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9.7 References
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Takwoingi Y, Guo B, Riley RD, Deeks JJ. Performance of methods for meta- analysis of
diagnostic test accuracy with few studies or sparse data. Statistical Methods in Medical
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Tosteson AN, Begg CB. A general regression methodology for ROC curve estimation.
Medical Decision Making 1988; 8: 204–215.
Treanor L, Frank RA, Cherpak LA, Dehmoobad Sharifabadi A, Salameh JP, Hallgrimson Z,
Fabiano N, McGrath TA, KraaijpoelN, Yao J, Korevaar DA, Bossuyt PM, McInnes MDF.
Publication bias in diagnostic imaging: conference abstracts with positive conclusions
are more likely to be published. European Radiology 2020; 30: 2964–2972.
Trikalinos TA, Hoaglin DC, Small KM, Terrin N, Schmid CH. Methods for the joint
meta- analysis of multiple tests. Research Synthesis Methods 2014; 5: 294–312.
Umemneku Chikere CM, Wilson K, Graziadio S, Vale L, Allen AJ. Diagnostic test evaluation
methodology: a systematic review of methods employed to evaluate diagnostic tests in
the absence of gold standard– an update. PloS One 2019; 14: e0223832.
Vacek PM. The effect of conditional dependence on the evaluation of diagnostic tests.
Biometrics 1985; 41: 959–968.
van Enst WA, Ochodo E, Scholten RJ, Hooft L, Leeflang MM. Investigation of publication
bias in meta- analyses of diagnostic test accuracy: a meta- epidemiological study. BMC
Medical Research Methodology 2014; 14: 70.
van Smeden M, Naaktgeboren CA, Reitsma JB, Moons KG, de Groot JA. Latent class models
in diagnostic studies when there is no reference standard—
a systematic review.
American Journal of Epidemiology 2014; 179: 423–431.
Veroniki AA, Tsokani S, Agarwal R, Pagkalidou E, Rücker G, Mavridis D, Takwoingi Y.
Diagnostic test accuracy network meta-
analysis methods: a scoping review and empiri-
cal assessment. Journal of Clinical Epidemiology 2022; 146: 86–96.
Walter SD, Irwig L, Glasziou PP. Meta-
analysis of diagnostic tests with imperfect reference
standards. Journal of Clinical Epidemiology 1999; 52: 943–951.
Wetterslev J, Thorlund K, Brok J, Gluud C. Estimating required information size by
quantifying diversity in random- effects model meta- analyses. BMC Medical Research
Methodology 2009; 9: 86.
Xie X, Sinclair A, Dendukuri N. Evaluating the accuracy and economic value of a new test in
the absence of a perfect reference test. Research Synthesis Methods 2017; 8: 321–332.
Zhou XH, Obuchowski N, McClish D. Statistical methods in diagnostic medicine. 2nd ed.
Chichester: John Wiley & Sons; 2011.
Zhou Y, Dendukuri N. Statistics for quantifying heterogeneity in univariate and bivariate
meta- analyses of binary data: the case of meta- analyses of diagnostic accuracy.
Statistics in Medicine 2014; 33: 2701–2717.
Zwinderman AH, Bossuyt PM. We should not pool diagnostic likelihood ratios in systematic
reviews. Statistics in Medicine 2008; 27: 687–697.
247
10
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Undertaking meta-
Yemisi Takwoingi, Nandini Dendukuri, Ian Schiller, Gerta Rücker, Hayley E. Jones,
Christopher Partlett and PetraMacaskill
KEY POINTS
Hierarchical models such as the bivariate and hierarchical summary receiver operating
•
characteristic (HSROC) models are recommended for test accuracy meta- analysis. The
models can be fitted in a frequentist or Bayesian statistics framework.
To fit these hierarchical models, several statistical software packages and user- written
•
programs are available for frequentist analyses, while Bayesian estimation within the
R software environment facilitates analyses by applied health researchers.
