Perform Bayesian pairwise or network meta-regression
Source:R/run.metareg_function.R
run_metareg.RdPerforms a one-stage pairwise or network meta-regression while addressing aggregate binary or continuous missing participant outcome data via the pattern-mixture model.
Usage
run_metareg(
full,
covariate,
covar_assumption,
cov_value,
n_chains,
n_iter,
n_burnin,
n_thin,
inits = NULL
)Arguments
- full
- covariate
A numeric vector or matrix for a trial-specific covariate that is a potential effect modifier. See 'Details'.
- covar_assumption
Character string indicating the structure of the intervention-by-covariate interaction, as described in Cooper et al. (2009). Set
covar_assumptionequal to"exchangeable","independent", or"common".- cov_value
Numeric for the covariate value of interest.
- 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_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_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
A list of R2jags outputs on the summaries of the posterior distribution, and the Gelman-Rubin convergence diagnostic (Gelman et al., 1992) for the following monitored parameters for a fixed-effect pairwise meta-analysis:
- EM
The estimated summary effect measure (according to the argument
measuredefined inrun_model).- beta_all
The estimated regression coefficient for all possible pairwise comparisons according to the argument
covar_assumption.- dev_o
The deviance contribution of each trial-arm based on the observed outcome.
- hat_par
The fitted outcome at each trial-arm.
- phi
The informative missingness parameter.
For a fixed-effect network meta-analysis, the output additionally includes:
- SUCRA
The surface under the cumulative ranking (SUCRA) curve for each intervention.
- effectiveneness
The ranking probability of each intervention for every rank.
For a random-effects pairwise meta-analysis, the output additionally includes the following elements:
- EM_pred
The predicted summary effect measure (according to the argument
measuredefined inrun_model).- delta
The estimated trial-specific effect measure (according to the argument
measuredefined inrun_model). For a multi-arm trial, we estimate T-1 effects, where T is the number of interventions in the trial.- tau
The between-trial standard deviation.
In network meta-analysis, EM and EM_pred refer to all
possible pairwise comparisons of interventions in the network. Furthermore,
tau is typically assumed to be common for all observed comparisons
in the network.
For a multi-arm trial, we estimate a total T-1 of delta for
comparisons with the baseline intervention of the trial (found in the first
column of the element t), with T being the number of
interventions in the trial.
Furthermore, the output includes the following elements:
- abs_risk
The adjusted absolute risks for each intervention. This appears only when
measure = "OR",measure = "RR", ormeasure = "RD".- 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.
The run_metareg function also returns the arguments data,
measure, model, assumption, covariate,
covar_assumption, n_chains, n_iter, n_burnin,
and n_thin to be inherited by other relevant functions of the
package.
Details
run_metareg inherits the arguments data,
measure, model, assumption, heter_prior,
mean_misspar, var_misspar, D, ref,
indic, and base_risk from run_model
(now contained in the argument full). 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_metareg (see 'Examples').
The model runs in JAGS and the progress of the simulation appears on
the R console. The output of run_metareg is used as an S3 object by
other functions of the package to be processed further and provide an
end-user-ready output. 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 models described in Spineli et al. (2021), and Spineli (2019) have been extended to incorporate one study-level covariate variable following the assumptions of Cooper et al. (2009) for the structure of the intervention-by-covariate interaction. The covariate can be either a numeric vector or matrix with columns equal to the maximum number of arms in the dataset.
References
Cooper NJ, Sutton AJ, Morris D, Ades AE, Welton NJ. Addressing between-study heterogeneity and inconsistency in mixed treatment comparisons: Application to stroke prevention treatments in individuals with non-rheumatic atrial fibrillation. Stat Med 2009;28(14):1861–81. doi: 10.1002/sim.3594
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, 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.baker2009")
# Read results from 'run_model' (using the default arguments)
res <- readRDS(system.file('extdata/res_baker.rds', package = 'rnmamod'))
# Publication year
pub_year <- c(1996, 1998, 1999, 2000, 2000, 2001, rep(2002, 5), 2003, 2003,
rep(2005, 4), 2006, 2006, 2007, 2007)
# \donttest{
# Perform a random-effects network meta-regression (exchangeable structure)
# Note: Ideally, set 'n_iter' to 10000 and 'n_burnin' to 1000
run_metareg(full = res,
covariate = pub_year,
covar_assumption = "exchangeable",
cov_value = 2007,
n_chains = 3,
n_iter = 1000,
n_burnin = 100,
n_thin = 1)
#> **Fixed baseline risk assigned**
#> JAGS generates initial values for the parameters.
#> Running the model ...
