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Re: st: McNemar test for survey data
From
Steve Samuels <[email protected]>
To
[email protected]
Subject
Re: st: McNemar test for survey data
Date
Mon, 6 Jan 2014 10:24:47 -0500
The only disagreement I have with Roger's elegant approach is with the
following statement:
"You can then set this dataset up as a -svyset- dataset with -patid-
identifying the clusters."
The sample output that Ankit displayed in another post indicates that there
are 31 primary sampling units (PSUs). It is that PSU variable, not
-patid-, which should appear in the -svyset- statement.
Steve
On Jan 6, 2014, at 4:16 AM, Roger B. Newson wrote:
Yes, that is the case. And the confidence interval for the PAR or PUF gives you information on the size of the difference or ratio.
Besst wishes
Roger
Roger B Newson BSc MSc DPhil
Lecturer in Medical Statistics
Respiratory Epidemiology, Occupational Medicine
and Public Health Group
National Heart and Lung Institute
Imperial College London
Royal Brompton Campus
Room 33, Emmanuel Kaye Building
1B Manresa Road
London SW3 6LR
UNITED KINGDOM
Tel: +44 (0)20 7352 8121 ext 3381
Fax: +44 (0)20 7351 8322
Email: [email protected]
Web page: http://www.imperial.ac.uk/nhli/r.newson/
Departmental Web page:
http://www1.imperial.ac.uk/medicine/about/divisions/nhli/respiration/popgenetics/reph/
Opinions expressed are those of the author, not of the institution.
On 05/01/2014 23:54, Ankit Sakhuja wrote:
> Thanks so much for the help and sharing the presentation. One last
> question regarding this. After using regpar and punaf if the p value
> for PAR or PUF is <0.05, does that mean that the PAR, PUF and PAF are
> significant and thus there is a significant difference between the two
> test results?
> Thanks
> Ankit
>
> On Sun, Jan 5, 2014 at 2:02 PM, Roger B. Newson <[email protected]> wrote:
>> The first step in the solution is probably to use -reshape long- (see online
>> help for -reshape-). If your test results are named -testres1- and
>> -testres2-, and your "Observation No" is a patient ID vriable -patid-, and
>> your stratum variable is -stratid-, and your sample-probability variable is
>> -samprob-, then you might type
>>
>> reshape long testres, i(stratid patid samprob) j(testid)
>> lab var testid "Test ID"
>>
>> and this will replace your dataset in memory with a long version, with a
>> variable -testid-. You can then set this dataset up as a -svyset- dataset,
>> with -patid- identifying the clusters, -stratid- identifying the strata, and
>> -samprob- as the sampling-probability weoghts. You can then use -logit-,
>> with the -svy:- prefix, with -testres- as the Y-variable and -testid- as
>> the predictive factor, to fit the model. Of course, not many people
>> understand odds or odds ratios. So the final step would be to use the SSC
>> package -regpar- to estimate the proportions positive under beach test,and
>> the differencee between the proportions, which are displayed as a confidence
>> interval. As in:
>>
>> regpar, at(testid=1) atzero(testid=2)
>>
>> More aboout -regpar- can be found in an articlee in the latest Stata Journal
>> (Newson, 2013), and in a presentation I gave at the 2012 UK Stata User
>> Meeting (Newson, 2012). It is designed to work after -svy:- commands, as it
>> is a wrapper for -margins-.
>>
>> I hope this helps. Let me know if you have any further queries.
>>
>> Best wishes
>>
>> Roger
>>
>> References
>>
>> Newson RB. Attributable and unattributable risks and fractions and other
>> scenario comparisons. The Stata Journal 2013; 13(4): 672–698. Purchase from
>> http://www.stata-journal.com/article.html?article=st0314
>>
>> Newson RB. Scenario comparisons: How much good can we do? Presented at the
>> 18th UK Stata User Meeting, 13–14 September, 2012. Download from
>> http://ideas.repec.org/p/boc/usug12/01.html
>>
>>
>> Roger B Newson BSc MSc DPhil
>> Lecturer in Medical Statistics
>> Respiratory Epidemiology, Occupational Medicine
>> and Public Health Group
>> National Heart and Lung Institute
>> Imperial College London
>> Royal Brompton Campus
>> Room 33, Emmanuel Kaye Building
>> 1B Manresa Road
>> London SW3 6LR
>> UNITED KINGDOM
>> Tel: +44 (0)20 7352 8121 ext 3381
>> Fax: +44 (0)20 7351 8322
>> Email: [email protected]
>> Web page: http://www.imperial.ac.uk/nhli/r.newson/
>> Departmental Web page:
>> http://www1.imperial.ac.uk/medicine/about/divisions/nhli/respiration/popgenetics/reph/
>>
>> Opinions expressed are those of the author, not of the institution.
