Re: st: McNemar test for survey data

[Ankit Sakhuja <a.sakhuja@gmail.com>]

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 <r.newson@imperial.ac.uk> 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: r.newson@imperial.ac.uk
> 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
>> <r.newson@imperial.ac.uk> 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: r.newson@imperial.ac.uk
>>> 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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>>
>>
>>
>>
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-- 
Ankit

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