Performs the unrelated mean effects model of Dias et al. (2013) that has been refined (Spineli, 2021) and extended to address aggregate binary and continuous missing participant outcome data via the pattern-mixture model (Spineli et al. 2021; Spineli, 2019). This model offers a global evaluation of the plausibility of the consistency assumption in the network.
Arguments
- full
- n_iter
Positive integer specifying the number of Markov chains for the MCMC sampling; an argument of the
jagsfunction of the R-package R2jags. The default argument is 10000.- n_burnin
Positive integer specifying the number of iterations to discard at the beginning of the MCMC sampling; an argument of the
jagsfunction of the R-package R2jags. The default argument is 1000.- n_chains
Positive integer specifying the number of chains for the MCMC sampling; an argument of the
jagsfunction of the R-package R2jags. The default argument is 2.- n_thin
Positive integer specifying the thinning rate for the MCMC sampling; an argument of the
jagsfunction of the R-package R2jags. The default argument is 1.- inits
A list with the initial values for the parameters; an argument of the
jagsfunction of the R-package R2jags. The default argument isNULL, and JAGS generates the initial values.
Value
An R2jags output on the summaries of the posterior distribution, and the Gelman-Rubin convergence diagnostic (Gelman et al., 1992) of the following monitored parameters:
- EM
The summary effect estimate (according to the argument
measuredefined inrun_model) for each pairwise comparison observed in the network.- dev_o
The deviance contribution of each trial-arm based on the observed outcome.
- hat_par
The fitted outcome at each trial-arm.
- tau
The between-trial standard deviation (assumed common across the observed pairwise comparisons) for the whole network, when a random-effects model has been specified.
- m_tau
The between-trial standard deviation (assumed common across the observed pairwise comparisons) for the subset of multi-arm trials, when a random-effects model has been specified.
The output also includes the following elements:
- leverage_o
The leverage for the observed outcome at each trial-arm.
- sign_dev_o
The sign of the difference between observed and fitted outcome at each trial-arm.
- model_assessment
A data-frame on the measures of model assessment: deviance information criterion, number of effective parameters, and total residual deviance.
- jagsfit
An object of S3 class
jagswith the posterior results on all monitored parameters to be used in themcmc_diagnosticsfunction.
Furthermore, run_ume returns a character vector with the pairwise
comparisons observed in the network, obs_comp, and a character
vector with comparisons between the non-baseline interventions observed in
multi-arm trials only, frail_comp. Both vectors are used in
ume_plot function.
Details
run_ume inherits the arguments data,
measure, model, assumption, heter_prior,
mean_misspar, var_misspar, and ref from
run_model.
This prevents specifying a different Bayesian model from that considered in
run_model.Therefore, the user needs first to apply
run_model, and then use run_ume (see 'Examples').
The run_ume function also returns the arguments data,
model, measure, assumption, n_chains,
n_iter, n_burnin, and n_thin as specified by the user
to be inherited by other relevant functions of the package.
Initially, run_ume calls the improved_ume function to
identify the frail comparisons, that is, comparisons between
non-baseline interventions in multi-arm trials not investigated in any
two-arm or multi-arm trial of the network (Spineli, 2021). The 'original'
model of Dias et al. (2013) omits the frail comparisons from the estimation
process. Consequently, the number of estimated summary effects is less
than those obtained by performing separate pairwise meta-analyses
(see run_series_meta).
For a binary outcome, when measure is "RR" (relative risk) or "RD"
(risk difference) in run_model, run_ume currently
considers the odds ratio as effect measure for being the base-case
effect measure in run_model for a binary outcome
(see also 'Details' in run_model).
run_ume calls the prepare_ume function which contains
the WinBUGS code as written by Dias et al. (2013) for binomial and normal
likelihood to analyse binary and continuous outcome data, respectively.
prepare_ume has been extended to incorporate the
pattern-mixture model with informative missingness parameters for binary
and continuous outcome data (see 'Details' in run_model).
prepare_ume has also been refined to account for the
multi-arm trials by assigning conditional univariate normal distributions
on the underlying trial-specific effect size of comparisons with the
baseline arm of the multi-arm trial (Spineli, 2021).
run_ume runs Bayesian unrelated mean effects model in JAGS.
