs, highlights unintended consequences, or identifies high-impact opportunities that were previously invisible. We help clients build the case for change with data that stands up to scrutiny from technical experts and non-technical leaders alike.

Equally important is sustainable capability. We don’t leave organisations dependent on us for every analysis. Through mentoring, documented workflows, and training, we help your team develop the statistical literacy needed to maintain, extend, and build on the analytical foundation we establish together.

When You Need This Service

• Evaluation and impact assessment: Measuring the effectiveness of programmes, policies, or interventions
• Prediction and forecasting: Building models that anticipate future outcomes — patient flow, demand, risk
• Comparative analysis: Identifying meaningful differences between groups, regions, or time periods
• Complex data structures: Handling clustered, longitudinal, or multilevel data that standard methods can’t address
• Regulatory or compliance reporting: Producing statistically sound evidence for audit or decision-making

What to Expect

We start with a scoping discussion to understand your data and objectives. From there, we deliver a clear analytical plan, execute the analysis, and present findings with transparent methodology and reproducible code. Every engagement includes a walkthrough of results and recommendations for next steps.