Convergence issues arising from analysis of sparse or atypical data are common, but
•
there are potential solutions, including simplifying hierarchical models.
For tests that produce a continuous numerical result, one of two promising approaches
•
that extend the bivariate model to allow the inclusion of multiple thresholds per study
can be used to obtain summary estimates of sensitivity and specificity at particular
thresholds and a summary curve across thresholds.
When the reference standard is imperfect, an extension of the bivariate model, assuming the
•
target condition cannot be observed, can be implemented via latent class meta- analysis.
analysis
10.1 Introduction
Hierarchical models are recommended for meta- analysis of test accuracy studies, as
explained in Chapter9. Cochrane’s primary authoring tool, Review Manager (RevMan; www.
training.cochrane.org/online- learning/core- software/revman), which can be used to write
systematic reviews of test accuracy, does not have the capability for fitting the bivariate and
HSROC models described in Chapter9. Therefore, external analysis using a statistical software package is required to fit such hierarchical models. If review authors are using RevMan,
This chapter should be cited as: Takwoingi Y, Dendukuri N, Schiller I, Rücker G, Jones HE, Partlett C, Macaskill
P. Chapter 10: Undertaking meta- analysis. In: Deeks JJ, Bossuyt PM, Leeflang MM, Takwoingi Y, editors.
Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy. 1st edition. Chichester (UK): John
Wiley & Sons, 2023: 249–326.
Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy, First Edition. Edited by
Jonathan J. Deeks, Patrick M. Bossuyt, Mariska M. Leeflang and Yemisi Takwoingi.
© 2023 The Cochrane Collaboration. Published 2023 by John Wiley & Sons Ltd.
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10 Undertaking meta- analysis
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parameter estimates can then be entered to generate summary receiver operating
characteristic (SROC) plots showing summary points or summary curves as appropriate.
The bivariate model is a generalized linear mixed model (GLMM) and several statistical
packages (e.g. SAS, Stata, R,BUGS and JAGS) are available for fitting the model using
binomial likelihoods to model within- study variability rather than a normal approximation (see the model specification in Chapter9, Section9.4.1). The HSROC model is a nonlinear generalized mixed model (GMM) and options for fitting this model are currently
limited to packages that can fit nonlinear GMMs (e.g. SAS, BUGS and JAGS).
The meta- analysis methods introduced in Chapter9 were presented using frequentist
estimation methods. However, several of the more advanced analyses can also be
undertaken using Bayesian estimation. Throughout this chapter we present analyses
performed using both frequentist and Bayesian approaches wherever possible. The frequentist approach is typically preferred for simpler models and analyses; as there is
substantial experience in the use of these methods, user-
written programs are more
widely available and easier to use. Frequentist methods also do not require the specification of prior distributions for the parameters that are estimated, which occasionally
can have undesirable influence on the results.
This chapter complements Chapter9. Depending on familiarity with the concepts
underpinning test accuracy meta- analysis and the software packages, the reader may
find some sections more challenging than others. The chapter gives an overview of how
to fit the hierarchical models within a frequentist framework using three software packages (SAS, Stata and R) and within a Bayesian framework using rjags (an interface from
R to the JAGS library for Bayesian data analysis).
A prerequisite to understanding the Bayesian estimation sections of this chapter is
knowledge of basic methods for Bayesian inference (Spiegelhalter 2004, Gelman 2013,
Kruschke 2015). Also, familiarity with using JAGS or BUGS languages (Lunn 2009,
Plummer2019) to fit simple models, such as models for estimating a single proportion
or for logistic regression, will aid in understanding of the more complex models
described in this chapter.
Bayesian model specification requires the user to provide (1) the likelihood function
and (2) the prior distribution functions for all unknown parameters. The software takes
care of implementing the necessary Monte Carlo Markov Chain (MCMC) algorithms in
the background to provide the user with samples from the posterior distributions of the
parameters of interest. Thus, the user needs only to be familiar with details of the syntax and does not have to carry out complex calculations. On the other hand, it is very
important that the user is familiar with methods for verifying whether the MCMC algorithms have converged and for carrying out sensitivity analyses to assess the impact of
different prior distribution functions.