The progress of the simulation appears on the R console. The model is
updated until convergence using the autojags
function of the R-package
R2jags with 2 updates and
number of iterations and thinning equal to n_iter and n_thin,
respectively.
The output of run_ume is not end-user-ready. The
ume_plot function uses the output of run_ume as an S3
object and processes it further to provide an end-user-ready output.
run_ume can be used only for a network of interventions. In the case
of two interventions, the execution of the function will be stopped and an
error message will be printed on the R console.
References
Dias S, Welton NJ, Sutton AJ, Caldwell DM, Lu G, Ades AE. Evidence synthesis for decision making 4: inconsistency in networks of evidence based on randomized controlled trials. Med Decis Making 2013;33(5):641–56. doi: 10.1177/0272989X12455847
Gelman A, Rubin DB. Inference from iterative simulation using multiple sequences. Stat Sci 1992;7(4):457–72. doi: 10.1214/ss/1177011136
Spineli LM. A Revised Framework to Evaluate the Consistency Assumption Globally in a Network of Interventions. Med Decis Making 2021. doi: 10.1177/0272989X211068005
Spineli LM, Kalyvas C, Papadimitropoulou K. Continuous(ly) missing outcome data in network meta-analysis: a one-stage pattern-mixture model approach. Stat Methods Med Res 2021;30(4):958–75. doi: 10.1177/0962280220983544
Spineli LM. An empirical comparison of Bayesian modelling strategies for missing binary outcome data in network meta-analysis. BMC Med Res Methodol 2019;19(1):86. doi: 10.1186/s12874-019-0731-y
Examples
data("nma.liu2013")
# Read results from 'run_model' (using the default arguments)
res <- readRDS(system.file('extdata/res_liu.rds', package = 'rnmamod'))
# \donttest{
# Run random-effects unrelated mean effects model
# Note: Ideally, set 'n_iter' to 10000 and 'n_burnin' to 1000
run_ume(full = res,
n_chains = 3,
n_iter = 1000,
n_burnin = 100,
n_thin = 1)
#> JAGS generates initial values for the parameters.
#> Running the model ...
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 59
#> Unobserved stochastic nodes: 80
#> Total graph size: 1291
#>
#> Initializing model
#>
#> ... Updating the model until convergence
#> $EM
#> mean sd 2.5% 25% 50% 75%
#> EM[2,1] 0.6411756 0.8640783 -1.1278313 0.1485561 0.6549098 1.1130284
#> EM[3,1] 0.2138138 0.7277686 -1.2075876 -0.2617058 0.1969826 0.6857658
#> EM[4,1] 0.7857477 0.5874192 -0.5002016 0.4266032 0.8165053 1.1751339
#> EM[5,1] 1.8692829 0.8058200 0.2071046 1.3404155 1.8799027 2.3984321
#> EM[4,2] -0.4371592 1.0188729 -2.5550948 -1.0588785 -0.4172995 0.2310008
#> EM[6,2] -1.3695623 1.0670491 -3.5249186 -2.0700454 -1.3415512 -0.7024801
#> EM[4,3] 1.1275144 0.9990354 -0.8842449 0.5492763 1.1473832 1.7029166
