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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
Sun, 5 Jan 2014 21:39:23 -0500
Unfortunately "test1" inherited the foreign's value
labels. Eliminated here.
SS
Here is the example in
(http://www.stata.com/statalist/archive/2010-03/msg00937.html),
specialized to your nomenclature. Roger's approach
requires an id variable for the reshape, but this does
not.
******************CODE BEGINS***********
sysuse auto, clear
gen test1 = foreign
svyset _n [pw = turn], strata(rep78)
set seed 2000
gen u=uniform()
sort u
gen test2 = _n<39
svy: tab test1 test2
lincom _b[p12] - _b[p21]
*******************CODE ENDS*************
As I stated in the post, the hypothesis _b[p12] = _b[p21] is exactly
the hypothesis tested in McNemar's test. And, it is equivalent to
the more useful formulation that the proportions positive are
the same for test 1 and test 1.
Steve
[email protected]
On Jan 5, 2014, at 2:05 PM, 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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>>
> *
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> * http://www.ats.ucla.edu/stat/stata/
--
Ankit
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