Out of sight, not out of mind: strategies for handling missing data.
OBJECTIVE
To describe and illustrate missing data mechanisms (MCAR, MAR, NMAR) and missing data techniques (MDTs) and offer recommended best practices for addressing missingness.


METHODS
We simulated data sets and employed ad hoc MDTs (deletion techniques, mean substitution) and sophisticated MDTs (full information maximum likelihood, Bayesian estimation, multiple imputation) in linear regression analyses.


RESULTS
MCAR data yielded unbiased parameter estimates across all MDTs, but loss of power with deletion methods. NMAR results were biased towards larger values and greater significance. Under MAR the sophisticated MDTs returned estimates closer to their original values.


CONCLUSIONS
State-of-the-art, readily available MDTs outperform ad hoc techniques.
Buhi
Eric R
ER
Department of Community and Family Health, College of Public Health, University of South Florida, Tampa 33612, FL, USA. ebuhi@health.usf.edu
Goodson
Patricia
P
Neilands
Torsten B
TB
D001499:Bayes Theorem; D035841:Behavioral Research / Q000706:statistics & numerical data*; D015982:Bias; D003627:Data Interpretation, Statistical; D015438:Health Behavior; D006801:Humans; D014481:United States