Using examples introduced in Chapter9, Sections10.2,10.3,10.4 and10.5 illustrate the
estimation and comparison of summary points and curves when assuming a perfect reference standard. In addition to inbuilt software commands, user- written programs and
macros that give outputs compatible with RevMan are highlighted. Section10.6 provides
suggestions on meta- analyses of problematic or atypical data sets, including simplifying
hierarchical models, one of the approaches recommended in Chapter9, Section9.4.8, for
meta- analysis of sparse data. Section10.7 uses an example to elaborate on meta- analysis
methods introduced in Chapter9, Section9.4.5, that allow multiple thresholds per study.
Using a Cochrane Review of diagnostic test accuracy as an example, Section10.8
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10.2 Estimation ofa summary point
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illustrates latent class meta- analysis, an approach introduced in Chapter9, Section9.5.1,
for meta- analysis with an imperfect reference standard. Section10.9 concludes the
chapter with a summary and information on additional resources.
The frequentist analyses were performed using SAS version 9.4 (SAS Institute, Cary,
NC, USA), Stata version 16 (Stata- Corp, College Station, TX, USA) and R version 4.1.0.
The corresponding SROC plots were produced using RevMan version 5.3.
10.2 Estimation ofa summary point
The focus of the bivariate model is the estimation of a summary point. As described in
Chapter9, Section9.4.1, the bivariate method models sensitivity and specificity directly
and has five parameters when no covariates are included: μ
parameters μA and μB are the mean logit(sensitivity) and mean logit(specificity), respectively, σ
2
and σ
A
2
describe the between- study variability in logit(sensitivity) and
B
logit(specificity), and σAB is the covariance between logit(sensitivity) and logit(specificity).
The model may also be parametrized using the correlation ρAB=σAB/(σAσB). Anti- CCP for
diagnosis of rheumatoid arthritis is one of the two index tests in the review (Nishimura
2007) introduced in Chapter9, Section9.2.3.1. Meta- analysis of anti- CCP was illustrated
using the bivariate model in Chapter9, Section9.4.2, and this example will be used
throughout this section. The sensitivities and specificities of the 37included studies are
shown on the forest plot in Chapter9, Figure9.2.a.
, μB, σ
A
2
2
, σ
and σAB. The
A
B
10.2.1 Fitting the bivariate model using SAS
Hierarchical models can be fitted using the NLMIXED and GLIMMIX procedures in SAS.
The NLMIXED procedure fits linear and nonlinear GMMs while GLIMMIX fits only
GLMMs. This chapter focuses on the NLMIXED procedure because it can be used to fit
both the bivariate and HSROC models. The NLMIXED procedure uses maximum likelihood to estimate the parameters of a model, and requires a regression equation and
declaration of parameters with their starting values. These starting values are required
for the iterative process, and it is essential to select good values in order to avoid excessively long computing time, and also to facilitate convergence of the optimization process for solving the maximum likelihood estimation problem. A single value can be
chosen for each parameter or a set of values by using the TO and BY keywords to specify
a number list for a grid search. For example, in the SAS code in the online supplementary material (10.S1 Code for undertaking meta- analysis, see Appendix 1), ‘msens =1
to 2 by 0.5’ defines the grid of values to search for starting values for ‘msens’, the
logit(sensitivity) parameter.
