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Re: st: using -mixed- with clustered data that includes probability weights


From   Stas Kolenikov <[email protected]>
To   "[email protected]" <[email protected]>
Subject   Re: st: using -mixed- with clustered data that includes probability weights
Date   Thu, 27 Feb 2014 22:25:04 -0500

Alfonso, I would say that this would lead to double counting of who
was oversampled. Doctors are domains, in terms of survey statistics;
and, as I said, I would not touch them since they were not sampled
directly.


-- Stas Kolenikov, PhD, PStat (ASA, SSC)
-- Principal Survey Scientist, Abt SRBI
-- Opinions stated in this email are mine only, and do not reflect the
position of my employer
-- http://stas.kolenikov.name



On Thu, Feb 27, 2014 at 10:09 PM, Alfonso Sánchez-Peñalver
<[email protected]> wrote:
> If doctors were not sampled, then they basically are a consequence of the sampling of the patients. Since we know that some patients have been over-sampled and others under-sampled, the question really is what proportion of each type of patients does each doctor have. Because a doctor would then be over-sampled or under-sampled depending on the over-sampling and under-sampling of the patients. Wouldn't they? Thus it may be appropriate to estimate a weighted average of the patients' weights for a doctor, where the weights for this weighted average could be the illness severity score, since it's the basis for the over-sampling. Don't you agree?
>
> Alfonso Sanchez-Penalver, PhD
>
> On Feb 27, 2014, at 9:48 PM, Steve Samuels <[email protected]> wrote:
>
>> To elaborate on Stas's post: if doctors were not sampled, then you can
>> define the doctor-level weight with pweight(1).
>>
>> Steve
>> [email protected]
>>
>> On Feb 27, 2014, at 8:21 PM, Stas Kolenikov <[email protected]> wrote:
>>
>> Whether your results are biased really depends on your study design.
>> -mixed- knows nothing about your design (Stata 14 request: a command
>> to read the pdf file and extract the design information from the
>> narrative -- may I suggest -pdf2svyset- as a prospective name? I am
>> sure a lot of researchers would find this handy), and just warns you
>> in case you had sampling at several levels. If there were no sampling
>> at the doctor's level, then weighting only at the patient level that
>> you have is appropriate.
>>
>> -- Stas Kolenikov, PhD, PStat (ASA, SSC)
>> -- Principal Survey Scientist, Abt SRBI
>> -- Opinions stated in this email are mine only, and do not reflect the
>> position of my employer
>> -- http://stas.kolenikov.name
>>
>>
>>
>> On Thu, Feb 27, 2014 at 7:55 PM, Stephen Henry <[email protected]> wrote:
>>> Hi,
>>>
>>> I want to know whether I can used -mixed- in Stata 13.1 to analyze
>>> clustered data that include probability weights.
>>>
>>> My data were collected to study patients during clinic visits.
>>> Each patient is unique, and patients are clustered within doctors.
>>> In addition, patients were sampled based on an illness severity score.
>>> Patients with more severe symptoms were over-sampled.
>>> Patient sampling was done independent of which doctor was seeing the
>>> patient.
>>>
>>> I have been analyzing data using the -reg- command and cluster option as
>>> follows:
>>>
>>> reg v1 v2 v3 [pweight=weight], cluster(doctor_id)
>>>
>>> However, I'd like to use -mixed- instead to take advantage of the
>>> additional postestimation commands.
>>>
>>> Stata will run the following command:
>>>
>>> mixed v1 v2 v3 [pweight=weight] || doctor_id:
>>>
>>> but warns me that "Sampling weights were specified only at the first level
>>> in a multilevel model."
>>> Are my results with the -mixed- command potentially biased?  If so, is
>>> there an easy way to fix this?
>>>
>>> Thanks in advance,
>>>
>>> Stephen Henry
>>> University of California Davis
>>> Sacramento, California
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