diagnostic test meta analysis using glmer
Dear Nathan, You can use the glht() function from the multcomp package to do post-hoc tests on contrasts. You should create a custom K matrix. See https://thebiobucket.blogspot.be/2011/06/glmm-with-custom-multiple-comparisons.html#more Best regards, Thierry ir. Thierry Onkelinx Statisticus / Statistician Vlaamse Overheid / Government of Flanders INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND FOREST Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance thierry.onkelinx at inbo.be Kliniekstraat 25, B-1070 Brussel www.inbo.be /////////////////////////////////////////////////////////////////////////////////////////// To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey /////////////////////////////////////////////////////////////////////////////////////////// [image: Van 14 tot en met 19 december 2017 verhuizen we uit onze vestiging in Brussel naar het Herman Teirlinckgebouw op de site Thurn & Taxis. Vanaf dan ben je welkom op het nieuwe adres: Havenlaan 88 bus 73, 1000 Brussel.] <https://overheid.vlaanderen.be/mobiliteitsplan-herman-teirlinckgebouw> Van 14 tot en met 19 december 2017 verhuizen we uit onze vestiging in Brussel naar het Herman Teirlinckgebouw op de site Thurn & Taxis. Vanaf dan ben je welkom op het nieuwe adres: Havenlaan 88 bus 73, 1000 Brussel. /////////////////////////////////////////////////////////////////////////////////////////// <https://www.inbo.be> 2017-11-14 7:04 GMT+01:00 Nathan Pace <n.l.pace at utah.edu>:
Hi,
I am modeling the sensitivity and specificity of 7 diagnostic tests in a
bivariate binomial model using glmer.
glmer(formula = cbind(true, n - true) ~ 0 + seM + spM + seMM + spMM +
seMouth + spMouth +
seSM + spSM + seTM + spTM + seULBT + spULBT +
seW + spW +
(0 + sens + spec | studyName), data =
Compare_DL.df, family = binomial, nAGQ = 1)
The model without separating diagnostic tests is
glmer(formula = cbind(true, n - true) ~ 0 + sens + spec + (0 + sens +
spec | studyName),
data = Compare_DL.df, family = binomial, nAGQ = 1)
The 7 test model assumes equal variances across tests.
The dataset includes sens, spec, seM, spM, etc as dummy index variables.
Both models can run and converge.
ANOVA shows improved fit:
Df AIC BIC logLik deviance
Chisq Chi Df Pr(>Chisq)
Simple model 5 8987.8 9007.6 -4488.9 8977.8
Separate tests 17 6878.5 6945.8 -3422.3 6844.5 2133.3 12 <
2.2e-16 ***
I need to identify any separation of sensitivity and specificity
properties among the 7 tests.
One possibility would be to jointly contrast seTesti ? seTestj = 0 and
spTesti ? spTestj = 0 for all pairwise comparisons of the 7 tests (with
multiplicity adjustment).
However, I am unable to construct such tests in lme4. Is this possible in
lme4? If so, what is the code?
I have looked at other packages (multcomp) without success.
As usual, all help will be appreciated.
Nathan Pace, MD, MStat
University of Utah
Salt Lake City, UT
n.l.pace at utah.edu
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