#> EM[5,4] -1.2543557 1.5643457 -4.3514011 -2.4355531 -1.1403605 -0.1933647
#> 97.5% Rhat n.eff
#> EM[2,1] 2.4165400 1.004742 590
#> EM[3,1] 1.6699797 1.009861 240
#> EM[4,1] 1.8133567 1.014485 290
#> EM[5,1] 3.4448129 1.013247 3000
#> EM[4,2] 1.4597717 1.009626 220
#> EM[6,2] 0.8093906 1.002493 1000
#> EM[4,3] 3.1397025 1.006827 650
#> EM[5,4] 1.7877831 1.035660 63
#>
#> $dev_o
#> mean sd 2.5% 25% 50%
#> dev.o[1,1] 1.087981797 1.4700033 0.0010246068 0.11492381 0.4905191
#> dev.o[2,1] 0.860650266 1.2173806 0.0008871066 0.08551090 0.3854807
#> dev.o[3,1] 1.401854355 1.9816491 0.0016828616 0.13838053 0.6074578
#> dev.o[4,1] 1.129676362 1.6053435 0.0014533556 0.11526583 0.5176434
#> dev.o[5,1] 1.019390392 1.4876829 0.0006432110 0.10105329 0.4296978
#> dev.o[6,1] 1.044869715 1.3608280 0.0008829905 0.11693039 0.5004370
#> dev.o[7,1] 1.034846204 1.3391678 0.0009033066 0.11024861 0.4984616
#> dev.o[8,1] 0.006036393 0.1063059 0.0000000000 0.00000000 0.0000000
#> dev.o[9,1] 0.891666859 1.2696417 0.0012679947 0.09723516 0.3965096
#> dev.o[10,1] 1.027408857 1.3053784 0.0013303410 0.11677908 0.5226921
#> dev.o[11,1] 0.870973830 1.2461908 0.0005384276 0.08471039 0.3877897
#> dev.o[1,2] 1.014184360 1.3843809 0.0010611204 0.10503386 0.4654616
#> dev.o[2,2] 1.166098235 1.5486182 0.0010256899 0.11866349 0.5588803
#> dev.o[3,2] 1.112410037 1.5901120 0.0007468840 0.10984866 0.4814913
#> dev.o[4,2] 1.081605776 1.4606211 0.0009950555 0.11670956 0.5146342
#> dev.o[5,2] 0.980369712 1.3315661 0.0009441174 0.10774468 0.4372487
#> dev.o[6,2] 1.148051934 1.4221610 0.0016532668 0.14568572 0.6016700
#> dev.o[7,2] 0.792879718 1.1097164 0.0007792466 0.08648384 0.3557735
#> dev.o[8,2] 0.013262129 0.1322449 0.0000000000 0.00000000 0.0000000
#> dev.o[9,2] 1.123605738 1.6248036 0.0011408239 0.09687636 0.4878892
#> dev.o[10,2] 1.406088275 1.7728873 0.0016861601 0.16705791 0.7491742
#> dev.o[11,2] 1.082492023 1.4734886 0.0010586106 0.10686120 0.5062203
#> dev.o[9,3] 1.094853460 1.5215918 0.0009251520 0.11277908 0.5299804
#> dev.o[10,3] 0.896401953 1.2136581 0.0012880443 0.10852684 0.4200497
#> dev.o[11,3] 0.991619333 1.4498019 0.0010066324 0.08708065 0.4508166
#> 75% 97.5% Rhat n.eff
#> dev.o[1,1] 1.512174e+00 5.48050170 1.001108 3000
#> dev.o[2,1] 1.143684e+00 4.26620120 1.001338 2400
#> dev.o[3,1] 1.852095e+00 7.36380260 1.012485 230
#> dev.o[4,1] 1.521927e+00 5.38805392 1.001300 3000
#> dev.o[5,1] 1.299893e+00 5.17458052 1.003216 770
#> dev.o[6,1] 1.492598e+00 4.82141773 1.001325 2400
#> dev.o[7,1] 1.413698e+00 4.75168842 1.001463 2100
#> dev.o[8,1] 3.197442e-14 0.01245678 1.200005 3000
#> dev.o[9,1] 1.186485e+00 4.45169332 1.000669 3000
#> dev.o[10,1] 1.397107e+00 4.81133473 1.002756 890
