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] 0.6222651 0.8692068 -1.2826497 0.1592340 0.6701062 1.0774445
#> EM[3,1] 0.1785900 0.7302484 -1.2391343 -0.2945278 0.1923722 0.6535423
#> EM[4,1] 0.7048289 0.6239851 -0.6803186 0.3323913 0.7588838 1.1252183
#> EM[5,1] 1.7565404 0.8503496 0.2491505 1.1558146 1.7789509 2.3079050
#> EM[4,2] -0.4242400 0.9904817 -2.4814668 -0.9998214 -0.3994952 0.1833293
#> EM[6,2] -1.4649326 1.2264658 -3.9236798 -2.2194803 -1.4138484 -0.6909996
#> EM[4,3] 1.1965780 0.9856488 -0.8578727 0.6203434 1.2428284 1.7883028
#> EM[5,4] -1.1398820 1.3441896 -3.4785908 -2.0416571 -1.2738581 -0.3243877
#> 97.5% Rhat n.eff
#> EM[2,1] 2.3419981 1.004551 1700
#> EM[3,1] 1.6456126 1.024294 130
#> EM[4,1] 1.8593559 1.107807 25
#> EM[5,1] 3.4184985 1.015384 230
#> EM[4,2] 1.4938821 1.003308 1900
#> EM[6,2] 0.9271331 1.008605 1500
#> EM[4,3] 3.0165227 1.009325 340
#> EM[5,4] 1.7984066 1.086650 38
#>
#> $dev_o
#> mean sd 2.5% 25% 50%
#> dev.o[1,1] 0.976003854 1.39607393 0.0011460044 0.10198725 0.4306728
#> dev.o[2,1] 0.871112577 1.28244294 0.0005982561 0.08359298 0.3722219
#> dev.o[3,1] 1.012839717 1.28769021 0.0010607370 0.11491937 0.5149106
#> dev.o[4,1] 1.006625466 1.39261857 0.0010794329 0.09353413 0.4673917
#> dev.o[5,1] 1.084174616 1.55459406 0.0010117277 0.10448676 0.4899437
#> dev.o[6,1] 1.067250743 1.41538097 0.0012672402 0.11100549 0.5209381
#> dev.o[7,1] 0.982751040 1.37240725 0.0014792830 0.10016528 0.4581158
#> dev.o[8,1] 0.004754167 0.05801256 0.0000000000 0.00000000 0.0000000
#> dev.o[9,1] 0.884455648 1.21515789 0.0012114430 0.08883597 0.4165631
#> dev.o[10,1] 0.926157225 1.27169874 0.0012500972 0.09447921 0.4457951
#> dev.o[11,1] 0.875404437 1.23549165 0.0009995200 0.09264021 0.4100028
#> dev.o[1,2] 0.976626458 1.39151757 0.0008428999 0.10140718 0.4577621
#> dev.o[2,2] 1.121945740 1.50437490 0.0011486483 0.12069271 0.5090218
#> dev.o[3,2] 1.035483482 1.53764933 0.0009423255 0.10341816 0.4734266
#> dev.o[4,2] 0.986717742 1.32939164 0.0009795902 0.11093454 0.4675963
#> dev.o[5,2] 1.239220480 1.73317596 0.0011687460 0.12158472 0.5352552
#> dev.o[6,2] 1.184696892 1.51161206 0.0013559866 0.14204325 0.6234204
#> dev.o[7,2] 0.840302115 1.17276054 0.0006249377 0.08772113 0.3860450
#> dev.o[8,2] 0.008773052 0.09801368 0.0000000000 0.00000000 0.0000000
#> dev.o[9,2] 1.022415547 1.43820852 0.0013067480 0.11263849 0.4595847
#> dev.o[10,2] 1.346960286 1.67058401 0.0015720712 0.15724162 0.7103110
#> dev.o[11,2] 1.003767779 1.36913040 0.0009935364 0.09850465 0.4688047
#> dev.o[9,3] 1.239462278 1.60772213 0.0012578733 0.12642030 0.6210999
