Re: st: Propensity score matching after multiple imputation

[Adam Olszewski <adam.olszewski@gmail.com>]

The technicalities depend on how you are storing your MI data (I use
the wide format) and what kind of propensity score adjustment you are
envisaging (matching, stratification, weighting). You could also
perhaps:
1) -mi estimate: logistic- your PS model in all datasets.
2) -mi predict- the propensity score.
3) use the predicted score in a -psmatch2, pscore()- call.
You may need to -mi unset- the dataset before #3, I believe that at
least -psgraph- does not work with MI data.
I'm not guaranteeing though that this is a statistically sound procedure.
Adam

On Fri, Mar 21, 2014 at 11:02 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 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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>>
>>
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