#> module glm loaded
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 100
#> Unobserved stochastic nodes: 157
#> Total graph size: 2848
#>
#> Initializing model
#>
#> ... Updating the model until convergence
#> $EM
#> mean sd 2.5% 25% 50% 75%
#> EM[2,1] -0.8196080 0.6271760 -2.1386425 -1.17260995 -0.80674604 -0.430126766
#> EM[3,1] -0.5815096 0.6366416 -1.8612497 -0.93750818 -0.60440867 -0.230527935
#> EM[4,1] -0.1062646 0.4667994 -0.9488425 -0.40282707 -0.14895265 0.165014530
#> EM[5,1] -0.5226620 0.3942326 -1.3734472 -0.77114716 -0.51152689 -0.257580243
#> EM[6,1] 0.1908378 0.3917882 -0.5264092 -0.06686532 0.16349473 0.434787837
#> EM[7,1] -0.2388989 0.2916703 -0.7808417 -0.44205507 -0.24174553 -0.045705053
#> EM[8,1] -0.4657902 0.2123778 -0.9388953 -0.59036182 -0.44444974 -0.318397942
#> EM[3,2] 0.2380985 0.7664907 -1.1737889 -0.21048685 0.18489111 0.668638250
#> EM[4,2] 0.7133434 0.7603938 -0.6289119 0.26879223 0.66046630 1.090182503
#> EM[5,2] 0.2969461 0.7093964 -1.1947691 -0.11443418 0.35025719 0.739744896
#> EM[6,2] 1.0104458 0.7032919 -0.1850560 0.58636381 0.93539717 1.374976688
#> EM[7,2] 0.5807091 0.6457758 -0.6045484 0.17100529 0.53239162 0.934778054
#> EM[8,2] 0.3538178 0.6131603 -0.8375095 -0.02551345 0.36439910 0.706919999
#> EM[4,3] 0.4752449 0.7438566 -0.9250920 0.03087679 0.45441130 0.840911092
#> EM[5,3] 0.0588476 0.7322301 -1.6346154 -0.32579889 0.12240877 0.515615660
#> EM[6,3] 0.7723473 0.7042767 -0.5110949 0.35981522 0.72179599 1.147581967
#> EM[7,3] 0.3426106 0.6456953 -0.9350189 -0.01111068 0.33362848 0.686462429
#> EM[8,3] 0.1157193 0.6429612 -1.3491618 -0.20486463 0.14162623 0.458463279
#> EM[5,4] -0.4163973 0.5717916 -1.6803050 -0.72809143 -0.34516093 -0.042792007
#> EM[6,4] 0.2971024 0.5216668 -0.8065611 -0.01337952 0.29403224 0.628734595
#> EM[7,4] -0.1326343 0.4721715 -1.0452672 -0.44009689 -0.14042390 0.195006956
#> EM[8,4] -0.3595256 0.4567728 -1.3380854 -0.63369541 -0.31254738 -0.048501978
#> EM[6,5] 0.7134997 0.5462199 -0.2039198 0.33696120 0.66247563 1.016527930
#> EM[7,5] 0.2837630 0.4869501 -0.5959440 -0.04908196 0.26707372 0.598905764
#> EM[8,5] 0.0568717 0.4174012 -0.7547308 -0.21857525 0.05208516 0.327152434
#> EM[7,6] -0.4297367 0.4224222 -1.3096761 -0.69533799 -0.41670092 -0.145366535
#> EM[8,6] -0.6566280 0.3963803 -1.5569647 -0.88835688 -0.60848271 -0.381470529
#> EM[8,7] -0.2268913 0.3044977 -0.8747741 -0.42674206 -0.21579557 -0.009449224
#> 97.5% Rhat n.eff
#> EM[2,1] 0.37290372 1.100285 27
#> EM[3,1] 0.82957997 1.017446 610
#> EM[4,1] 0.88851118 1.183641 16
#> EM[5,1] 0.24531281 1.081867 32
#> EM[6,1] 1.02909607 1.054470 50
#> EM[7,1] 0.36877190 1.047620 79
#> EM[8,1] -0.10526562 1.008542 370
#> EM[3,2] 1.98767929 1.035595 62
#> EM[4,2] 2.54073280 1.146682 19
#> EM[5,2] 1.64483399 1.099756 29
#> EM[6,2] 2.67637095 1.042813 67
#> EM[7,2] 2.10109197 1.083512 29
#> EM[8,2] 1.68582618 1.087283 30
#> EM[4,3] 2.20945591 1.070789 38
#> EM[5,3] 1.36057646 1.035284 120
#> EM[6,3] 2.36971830 1.009843 390
#> EM[7,3] 1.69177982 1.011648 240
#> EM[8,3] 1.37169705 1.013696 1700
#> EM[5,4] 0.55054454 1.033287 110
#> EM[6,4] 1.34839043 1.088715 27
#> EM[7,4] 0.79786604 1.309932 10
#> EM[8,4] 0.39941401 1.173637 16
#> EM[6,5] 1.99222703 1.020142 120
#> EM[7,5] 1.29049916 1.114792 23
#> EM[8,5] 0.89392807 1.059789 42
#> EM[7,6] 0.41292098 1.077556 31
#> EM[8,6] 0.01321147 1.027212 78
#> EM[8,7] 0.32476536 1.043075 66
#>
#> $EM_pred
#> mean sd 2.5% 25% 50%
#> EM.pred[2,1] -0.82208851 0.6412103 -2.2047628 -1.18278915 -0.81430106
#> EM.pred[3,1] -0.57644638 0.6537302 -1.9112643 -0.93944590 -0.59528721
#> EM.pred[4,1] -0.10977424 0.4940997 -1.0653280 -0.41471920 -0.14211842
#> EM.pred[5,1] -0.52342783 0.4259826 -1.4282021 -0.77778272 -0.50432047
#> EM.pred[6,1] 0.19103940 0.4160182 -0.5904471 -0.07485968 0.16739448
#> EM.pred[7,1] -0.24132933 0.3255592 -0.8632799 -0.45356102 -0.24031741
#> EM.pred[8,1] -0.46338742 0.2532425 -1.0639891 -0.58968996 -0.43433061
#> EM.pred[3,2] 0.23665132 0.7816897 -1.2436187 -0.23172670 0.19184503
#> EM.pred[4,2] 0.71501917 0.7735912 -0.6730840 0.25318096 0.65793139
