Missing data in clinical trials can quietly undermine results, not because the sample is smaller, but because missingness can bias the estimated treatment effect. This short explainer clarifies what missing data is, why it matters for regulatory confidence, and what data management and clinical operations teams can do to prevent avoidable gaps during conduct.
We explain how the estimand framework helps teams define the clinical question first, then align follow-up and analysis with how intercurrent events are handled, such as treatment discontinuation, rescue medication, or death. You’ll also hear practical ways to reduce missingness in the first place, including simpler schedules and visit windows, remote or hybrid assessments where appropriate, stronger retention processes, and monitoring that flags emerging patterns early so teams can intervene.
At Quanticate, our biometrics, statistics, and data management teams support sponsors with practical, inspection-ready approaches to missing data that balance operational feasibility with credible, well-justified analysis. Request a consultation below today.