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| From | Ankit Sakhuja <a.sakhuja@gmail.com> |
| To | statalist@hsphsun2.harvard.edu |
| Subject | Re: st: McNemar test for survey data |
| Date | Sun, 5 Jan 2014 17:54:17 -0600 |
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 >>>> * >>>> * For searches and help try: >>>> * http://www.stata.com/help.cgi?search >>>> * http://www.stata.com/support/faqs/resources/statalist-faq/ >>>> * http://www.ats.ucla.edu/stat/stata/ >>>> >>> * >>> * For searches and help try: >>> * http://www.stata.com/help.cgi?search >>> * http://www.stata.com/support/faqs/resources/statalist-faq/ >>> * http://www.ats.ucla.edu/stat/stata/ >> >> >> >> > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/faqs/resources/statalist-faq/ > * http://www.ats.ucla.edu/stat/stata/ -- Ankit * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/