#> EM.pred[5,2] 0.29513489 0.7265533 -1.2163475 -0.13895830 0.34834121
#> EM.pred[6,2] 1.00947262 0.7190856 -0.2286345 0.56328541 0.93829209
#> EM.pred[7,2] 0.57688886 0.6575928 -0.6119738 0.15292056 0.51719647
#> EM.pred[8,2] 0.35723191 0.6322096 -0.8679137 -0.03468241 0.36375945
#> EM.pred[4,3] 0.47927343 0.7580434 -0.9369816 0.03161668 0.45524964
#> EM.pred[5,3] 0.05429031 0.7480675 -1.6486298 -0.34440490 0.10793241
#> EM.pred[6,3] 0.77101709 0.7204353 -0.5586301 0.34261159 0.72380689
#> EM.pred[7,3] 0.34381981 0.6598643 -0.9785287 -0.01326791 0.33159249
#> EM.pred[8,3] 0.11911118 0.6570691 -1.3609763 -0.20822653 0.14424161
#> EM.pred[5,4] -0.41686353 0.5890869 -1.7425244 -0.73742864 -0.34943183
#> EM.pred[6,4] 0.30097821 0.5429871 -0.8461116 -0.02600992 0.29740847
#> EM.pred[7,4] -0.12862479 0.4902799 -1.0802615 -0.45449260 -0.14463768
#> EM.pred[8,4] -0.36459307 0.4813046 -1.4417954 -0.64597377 -0.30806506
#> EM.pred[6,5] 0.71615196 0.5636796 -0.2414872 0.33039716 0.66563185
#> EM.pred[7,5] 0.28158647 0.5100410 -0.6398270 -0.07305325 0.26877984
#> EM.pred[8,5] 0.05874807 0.4427305 -0.8580747 -0.23294965 0.06298908
#> EM.pred[7,6] -0.43165507 0.4533232 -1.3982242 -0.70571624 -0.42491467
#> EM.pred[8,6] -0.65870922 0.4248332 -1.6435015 -0.89914706 -0.60530414
#> EM.pred[8,7] -0.22665224 0.3443028 -0.9466146 -0.44746255 -0.21597669
#> 75% 97.5% Rhat n.eff
#> EM.pred[2,1] -0.43186016 0.43949605 1.094050 28
#> EM.pred[3,1] -0.21089507 0.86608539 1.016115 570
#> EM.pred[4,1] 0.17706021 0.91069315 1.151560 18
#> EM.pred[5,1] -0.24264232 0.28740290 1.058630 44
#> EM.pred[6,1] 0.44541505 1.05428476 1.043035 64
#> EM.pred[7,1] -0.02747190 0.40427925 1.034048 110
#> EM.pred[8,1] -0.30073779 -0.04067162 1.004043 570
#> EM.pred[3,2] 0.66701510 1.99195936 1.033823 65
#> EM.pred[4,2] 1.09069229 2.53954065 1.139090 20
#> EM.pred[5,2] 0.73721756 1.69306267 1.098518 29
#> EM.pred[6,2] 1.39041248 2.65864433 1.037777 78
#> EM.pred[7,2] 0.94949698 2.10850466 1.078112 31
#> EM.pred[8,2] 0.71449785 1.72693099 1.079286 33
#> EM.pred[4,3] 0.86605573 2.22239186 1.069602 38
#> EM.pred[5,3] 0.52075472 1.42909108 1.033792 110
#> EM.pred[6,3] 1.17258121 2.41039866 1.008994 380
#> EM.pred[7,3] 0.69401088 1.70928797 1.010085 270
#> EM.pred[8,3] 0.46749295 1.40551025 1.013838 1800
#> EM.pred[5,4] -0.03263088 0.61840587 1.031448 110
#> EM.pred[6,4] 0.63988708 1.39553124 1.082975 29
#> EM.pred[7,4] 0.21596148 0.82390240 1.285116 11
#> EM.pred[8,4] -0.04617574 0.45631233 1.159363 17
#> EM.pred[6,5] 1.04200768 2.02578719 1.016565 150
#> EM.pred[7,5] 0.61500869 1.30585745 1.105882 25
#> EM.pred[8,5] 0.34208385 0.93541826 1.052849 48
#> EM.pred[7,6] -0.12590840 0.46112541 1.068034 34
#> EM.pred[8,6] -0.37281011 0.07964278 1.021608 97
#> EM.pred[8,7] 0.01635739 0.39361591 1.029561 88
#>
#> $tau
#> mean sd 2.5% 25% 50% 75%
#> 0.111451605 0.099592406 0.003567396 0.023183338 0.087505355 0.172056243
#> 97.5% Rhat n.eff
#> 0.361862606 1.085650200 49.000000000
#>
#> $delta
#> mean sd 2.5% 25% 50% 75%
#> delta[1,2] -0.31344472 0.3330966 -1.0175619 -0.4819075 -0.3224652 -0.149826846
#> delta[2,2] -0.28411458 0.3043354 -0.8857541 -0.4633930 -0.3112973 -0.135107601
#> delta[3,2] -0.44217048 0.2239801 -0.8815871 -0.6016410 -0.4635511 -0.268725914
#> delta[4,2] -0.46841572 0.1478879 -0.7816247 -0.5530570 -0.4584894 -0.394211905
#> delta[5,2] -0.40535254 0.2298095 -0.8172089 -0.5771038 -0.4300604 -0.251765897
#> delta[6,2] -0.35217037 0.2165907 -0.6938583 -0.5387866 -0.3450091 -0.219055441
#> delta[7,2] -0.46857011 0.1474769 -0.7862753 -0.5578362 -0.4582484 -0.392339259
#> delta[8,2] -0.38779039 0.1976694 -0.6959094 -0.5488258 -0.4013136 -0.249181211
#> delta[9,2] -0.41239483 0.2002298 -0.7522420 -0.5729314 -0.4321506 -0.265176153
#> delta[10,2] -0.24640147 0.3267134 -0.8597941 -0.4532805 -0.2663884 -0.075125459
#> delta[11,2] -0.16784815 0.2231294 -0.5917196 -0.2923170 -0.1665786 -0.048649815
#> delta[12,2] -0.26626504 0.2891308 -0.8181502 -0.4561262 -0.2795729 -0.117445308
#> delta[13,2] -0.93356934 0.3983850 -1.7293247 -1.1830924 -0.9204267 -0.745558291
