Re: st: Propensity score matching after multiple imputation

[natalia malancu <natalia.malancu@gmail.com>]

The references (totally skipped my mind, apologizes):

Mitra, R. and Reiter, JP. (2011) Propensity score matching with
missing covariates via iterated, sequential multiple imputation
[Working Paper]

Hill, J (2004) Reducing Bias in Treatment Effect Estimation in
Observational Studies Suffering from Missing Datap [ISERP Working
Papers]


Adam: the paper I am referring to seems to be the earlier version of
the one you are mentioning.

a. I completely share your concern and I cannot come up with a
fix-maybe others have some suggestions
b. On the technical end I presume the scenario to deal with things
would be (please do correctly if I am wrong): mi extract to get the
datasets, psmatch2 to obtain the PS in each of the datasets,
reconstructing a master containing all PS variables, constructing a
variable containing the average PS, estimating the treatment effect.

Thanks,
Natalia

On Sat, Mar 22, 2014 at 1:49 AM, natalia malancu
<natalia.malancu@gmail.com> wrote:
> The references (totally skipped my mind, apologizes):
>
> Mitra, R. and Reiter, JP. (2011) Propensity score matching with missing
> covariates via iterated, sequential multiple imputation [Working Paper]
>
> Hill, J (2004) Reducing Bias in Treatment Effect Estimation in Observational
> Studies Suffering from Missing Datap [ISERP Working Papers]
>
>
> Adam: the paper I am referring to seems to be the earlier version of the one
> you are mentioning.
>
> a. I completely share your concern and I cannot come up with a fix-maybe
> other have some suggestions
> b. On the technical end I presume the scenario to deal with things would be
> (please do correctly if I am wrong): mi extract to get the datasets,
> psmatch2 to obtain the PS in each of the datasets, reconstructing a master
> containing all PS variables, constructing a variable containing the average
> PS, estimating the treatment effect.
>
> Thanks,
> Natalia
>
>
> On Sat, Mar 22, 2014 at 1:34 AM, Adam Olszewski <adam.olszewski@gmail.com>
> wrote:
>>
>> In their most recent paper:
>> Mitra R1, Reiter JP. A comparison of two methods of estimating
>> propensity scores after multiple imputation. Stat Methods Med Res.
>> 2012
>> they recommend:
>> 1) calculating PS in each imputed dataset
>> 2) averaging PS accross the imputations
>> 3) estimating treatment effect using the averaged PS
>> I am not sure how this addresses the problem of uncertainty of
>> estimates though. I am not aware of a method that would estimate the
>> treatment effect taking into consideration the uncertainty about the
>> propensity score.
>> AO
>>
>> On Fri, Mar 21, 2014 at 8:41 AM, natalia malancu
>> <natalia.malancu@gmail.com> wrote:
>> > Hi guys!
>> >
>> > After reading  Mitra, Robin and Reiter, Jerome P. (2011) and Hill's
>> > 2004 paper, I was wondering whether there is a way to:
>> > a. compute and then
>> > b. average propensity scores after multiple imputation. Causal
>> > inference to follow
>> >
>> > In STATA 12, which I am using, this is not possible with psmatch2. Is
>> > is possible in STATA 13 with teffects? Are there are options I am
>> > missing on?
>> >
>> > Any suggestions are much appreciated,
>> > Natalia
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