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From |
"Ben Dwamena" <bdwamena@med.umich.edu> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: RE: random-effects SROC model in gllamm |

Date |
Sun, 16 Dec 2007 18:52:20 -0500 |

Dear Jay, Thanks. However, the code you provide allows modeling the generic bivariate random-effects model for multiple 2 by 2 table which have implemented in midas ( ssc install midas). In that case you could avoid expanding by using bin(count) to convert binary to binomial regression. My question is related to an exact binomial equivalent of the following gaussian model: *logit transformation of sensitivity and specificity gen float lno0=logit(tp/(tp+fn)) gen float lno1=logit(fp/(tn+fp)) gen ds1=lno0-lno1 gen ds0=lno0+lno1 reshape long ds, i(study) j(dtruth) tabulate dtruth, generate(disgrp) eq het: disgrp1 disgrp2 eq disgrp1: disgrp1 eq disgrp2: disgrp2 gllamm ds disgrp1 disgrp2, nocons i(study) nrf(2) eqs(disgrp1 disgrp2) /// s(het) adapt ip(m) trace Ben Ben Adarkwa Dwamena, MD Assistant Professor of Radiology Division of Nuclear Medicine Department of Radiology University of Michigan Health Systems B1G505 UH SPC 5028 1500 E. Medical Center Drive Ann Arbor, MI 48109-5028 bdwamena@umich.edu http://sitemaker.umich.edu/metadiagnosis Staff Physician D748 Nuclear Medicine Service (115), VA Ann Arbor Health Care System 2215 Fuller Road Ann Arbor, MI 48105 734-845-3775 Phone 734-845-3252 Fax >>> "Verkuilen, Jay" <JVerkuilen@gc.cuny.edu> 12/16/2007 1:25 PM >>> >>I would appreciate assistance with a gllamm or xtmelogit code for the model described below for combining multiple 2 by 2 results of a binary diagnostic test with binary reference test outcome:<<< Coincidentally I set up something like this for my students in categorical data analysis recently. (They had to consider the output since the copy of Stata they have can't actually run this model.) Not sure if it's exactly what you want, but here it is. Uses the classic "betablocker" dataset (see below for plain text of it), modeling the probability of death. This study has 22 2X2 tables. i indexes study (1... 22) betablocker_i = 1 for treatment group and 0 for controls logit(mu_i) = alpha_i0 + alpha_i1*(betablocker_i) alpha_i0 = alpha_0 + u_i0 alpha_i1 = alpha_1 + u_i1 The u terms are unstructured MVN with mean 0. (It turns out for these data you can restrict things further and here really only need the random intercept.) Hopefully I didn't hatchet the notation here. This code fits the above model: xtmelogit death betablocker, || study: betablocker, covariance(unstructured) I had the data in long form already from Stata 9.2, and didn't rearrange things to use binomial processing, which would definitely be more efficient, though this only takes thirty seconds or so on my Macbook running Stata SE. <shrug> SAS nlmixed lets you use weights, and is therefore quicker. I didn't try GLLAMM. //need to run expand count on this first since xtmelogit doesn't like frequency weights. study betablocker death count 1 0 0 36 1 0 1 3 1 1 0 35 1 1 1 3 2 0 0 102 2 0 1 14 2 1 0 107 2 1 1 7 3 0 0 82 3 0 1 11 3 1 0 64 3 1 1 5 4 0 0 1393 4 0 1 127 4 1 0 1431 4 1 1 102 5 0 0 338 5 0 1 27 5 1 0 327 5 1 1 28 6 0 0 46 6 0 1 6 6 1 0 55 6 1 1 4 7 0 0 787 7 0 1 152 7 1 0 847 7 1 1 98 8 0 0 423 8 0 1 48 8 1 0 572 8 1 1 60 9 0 0 245 9 0 1 37 9 1 0 253 9 1 1 25 10 0 0 1733 10 0 1 188 10 1 0 1778 10 1 1 138 11 0 0 531 11 0 1 52 11 1 0 809 11 1 1 64 12 0 0 219 12 0 1 47 12 1 0 218 12 1 1 45 13 0 0 277 13 0 1 16 13 1 0 282 13 1 1 9 14 0 0 838 14 0 1 45 14 1 0 801 14 1 1 57 15 0 0 116 15 0 1 31 15 1 0 129 15 1 1 25 16 0 0 175 16 0 1 38 16 1 0 174 16 1 1 33 17 0 0 110 17 0 1 12 17 1 0 223 17 1 1 28 18 0 0 148 18 0 1 6 18 1 0 143 18 1 1 8 19 0 0 131 19 0 1 3 19 1 0 168 19 1 1 6 20 0 0 178 20 0 1 40 20 1 0 177 20 1 1 32 21 0 0 321 21 0 1 43 21 1 0 364 21 1 1 27 22 0 0 635 22 0 1 39 22 1 0 658 22 1 1 22 ********************************************************** Electronic Mail is not secure, may not be read every day, and should not be used for urgent or sensitive issues * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**st: RE: RE: random-effects SROC model in gllamm***From:*"Verkuilen, Jay" <JVerkuilen@gc.cuny.edu>

**References**:**st: random-effects SROC model in gllamm***From:*"Ben Dwamena" <bdwamena@med.umich.edu>

**st: RE: random-effects SROC model in gllamm***From:*"Verkuilen, Jay" <JVerkuilen@gc.cuny.edu>

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