Biostatistics & Statistical Analysis

When this is needed

• Protocol or analysis plans need statistical coherence (estimands, endpoints, operating characteristics)
• Analyses require disciplined methods, transparent assumptions and hand-over documentation
• Teams need interpretable outputs for RA, governance, DSMB or publication pathways
• Biometrics capacity is constrained during a surge of work

How we work

We treat statistical work as part of the wider evidence story — not as isolated tables. Typical flow:

1. Clarify questions — estimands, endpoints, constraints and decision use
2. Plan — analysis strategy and documentation appropriate to the programme
3. Execute — analysis support as scoped, with transparent assumptions
4. Interpret — outputs usable by scientific and non-statistical stakeholders
5. Hand over — materials the client can own in review or submission pathways

Programme stages: How we work. Study design and protocols: Clinical study design.

Languages and environments

We work in the stack the programme already uses — or recommend one when you are choosing tools for reproducibility and review.

• R — flexible analysis, reporting pipelines and specialised packages (including survival and Bayesian workflows)
• Python — analysis and modelling pipelines where the scientific stack is Python-first
• SAS — common in regulated clinical and biometrics environments
• SPSS — applied analyses and reporting where SPSS is the institutional standard
• Stata — epidemiology, survey and longitudinal work where Stata is preferred

Tool choice follows data governance, validation expectations and the client’s SOPs — not a single house language.

Methods and capabilities

Below is what we emphasise on engagements. Everyday descriptive summaries and routine group comparisons are included when needed, but they are not the value proposition.

Clinical trial planning and analysis

• Sample size and power — sizing studies to detect effects that matter under stated assumptions and operating characteristics
• Randomisation design — block, stratified and adaptive allocation schemes, with documentation suitable for protocol and analysis plans
• Interim analyses — planned looks, spending / stopping frameworks and materials for DSMB or sponsor decision (client owns the decision)
• Analysis planning — estimands, populations, missing-data strategy and SAP-aligned analysis support

Survival analysis

• Time-to-event endpoints: Kaplan–Meier, Cox and parametric survival models
• Competing risks, landmarking and other extensions where the clinical question requires them
• Reporting that stays interpretable for clinical and regulatory readers

Bayesian statistics

• Bayesian design and analysis where priors, posteriors and decision rules are explicit
• Borrowing of information, hierarchical models and predictive probabilities for interim or adaptive contexts
• Clear documentation of assumptions so non-statisticians can follow the reasoning

Epidemiological effect measures

• Odds ratios, relative risks and hazard ratios — estimation, confounding control and careful interpretation
• Cohort, case–control and related observational designs when statistics support evidence generation (not clinical care)

Infectious-disease compartment models (e.g. SIR/SEIR) and spatial epidemiology are available when a programme genuinely needs them — we do not list them as default brochure services.

Longitudinal and hierarchical modelling

• Mixed models (LMM / GLMM) — repeated measures, clustering (e.g. patients within sites) and correlated outcomes
• GEE — population-average approaches for longitudinal or clustered data
• Joint models — linking longitudinal biomarkers with time-to-event outcomes when that is the scientific question

High-dimensional and omics support

• Statistical genetics / association-style analyses (including GWAS-style workflows) where the engagement scope includes them
• Transcriptomic, proteomic or metabolomic matrices: preprocessing, normalisation and analysis plans agreed in the SOW
• Multiple-testing control (e.g. FDR, family-wise procedures) when thousands of tests are in play

We support analysis and documentation — we are not a sequencing laboratory or a diagnostic service.

Evidence synthesis

• Classical meta-analysis (fixed- and random-effects)
• Network meta-analysis when comparing multiple interventions under a coherent evidence network
• Heterogeneity, bias assessment and transparent reporting of assumptions

Prediction and diagnostic performance

• Diagnostic accuracy: ROC, sensitivity, specificity, predictive values — with attention to spectrum and verification bias
• Prediction and machine-learning models for research or product-adjacent questions where evaluation, calibration and leakage control matter
• Language stays precise: we do not claim certified clinical AI or guaranteed personalised treatment algorithms

Example deliverables

• Statistical input to protocols, SAPs and analysis plans
• Analysis code, outputs and technical notes agreed in the SOW (R, Python, SAS, SPSS or Stata as scoped)
• Interim / DSMB-oriented statistical packages prepared for client-owned decision pathways
• Interpretation support for internal review, publication or client-owned submissions

Out of scope

• Clinical care, patient diagnosis or acting as treating physician
• Guaranteeing study success, ethics approval or regulator acceptance
• Receiving identifiable patient datasets or PHI via the public website
• Operating as a sequencing lab, biobank or diagnostic manufacturer

Related

Clinical study design · Regulatory support · Research organisations · Biotech & pharma