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Re: st: Propensity score matching after multiple imputation
From
natalia malancu <[email protected]>
To
[email protected]
Subject
Re: st: Propensity score matching after multiple imputation
Date
Sat, 22 Mar 2014 18:54:10 +1100
Just realized that there is a scenario in which things do not work out
- that in which you are MI on the treatment variable (since psmatch2 /
teffects do not work with mi). Any thoughts on how one could go around
that?
Once again thanks,
Natalia
On Sat, Mar 22, 2014 at 5:25 PM, natalia malancu
<[email protected]> wrote:
> 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
> <[email protected]> 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
>> <[email protected]> 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
>>> <[email protected]> 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 <[email protected]>
>>>> 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
>>>>> <[email protected]> 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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