#> dev.o[10,3] 1.024712072 1.38329319 0.0009337266 0.09562252 0.4951711
#> dev.o[11,3] 0.925211777 1.21735756 0.0004953771 0.09541532 0.4615575
#> 75% 97.5% Rhat n.eff
#> dev.o[1,1] 1.262698e+00 5.04196552 1.002145 1600
#> dev.o[2,1] 1.131356e+00 4.69754548 1.001454 2100
#> dev.o[3,1] 1.457124e+00 4.61884275 1.004786 900
#> dev.o[4,1] 1.340516e+00 5.16874191 1.000808 3000
#> dev.o[5,1] 1.411297e+00 5.34265663 1.002291 1100
#> dev.o[6,1] 1.444541e+00 5.27409078 1.002632 940
#> dev.o[7,1] 1.315281e+00 4.98523872 1.002468 1000
#> dev.o[8,1] 1.065814e-14 0.01508521 1.135482 1800
#> dev.o[9,1] 1.181916e+00 4.31036097 1.004013 1400
#> dev.o[10,1] 1.287612e+00 4.36733772 1.005622 390
#> dev.o[11,1] 1.144427e+00 4.41962804 1.006728 320
#> dev.o[1,2] 1.267749e+00 5.04021427 1.000523 3000
#> dev.o[2,2] 1.567881e+00 5.48292482 1.001445 2100
#> dev.o[3,2] 1.352937e+00 5.30072320 1.007514 290
#> dev.o[4,2] 1.329288e+00 4.73407766 1.001334 2400
#> dev.o[5,2] 1.658457e+00 6.29615277 1.003127 760
#> dev.o[6,2] 1.697070e+00 5.42660073 1.005507 410
#> dev.o[7,2] 1.107023e+00 4.12548514 1.000854 3000
#> dev.o[8,2] 2.664535e-14 0.04779659 1.153474 3000
#> dev.o[9,2] 1.340815e+00 5.34468338 1.000770 3000
#> dev.o[10,2] 1.929184e+00 5.87402768 1.014574 200
#> dev.o[11,2] 1.332556e+00 5.03517099 1.002733 900
#> dev.o[9,3] 1.718315e+00 5.71985767 1.000656 3000
#> dev.o[10,3] 1.398576e+00 4.78993128 1.003662 630
#> dev.o[11,3] 1.282196e+00 4.30264923 1.016013 130
#>
#> $hat_par
#> mean sd 2.5% 25% 50% 75%
#> hat.par[1,1] 26.662334 4.49353591 18.2276579 23.554113 26.466278 29.557409
#> hat.par[2,1] 2.743415 1.34821374 0.7710154 1.755521 2.544492 3.509565
#> hat.par[3,1] 10.139600 1.17655407 7.2667402 9.498921 10.386991 11.036401
#> hat.par[4,1] 22.865137 2.51344298 17.5015652 21.199076 23.051587 24.662264
#> hat.par[5,1] 8.145112 2.15501404 4.1355217 6.579734 8.110985 9.584765
#> hat.par[6,1] 3.294189 0.97510995 1.3617792 2.616085 3.306796 3.996286
#> hat.par[7,1] 2.619953 1.30884848 0.6675262 1.676552 2.430515 3.360208
#> hat.par[8,1] 11.997657 0.02804988 11.9924598 12.000000 12.000000 12.000000
#> hat.par[9,1] 15.647277 2.57537212 10.4975275 13.887042 15.655014 17.465822
#> hat.par[10,1] 3.477230 1.27419879 1.3563752 2.525444 3.397467 4.295578
#> hat.par[11,1] 4.768414 1.53313256 2.0334519 3.672171 4.698947 5.737874
#> hat.par[1,2] 38.247122 5.05649690 28.7869946 34.705492 38.134221 41.616114
#> hat.par[2,2] 4.251885 1.65638865 1.5439555 3.033635 4.088644 5.286088
#> hat.par[3,2] 7.889164 1.39733776 4.9964309 6.927980 7.964043 8.941345
#> hat.par[4,2] 16.222297 2.45068976 11.2254307 14.544803 16.322644 17.934953
