Avoiding pitfalls when combining multiple imputation and propensity scores

Emily Granger, Jamie Sergeant, Mark Lunt

Research output: Contribution to journalArticlepeer-review


Overcoming bias due to confounding and missing data is challenging when analysing 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 investigate whether two different approaches to combining propensity scores and multiple imputation (Across and Within) lead to differences in the accuracy or precision of exposure effect estimates. Both approaches start by imputing missing values multiple times. Propensity scores are then estimated for each resulting dataset. Using the Across approach, the mean propensity score across imputations for each subject is used in a single subsequent analysis. Alternatively, theWithin approach uses propensity scores individually to obtain exposure effect estimates in each imputation, which are combined to produce an overall estimate. These approaches were compared in a series of Monte Carlo simulations and applied to data from the British Society for Rheumatology Biologics Register. Results indicated that the Within approach produced unbiased estimates with appropriate confidence intervals, whereas the Across approach produced biased results and unrealistic confidence intervals. Researchers are encouraged to implement the Within approach when conducting propensity score analyses with incomplete data.
Original languageEnglish
Pages (from-to)5120-5132
JournalStatistics in medicine
Issue number26
Early online date11 Sept 2019
Publication statusPublished - 20 Nov 2019


  • propensity scores
  • multiple imputation
  • simulation study
  • observational data
  • confounding
  • missing data


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