#> delta[14,2] -0.09520431 0.1985461 -0.4881772 -0.2239622 -0.1066721 0.061655552
#> delta[15,2] -0.11826553 0.2374541 -0.5423705 -0.2577843 -0.1269389 -0.000114772
#> delta[16,2] -0.37413952 0.1557617 -0.6331056 -0.4683810 -0.4066820 -0.288597314
#> delta[17,2] -0.38956596 0.2820345 -1.0554280 -0.5365988 -0.3559837 -0.213044872
#> delta[18,2] -0.46780819 0.3312653 -1.0168226 -0.7423707 -0.5142281 -0.215672093
#> delta[19,2] -0.46073468 0.3391649 -1.0206997 -0.7467570 -0.5060280 -0.212848387
#> delta[20,2] -0.39072106 0.2225448 -0.7705448 -0.5642457 -0.4055041 -0.247919271
#> delta[21,2] -0.52807015 0.1956919 -0.9865626 -0.6319605 -0.4934403 -0.416727552
#> delta[9,3] -0.51745270 0.1643962 -0.8847621 -0.6177813 -0.4852735 -0.415252576
#> delta[10,3] -0.44571745 0.3419034 -1.0384120 -0.7384624 -0.4742662 -0.200734326
#> delta[12,3] -0.37102797 0.3394275 -0.8777831 -0.6486123 -0.3927725 -0.151464738
#> delta[13,3] -0.70257533 0.3493721 -1.4454683 -0.9254612 -0.7050986 -0.437719374
#> delta[19,3] -0.43802364 0.2482332 -0.9376325 -0.6047133 -0.4573054 -0.260872608
#> delta[10,4] -0.43046451 0.2497584 -0.9377046 -0.6016863 -0.4570082 -0.258674462
#> delta[12,4] -0.34768365 0.2331529 -0.7180945 -0.5367894 -0.3620926 -0.215362540
#> delta[13,4] -0.20085191 0.2642915 -0.7867643 -0.3444155 -0.1885991 -0.054261934
#> 97.5% Rhat n.eff
#> delta[1,2] 0.360847222 1.182476 16
#> delta[2,2] 0.358076886 1.210522 14
#> delta[3,2] 0.006716708 1.065075 53
#> delta[4,2] -0.175087592 1.026888 140
#> delta[5,2] 0.038515313 1.051966 60
#> delta[6,2] 0.083821950 1.065698 58
#> delta[7,2] -0.176945596 1.039368 59
#> delta[8,2] 0.015846271 1.081471 39
#> delta[9,2] 0.005239083 1.069125 49
#> delta[10,2] 0.448807576 1.254205 12
#> delta[11,2] 0.318338383 1.179915 19
#> delta[12,2] 0.347207101 1.294584 11
#> delta[13,2] -0.171939364 1.352447 10
#> delta[14,2] 0.242532695 1.200341 15
#> delta[15,2] 0.417429932 1.118463 24
#> delta[16,2] -0.004393567 1.050929 51
#> delta[17,2] 0.141028619 1.060497 49
#> delta[18,2] 0.163818056 1.399052 9
#> delta[19,2] 0.232430190 1.386578 9
#> delta[20,2] 0.067201643 1.076446 40
#> delta[21,2] -0.199678010 1.053864 54
#> delta[9,3] -0.234116929 1.038583 76
#> delta[10,3] 0.229376925 1.448411 8
#> delta[12,3] 0.369536208 1.420768 8
#> delta[13,3] -0.054625651 1.013648 180
#> delta[19,3] 0.026019031 1.056580 56
#> delta[10,4] 0.051921436 1.022914 150
#> delta[12,4] 0.123618001 1.057444 79
#> delta[13,4] 0.305390624 1.170207 18
#>
#> $beta_all
#> mean sd 2.5% 25% 50%
#> beta.all[2,1] 0.0209414908 0.12252995 -0.25348864 -0.030565374 2.417452e-02
#> beta.all[3,1] 0.0217826179 0.12481208 -0.24406235 -0.029381635 2.248120e-02
#> beta.all[4,1] 0.0410533911 0.05953676 -0.07171605 0.003106506 3.777484e-02
#> beta.all[5,1] -0.0199902531 0.08962584 -0.23546205 -0.065262446 -6.433847e-03
#> beta.all[6,1] 0.0778399163 0.08333500 -0.05902167 0.022279490 6.766849e-02
#> beta.all[7,1] 0.0376679534 0.04524921 -0.04848907 0.008459788 3.938213e-02
#> beta.all[8,1] 0.0044733854 0.04147735 -0.08404615 -0.022986437 6.812862e-03
#> beta.all[3,2] 0.0008411271 0.14391632 -0.32008816 -0.055379829 7.237341e-05
#> beta.all[4,2] 0.0201119003 0.13145938 -0.22356570 -0.041105972 4.485822e-03
#> beta.all[5,2] -0.0409317439 0.13879758 -0.37619482 -0.100698599 -1.867054e-02
#> beta.all[6,2] 0.0568984255 0.14082669 -0.18355592 -0.014280855 2.921410e-02
#> beta.all[7,2] 0.0167264626 0.12336331 -0.21226250 -0.036003927 7.461138e-03
#> beta.all[8,2] -0.0164681054 0.12423138 -0.27457379 -0.073818830 -1.380759e-02
#> beta.all[4,3] 0.0192707732 0.13405280 -0.24387459 -0.036737707 8.098744e-03
#> beta.all[5,3] -0.0417728710 0.14406948 -0.40994626 -0.091860972 -1.750514e-02
#> beta.all[6,3] 0.0560572983 0.14090345 -0.17857806 -0.013978289 2.720297e-02
#> beta.all[7,3] 0.0158853355 0.12462363 -0.24422374 -0.034990243 6.581649e-03
#> beta.all[8,3] -0.0173092326 0.12614354 -0.29067050 -0.069356218 -1.216009e-02
#> beta.all[5,4] -0.0610436442 0.11161839 -0.33602135 -0.114386156 -3.439824e-02
