title: Out of sight, not out of mind: strategies for handling missing data.
abstract: 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.
authors.affiliation: Department of Community and Family Health, College of Public Health, University of South Florida, Tampa 33612, FL, USA. ebuhi@health.usf.edu
authors.forename: Eric R
authors.initials: ER
authors.lastname: Buhi
authors.forename: Patricia
authors.initials: P
authors.lastname: Goodson
authors.forename: Torsten B
authors.initials: TB
authors.lastname: Neilands
mesh_terms: D001499:Bayes Theorem; D035841:Behavioral Research / Q000706:statistics & numerical data*; D015982:Bias; D003627:Data Interpretation, Statistical; D015438:Health Behavior; D006801:Humans; D014481:United States