#> dev.o[11,1] 1.133162e+00 4.41842136 1.006116 460
#> dev.o[1,2] 1.402976e+00 5.11420444 1.000508 3000
#> dev.o[2,2] 1.591358e+00 5.71243576 1.001438 2100
#> dev.o[3,2] 1.494369e+00 5.45854660 1.002572 3000
#> dev.o[4,2] 1.472286e+00 5.29032044 1.002509 3000
#> dev.o[5,2] 1.369540e+00 4.61228484 1.000710 3000
#> dev.o[6,2] 1.670931e+00 4.99957367 1.004529 510
#> dev.o[7,2] 1.047903e+00 3.92595284 1.001446 2100
#> dev.o[8,2] 6.994405e-14 0.03395512 1.147125 660
#> dev.o[9,2] 1.503558e+00 5.71786291 1.001738 1700
#> dev.o[10,2] 2.027033e+00 6.30535070 1.001382 2300
#> dev.o[11,2] 1.486976e+00 5.36049366 1.001164 3000
#> dev.o[9,3] 1.438287e+00 5.32127557 1.006299 480
#> dev.o[10,3] 1.228097e+00 4.32514351 1.002036 1500
#> dev.o[11,3] 1.329610e+00 4.93126278 1.001104 3000
#>
#> $hat_par
#> mean sd 2.5% 25% 50% 75%
#> hat.par[1,1] 26.845878 4.78837744 18.0684546 23.510929 26.537944 29.979851
#> hat.par[2,1] 2.721465 1.33628820 0.7648237 1.747671 2.511186 3.523310
#> hat.par[3,1] 10.226134 1.23386829 7.3489904 9.528879 10.466885 11.182142
#> hat.par[4,1] 22.953304 2.64548645 17.4164863 21.207513 23.154867 24.820044
#> hat.par[5,1] 8.052856 2.09552480 4.1279610 6.573381 7.970784 9.405726
#> hat.par[6,1] 3.288968 0.95995239 1.4522109 2.589933 3.315218 3.995785
#> hat.par[7,1] 2.488775 1.26179284 0.6749404 1.560291 2.288980 3.202030
#> hat.par[8,1] 11.997093 0.04880353 11.9937732 12.000000 12.000000 12.000000
#> hat.par[9,1] 15.161336 2.64781492 10.0436460 13.328541 15.179412 16.933131
#> hat.par[10,1] 3.325602 1.27060929 1.2289497 2.391501 3.184565 4.140561
#> hat.par[11,1] 4.724338 1.55100410 2.0156863 3.636192 4.623136 5.694326
#> hat.par[1,2] 38.078742 5.13882373 28.3468268 34.485307 38.103953 41.523992
#> hat.par[2,2] 4.193637 1.66658573 1.5451085 2.973127 3.983172 5.200298
#> hat.par[3,2] 7.803955 1.47824818 4.5555184 6.843399 7.975981 8.886889
#> hat.par[4,2] 16.181571 2.56264961 11.0913833 14.392261 16.268928 18.014792
#> hat.par[5,2] 2.930561 1.45790136 0.7393941 1.835342 2.703530 3.828519
#> hat.par[6,2] 3.728065 0.92510664 1.7726629 3.095352 3.791322 4.425271
#> hat.par[7,2] 2.497733 1.19808864 0.6662640 1.601848 2.354538 3.195539
#> hat.par[8,2] 14.993513 0.06390693 14.9830320 15.000000 15.000000 15.000000
#> hat.par[9,2] 17.017037 2.85415906 11.5449462 15.113568 16.926601 18.981203
#> hat.par[10,2] 3.240577 1.31611663 1.0104264 2.256722 3.147384 4.123880
#> hat.par[11,2] 7.006276 1.53290123 3.9524878 5.947098 7.049734 8.076096
#> hat.par[9,3] 21.748889 2.28366106 16.9516925 20.269483 21.867515 23.389315
#> hat.par[10,3] 8.402404 1.36984637 5.5470297 7.447513 8.480455 9.467578
#> hat.par[11,3] 11.437128 1.62427132 7.9150357 10.448154 11.540144 12.608576
#> 97.5% Rhat n.eff
#> hat.par[1,1] 37.089281 1.001602 1800
