Avoiding pitfalls when combining multiple imputation and propensity scores

• Published in: Statistics in medicine Vol. 38; no. 26; pp. 5120 - 5132
• Main Authors: Granger, Emily, Sergeant, Jamie C., Lunt, Mark
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 20.11.2019; John Wiley and Sons Inc
• Subjects: Algorithms; Bias; Confidence Intervals; confounding; Data Interpretation, Statistical; Estimates; Missing data; Models, Statistical; Monte Carlo Method; multiple imputation; observational data; Observational Studies as Topic - statistics & numerical data; Propensity Score; propensity scores; simulation study; multiple imputation; confounding; propensity scores; observational data; missing data; simulation study

Overcoming bias due to confounding and missing data is challenging when analyzing observational data. Propensity scores are commonly used to account for the first problem and multiple imputation for the latter. Unfortunately, it is not known how best to proceed when both techniques are required. We...