The parameter estimates from the bivariate model fitted in SAS using the NLMIXED
code in Appendix 1 of the online supplementary material (10.S1 Code for undertaking
meta- analysis) are shown in Box10.2.a. The parameter estimates in the red and blue
boxes can be entered into the corresponding analysis in RevMan to generate the summary point, as can the 95% confidence and 95% prediction regions shown in Chapter9,
Figure 9.4.a. The bivariate output box in RevMan requires the estimate for mean
logit(sensitivity), which is 0.6534; the estimate for mean logit(specificity), which is
3.1090; and the variances of the random effects for logit(sensitivity), logit(specificity)
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10 Undertaking meta- analysis
exp exp 0.6534
0.66
logit sensitivity
Sensitivity
exp exp 3.1090
0.96
logit specificity
Specificity
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Box 10.2.a SAS output of bivariate model parameters
Parameter Estimates
Parameter
msens
mspec
s2usens
s2uspec
covsesp
Estimate
–0.2704
Standard
Error
0.6534
3.1090
0.5426
0.5717
msens
mspec
s2usens
s2uspec
covsesp
0.1275
0.1459
0.1463
0.1873
0.1199
Covariance Matrix of Parameter Estimates
–0.00741
0.000890
–0.00004
–0.00004
DF t Value Pr > |t| 95% Confidence Limits Gradient
5.13
35
21.31
35
3.71
35
3.05
35
2.26
35
msens mspec s2usens s2uspec covsesp
0.01625
–0.00741
0.02128
–0.00006
0.004287
–0.00116
0.000890
–0.00006
0.02142
0.003997
–0.00874
<.0001
<.0001
0.0007
0.0043
0.0304
0.3946
2.8128
0.2455
0.1914
–0.5137
–0.00004
0.004287
0.003997
0.03509
–0.01184
–0.02710
–0.00004
–0.00116
–0.00874
–0.01184
0.01436
0.9122
3.4052
0.8397
0.9520
3.959E-6
3.472E-8
–6.62E-6
1.36E-6
–1.59E-6
and their covariance, which are 0.5426, 0.5717, and −0.2704, respectively. Computation
of confidence and prediction regions also requires the standard error of the estimates
for mean logit(sensitivity), mean logit(specificity) and their covariance, which are
0.1275, 0.1459 and −0.00741, respectively (shown in the blue boxes).
The summary sensitivity and specificity can be obtained by inverse transformation of
the estimates for mean logit(sensitivity) and logit(specificity) (0.6534 and 3.1090) to
give a sensitivity and specificity of 0.66 and 0.96, respectively. This calculation can be
done using the following equations.
1 exp 1 exp 0.6534
1 exp 1 exp 3.1090
logit sensitivity
logit specificity
The 95% confidence intervals (CI) for the summary estimates can be similarly obtained
by inverse transformation of the 95% CI of the mean logit estimates.
The SAS macro MetaDAS is a wrapper for NLMIXED to automate fitting bivariate
and HSROC models to produce parameter and summary estimates (Takwoingi 2010).
The macro requires a minimum of two or three input parameters, depending on whether
data import (e.g. from a spreadsheet) is required or a SAS data setalready exists.
Theoutput from the analysis is saved in a rich text Word file and presented in tables,
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10.2 Estimation ofa summary point
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from which parameter estimatescan be copied and pasted into RevMan to generate
SROC plots. Example code is available in Appendix 1 of the online supplementary
material (10.S1 Code for undertaking meta- analysis) and a user guide with a worked
example can be found on the Cochrane Screening and Diagnostic Tests Methods Group
website (www. methods.cochrane.org/sdt).
10.2.2 Fitting thebivariate model using Stata
The user- written programs metandi (Harbord 2008, Harbord 2009) and midas
(Dwamena 2007) use the xtmelogit or gllamm commands to perform bivariate
meta- analysis of sensitivity and specificity using a GLMM approach. However, midas
does not give parameter estimates in the output and so cannot be used with RevMan.
Therefore, only metandi will be illustrated in this section.
The GLMM estimation routine xtmelogit was introduced in Stata 10 and replaced
with meqrlogit in Stata 13. In Stata 10 and above, metandi fits the model using the
command xtmelogit by default. In Stata 8 or 9, metandi uses gllamm, which
must also be installed. The ssc command allows users to download a user-
written
package from the Boston College Statistical Software Components (SSC) archive. For
example, to download and install metandi, type the following in the command window in Stata:
ssc install metandi
The metandi command requires four input variables: the number of true positives
(tp), false positives (fp), false negatives (fn) and true negatives (tn) within each study.
metandi tp fp fn tn
The results of the meta-analysis of anti-CCP using the metandi command are shown in
Box 10.2.b.
