Design · Analysis · Submission
Biostatistical services for medtech clinical trials.
From first protocol to final submission — study design, statistical analysis and regulatory-ready reporting, built specifically for medical device and digital health development.
Statistics that carry your study end to end.
Every engagement is led by an experienced biostatistician and scoped to your regulatory pathway — no boilerplate, no black boxes.
Efficient, cost-aware designs — endpoints, hypotheses, randomisation and analysis strategy defined before enrolment begins. Simulation-based sample size justifications that stand up to regulators and notified bodies: right-sized, never padded.
The statistical sections of your protocol written to be review-proof, followed by a detailed Statistical Analysis Plan covering every output, model and sensitivity analysis — agreed before database lock.
Planned looks at emerging data to guide study adjustments — sample size re-estimation, arm selection, early stopping — with operating characteristics documented and defensible.
Independent statistical membership for Data Monitoring Committees, and statistical representation in sponsor and regulatory meetings — so the numbers always have an expert voice in the room.
Comprehensive final analysis with structured reports and tables, listings and figures that present findings clearly — ready for submission, publication or your next investment round.
Independent statistical review of existing designs, data or analyses — including expert witness support when findings are challenged.
Statistics across the whole study lifecycle.
Engage us for a single phase or the full journey — the earlier we’re involved, the more cost and delay we can design out.
Study Design
Endpoints, hypotheses, sample size and analysis strategy — set before a single patient is enrolled.
Analysis Planning
The statistical scaffolding agreed up front, so there are no surprises at database lock.
Trial Oversight
Planned interim looks and independent oversight while your study is in the field.
Analysis & Reporting
Final analysis, TLFs and interpreted reports — plus answers when regulators ask questions.
Deliverables, regulatory-ready.
A complete biometrics workstream — documented, reproducible and formatted for review.
Study Design Report
Design rationale, endpoints, sample size and analysis strategy.
Statistical Analysis Plan
Full analytical specification agreed before unblinding.
Randomisation Schedule
Validated allocation schedules for your trial supply.
Tables, Listings & Figures
Complete, QC’d output packages in your house style.
Statistical Analysis Report
Interpreted findings written for clinical and regulatory readers.
Submission Support
Responses to statistical questions from regulators and notified bodies.
- Study Design, Statistical Analysis and Reporting
- Bayesian Techniques & Clinical Trial Design
- Advanced Trial Architecture: Adaptive Designs & Master Protocols
- Clinical Survey Design & Analysis
- Statistical Programming
A clinical trial is too expensive and too highly scrutinised to leave statistical planning as an afterthought. Whether entering at the protocol stage or during data collection, the focus must be on building a framework that withstands regulatory review.
Study Design & Protocol Development
Defining precise estimands, clinical endpoints, and eligibility criteria isn't just paperwork—it is the foundation of the trial. Getting this right ensures the scientific rigor required to prove your device or therapeutic actually works.
Sample Size Calculation
Under-powering a trial means wasting millions on inconclusive results; over-powering means wasting resources on unnecessary patient recruitment. Accurately calculating the optimal sample size—factoring in effect sizes, variability, and statistical power—strikes the exact balance needed.
Randomisation Schedules
Selection bias destroys regulatory credibility. Implementing simple, stratified, or block randomisation algorithms ensures treatment groups are strictly comparable from day one.
The Statistical Analysis Plan (SAP)
The SAP is the definitive rulebook for the trial. It explicitly details primary and secondary endpoints, analysis populations, and strategies for handling missing data, guaranteeing that the final analysis is entirely transparent and replicable.
CDISC Compliant SDTM and ADaM Preparation
Raw trial data is useless to regulators if it isn't formatted correctly. Converting raw data into standard Study Data Tabulation Model (SDTM) and Analysis Data Model (ADaM) formats eliminates integration bottlenecks and accelerates the submission process.
