Hi all,

Thanks to everyone who shared their experiences with multiple imputation
and matching. Below you can find a summary of the responses I recieved.
Similar to matching with complex survey designs, there seems to be
relatively little consensus in the literature
​about the ​
best practices.

Cyrus Samii and colleagues first created multiply imputed data sets and
then carried out matching on each completed data set. They also provide
detailed and efficient R code they used in their JCR paper:
http://m.jcr.sagepub.com/content/57/4/598.

​Here are a couple of papers that systematically explore different
approaches using simulations:

Mitra, R., & Reiter, J. P. (2012). A comparison of two methods of
estimating propensity scores after multiple imputation.
*Statistical ​M​ethods in ​M​edical ​R​esearch ​: *
https://stat.duke.edu/~jerry/Papers/smimr13.pdf
​.

​
Qu, Y., & Lipkovich, I. (2009). Propensity score estimation with missing
values using a multiple imputation missingness pattern (MIMP) approach.
*Statistics in ​M​edicine*, *28*(9), 1402-1414.

​Bernard and colleagues use principal stratification to recover missing
data in a randomized experiment. I am not sure, though, to what extent this
framework can be extended to an observational study:
http://biosun01.biostat.jhsph.edu/~cfrangak/papers/sc/vouchers.pdf
​.

Here is some code I received that shows how to go about using MatchIt and
Amelia in combination:
http://www.forrestlane.com/uploads/ECLS_PSM_syntax.txt
​.

Not
​about
 multiple imputation, but
​the
 approach Paul Rosenbaum suggests
​ in his 2002 book​

​is to impute m
issing data on
​the covariates
 with their
​mean
value
​,​

​create ​
​a dummy variable

indicating
 missing cases
​on each imputed covariate, and use the two variables, along with other
predictors, in estimating the propensity score.

​Here's another paper that ​does not use multiple imputation but that is
useful in dealing with missing data in propensity score matching: D'Agostino
Jr, R. B., & Rubin, D. B. (2000). Estimating and using propensity scores
with partially missing data. *Journal of the American Statistical
Association*, *95*(451), 749-759.

Thanks once again to all who shared these resources.

Best,
Valerio

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