Missing Data Section
STROBE includes missing-data handling within statistical methods for observational studies, making the chosen handling method and its analytical implications part of transparent reporting. Not every report needs a standalone heading, and the appropriate method depends on design and assumptions. This page does not prescribe one imputation or exclusion strategy as universally correct.
A Missing Data section explains what data were missing, how missingness was handled in the analysis, and any design-appropriate checks or assumptions readers need to understand.
Key details
Important caveats
Scope Boundary
Not every report needs a standalone heading, and the appropriate method depends on design and assumptions. This page does not prescribe one imputation or exclusion strategy as universally correct.
Further guidance
Content
Report the relevant extent or pattern of missingness when known, identify the handling approach used, and describe sensitivity or assumption checks when they materially affect interpretation.
Purpose
A Missing Data section explains what data were missing, how missingness was handled in the analysis, and any design-appropriate checks or assumptions readers need to understand.
Sources and evidence
Sources are shown with the role they play in this guide. Historical or style-sensitive claims are kept within the evidence boundary described above.
- Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration (opens in a new tab)PLOS Medicine / STROBE Initiative · Primary reporting-guideline evidence for observational-study setting, study size, bias, participant flow, and statistical handling including missing data and confounding.