Users of Stata 10 and above may choose to use option gllamm with metandi,
which runs slower than xtmelogit but can sometimes solve convergence issues commonly encountered in meta- analysis of sparse data (see Section10.6). Use the help
command in Stata to learn more about metandi and its options, some of which are
included in the code in Appendix 2 of the online supplementary material (10.S1 Code for
undertaking meta- analysis). Although metandi only fits the bivariate model, it can
output HSROC model parameters using functions of the parameter estimates from the
bivariate model, since the two models are mathematically equivalent when no covariates are fitted (see Chapter9, Section9.4). Summary test accuracy measures are also
reported as shown in the green box in Box10.2.b. The parameter estimates in the red
and blue boxes can be entered into RevMan as explained in Section10.2.1. The metandi
command is straightforward to use, but does not have an option for including a covariate in the bivariate model, i.e. one cannot perform meta- regression to investigate heterogeneity or compare test accuracy. Furthermore, there are limited options to try
when convergence issues are encountered (see Section10.6). Therefore it is useful to
know how to fit the model using the meqrlogit command for greater flexibility.
The code provided in Appendix 3 of the online supplementary material (10.S1 Code for
undertaking meta- analysis) uses meqrlogit but can be replaced with xtmelogit
253
10 Undertaking meta- analysis
Meta-analysis of diagnostic accuracy
al]
.9035981
3.395928
1
1.098663
–.1092559
4.251751
–.796087
.4157994
1.144094
.7082225
.7116884
7
8
19.82764
.4195441
3.282909
37
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Box 10.2.b Stata output of bivariate model parameters using metandi
Log likelihood Number of studies =
Bivariate
E(logitSe) .6534697 .1276188 .4033414
E(logitSp) 3.108991 .1463993 2.822053
Var(logitSe) .5438584 .1467547 .3204777 .92294
Var(logitSp) .5769244 .1896049 .3029517
Corr(logits) –.4820203 .1629024 –.7359252
HSROC
Lambda 3.726636 .2679207 3.201521
Theta –1.206134 .2061505 –1.604182
beta .6295111 .1970895 0.150.881 –.3567771
s2alpha .58029 .2009854 .2943259
s2theta .415075 .1131522 .2432671
Summary pt.
Se .6577919 .0287272 .5994902
Sp
DOR
LR+
LR-
1/LR-
Covariance between estimates of E(logitSe) & E(logitSp) -.0074001
= -273.09555
Coef. Std. Err.zP>|z| [95% Conf. Interv
.9572621 .0059894 .943856 .96757
43.05422 6.517999 32.00015 57.926
15.39129 1.988927 11.94756
.3574863 .0291962 .3046079
2.79731 .2284584 2.383539
without changing the code syntax. The output in Box10.2.c shows the meqrlogit command line that was executed along with the estimation log and two tables. The contents
of the command line are explained alongside the code in Appendix 3 of the online supplementary material (10.S1 Code for undertaking meta- analysis). The estimation log
includes a set of iterations used to refine starting values and a set of gradient- based iterations. By default, these are Newton- Raphson iterations, but other methods are available
by specifying the appropriate maximize options. The first estimation table reports the
fixed effects and the second table reports the variance components. The first section of
the latter is labelled ‘studyid: Unstructured’, meaning these are random effects at
the study level (the studyid variable identifies each study) with unstructured covariance,
i.e. each variance and covariance are estimated uniquely from the data.