#> beta.all[6,4] 0.0367865252 0.08868124 -0.12467250 -0.014426761 2.275223e-02
#> beta.all[7,4] -0.0033854377 0.06312221 -0.13289204 -0.040343880 -2.146679e-03
#> beta.all[8,4] -0.0365800057 0.06617518 -0.18451085 -0.074427476 -2.779040e-02
#> beta.all[6,5] 0.0978301693 0.12775617 -0.08286942 0.006897341 6.582156e-02
#> beta.all[7,5] 0.0576582065 0.09543719 -0.08508619 -0.004584217 3.694681e-02
#> beta.all[8,5] 0.0244636384 0.09147886 -0.12938847 -0.026979811 8.695884e-03
#> beta.all[7,6] -0.0401719628 0.08183562 -0.22848051 -0.085347410 -2.530409e-02
#> beta.all[8,6] -0.0733665309 0.08681624 -0.27014726 -0.126574481 -5.799631e-02
#> beta.all[8,7] -0.0331945681 0.05553703 -0.15277995 -0.066504593 -2.714174e-02
#> 75% 97.5% Rhat n.eff
#> beta.all[2,1] 0.082255184 0.26076244 1.009366 1400
#> beta.all[3,1] 0.074887159 0.29978742 1.034867 310
#> beta.all[4,1] 0.077628872 0.16749073 1.046075 56
#> beta.all[5,1] 0.037380273 0.12163327 1.060608 46
#> beta.all[6,1] 0.125561224 0.26598070 1.010637 340
#> beta.all[7,1] 0.068208620 0.12372177 1.021385 100
#> beta.all[8,1] 0.033006746 0.07895939 1.035236 68
#> beta.all[3,2] 0.053392032 0.32428000 1.011311 790
#> beta.all[4,2] 0.066974290 0.35021302 1.008480 530
#> beta.all[5,2] 0.021976823 0.21389134 1.043322 70
#> beta.all[6,2] 0.109809157 0.40668952 1.007384 350
#> beta.all[7,2] 0.061327402 0.30433629 1.013195 310
#> beta.all[8,2] 0.032000041 0.26705061 1.015765 230
#> beta.all[4,3] 0.065790983 0.33866111 1.017161 3000
#> beta.all[5,3] 0.023275453 0.20300656 1.066010 59
#> beta.all[6,3] 0.109812326 0.42307554 1.012957 220
#> beta.all[7,3] 0.063544609 0.30034076 1.027195 230
#> beta.all[8,3] 0.028217745 0.27294787 1.038497 150
#> beta.all[5,4] 0.007215255 0.10939548 1.087782 32
#> beta.all[6,4] 0.083663266 0.23578158 1.031171 70
#> beta.all[7,4] 0.031743629 0.12736681 1.053904 46
#> beta.all[8,4] 0.005443383 0.07984877 1.082008 34
#> beta.all[6,5] 0.166619291 0.40875779 1.018682 190
#> beta.all[7,5] 0.107720864 0.27830605 1.039500 82
#> beta.all[8,5] 0.068280833 0.24824274 1.031711 110
#> beta.all[7,6] 0.010417003 0.10184474 1.001317 2400
#> beta.all[8,6] -0.007496854 0.05806587 1.000679 3000
#> beta.all[8,7] 0.002735289 0.06775273 1.004286 530
#>
#> $dev_o
#> mean sd 2.5% 25% 50% 75%
#> dev.o[1,1] 2.0840202 2.2708292 0.0061680730 0.38974364 1.3224503 3.0386255
#> dev.o[2,1] 0.9289126 1.3494737 0.0008104883 0.09092910 0.4081544 1.1849373
#> dev.o[3,1] 0.9721415 1.3084081 0.0007933919 0.10814585 0.4679431 1.3178405
#> dev.o[4,1] 0.7452320 1.0383537 0.0003971443 0.07048411 0.3363442 0.9846712
#> dev.o[5,1] 0.6800817 0.9370864 0.0008384411 0.07559599 0.3228097 0.8973414
#> dev.o[6,1] 1.1023229 1.4292462 0.0016474555 0.13126231 0.5531676 1.5251843
#> dev.o[7,1] 0.7362819 1.0673147 0.0006234364 0.06899069 0.3199211 0.9585959
#> dev.o[8,1] 0.7097268 1.0206868 0.0008226539 0.07472648 0.3147305 0.9212721
#> dev.o[9,1] 0.7536152 0.9838389 0.0007388539 0.08479005 0.3757948 1.0300071
#> dev.o[10,1] 0.6161280 0.8580041 0.0006284813 0.06426732 0.2957309 0.8118986
#> dev.o[11,1] 0.8152811 1.1734825 0.0006961243 0.08293642 0.3697132 1.0452022
#> dev.o[12,1] 1.3756312 1.6179867 0.0016563608 0.21055506 0.8267203 1.9886327
#> dev.o[13,1] 1.3132028 1.5804677 0.0018406847 0.15682281 0.6993795 1.9677861
#> dev.o[14,1] 0.8290510 1.1964598 0.0009011333 0.08464446 0.3743987 1.0955884
#> dev.o[15,1] 0.8780932 1.1774676 0.0012146454 0.10223420 0.4208052 1.1568947
#> dev.o[16,1] 1.3329763 1.8702708 0.0008157606 0.13549395 0.6334885 1.8035618
#> dev.o[17,1] 1.8286918 2.0427301 0.0022872641 0.32121959 1.1231831 2.7029280
#> dev.o[18,1] 1.1624310 1.6431947 0.0011947778 0.11271439 0.5097104 1.5265721
#> dev.o[19,1] 1.7931160 1.7768827 0.0060991712 0.45403610 1.2513715 2.5772980
#> dev.o[20,1] 0.7739586 1.0582239 0.0009289007 0.08371238 0.3661066 1.0296939
#> dev.o[21,1] 1.3363425 1.6815575 0.0016459944 0.17071775 0.7254844 1.8563331
#> dev.o[1,2] 2.8902037 1.8519585 0.5054647710 1.55389674 2.5212037 3.7040197
#> dev.o[2,2] 0.9134849 1.2942142 0.0011185846 0.10843560 0.4293680 1.1753012