#> hat.par[5,2] 2.803788 1.53859782 0.5239361 1.599391 2.608436 3.729748
#> hat.par[6,2] 3.734348 0.93602908 1.7213532 3.117686 3.813339 4.449655
#> hat.par[7,2] 2.340276 1.20160731 0.4621918 1.430560 2.196674 3.101845
#> hat.par[8,2] 14.995692 0.04690426 14.9761207 15.000000 15.000000 15.000000
#> hat.par[9,2] 16.909992 2.75074591 11.7351359 14.917093 16.897924 18.852387
#> hat.par[10,2] 3.126981 1.34051348 0.8040481 2.140357 3.002424 4.021114
#> hat.par[11,2] 7.033723 1.47500240 4.0627918 6.031211 7.051608 8.086801
#> hat.par[9,3] 21.354232 2.26556712 16.7861116 19.836315 21.472542 22.994460
#> hat.par[10,3] 8.390388 1.47518089 5.3610433 7.397323 8.558933 9.476101
#> hat.par[11,3] 11.244381 1.65432490 7.8045619 10.161343 11.391136 12.489974
#> 97.5% Rhat n.eff
#> hat.par[1,1] 35.947908 1.004134 1100
#> hat.par[2,1] 6.048701 1.001430 2100
#> hat.par[3,1] 11.656457 1.065176 58
#> hat.par[4,1] 27.245950 1.002279 1100
#> hat.par[5,1] 12.567582 1.002344 1600
#> hat.par[6,1] 5.074586 1.006880 360
#> hat.par[7,1] 5.605217 1.044541 50
#> hat.par[8,1] 12.000000 1.135482 1800
#> hat.par[9,1] 20.616886 1.011655 270
#> hat.par[10,1] 6.174334 1.035384 64
#> hat.par[11,1] 8.011958 1.018654 490
#> hat.par[1,2] 48.513713 1.000854 3000
#> hat.par[2,2] 7.945940 1.000543 3000
#> hat.par[3,2] 10.307465 1.012666 190
#> hat.par[4,2] 20.784389 1.002847 850
#> hat.par[5,2] 6.294247 1.002597 2700
#> hat.par[6,2] 5.306243 1.030664 120
#> hat.par[7,2] 5.022927 1.080634 36
#> hat.par[8,2] 15.000000 1.153474 3000
#> hat.par[9,2] 22.463739 1.004174 600
#> hat.par[10,2] 5.892114 1.119005 27
#> hat.par[11,2] 9.782071 1.000729 3000
#> hat.par[9,3] 25.407572 1.004801 470
#> hat.par[10,3] 10.876516 1.017648 180
#> hat.par[11,3] 14.012652 1.012897 480
#>
#> $leverage_o
#> [1] 0.970672037 0.618638263 1.000686208 1.003830639 1.079642517
#> [6] 0.723193503 0.919975696 -0.008962488 0.833453335 0.814030242
#> [11] 0.692919832 0.974314842 0.965433848 1.029936803 0.979103745
#> [16] 1.223074410 0.813964813 0.777488574 0.003313173 0.924926052
#> [21] 0.726549865 0.711013615 0.780462644 0.872159425 0.907513141
#>
#> $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%
#> 0.68984565 0.41890551 0.04736705 0.36601168 0.62758949 1.00237176
#> 97.5% Rhat n.eff
#> 1.50714572 1.03188264 90.00000000
#>
#> $m_tau
#> mean sd 2.5% 25% 50% 75%
#> 0.72462102 0.49998405 0.04744372 0.30360757 0.66055672 1.03020817
#> 97.5% Rhat n.eff
#> 1.94814690 1.09200846 36.00000000
#>
#> $model_assessment
#> DIC pD dev
#> 1 43.98516 20.33733 23.64783
#>
#> $obs_comp
#> [1] "2vs1" "3vs1" "4vs1" "5vs1" "4vs2" "6vs2" "4vs3" "5vs4"
#>
#> $jagsfit
