Analytical engagements scoped to your institutional context, delivered with documented methodology and full auditability.
Development and validation of mortality, morbidity, and reserving models to regulatory and internal governance standards. All models are scripted, version-controlled, and accompanied by technical documentation sufficient for independent review.
Typically engaged for basis risk assessments, assumption setting, model validation, or actuarial support where independence from the primary team is required.
All model outputs carry a version-stamped audit trail from data ingestion to final estimates.
Independent validation of existing models is available as a separate engagement. Read about our model validation service →
Quantitative analyses of population health data using validated epidemiological frameworks. Outputs are structured for use in policy submissions, regulatory filings, or peer-reviewed publication.
Work includes burden of disease estimation, survival analysis, health economic modelling, and evidence synthesis from administrative or survey data.
Every estimate is traceable to its source dataset and methodological reference.
Design and implementation of reproducible analytical pipelines for teams producing regulatory, policy, or research outputs. Includes audit of existing workflows against reproducibility standards and structured migration plans.
Outputs enable any qualified analyst to re-run, verify, and extend the work without reliance on the original author's undocumented knowledge.
Infrastructure is designed so internal teams can maintain and audit it without ongoing external dependency.
Independent review of statistical outputs, methods, and analytical processes for internal governance, regulatory response, or quality assurance purposes. Findings are documented to a standard suitable for committee-level reporting.
Reviews are conducted independently of the team whose work is under examination.
Structured training programmes for analysts and research teams, covering statistical foundations, applied modelling, and reproducible workflow practice. Delivered as open workshops, in-house courses, or individual coaching. See the training page for full programme details.
Scoping a project accurately requires clear information upfront. The following reduces time to proposal and improves the precision of the engagement specification.
Defined deliverables, timeline, and fee. Appropriate for discrete analyses with clear inputs and outputs.
Ongoing analytical support on a monthly basis. Appropriate for teams requiring regular independent review or modelling capacity.
Fixed-duration programme with agreed syllabus. Priced per session or per participant cohort.
Do you work with proprietary or sensitive data?
Yes. Data handling arrangements, including transfer mechanisms and confidentiality obligations, are specified in the engagement agreement before any data is shared. We do not retain client data beyond the agreed project period.
Can outputs be used in regulatory submissions?
Analytical outputs are structured and documented to a standard appropriate for regulatory review. Responsibility for submission rests with the client; we provide the documented methodology and code necessary to support examination.
What software tools do you use?
Primary tools are R, Python, Quarto, and Git. Outputs can be formatted for Excel or other platforms where required. We do not use proprietary analytical platforms that limit client access to the underlying code.
How do you handle disagreements about findings?
Analytical conclusions are determined by the data and the stated methodology. Where a client disagrees with a finding, we will examine whether the concern reflects a methodological question that warrants investigation. We do not revise conclusions to reflect client preference.
What is the typical turnaround for a scoping proposal?
A scoping call followed by a written proposal typically takes five to seven working days, provided the information listed above is available at first contact.