#> hat.par[2,1] 5.800153 1.017302 130
#> hat.par[3,1] 11.854543 1.025668 93
#> hat.par[4,1] 27.559029 1.010784 340
#> hat.par[5,1] 12.400661 1.005619 750
#> hat.par[6,1] 5.141731 1.005754 600
#> hat.par[7,1] 5.523761 1.001077 3000
#> hat.par[8,1] 12.000000 1.200005 3000
#> hat.par[9,1] 20.386412 1.020162 110
#> hat.par[10,1] 6.082728 1.006205 1300
#> hat.par[11,1] 8.064451 1.002125 1200
#> hat.par[1,2] 48.572661 1.001683 1700
#> hat.par[2,2] 7.937909 1.005902 600
#> hat.par[3,2] 10.229482 1.011413 190
#> hat.par[4,2] 20.890703 1.010646 220
#> hat.par[5,2] 6.288531 1.003751 730
#> hat.par[6,2] 5.254138 1.004907 540
#> hat.par[7,2] 5.228095 1.003493 690
#> hat.par[8,2] 15.000000 1.147125 660
#> hat.par[9,2] 22.550706 1.001303 2500
#> hat.par[10,2] 6.040552 1.002452 1000
#> hat.par[11,2] 9.914263 1.004036 570
#> hat.par[9,3] 25.680903 1.003461 680
#> hat.par[10,3] 10.712773 1.002731 3000
#> hat.par[11,3] 14.174022 1.006412 720
#>
#> $leverage_o
#> [1] 1.086874797 0.621703407 1.369141051 1.129339977 1.018787904
#> [6] 0.695834321 0.918140593 -0.004492432 0.888489137 0.837975523
#> [11] 0.708197291 1.013949276 0.983048373 1.095246780 1.076530228
#> [16] 0.978393958 0.783831521 0.663133915 0.011631843 1.001847886
#> [21] 0.666806462 0.773078597 0.824130650 0.749602194 0.934094743
#>
#> $sign_dev_o
#> [1] 1 -1 -1 1 -1 1 1 -1 -1 1 -1 -1 1 1 -1 1 -1 -1 -1 -1 -1 1 1 1 -1
#>
#> $tau
#> mean sd 2.5% 25% 50% 75%
#> 0.68600986 0.36465586 0.08759403 0.42396051 0.64759612 0.90332490
#> 97.5% Rhat n.eff
#> 1.43352676 1.11418436 42.00000000
#>
#> $m_tau
#> mean sd 2.5% 25% 50% 75%
#> 0.76712939 0.51778577 0.07013626 0.36532302 0.69364887 1.07378061
#> 97.5% Rhat n.eff
#> 1.98854231 1.02921672 92.00000000
#>
#> $model_assessment
#> DIC pD dev
#> 1 45.1046 20.82532 24.27928
#>
#> $obs_comp
#> [1] "2vs1" "3vs1" "4vs1" "5vs1" "4vs2" "6vs2" "4vs3" "5vs4"
#>
#> $jagsfit
#> Inference for Bugs model at "10", fit using jags,
#> 3 chains, each with 1000 iterations (first 0 discarded)
#> n.sims = 3000 iterations saved. Running time = secs
#> mu.vect sd.vect 2.5% 25% 50% 75% 97.5% Rhat n.eff
#> EM[2,1] 0.641 0.864 -1.128 0.149 0.655 1.113 2.417 1.005 590
#> EM[3,1] 0.214 0.728 -1.208 -0.262 0.197 0.686 1.670 1.010 240
#> EM[4,1] 0.786 0.587 -0.500 0.427 0.817 1.175 1.813 1.014 290
#> EM[5,1] 1.869 0.806 0.207 1.340 1.880 2.398 3.445 1.013 3000
#> EM[4,2] -0.437 1.019 -2.555 -1.059 -0.417 0.231 1.460 1.010 220
#> EM[6,2] -1.370 1.067 -3.525 -2.070 -1.342 -0.702 0.809 1.002 1000
#> EM[4,3] 1.128 0.999 -0.884 0.549 1.147 1.703 3.140 1.007 650
#> EM[5,4] -1.254 1.564 -4.351 -2.436 -1.140 -0.193 1.788 1.036 63
#> dev.o[1,1] 1.088 1.470 0.001 0.115 0.491 1.512 5.481 1.001 3000
