Re: st: Propensity score matching after multiple imputation

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

Thanks Adam. I'll give it a try and see how to it works.

On Sat, Mar 22, 2014 at 6:38 AM, Adam Olszewski
<adam.olszewski@gmail.com> wrote:
> 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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