#> dev.o[3,2] 0.9519102 1.2679680 0.0007451813 0.10853653 0.4432290 1.3365516
#> dev.o[4,2] 0.7866066 1.1431691 0.0009759188 0.07920716 0.3479980 1.0397895
#> dev.o[5,2] 0.5576779 0.8152147 0.0007989881 0.05477677 0.2625884 0.7340540
#> dev.o[6,2] 1.2023203 1.5188610 0.0022783923 0.15332466 0.6536289 1.6946755
#> dev.o[7,2] 0.8839912 1.2662925 0.0007654681 0.08452526 0.4086483 1.1915268
#> dev.o[8,2] 0.6992235 0.9836962 0.0010923829 0.06353256 0.3204135 0.9563985
#> dev.o[9,2] 0.6849584 0.9343667 0.0007126209 0.06759063 0.3094297 0.9303177
#> dev.o[10,2] 1.7426342 1.9324160 0.0024348146 0.27683148 1.0825676 2.5568612
#> dev.o[11,2] 0.8842579 1.2333448 0.0008803396 0.09024282 0.4267014 1.1970159
#> dev.o[12,2] 0.8746937 1.2077340 0.0008853638 0.08487871 0.3887149 1.1825417
#> dev.o[13,2] 1.0417439 1.4407742 0.0007346010 0.10550799 0.4506356 1.4158513
#> dev.o[14,2] 0.8155228 1.1155543 0.0010667087 0.09829110 0.3841322 1.1051859
#> dev.o[15,2] 0.9258506 1.2662941 0.0010466258 0.09054163 0.4372935 1.2772273
#> dev.o[16,2] 1.4724940 1.8956343 0.0018904757 0.19353660 0.7508370 2.0386159
#> dev.o[17,2] 2.0684527 1.9273403 0.0134850692 0.59116458 1.5369738 2.9940784
#> dev.o[18,2] 1.0474703 1.4126403 0.0014140290 0.10864853 0.4951470 1.4253161
#> dev.o[19,2] 0.4018820 0.6134104 0.0004571646 0.03992901 0.1709780 0.5151919
#> dev.o[20,2] 0.7301221 1.0235028 0.0008209134 0.07445181 0.3209612 0.9964580
#> dev.o[21,2] 1.2926590 1.5615266 0.0017819947 0.18553440 0.7334534 1.8423990
#> dev.o[9,3] 0.9106497 1.1996136 0.0007106724 0.10034174 0.4612656 1.2507992
#> dev.o[10,3] 0.7989638 1.0987469 0.0008315266 0.07511460 0.3592328 1.0798147
#> dev.o[12,3] 1.2813486 1.5840668 0.0017397081 0.15507369 0.6781681 1.8355119
#> dev.o[13,3] 0.9924178 1.3586551 0.0015809605 0.09863596 0.4776325 1.3106409
#> dev.o[19,3] 1.7878129 1.5009845 0.0389779636 0.69800949 1.4204650 2.4618838
#> dev.o[10,4] 1.1075140 1.4092561 0.0013057824 0.13527957 0.5651486 1.5815997
#> dev.o[12,4] 0.8274388 1.0833967 0.0007465536 0.09231834 0.3946564 1.1378258
#> dev.o[13,4] 1.1013749 1.4531438 0.0013912717 0.11732088 0.5480071 1.5304916
#> 97.5% Rhat n.eff
#> dev.o[1,1] 8.105743 1.001551 1900
#> dev.o[2,1] 4.928486 1.000826 3000
#> dev.o[3,1] 4.668645 1.004721 480
#> dev.o[4,1] 3.642937 1.005785 380
#> dev.o[5,1] 3.273014 1.004537 770
#> dev.o[6,1] 5.087944 1.000559 3000
#> dev.o[7,1] 3.771921 1.001556 1900
#> dev.o[8,1] 3.478995 1.003074 780
#> dev.o[9,1] 3.492825 1.003962 580
#> dev.o[10,1] 3.019194 1.003106 770
#> dev.o[11,1] 4.197070 1.004987 450
#> dev.o[12,1] 5.769885 1.049503 53
#> dev.o[13,1] 5.666193 1.030032 72
#> dev.o[14,1] 4.304712 1.002180 1200
#> dev.o[15,1] 4.223225 1.003191 1400
#> dev.o[16,1] 6.691469 1.001337 2400
#> dev.o[17,1] 7.357737 1.008086 330
#> dev.o[18,1] 5.923008 1.004858 460
#> dev.o[19,1] 6.445714 1.002585 1300
#> dev.o[20,1] 3.796048 1.001769 1600
#> dev.o[21,1] 6.079287 1.006291 380
#> dev.o[1,2] 7.423673 1.003701 1300
#> dev.o[2,2] 4.385290 1.002083 1300
#> dev.o[3,2] 4.572636 1.013553 180
#> dev.o[4,2] 3.811468 1.003818 790
#> dev.o[5,2] 2.784385 1.001673 1700
#> dev.o[6,2] 5.401865 1.002691 910
#> dev.o[7,2] 4.280966 1.001175 3000
#> dev.o[8,2] 3.489407 1.002288 1400
#> dev.o[9,2] 3.448639 1.002088 1300
#> dev.o[10,2] 6.972619 1.002778 1300
#> dev.o[11,2] 4.329900 1.001648 2400
#> dev.o[12,2] 4.196106 1.010885 250
#> dev.o[13,2] 4.992179 1.003543 660
#> dev.o[14,2] 4.128379 1.004827 480
#> dev.o[15,2] 4.427983 1.002628 940
#> dev.o[16,2] 6.563624 1.000980 3000
#> dev.o[17,2] 7.066465 1.015221 250
#> dev.o[18,2] 4.984158 1.012018 230
#> dev.o[19,2] 2.117000 1.009517 240
#> dev.o[20,2] 3.680479 1.001044 3000
#> dev.o[21,2] 5.786009 1.002457 1900
#> dev.o[9,3] 4.405829 1.012064 210
#> dev.o[10,3] 3.983752 1.002892 840
#> dev.o[12,3] 5.652631 1.001890 1400
#> dev.o[13,3] 4.989589 1.001676 3000
#> dev.o[19,3] 5.486405 1.031860 100
#> dev.o[10,4] 5.026160 1.001330 2400
#> dev.o[12,4] 3.956494 1.000936 3000
#> dev.o[13,4] 5.080747 1.002681 920
#>
#> $hat_par
#> mean sd 2.5% 25% 50%
#> hat.par[1,1] 1.710121 0.8340560 0.3983698 1.0702409 1.623772
