Medical coding and reconciliation
Adverse events, medical history, and concomitant medications have to be coded to a standard dictionary so they can be grouped and analysed consistently. We code adverse events and medical history to MedDRA and medications to WHODrug, following your versioning and auto-coding conventions, and we reconcile coded terms against the verbatim source and, where relevant, against safety data. Consistent coding is what lets a reviewer count how many participants had a given class of event rather than sifting hundreds of near-duplicate free-text strings. For teams whose primary need is safety literature rather than trial data, this pairs naturally with our pharmacovigilance literature screening work.
CDISC-aware datasets and database lock
When collection is complete, we clean the final discrepancies, confirm coding and reconciliation are closed, and prepare for database lock, the point at which the dataset is frozen for analysis. We structure the delivered data to be CDISC-aware: organised along SDTM (Study Data Tabulation Model) principles with controlled terminology, so that if your study later needs formal SDTM and ADaM datasets for a regulatory submission, the groundwork is already in place. At lock you receive the analysis-ready dataset, the data dictionary, the full query and audit trail, and lock documentation. From there our biostatisticians can pick the analysis up directly, since statistical analysis and reporting is delivered by the same team, or you can hand the locked dataset to your own statistician.
Data management for grants, dissertations, and academic trials
Not every study is a registered clinical trial, and our service is scoped for the reality of academic research. A PhD candidate cleaning a longitudinal dataset, a research group running a single-site registry, and an investigator-led trial preparing for a first regulatory conversation all need the same fundamentals: a written plan, a validated database, disciplined cleaning, and a defensible audit trail. We size the engagement to the study. For a doctoral project that might mean a DMP, a REDCap build, and a cleaned dataset; for a small sponsor it might extend through coding, reconciliation, and a formal lock. Because pricing is scoped to the work and held constant regardless of where you are based, you get the same standard whether you are in Riyadh, London, or Accra.
Standards, quality, and audit readiness
Data handling for clinical research is judged against Good Clinical Practice and the ALCOA data-integrity principles: data should be attributable, legible, contemporaneous, original, and accurate. We work to those principles and design our documentation so an engagement is inspection-ready, with awareness of 21 CFR Part 11 expectations for electronic records where your platform and study require it. This is the same documentation-first discipline behind our regulatory literature reviews: the goal is that every value in the final dataset can be traced back to its source through a complete, dated trail. We are transparent about scope. We are a specialist academic and research data management team, not a full-service contract research organisation, and we will tell you plainly at the quote stage which parts of your study we are the right fit for.
Most datasets that reach a statistician in poor shape share the same handful of failures, and every one of them is cheaper to prevent than to repair. Free-text fields that should have been drop-downs produce dozens of spellings of the same value. Dates stored as text break every calculation that depends on them. Missing data goes unrecorded, so nobody can tell a true zero from an unanswered question. Silent duplication of participants inflates the sample. Undocumented mid-study changes to how a variable was collected quietly bias the results. Our up-front data dictionary, typed fields, controlled terminology, and edit checks close each of these off at entry, and our query management log catches the rest before database lock rather than during analysis, when a single fix can mean re-running the entire statistical plan.
What we need from you, and how we scope
To quote accurately we need three things: your protocol or study plan, the platform you use or intend to use, and your timeline. From those we scope the engagement to what your study actually requires. A doctoral project may need only a data management plan, a REDCap build, and a cleaned dataset; a registry may add ongoing cleaning and periodic exports; a small trial may run the full path through medical coding, reconciliation, and a formal database lock. Pricing is fixed per scope and held constant regardless of your location, so the number you approve is the number you pay. Where your need is really statistical rather than data-handling, we will point you to the right service instead of overselling this one, whether that is our biostatistics team or funder-ready methodology writing.
Your engagement is led by a named data manager and reviewed by a senior biostatistician, so there is real, credentialed accountability on every project rather than an anonymous queue. We work under a mutual non-disclosure agreement on request, accept purchase orders for institutional work, and run every engagement against a defined written scope agreed before work begins. If an auditor or reviewer later questions the data handling, cleaning, or documentation, we revise it at no charge. Tell us your study design, your platform, and your timeline, and a named methodologist will reply with a scoped plan and a fixed price.