TLF (Tables, Listings, and Figures) Mock Shells
Designing the layout and structure of your final reporting outputs before the data even arrives prevents massive bottlenecks at the end of the trial, ensuring visual consistency across all submissions.
Meta-Analysis & Bayesian Simulation (MCMC)
Don't rely on guesswork to plan a multi-million dollar trial. Meta-analysis pools existing data to establish robust baseline estimates. Meanwhile, Bayesian simulations using Markov Chain Monte Carlo (MCMC) methods allow you to test hundreds of trial scenarios, quantify uncertainty, and optimize the study design before enrollment even begins.
Statistical Analysis & The CSR
The SAP dictates the execution; the Clinical Study Report (CSR) or Statistical Analysis Report (SAR) is the final deliverable. This phase involves rigorous inferential testing and transparent documentation of all methodologies. For long-term trials, pre-planned interim analyses are executed to allow for early stopping or sample size adjustments, ensuring resources are used efficiently without compromising the trial's overall integrity.
Bayesian Meta-Analysis & Evidence Synthesis
Designing a new trial without accurately leveraging historical data leaves valuable insights on the table. While traditional frequentist methods often struggle to combine multiple data sources accurately, Bayesian meta-analysis uses a hierarchical framework to reliably pool prior evidence. Far from being just a standalone exercise - it is the essential, foundational starting point for building any robust adaptive clinical trial design.
Bayesian Adaptive Designs (Phases 1-3)
Clinical environments shift, and new data emerges. Rigid trial designs force you to either ignore new data or start entirely new studies. Adaptive designs provide pre-planned, regulatory-compliant flexibility to manage this uncertainty. By incorporating these methods - ideally at Phase 1, though applicable at any blinded stage—you gain the ability to update treatment doses, optimise resources, or end an unproductive trial early. This seamlessly connects your entire Phase 1 through 3 sequence into a single, highly efficient pathway.
Bayesian Predictive & Generative Models
Complex med-tech systems require models that can cut through noise to isolate the specific factors driving outcomes. Rather than relying on overly simplistic models, Bayesian model development progressively builds a precise mathematical picture of reality:
- The Framework: Establishing a prior distribution based on historical data, determining the likelihood function, and forming a posterior distribution to summarise exact probabilities and point estimates.
- MCMC Based Methods: Solving these complex models requires advanced computational engines. We utilise algorithms including Metropolis-Hastings, Reversible Jump MCMC, Hamiltonian Monte Carlo, Gibbs Sampler, Particle MCMC, and Evolutionary Monte Carlo.
- Additional Computational Methods: Depending on the system, we also apply Sequential Monte Carlo, Approximate Bayesian Computation (ABC), Integrated Nested Laplace Approximations, and Variational Bayes.
When standard fixed trials are too rigid, slow, or expensive for your clinical questions, Bayesian methods allow you to fundamentally change how a trial is structured. This goes beyond the underlying statistics — it is about building a dynamic trial architecture that responds to real-time evidence.
The Mechanics of Bayesian Adaptation
Instead of being locked into a rigid protocol, a Bayesian framework allows for pre-planned pivots. This architecture provides:
- Early Stopping for Futility or Efficacy: Sparing patients from ineffective treatments and halting trials that aren't working, or accelerating successful therapies to market.
- Seamless Adaptation: Adjusting treatment arms, patient allocation ratios, or eligibility criteria as new data emerges.
- Adaptive Randomisation: Shifting the probability of assigning patients to the superior performing arm as the trial progresses, while maintaining strict statistical integrity and controlling for bias.
- Interim Analyses: Conducting rigorous, pre-planned data checks. This allows for data-driven decisions (like early stopping or sample size adjustments) without compromising the blind or invalidating the final regulatory submission.
Master Protocols: Unifying Complex Trials
For med-tech and pharma companies evaluating multiple therapies, running separate clinical trials is massively inefficient. Master Protocols unify multiple sub-studies under a single, shared infrastructure:
- Umbrella Trials: Grouping patients with a single disease but different biomarkers, allowing you to test multiple targeted therapies simultaneously within one protocol.
