Your Easy Guide to Clinical Studies

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)

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).

References

ICH Topic E9 – see in particular

• 5.3 Missing values and outliers