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Implications of Missing Data Mechanisms in Randomized Controlled Trials: Current Practices and Suggestions Utilizing
Jody S Nicholson1, Pascal R Deboeck2, Waylon Howard3
1Department of Psychology, Clemson University, Clemson, USA.
Journal of the American Geriatrics Society
|August 8, 2026
Summary
Missing data in randomized controlled trials (RCTs) can affect results. This study used advanced models to analyze missingness in cognitive training for dementia prevention, finding no major bias but highlighting data completeness differences.
Area of Science:
- Gerontology
- Clinical Trials
- Biostatistics
Background:
- Missing data in randomized controlled trials (RCTs) can significantly impact study outcomes and confidence in results.
- Investigating missing data is crucial for accurate interpretation of intervention effects, particularly in long-term studies.
Purpose of the Study:
- To establish confidence in results despite missing data in a randomized controlled trial (RCT) on computerized cognitive training for dementia prevention.
- To examine the preventive potential of cognitive training and assess potential biases introduced by missing participant data.
Main Methods:
- Secondary data analysis of the Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) study (n=2832, ages 65-94).
- Identification of auxiliary variables (demographic, physical, cognitive, psychological) predicting missingness.
- Application of two Missing Not At Random (MNAR) models: selection and pattern mixture modeling.
Main Results:
- Auxiliary variables revealed minor shifts in intervention effect estimates due to missing data, without altering overall trial interpretation.
- MNAR model analysis indicated no systemic bias in study outcomes attributable to missing data.
- Potential differences were identified between participants with complete versus incomplete data.
Conclusions:
- The study demonstrates a robust approach to examining missing data using multiple methods and auxiliary variables.
- Recommendations include amending reporting guidelines (e.g., CONSORT-ROUTINE) for greater specificity regarding missing data in RCTs.
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