What is it? Why is it important?

Missing study data is incomplete data. It can affect the study’s statistical power and/or create bias (i.e. distortion of statistical results).

 

Missing data can affect the reliability and credibility of study results. It is therefore important to:

  • Take preventive measures to limit the amount of missing data
  • Plan appropriate statistical strategies on how to handle missing data

 

Missing data may have different root causes, such as:

  • Incomplete survey questionnaires: participants may not know, or decide not to respond to study questions (e.g. due to privacy concerns, topic sensitivity)
  • Missing visit(s): in longitudinal studies, participants may, voluntarily or not (e.g. for health reasons), miss follow-up visits
  • Early study termination: participants may not want or are not able to complete the study (e.g. early study withdrawal, lost to follow-up, death)
  • Technical issues: problems with study relevant technical equipment resulting in data loss (e.g. failure of laboratory equipment during analysis)

What do I need to do?

As a SP-INV, define measures limiting the amount of missing data in your study.

 

Potential strategies include to:

  • Implement a study design that facilitates data collection (e.g. the use of an electronic Data Capture System (eCRF) with built-in validation checks)
  • Train study staff on data collection and documentation procedures (e.g. correct and complete documentation during study visits)
  • Implement supporting strategies for study participants to ensure compliance and minimize study drop-outs (e.g. clear study instruction leaflets, provide participants with a smart pillbox (i.e. a digital solution for monitoring and improve adherence to medication intake), use of e-mail reminder systems reminding participants of pending study visits)
  • Plan on-site monitoring visits, and in particular central data monitoring

 

Discuss with a statistician on who to handle missing data. Plan statistical analyses, which take missing data into consideration.

 

Describe methods used to handle missing data in the study protocol and, if applicable, in the Statistical Analysis Plan (SAP).

Where can I get help?

Your local CTU can support you with experienced staff regarding this topic

References

ICH Topic E9 – see in particular

  • 5.3 Missing values and outliers
Abbreviations
  • CTU – Clinical Trials Unit
  • ICH – International Council for Harmonisation
  • eCRF – electronic Data Capture System
  • SAP – Statistical Analysis Plan
  • SP-INV – Sponsor Investigator
Development ↦ Statistic Methodology ↦ Statistics in the Protocol ↦ Missing Data
Study
Basic

Provides some background knowledge and basic definitions

Basic Monitoring
Basic Drug or Device
Concept

Starts with a study idea

Ends after having assessed and evaluated study feasibility

Concept Statistic Methodology
Concept Drug or Device
Development

Starts with confidence that the study is feasible

Ends after having received ethics and regulatory approval

Development Drug or Device
Set-Up

Starts with ethics and regulatory approval

Ends after successful study initiation

Set-Up Ethics and Laws
Set-Up Statistic Methodology
Set-Up Quality and Risk
Set-Up Drug or Device
Conduct

Starts with participant recruitment

Ends after the last participant has completed the last study visit

Conduct Statistic Methodology
Conduct Drug or Device
Completion

Starts with last study visit completed

Ends after study publication and archiving

Completion Statistic Methodology
Completion Drug or Device
Current Path (click to copy): Development ↦ Statistic Methodology ↦ Statistics in the Protocol ↦ Missing Data

Please note: the Easy-GCS tool is currently under construction.