#> Inference for Bugs model at "40", 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] 0.622 0.869 -1.283 0.159 0.670 1.077 2.342 1.005 1700
#> EM[3,1] 0.179 0.730 -1.239 -0.295 0.192 0.654 1.646 1.024 130
#> EM[4,1] 0.705 0.624 -0.680 0.332 0.759 1.125 1.859 1.108 25
#> EM[5,1] 1.757 0.850 0.249 1.156 1.779 2.308 3.418 1.015 230
#> EM[4,2] -0.424 0.990 -2.481 -1.000 -0.399 0.183 1.494 1.003 1900
#> EM[6,2] -1.465 1.226 -3.924 -2.219 -1.414 -0.691 0.927 1.009 1500
#> EM[4,3] 1.197 0.986 -0.858 0.620 1.243 1.788 3.017 1.009 340
#> EM[5,4] -1.140 1.344 -3.479 -2.042 -1.274 -0.324 1.798 1.087 38
#> dev.o[1,1] 0.976 1.396 0.001 0.102 0.431 1.263 5.042 1.002 1600
#> dev.o[2,1] 0.871 1.282 0.001 0.084 0.372 1.131 4.698 1.001 2100
#> dev.o[3,1] 1.013 1.288 0.001 0.115 0.515 1.457 4.619 1.005 900
#> dev.o[4,1] 1.007 1.393 0.001 0.094 0.467 1.341 5.169 1.001 3000
#> dev.o[5,1] 1.084 1.555 0.001 0.104 0.490 1.411 5.343 1.002 1100
#> dev.o[6,1] 1.067 1.415 0.001 0.111 0.521 1.445 5.274 1.003 940
#> dev.o[7,1] 0.983 1.372 0.001 0.100 0.458 1.315 4.985 1.002 1000
#> dev.o[8,1] 0.005 0.058 0.000 0.000 0.000 0.000 0.015 1.135 1800
#> dev.o[9,1] 0.884 1.215 0.001 0.089 0.417 1.182 4.310 1.004 1400
#> dev.o[10,1] 0.926 1.272 0.001 0.094 0.446 1.288 4.367 1.006 390
#> dev.o[11,1] 0.875 1.235 0.001 0.093 0.410 1.144 4.420 1.007 320
#> dev.o[1,2] 0.977 1.392 0.001 0.101 0.458 1.268 5.040 1.001 3000
#> dev.o[2,2] 1.122 1.504 0.001 0.121 0.509 1.568 5.483 1.001 2100
#> dev.o[3,2] 1.035 1.538 0.001 0.103 0.473 1.353 5.301 1.008 290
#> dev.o[4,2] 0.987 1.329 0.001 0.111 0.468 1.329 4.734 1.001 2400
#> dev.o[5,2] 1.239 1.733 0.001 0.122 0.535 1.658 6.296 1.003 760
#> dev.o[6,2] 1.185 1.512 0.001 0.142 0.623 1.697 5.427 1.006 410
#> dev.o[7,2] 0.840 1.173 0.001 0.088 0.386 1.107 4.125 1.001 3000
#> dev.o[8,2] 0.009 0.098 0.000 0.000 0.000 0.000 0.048 1.153 3000
#> dev.o[9,2] 1.022 1.438 0.001 0.113 0.460 1.341 5.345 1.001 3000
#> dev.o[10,2] 1.347 1.671 0.002 0.157 0.710 1.929 5.874 1.015 200
#> dev.o[11,2] 1.004 1.369 0.001 0.099 0.469 1.333 5.035 1.003 900
#> dev.o[9,3] 1.239 1.608 0.001 0.126 0.621 1.718 5.720 1.001 3000
#> dev.o[10,3] 1.025 1.383 0.001 0.096 0.495 1.399 4.790 1.004 630
#> dev.o[11,3] 0.925 1.217 0.000 0.095 0.462 1.282 4.303 1.016 130
#> hat.par[1,1] 26.662 4.494 18.228 23.554 26.466 29.557 35.948 1.004 1100
#> hat.par[2,1] 2.743 1.348 0.771 1.756 2.544 3.510 6.049 1.001 2100
#> hat.par[3,1] 10.140 1.177 7.267 9.499 10.387 11.036 11.656 1.065 58
#> hat.par[4,1] 22.865 2.513 17.502 21.199 23.052 24.662 27.246 1.002 1100
#> hat.par[5,1] 8.145 2.155 4.136 6.580 8.111 9.585 12.568 1.002 1600
