The Decisions You Make Before Enrolling Your First Patient Define Everything That Follows

Trial design isn't just a statistical exercise. It's a regulatory commitment. The endpoints you choose, the sample size you calculate, the analysis plan you pre-specify — these decisions lock in your path to approval before a single subject is enrolled. Get them right, and everything downstream gets easier. Get them wrong, and no amount of brilliant analysis can fix them later.

The challenge is that most sponsors don't know what they don't know. FDA expectations aren't always written down. The guidance documents leave room for interpretation. And the cost of a flawed design isn't visible until it's too late to change course.

BSC has spent nearly four decades supporting FDA submissions across medical devices. We know what reviewers look for, where designs tend to break down, and how to build trials that hold up under scrutiny — before the protocol is ever finalized.

What We Bring to the Design Stage

Scientific Rigor

Sound design starts with the right methods — not the most familiar ones. We apply modern statistical approaches appropriate to the clinical question: proper endpoint selection and hierarchy, type I error control, power calculations grounded in realistic assumptions, and decision rules that hold up when regulators examine them closely.

Regulatory Alignment

We build FDA expectations into the design from the start. Our experience across 510(k)s, PMAs, IDEs, and De Novo submissions means we know how agency reviewers evaluate design choices — and we anticipate their questions before they're asked. Sponsors who partner with BSC early spend far less time responding to deficiencies later.

Operational Feasibility

The best statistical design in the world fails if it can't be executed. We embed real-world constraints into every design — enrollment projections, site readiness, visit burden, dropout assumptions — so your protocol reflects what's actually achievable, not just what's theoretically optimal.

What We Do

The Plan

Statistical strategy and study design, sample size and power analyses including simulation-based approaches, endpoint selection and hierarchy development, decision rules and stopping boundaries, Statistical Analysis Plan (SAP) development, and mock Tables, Listings, and Figures (TLFs).

The Data

CRF review and annotation, database design consultation, data entry procedures, and data handling and screening plans that anticipate the cleaning work ahead.

Specialized Methods

Adaptive design with pre-specified interim decision rules, propensity score methods for non-randomized comparisons, Bayesian frameworks for prior-informed designs, and simulation modeling to validate operating characteristics before the protocol is locked.

The Cost of Starting Wrong

A trial that isn't designed with FDA in mind doesn't fail at submission — it fails at design. By the time a deficiency letter arrives, the study is complete, the data is locked, and your options are narrow. The sponsors who avoid this outcome are the ones who invest in rigorous statistical strategy before the first site is activated.

Starting right isn't just good science. It's the most efficient path to approval.

Ready to design your trial right the first time? [Talk to us.]