Addressing missing participant outcome data in dental clinical trials

Area of Science:

• Clinical Trials
• Biostatistics
• Health Research Methodology

Background:

• Missing outcome data is a frequent challenge in clinical trials, even with robust protocols.
• Participant attrition and exclusion post-randomization lead to information loss, potentially introducing attrition bias and complicating result interpretation.
• The mechanisms of missingness can significantly impact the reliability and credibility of clinical trial findings.

Purpose of the Study:

• To highlight key issues surrounding missing outcome data in clinical trials.
• To describe recognized methods for handling missing data.
• To explain the principles of intention-to-treat (ITT) and per-protocol (PP) analyses.

Main Methods:

• Utilized a worked example from a published dental study.
• Discussed common causes and implications of missing data.
• Reviewed established approaches for managing missing outcome data.
• Explained the theoretical underpinnings of ITT and PP analysis.

Main Results:

• Identified attrition bias as a significant consequence of missing data.
• Emphasized that the 'mechanism of missingness' affects result credibility.
• Demonstrated the importance of appropriate assumptions and imputation for true ITT analysis.

Conclusions:

• Handling missing outcome data appropriately is crucial for valid clinical trial interpretation.
• Intention-to-treat (ITT) analysis is generally recommended over per-protocol (PP) analysis when dealing with missing data.
• Effective management of missing data, including imputation, is essential for maintaining the integrity of trial results.