#> hat.par[6,1] 3.294 0.975 1.362 2.616 3.307 3.996 5.075 1.007 360
#> hat.par[7,1] 2.620 1.309 0.668 1.677 2.431 3.360 5.605 1.045 50
#> hat.par[8,1] 11.998 0.028 11.992 12.000 12.000 12.000 12.000 1.135 1800
#> hat.par[9,1] 15.647 2.575 10.498 13.887 15.655 17.466 20.617 1.012 270
#> hat.par[10,1] 3.477 1.274 1.356 2.525 3.397 4.296 6.174 1.035 64
#> hat.par[11,1] 4.768 1.533 2.033 3.672 4.699 5.738 8.012 1.019 490
#> hat.par[1,2] 38.247 5.056 28.787 34.705 38.134 41.616 48.514 1.001 3000
#> hat.par[2,2] 4.252 1.656 1.544 3.034 4.089 5.286 7.946 1.001 3000
#> hat.par[3,2] 7.889 1.397 4.996 6.928 7.964 8.941 10.307 1.013 190
#> hat.par[4,2] 16.222 2.451 11.225 14.545 16.323 17.935 20.784 1.003 850
#> hat.par[5,2] 2.804 1.539 0.524 1.599 2.608 3.730 6.294 1.003 2700
#> hat.par[6,2] 3.734 0.936 1.721 3.118 3.813 4.450 5.306 1.031 120
#> hat.par[7,2] 2.340 1.202 0.462 1.431 2.197 3.102 5.023 1.081 36
#> hat.par[8,2] 14.996 0.047 14.976 15.000 15.000 15.000 15.000 1.153 3000
#> hat.par[9,2] 16.910 2.751 11.735 14.917 16.898 18.852 22.464 1.004 600
#> hat.par[10,2] 3.127 1.341 0.804 2.140 3.002 4.021 5.892 1.119 27
#> hat.par[11,2] 7.034 1.475 4.063 6.031 7.052 8.087 9.782 1.001 3000
#> hat.par[9,3] 21.354 2.266 16.786 19.836 21.473 22.994 25.408 1.005 470
#> hat.par[10,3] 8.390 1.475 5.361 7.397 8.559 9.476 10.877 1.018 180
#> hat.par[11,3] 11.244 1.654 7.805 10.161 11.391 12.490 14.013 1.013 480
#> m.tau 0.725 0.500 0.047 0.304 0.661 1.030 1.948 1.092 36
#> resdev.o[1] 1.953 1.999 0.055 0.547 1.292 2.684 7.337 1.002 1400
#> resdev.o[2] 1.993 1.890 0.052 0.594 1.442 2.742 6.976 1.001 3000
#> resdev.o[3] 2.048 1.977 0.061 0.633 1.487 2.881 7.090 1.014 150
#> resdev.o[4] 1.993 1.894 0.053 0.603 1.433 2.831 6.971 1.002 2200
#> resdev.o[5] 2.323 2.296 0.057 0.672 1.533 3.232 8.386 1.009 240
#> resdev.o[6] 2.252 1.878 0.111 0.884 1.837 3.074 7.081 1.014 190
#> resdev.o[7] 1.823 1.862 0.047 0.565 1.269 2.484 7.163 1.002 2800
#> resdev.o[8] 0.014 0.134 0.000 0.000 0.000 0.000 0.078 1.035 3000
#> resdev.o[9] 3.146 2.482 0.237 1.296 2.481 4.371 9.702 1.002 1100
#> resdev.o[10] 3.298 2.403 0.235 1.480 2.813 4.509 9.122 1.033 71
#> resdev.o[11] 2.804 2.097 0.256 1.256 2.351 3.819 8.098 1.017 130
#> tau 0.690 0.419 0.047 0.366 0.628 1.002 1.507 1.032 90
#> totresdev.o 23.648 6.595 12.122 18.963 23.260 27.719 37.834 1.030 70
#>
#> 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%
#> 0.72462102 0.49998405 0.04744372 0.30360757 0.66055672 1.03020817
#> 97.5% Rhat n.eff
#> 1.94814690 1.09200846 36.00000000
#>
#> $frail_comp
#> [1] "4vs3"
#>
#> attr(,"class")
#> [1] "run_ume"
# }