>>
>> On 05/01/2014 19:05, Ankit Sakhuja wrote:
>>>
>>> Thanks for the input. The survey sample that I am working on is a
>>> stratified sample using probability weights. It is probability the
>>> naivety and ignorance on my part but I am still not sure how to make
>>> the variable -testid- as all observations underwent both tests. To
>>> give an example my dataset looks like this:
>>>
>>> Observation No Result of Test 1 Result of Test 2
>>> 1 1 1
>>> 2 1 0
>>> 3 1 1
>>> 4 1 0
>>> 5 1 1
>>> 6 1 0
>>> 7 1 1
>>> 8 0 0
>>> 9 1 1
>>> 10 0 0
>>>
>>> So that in the above example the result of test 1 is 80% and for test
>>> 2 is 50% but all 10 observations got both tests.
>>>
>>> Or a different example could be that 10 patients were given medication
>>> A for asthma and after a washout period taking a medication B for the
>>> same. Then say with first medication 80% had a response and with
>>> second medication 50% had a response. So all observations got both
>>> medications (or tests) and therefore I am not sure if variable
>>> -testid- or -cat- (as in Samuel's example) can be made.
>>> Thanks again
>>> Ankit
>>>
>>> On Sun, Jan 5, 2014 at 11:39 AM, Roger B. Newson
>>> <[email protected]> wrote:
>>>>
>>>> This problem can probably be solved using -somersd-, -regpar-, -binreg-,
>>>> -glm-, or some other package that can estimate diferences between 2
>>>> proportions for clustered data. The first step would be to reshape your
>>>> data
>>>> (using either -reshape- or -expgen-) to have 1 observation per study
>>>> subject
>>>> per binary test (and therefore 2 observations per study subject as there
>>>> are
>>>> 2 binary tests). The binary outcome, in this dataset, would be the test
>>>> result. For each study subject, it would be the outcome of the first
>>>> binary
>>>> test in the first observation for that subject, and the outcome of the
>>>> second binary test in the second outcome. And the dataset would contain a
>>>> variable, maybe called -testid-, with the value 1 in observations
>>>> representing the first test, and 2 in observations representing the
>>>> second
>>>> test. The confidence interval to be calculated would be for the
>>>> difference
>>>> between 2 proportions, namely the proportion of positive outcomes where
>>>> -testid- is 2 and the proportion o positive results where -testid- is 1.
>>>>
>>>> You do not say what the sampling design is for your complex survey data.
>>>> However, if this design has clusters, then they will be the clusters to
>>>> use
>>>> when estimating your difference between proportions. And, if this design
>>>> does not have clusters, then the clusters used, when stimating your
>>>> difference between proportions, will be the study subjects themselves.
>>>> Either way, your final estimate will be clustered.
>>>>
>>>> I hope thhis helps. Let me know if you have any further queries.
>>>>
>>>> Best wishes
>>>>
>>>> Roger
>>>>
>>>> Roger B Newson BSc MSc DPhil
>>>> Lecturer in Medical Statistics
>>>> Respiratory Epidemiology, Occupational Medicine
>>>> and Public Health Group
>>>> National Heart and Lung Institute
>>>> Imperial College London
>>>> Royal Brompton Campus
>>>> Room 33, Emmanuel Kaye Building
>>>> 1B Manresa Road
>>>> London SW3 6LR
>>>> UNITED KINGDOM
>>>> Tel: +44 (0)20 7352 8121 ext 3381
>>>> Fax: +44 (0)20 7351 8322
>>>> Email: [email protected]
>>>> Web page: http://www.imperial.ac.uk/nhli/r.newson/
>>>> Departmental Web page:
>>>>
>>>> http://www1.imperial.ac.uk/medicine/about/divisions/nhli/respiration/popgenetics/reph/
>>>>
>>>> Opinions expressed are those of the author, not of the institution.
>>>>
>>>>
>>>> On 05/01/2014 16:55, Ankit Sakhuja wrote:
>>>>>
>>>>>
>>>>> Dear Members,
>>>>> I am trying to compare two categorical variables which are not
>>>>> mutually exclusive such that participants with a positive result in
>>>>> one group (using method 1) also have a positive result in second group
>>>>> (using method 2). Now say 30% have positive result by method 1 and 20%
>>>>> by method two, how can I say that these results are in fact similar or
>>>>> different? I could potentially use McNemar's but it is a complex
>>>>> survey data and I am not sure how to go ahead with that. I have seen
>>>>> discussions about using -somersd- but not sure how to exactly use it
>>>>> with this data. Would really appreciate any help.
>>>>> Ankit
>>>>> *
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