#> dev.o[2,1] 0.861 1.217 0.001 0.086 0.385 1.144 4.266 1.001 2400
#> dev.o[3,1] 1.402 1.982 0.002 0.138 0.607 1.852 7.364 1.012 230
#> dev.o[4,1] 1.130 1.605 0.001 0.115 0.518 1.522 5.388 1.001 3000
#> dev.o[5,1] 1.019 1.488 0.001 0.101 0.430 1.300 5.175 1.003 770
#> dev.o[6,1] 1.045 1.361 0.001 0.117 0.500 1.493 4.821 1.001 2400
#> dev.o[7,1] 1.035 1.339 0.001 0.110 0.498 1.414 4.752 1.001 2100
#> dev.o[8,1] 0.006 0.106 0.000 0.000 0.000 0.000 0.012 1.200 3000
#> dev.o[9,1] 0.892 1.270 0.001 0.097 0.397 1.186 4.452 1.001 3000
#> dev.o[10,1] 1.027 1.305 0.001 0.117 0.523 1.397 4.811 1.003 890
#> dev.o[11,1] 0.871 1.246 0.001 0.085 0.388 1.133 4.418 1.006 460
#> dev.o[1,2] 1.014 1.384 0.001 0.105 0.465 1.403 5.114 1.001 3000
#> dev.o[2,2] 1.166 1.549 0.001 0.119 0.559 1.591 5.712 1.001 2100
#> dev.o[3,2] 1.112 1.590 0.001 0.110 0.481 1.494 5.459 1.003 3000
#> dev.o[4,2] 1.082 1.461 0.001 0.117 0.515 1.472 5.290 1.003 3000
#> dev.o[5,2] 0.980 1.332 0.001 0.108 0.437 1.370 4.612 1.001 3000
#> dev.o[6,2] 1.148 1.422 0.002 0.146 0.602 1.671 5.000 1.005 510
#> dev.o[7,2] 0.793 1.110 0.001 0.086 0.356 1.048 3.926 1.001 2100
#> dev.o[8,2] 0.013 0.132 0.000 0.000 0.000 0.000 0.034 1.147 660
#> dev.o[9,2] 1.124 1.625 0.001 0.097 0.488 1.504 5.718 1.002 1700
#> dev.o[10,2] 1.406 1.773 0.002 0.167 0.749 2.027 6.305 1.001 2300
#> dev.o[11,2] 1.082 1.473 0.001 0.107 0.506 1.487 5.360 1.001 3000
#> dev.o[9,3] 1.095 1.522 0.001 0.113 0.530 1.438 5.321 1.006 480
#> dev.o[10,3] 0.896 1.214 0.001 0.109 0.420 1.228 4.325 1.002 1500
#> dev.o[11,3] 0.992 1.450 0.001 0.087 0.451 1.330 4.931 1.001 3000
#> hat.par[1,1] 26.846 4.788 18.068 23.511 26.538 29.980 37.089 1.002 1800
#> hat.par[2,1] 2.721 1.336 0.765 1.748 2.511 3.523 5.800 1.017 130
#> hat.par[3,1] 10.226 1.234 7.349 9.529 10.467 11.182 11.855 1.026 93
#> hat.par[4,1] 22.953 2.645 17.416 21.208 23.155 24.820 27.559 1.011 340
#> hat.par[5,1] 8.053 2.096 4.128 6.573 7.971 9.406 12.401 1.006 750
#> hat.par[6,1] 3.289 0.960 1.452 2.590 3.315 3.996 5.142 1.006 600
#> hat.par[7,1] 2.489 1.262 0.675 1.560 2.289 3.202 5.524 1.001 3000
#> hat.par[8,1] 11.997 0.049 11.994 12.000 12.000 12.000 12.000 1.200 3000
#> hat.par[9,1] 15.161 2.648 10.044 13.329 15.179 16.933 20.386 1.020 110
#> hat.par[10,1] 3.326 1.271 1.229 2.392 3.185 4.141 6.083 1.006 1300
#> hat.par[11,1] 4.724 1.551 2.016 3.636 4.623 5.694 8.064 1.002 1200
#> hat.par[1,2] 38.079 5.139 28.347 34.485 38.104 41.524 48.573 1.002 1700
#> hat.par[2,2] 4.194 1.667 1.545 2.973 3.983 5.200 7.938 1.006 600
#> hat.par[3,2] 7.804 1.478 4.556 6.843 7.976 8.887 10.229 1.011 190
#> hat.par[4,2] 16.182 2.563 11.091 14.392 16.269 18.015 20.891 1.011 220
#> hat.par[5,2] 2.931 1.458 0.739 1.835 2.704 3.829 6.289 1.004 730
