Service
Data management & validation
Clients routinely tell us their database is ready for analysis. In our experience, a meaningful proportion of records in a multicentre retrospective dataset need at least one correction, recoding decision or eligibility review.
Clinical data validation checks every variable against its expected range, coding system, clinical definition and relationship with other variables before any analysis is run. It identifies duplicate records, physiologically impossible values, dates in inconsistent formats or impossible order, continuous variables stored as text, comorbidity definitions that differ between centres, patients who do not meet the eligibility criteria, and the pattern and extent of missing data. It matters because these problems do not announce themselves in the output — a model runs perfectly happily on a corrupted dataset and produces a confident, wrong answer.
What this service includes
- Structured validation protocol
- A written validation plan applied variable by variable before any statistical analysis begins.
- Automated + manual review
- Range, logic and consistency rules flag suspect records automatically; a clinical researcher then reviews every flag rather than deleting it.
- Duplicate detection
- Identification of duplicate records using combinations of identifiers, admission dates, demographics and clinical variables — not identifier matching alone.
- Multicentre harmonisation
- A standardised data dictionary so every centre's coding maps to a single definition, which is where most multicentre datasets silently break.
- Eligibility verification
- Every record re-checked against the stated inclusion and exclusion criteria before it enters the analysis set.
- Missing-data assessment
- Extent and pattern of missingness examined per variable, with a documented strategy instead of automatic deletion.
- Restructuring
- Wide-to-long reshaping, date parsing, unit standardisation, derived-variable construction and type correction, all in code.
- Audit trail
- Raw value → issue identified → decision → final dataset → statistical output → manuscript table, documented end to end.
What you receive
- Data validation report listing every issue found
- Cleaned, documented, analysis-ready dataset
- Data dictionary with harmonised definitions
- Reproducible cleaning scripts
- Missingness assessment and strategy
- Complete audit trail
Independently verified before it reaches you
Every primary result is reproduced from the cleaned dataset by a second statistician before delivery, and you receive the analysis scripts so any number in the manuscript can be traced back to a line of code and a row of data.
Common questions
Related
Often needed alongside this.
Biostatistics & data analysis
Descriptive statistics through to advanced modelling — with assumptions tested, methods justified, and every number reproducible.
ExploreStudy design & protocol development
The cheapest place to fix a study is before the first patient is enrolled. Design, sample size and analysis plan, agreed in advance.
ExploreManuscript & publication support
Writing, scientific editing, reporting-guideline compliance, journal selection, submission and point-by-point reviewer responses.
ExploreTell us about your project.
Book a free 15-minute consultation. Tell us the research question, the data you have, and the deadline — we will tell you honestly what is achievable.
Or call +20 100 163 8864 · Sunday–Thursday, 09:00–18:00 (GMT+2, Cairo)