Analytics & Biostatistics
Statistical analysis, biostatistics, and applied data science — turning routine programme, registry, and study data into defensible findings, peer-reviewable papers, and decisions a board will fund.
Data collection on its own does not produce evidence. The analytical work — picking the right method, surviving peer review, writing the methods up so the next analyst can reproduce them — is what turns numbers into decisions, papers, and policy. We treat the methods write-up as a deliverable, not an afterthought, because findings only matter if the analysis survives the question “how did you arrive at that number?”
If you have already read something on our blog or Data Hub and recognised the way we think about data, this is the service that makes that thinking available to your programme directly.
Concrete deliverables, not vague promises.
- Descriptive and inferential analysis of routine programme, registry, and survey data
- Biostatistical analysis for clinical, epidemiological, and public-health studies
- Hypothesis testing, regression modelling, survival analysis
- Predictive modelling and applied machine learning where the data supports it
- Reproducible analysis pipelines in R, Python, or Stata — with methods documented for the next analyst
- Pre-publication statistical review and reviewer-response support for journal submissions
- — Researchers and study teams needing statistical support
- — NGOs and programmes turning routine data into evidence
- — Funders requiring rigorous monitoring & evaluation analysis
- — Clinicians and PIs preparing peer-reviewed publications
Project-based, scoped to a clear analytical question. Per-paper or per-study pricing for academic work; per-question pricing for programme analytics. Methods documentation is part of every deliverable.