#> hat.par[6,2] 3.728 0.925 1.773 3.095 3.791 4.425 5.254 1.005 540
#> hat.par[7,2] 2.498 1.198 0.666 1.602 2.355 3.196 5.228 1.003 690
#> hat.par[8,2] 14.994 0.064 14.983 15.000 15.000 15.000 15.000 1.147 660
#> hat.par[9,2] 17.017 2.854 11.545 15.114 16.927 18.981 22.551 1.001 2500
#> hat.par[10,2] 3.241 1.316 1.010 2.257 3.147 4.124 6.041 1.002 1000
#> hat.par[11,2] 7.006 1.533 3.952 5.947 7.050 8.076 9.914 1.004 570
#> hat.par[9,3] 21.749 2.284 16.952 20.269 21.868 23.389 25.681 1.003 680
#> hat.par[10,3] 8.402 1.370 5.547 7.448 8.480 9.468 10.713 1.003 3000
#> hat.par[11,3] 11.437 1.624 7.915 10.448 11.540 12.609 14.174 1.006 720
#> m.tau 0.767 0.518 0.070 0.365 0.694 1.074 1.989 1.029 92
#> resdev.o[1] 2.102 2.010 0.051 0.598 1.479 3.053 7.222 1.001 3000
#> resdev.o[2] 2.027 1.894 0.056 0.642 1.506 2.837 7.127 1.006 390
#> resdev.o[3] 2.514 2.531 0.063 0.658 1.740 3.572 9.318 1.007 400
#> resdev.o[4] 2.211 2.160 0.065 0.663 1.593 3.054 7.893 1.002 3000
#> resdev.o[5] 2.000 1.981 0.053 0.569 1.372 2.838 7.146 1.003 730
#> resdev.o[6] 2.193 1.745 0.088 0.889 1.832 3.050 6.514 1.006 350
#> resdev.o[7] 1.828 1.712 0.058 0.575 1.324 2.537 6.418 1.003 830
#> resdev.o[8] 0.019 0.209 0.000 0.000 0.000 0.000 0.065 1.175 910
#> resdev.o[9] 3.110 2.566 0.241 1.239 2.489 4.210 9.850 1.001 2900
#> resdev.o[10] 3.330 2.346 0.315 1.589 2.838 4.494 9.076 1.002 1100
#> resdev.o[11] 2.945 2.403 0.211 1.218 2.314 3.993 9.320 1.011 400
#> tau 0.686 0.365 0.088 0.424 0.648 0.903 1.434 1.114 42
#> totresdev.o 24.279 6.906 12.751 19.240 23.655 28.503 39.696 1.007 320
#>
#> For each parameter, n.eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor (at convergence, Rhat=1).
#>
#> $data
#> study t1 t2 t3 r1 r2 r3 m1 m2 m3 n1 n2 n3
#> 356 Richard, 2012 1 3 4 15 16 23 6 8 4 39 42 34
#> 357 Barone, 2010 1 2 NA 27 38 NA 19 20 NA 152 144 NA
#> 358 Weinbtraub, 2010 1 3 NA 2 5 NA 6 6 NA 27 28 NA
#> 359 Menza, 2009 1 4 5 4 2 9 6 7 5 17 18 17
#> 360 Devos, 2008 1 4 5 4 8 11 0 2 1 16 15 17
#> 361 Antonini, 2006 4 5 NA 10 8 NA 4 4 NA 16 15 NA
#> 362 Barone, 2006 2 4 NA 23 16 NA 1 7 NA 33 34 NA
#> 363 Rektorova, 2003 2 6 NA 8 3 NA 3 2 NA 22 19 NA
#> 364 Leentjens, 2003 1 4 NA 4 3 NA 0 0 NA 6 6 NA
#> 365 Wermuth, 1998 1 4 NA 3 2 NA 2 5 NA 19 18 NA
#> 366 Rabey, 1996 4 5 NA 12 15 NA 8 12 NA 20 27 NA
#>
#> $model
#> [1] "RE"
#>
#> $measure
#> [1] "OR"
#>
#> $assumption
#> [1] "IDE-ARM"
#>
#> $phi
#> NULL
#>
#> $n_chains
#> [1] 3
#>
#> $n_iter
#> [1] 1000
#>
#> $n_burnin
#> [1] 100
#>
#> $n_thin
#> [1] 1
#>
#> $m_tau
#> mean sd 2.5% 25% 50% 75%
#> 0.76712939 0.51778577 0.07013626 0.36532302 0.69364887 1.07378061
#> 97.5% Rhat n.eff
#> 1.98854231 1.02921672 92.00000000
#>
#> $frail_comp
#> [1] "4vs3"
#>
#> attr(,"class")
#> [1] "run_ume"
# }