#> hat.par[2,1] 51.410263 5.0404755 41.6830334 47.9580988 51.253501
#> hat.par[3,1] 44.615044 4.5804783 35.9511389 41.5104362 44.432950
#> hat.par[4,1] 42.420048 4.7134862 33.5381157 39.1513533 42.233046
#> hat.par[5,1] 17.270357 2.4851551 12.6128090 15.5296162 17.212215
#> hat.par[6,1] 44.469241 4.0653705 36.5756653 41.7440824 44.431836
#> hat.par[7,1] 156.965701 7.2593334 142.9501176 152.2518575 156.809906
#> hat.par[8,1] 68.266342 5.4560369 57.6205278 64.6058886 68.129658
#> hat.par[9,1] 89.063576 4.6955874 80.1484971 85.7942829 89.042892
#> hat.par[10,1] 78.606772 3.7587612 71.3556828 75.9711390 78.626504
#> hat.par[11,1] 74.363590 5.6515857 63.1044301 70.5351829 74.353331
#> hat.par[12,1] 77.776686 4.1950513 69.5718054 75.0320875 77.872298
#> hat.par[13,1] 48.832241 4.3675751 40.5981216 45.6880922 48.873073
#> hat.par[14,1] 34.644471 4.7479628 25.8418691 31.3596369 34.511324
#> hat.par[15,1] 35.252643 4.8533413 26.1274984 31.8177724 35.267870
#> hat.par[16,1] 304.488911 13.4685647 278.3252555 295.4437645 304.365009
#> hat.par[17,1] 10.963156 2.5813337 6.5313562 9.0909704 10.767307
#> hat.par[18,1] 22.026839 3.5595499 15.1770360 19.5578919 22.002165
#> hat.par[19,1] 3.873408 1.3065230 1.7831153 2.9568888 3.750109
#> hat.par[20,1] 23.843022 3.7846861 16.9541976 21.1545259 23.650343
#> hat.par[21,1] 31.517891 4.5214721 23.3532280 28.4065005 31.166374
#> hat.par[1,2] 1.279596 0.7210364 0.2487820 0.7404126 1.166300
#> hat.par[2,2] 44.452279 5.0218809 34.7191236 40.9110801 44.329977
#> hat.par[3,2] 30.499150 4.0441881 22.7203248 27.7595037 30.355053
#> hat.par[4,2] 43.956198 5.2040050 34.1952503 40.5118167 43.727195
#> hat.par[5,2] 11.577535 2.0770307 7.8557739 10.1051259 11.517843
#> hat.par[6,2] 34.509843 3.9384949 27.0911903 31.8260548 34.281780
#> hat.par[7,2] 197.130284 10.1027152 177.4634092 190.3581814 197.313912
#> hat.par[8,2] 51.390472 5.0029633 41.9052408 47.9607692 51.418299
#> hat.par[9,2] 81.722847 5.6572146 70.6383017 77.7287153 81.913817
#> hat.par[10,2] 72.525536 3.9054532 64.8158695 69.8675165 72.537884
#> hat.par[11,2] 117.728437 8.1695615 102.3414432 112.1262675 117.557479
#> hat.par[12,2] 81.076359 4.8228618 70.9456367 78.0057495 81.108669
#> hat.par[13,2] 26.436610 4.6689132 17.7090424 23.2445316 26.257895
#> hat.par[14,2] 31.394125 4.6486321 22.7837206 28.0583036 31.300525
#> hat.par[15,2] 33.536936 4.7507149 25.0290757 30.2272407 33.289774
#> hat.par[16,2] 245.831935 12.9953079 221.8342621 236.9104350 245.641769
#> hat.par[17,2] 7.035317 1.9192934 3.7227511 5.6637195 6.861026
#> hat.par[18,2] 12.946596 2.7084529 8.1527929 10.9862850 12.772704
#> hat.par[19,2] 2.378856 0.9557357 0.9443979 1.6853521 2.245173
#> hat.par[20,2] 20.237848 3.5064370 14.1099981 17.8025965 20.036672
#> hat.par[21,2] 22.383442 3.8548729 15.0167938 19.8219105 22.295832
#> hat.par[9,3] 80.263822 5.7519786 68.3466824 76.5749185 80.376394
#> hat.par[10,3] 69.063879 4.4691099 59.8297216 66.0802561 69.004184
#> hat.par[12,3] 66.882701 4.7786884 57.7323663 63.5491992 66.817410
#> hat.par[13,3] 35.482203 5.1467430 25.6180726 31.8366675 35.337811
#> hat.par[19,3] 2.818812 1.0654099 1.1734334 2.0632416 2.665969
#> hat.par[10,4] 66.589455 4.2144171 58.0753239 63.9350608 66.617412
#> hat.par[12,4] 62.618010 4.3608463 54.1459038 59.5799872 62.601078
#> hat.par[13,4] 41.296590 4.6410621 32.3661277 38.1021209 41.177650
#> 75% 97.5% Rhat n.eff
#> hat.par[1,1] 2.243340 3.542052 1.006597 640
#> hat.par[2,1] 54.670927 61.851055 1.010378 200
#> hat.par[3,1] 47.743118 53.934973 1.016632 130
#> hat.par[4,1] 45.524314 52.010465 1.016756 160
#> hat.par[5,1] 18.991039 22.129632 1.001715 3000
#> hat.par[6,1] 47.178620 52.532668 1.002642 940
#> hat.par[7,1] 161.834724 171.402023 1.007736 320
#> hat.par[8,1] 71.899287 78.978566 1.013489 160
#> hat.par[9,1] 92.133993 98.414216 1.009299 280
#> hat.par[10,1] 81.149886 86.126852 1.019583 110
#> hat.par[11,1] 78.079650 85.653806 1.014309 310
#> hat.par[12,1] 80.671092 85.860293 1.058089 39
#> hat.par[13,1] 51.741421 57.617236 1.067689 36
#> hat.par[14,1] 37.792991 44.338227 1.011414 190
