Abstract
Missing data are ubiquitous in studies examining preventive interventions. This missing data need to be handled appropriately for data analyses to yield unbiased results. After a brief discussion of missing data mechanisms, inappropriate missing data treatments and appropriate missing data treatments, we review the current state of missing data treatments in intervention studies as well as how they have evolved over the years. Although missing data treatments have improved over the years, antiquated missing data treatments associated with biased results are still prevalent. Furthermore, many studies do not appropriately report their rates of missing data and missing data treatments. Using appropriate missing data treatments is elemental to accurately identify effective preventive interventions and properly inform practice and policy.
Original language | English |
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Pages (from-to) | 51-58 |
Number of pages | 8 |
Journal | International Journal of Behavioral Development |
Volume | 45 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2021 |
Keywords
- Missing data
- attrition
- clinical trials
- dropout
- full information maximum likelihood
- interventions
- multiple imputation