The five parameters (mean logits, variance and covariance estimates) of the bivariate
model are shown in the red boxes. This covariance estimate is the covariance of the
logits across studies. Unlike metandi, the output in Box10.2.c shows the estimate for
the covariance instead of the correlation parameter. This is because the variance
option displays the random- effects parameter estimates as variances and covariances;
to display them as standard deviations and correlations, use the option stddevia-
tions to obtain the output in the red box in Box10.2.d.
254
Box 10.2.c Stata output of bivariate model parameters using meqrlogit
1
5
5
studyid:
]
4
4
]
Lo
=0
0
Integratio
=6
0
2
0
2
Grou
=37
Binomial
Mixed-effect
74
Iteratio
Iteratio
Iteratio
Iteratio
Performing
Iteratio
Iteratio
Iteratio
Iteratio
Refining
>n
.meqrlogit true sens spec, nocons|| studyid: sens spec,///
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oconscov(un) binomial(n) refineopts(iterate(3))intpoints(5) variance
starting values:
n0:log likelihood=-279.47997
n1:log likelihood=-273.46004
n2:log likelihood=-273.13205
n3:log likelihood=-273.21507
gradient-based optimization:
n0:log likelihood=-273.21507
n1:log likelihood=-273.07355
n2:log likelihood=-273.07286
n3:log likelihood=-273.07286
slogistic regression Number of obs=
variable:n
pvariable: studyidNumberofgroups
10.2 Estimation ofa summary point
glikelihood=-273.07286Prob>chi2
Random-effects Parameters EstimateStd.Err.[95%Conf. Interval
npoints= 5Waldchi2(2)
true Coef. Std. Err. zP>|z| [95% Conf. Interval
sens .6534704 .1276192 5.12 0.000.4033414 .903599
spec 3.109004 .1464056 21.240.000 2.822054 3.39595
Unstructured
cov(sens,spec) -.2699989.1203215-.5058247 -.034173
The contents of the variance–covariance matrix need to be displayed using the
matrix list command to obtain the covariance between mean logit(sensitivity) and
logit(specificity), as shown in the blue box in Box10.2.e. This covariance and the standard errors of the estimates for mean logit(sensitivity) and logit(specificity) are needed
to draw confidence and prediction regions in RevMan.
There are negligible differences in results between metandi and meqrlogit due
to the iterative nature of the maximum likelihood estimation and the choice of
Obsper group:
var(sens) .5438606.1467555.3204787 .922945
var(spec) .5769862.1896171.3029926 1.0987
min=
avg= 2.
max=
68.1
.000
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10 Undertaking meta- analysis
atr1_1_1_2:_cons .00040708-.00037087-.00526803-.00502673.04503202
:
symmetri
.matrix list e(V)
7
3
8
studyid:
]
4
4
]
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Box 10.2.d Stata output of random- effects parameters of the bivariate model as
standard deviations using meqrlogit
true Coef.Std.Err.zP>|z|[95%Conf. Interval
sens .6534704 .1276192 5.12 0.000.4033414.903599
spec 3.109004 .1464056 21.240.0002.8220543.39595
Random-effects Parameters Estimate Std. Err.[95%Conf. Interval
Unstructured
corr(sens,spec) -.4819871 .1629037 -.735899 -.10922
sd(sens) .7374691 .0994995 .5661083 .960700
sd(spec) .7595962 .1248144 .5504477 1.04821
Box 10.2.e Stata output of variance–covariance matrix after estimation with
meqrlogit
ce(V)[5,5]
eq1:sens .01628658
eq1:spec -.00740014 .02143278
lns1_1_1:_cons .00082191-.00005911.01820336
lns1_1_2:_cons -.00003298 .00376008.00318077.02700229
eq1: eq1: lns1_1_1: lns1_1_2:atr1_1_1_2
sens spec _cons_cons _cons
options– intpoints() and refineopts()– that control the process. The option
intpoints(5)specifies the number of integration points for adaptive Gaussian
quadrature, while option refineopts(iterate(3)) controls the maximization
process during the refinement of starting values. Two iterations is the default. Should
the analysis fail to converge, one possible solution is to increase the number of integration points and/or iterations. An alternative is to use the gllamm command, which
appears to be better at obtaining feasible starting values for the likelihood estimation
than xtmelogit or meqrlogit. The code for fitting the bivariate model using
gllamm and part of the output are available in Appendix 4 of the online supplementary
material (10.S1 Code for undertaking meta-
analysis). Other suggestions for dealing
with convergence issues are considered in Section10.6.