- Basket Trials: Grouping patients with different diseases but a shared genetic alteration, allowing you to test one targeted therapy across diverse, hard-to-reach populations.
- Platform Adaptation Trials: Dynamic infrastructure that allows you to seamlessly add or drop treatment arms over time without starting entirely new studies.
Navigating the Statistical Complexity
Building these dynamic architectures requires careful upfront planning. We handle the complexities of Bayesian sample size calculations and continuous data monitoring, ensuring that this immense flexibility still results in robust, regulatory-compliant evidence.
Engineering a clinical survey means building a tool that directly captures the evidence required for your research goals. The development process involves selecting the optimal response format for your study: qualitative open responses, quantitative scales like Likert, or a strategic combination of both. Establishing this foundation early ensures every question is fine-tuned to evaluate your target constructs with maximum precision.
Strategic Sampling Methods
Choosing the right cohort requires balancing statistical rigor against real-world constraints. Selecting between randomised probability, stratified, convenience, or targeted selective sampling ensures you gather the necessary data without overextending your budget, timeline, or distribution capabilities.
Platform Implementation
Seamless execution across your chosen platform - whether Qualtrics, SurveyMonkey, or Google Forms - eliminates technical data-collection errors before they happen.
Statistical Pilot Testing
Launching a clinical survey without statistical validation is a risk. Pilot testing establishes test-retest reliability and calculates Cronbach’s alpha to guarantee construct validity. Confirmatory factor analysis (CFA) is then applied to lock in the survey's statistical power, allowing for an exact, justified sample size calculation before the full rollout.
Advanced Final Analysis
Basic survey summaries are rarely enough for clinical decision-making. We extract definitive insights from your final data using advanced methodologies, including factor analysis, principal components analysis (PCA), latent variable models, and predictive modelling, tailored directly to your specific research questions.
Statistical Programming & Regulatory Execution
A Statistical Analysis Plan (SAP) is just a theoretical document until translated into flawless, executable code. Any disconnect between the protocol and the programming compromises a regulatory submission. Accurate data management and strict alignment with regulatory standards from start to finish prevent this.
Data Engineering & Transformation
Structuring and cleaning raw clinical data into compliant SDTM (Study Data Tabulation Model) and ADaM (Analysis Data Model) formats eliminates downstream errors. This foundational step creates secure, efficient databases ready for analysis.
SAP Execution & Repeatable Analysis
Translating the SAP directly into efficient code and building programmable datasets creates a system of repeatable analysis. Statistical tests and models can then be executed identically at any stage, drastically reducing human error and guaranteeing consistent results.
Regulatory-Ready TLFs
Comprehensive Tables, Listings, and Figures (TLFs) summarise demographics, individual patient data, and treatment outcomes into a highly visual, submission-ready format.
Quality Control & Absolute Traceability
Regulatory agencies like the MRHA, EMA and FDA demand absolute data accuracy. Integrating rigorous Quality Control (QC) processes throughout the programming phase catches discrepancies against source data before they compound. Pairing this with Traceability Matrices that link every SAP element to its corresponding program output ensures an unbroken, audit-proof trail.
Core Deliverables:
- Standardised SDTM and ADaM datasets
- Executable statistical analysis programs
- Complete sets of Tables, Listings, and Figures (TLFs)
- Detailed Clinical Study Reports
- Traceability matrices and full validation documentation
Statistical programmers are integral to the successful execution of clinical trials, facilitating the accurate management, analysis and reporting of study data.Their expertise in programming and data handling facilitates the generation of reliable and compliant results, which are essential for the evaluation of clinical interventions and regulatory submissions.
Tell us about your study — we’ll shape the statistics.
A no-obligation conversation with a senior biostatistician. If we’re the right fit, we’ll prepare a complete proposal for the work — nothing is committed until the contract is signed.
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