Missing data treatments in intervention studies: What was, what is, and what should be

Date

2021

Journal Title

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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.

Description

Keywords

Missing Data, Attrition, Dropout, Multiple Imputation, Full Information Maximum Likelihood, Interventions, Clinical Trials

Citation

Rioux, C. & Little, T.D. (2021). Missing data treatments in intervention studies: What was, what is, and what should be. International Journal of Behavioral Development, 45(1), 51-58. https://doi.org/10.1177/0165025419880609

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