10.2.3 Fitting thebivariate model using R
Dewey (2018) provides an overview of a range of packages in R for meta- analysis, including meta- analysis of test accuracy studies. Table10.2.a summarizes the functionality of R
packages for meta- analysis of test accuracy that we were aware of in August 2021. It is
important to note that not all of the packages in Table10.2.a are compatible with RevMan,
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10.2 Estimation ofa summary point
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Table10.2.a Summary ofpackages formeta- analysis oftest accuracy inR
Package (reference) Model Meta- regression Compatible
with RevMan
lme4 (glmer function in
lme4 for fitting GLMMs in R)
(Bates 2016)
mvmeta (Gasparrini2015)
mada (Doebler2015)
CopulaDTA (Nyaga 2017) Bivariate beta- binomial Yes No None
meta4diag (Guo 2015) Bivariate Bayesian Yes No INLA
bamdit (Verde2018) Bivariate Bayesian No No JAGS
diagmeta (Rücker 2020) Bivariate No No None
CopulaREMADA
(Nikoloulopoulos2015)
NMADiagT (Lu 2020) Network meta- analysis No No JAGS
GLMM, generalized linear mixed model; INLA, Integrated Nested Laplace Approximation; JAGS, Just Another
Gibbs Sampler.
a
These packages implement the bivariate model with a normal within- study likelihood (i.e. a linear mixed
model rather than a GLMM) and are not recommended for Cochrane Reviews of diagnostic test accuracy.
b
If there are no covariates in the model, mada also generates parameters for the hierarchical summary receiver
operating characteristic (HSROC) curve by exploiting the relationship between bivariate and HSROC models.
c
The trivariate model jointly synthesizes the sensitivity, specificity and prevalence of the target condition.
Source: Adapted from Partlett 2021.
Bivariate binomial Yes Yes None
a
Bivariate normal Yes Yes None
a
Bivariate normal
Trivariate
b
c
Yes Yes None
No No None
Other software
requirements
because RevMan is not set up for parameters from such analyses. Ideally, a binomial likelihood should be used to model within- study variability (Chu 2006). Therefore, packages
such as mada and mvmeta that use a normal approximation (i.e. a linear mixed model
rather than a GLMM), as described by Reitsma (2005), are not recommended forCochrane
Reviews of diagnostic test accuracy. The approximation may lead to biased results due to
the unmet assumption of large sample sizes for the number of cases and non- cases, and
the use of an ad hoc continuity correction when any of the cells of the 2×2 table is zero.
This section and Section10.4.3 will focus on the glmer function in the R package
lme4 because it fits a bivariate model using a GLMM approach, and also gives output
that is compatible with RevMan for generating SROC plots with summary points. The
diagmeta package performs meta-
analysis using multiple thresholds from each
study and will be illustrated in Section10.7.1.
A free web- based interactive tool, MetaDTA, performs meta- analysis of test accuracy
by using the glmer function (Freeman 2019, Patel 2021). MetaDTA is an app powered by RShiny and produces parameter estimates in a format compatible with RevMan.
However, there is no option for meta- regression in the current version (version 2.0).
MetaDTA supports the upload of different file formats; Figure10.2.a shows the
anti- CCP data uploaded from a .csv file.
The results of the meta- analysis can be viewed on the ‘Meta- analysis’ tab (Figure10.2.b).
The SROC plot is displayed and the parameter estimates needed for input into RevMan
can be downloaded as a .csv file from the ‘Parameters for RevMan’ tab.
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Соседние файлы в папке Библиотека им академика М.И. Перельмана