#> hat.par[15,1] 38.409083 44.997596 1.009347 230
#> hat.par[16,1] 313.562942 330.912256 1.004280 940
#> hat.par[17,1] 12.511974 16.661129 1.007599 280
#> hat.par[18,1] 24.446135 29.027651 1.015795 140
#> hat.par[19,1] 4.606080 6.818777 1.003140 950
#> hat.par[20,1] 26.338311 31.753423 1.008515 280
#> hat.par[21,1] 34.378536 41.144436 1.006328 410
#> hat.par[1,2] 1.653266 2.969789 1.004131 1200
#> hat.par[2,2] 47.861220 54.446537 1.014767 140
#> hat.par[3,2] 33.233397 38.718711 1.020746 100
#> hat.par[4,2] 47.337647 54.667720 1.004876 460
#> hat.par[5,2] 12.953333 15.787258 1.006253 350
#> hat.par[6,2] 37.106099 42.429473 1.005517 400
#> hat.par[7,2] 203.932652 216.689330 1.004198 540
#> hat.par[8,2] 54.748323 61.370026 1.017541 120
#> hat.par[9,2] 85.394349 92.840690 1.053605 43
#> hat.par[10,2] 75.267831 80.122510 1.002779 1200
#> hat.par[11,2] 123.439299 133.719045 1.003356 700
#> hat.par[12,2] 84.441843 90.091264 1.029352 73
#> hat.par[13,2] 29.454899 36.087384 1.064185 36
#> hat.par[14,2] 34.377716 41.232651 1.011170 190
#> hat.par[15,2] 36.433049 43.978015 1.013561 160
#> hat.par[16,2] 254.295944 272.119077 1.000717 3000
#> hat.par[17,2] 8.245559 11.227090 1.009453 230
#> hat.par[18,2] 14.720317 18.593612 1.022967 93
#> hat.par[19,2] 2.888093 4.638545 1.030308 74
#> hat.par[20,2] 22.405144 27.685592 1.003470 680
#> hat.par[21,2] 24.908872 30.315697 1.000868 3000
#> hat.par[9,3] 84.150061 91.296757 1.011953 180
#> hat.par[10,3] 72.239918 77.719328 1.017063 120
#> hat.par[12,3] 70.119635 76.319558 1.002980 810
#> hat.par[13,3] 38.958399 45.784041 1.013958 150
#> hat.par[19,3] 3.403635 5.232737 1.028845 75
#> hat.par[10,4] 69.435358 74.655117 1.004107 560
#> hat.par[12,4] 65.604715 71.185003 1.002589 960
#> hat.par[13,4] 44.450531 50.450789 1.005499 400
#>
#> $leverage_o
#> [1] 0.9477387 0.9228582 0.7706020 0.6817679 0.6279379 0.6323183 0.7235389
#> [8] 0.7080462 0.5412496 0.6095154 0.8051637 0.6178163 0.6989897 0.7693097
#> [15] 0.8220856 0.9506277 0.8912590 0.9066585 0.7116912 0.7726444 0.8713078
#> [22] 0.2220185 0.9029354 0.6764544 0.7571215 0.5167200 0.6967676 0.8775203
#> [29] 0.6891515 0.6833286 0.7002499 0.8772793 0.7406414 1.0327556 0.7454260
#> [36] 0.8487540 0.9923144 0.3686916 0.6577622 0.3355308 0.7267375 0.6820920
#> [43] 0.6871892 0.7569351 0.7341026 0.9837893 0.1635183 0.6506538 0.6376791
#> [50] 0.7186492
#>
#> $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
#> [26] -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
#>
#> $phi
#> mean sd 2.5% 25% 50% 75%
#> phi[1] -0.28038713 0.5347203 -1.437745 -0.6072159 -0.25137959 0.05094547
#> phi[2] 0.12955778 1.0186466 -1.960638 -0.4929084 0.14459331 0.80763801
#> phi[3] 0.08375382 0.9500225 -1.904737 -0.5123342 0.11144796 0.73126453
#> phi[4] -1.17524328 0.7367409 -2.618846 -1.6475151 -1.16896454 -0.72043436
#> phi[5] -0.46411553 0.9474779 -2.318283 -1.1062131 -0.47402586 0.18152223
#> phi[6] 0.73926334 0.8239143 -1.018537 0.2570548 0.76586035 1.25581013
#> phi[7] -0.10418615 0.7673138 -1.597297 -0.6418194 -0.08519369 0.45060495
#> phi[8] -0.21125014 0.9416103 -2.056709 -0.8510343 -0.18273639 0.44628607
#> 97.5% Rhat n.eff
#> phi[1] 0.7829486 1.095242 31
#> phi[2] 2.0032472 1.154370 19
#> phi[3] 1.8561171 1.018248 120
#> phi[4] 0.3939858 1.058072 40
#> phi[5] 1.3911158 1.173903 16
#> phi[6] 2.2842129 1.051670 50
#> phi[7] 1.2797625 1.264697 12
#> phi[8] 1.6181194 1.001471 3000
#>
#> $model_assessment
#> DIC pD dev
#> 1 90.48881 36.0459 54.44292
#>
#> $measure
#> [1] "OR"
#>
#> $model
#> [1] "RE"
#>
#> $assumption
#> [1] "IDE-ARM"
#>
#> $covariate
#> [1] 1996 1998 1999 2000 2000 2001 2002 2002 2002 2002 2002 2003 2003 2005 2005
#> [16] 2005 2005 2006 2006 2007 2007
#>
#> $covar_assumption
#> [1] "exchangeable"
#>
#> $cov_value
#> [1] 2007
#>
#> $jagsfit
#> Inference for Bugs model at "4", fit using jags,
#> 3 chains, each with 1000 iterations (first 0 discarded)
#> n.sims = 3000 iterations saved
#> mu.vect sd.vect 2.5% 25% 50% 75% 97.5%
#> EM[2,1] -0.820 0.627 -2.139 -1.173 -0.807 -0.430 0.373
#> EM[3,1] -0.582 0.637 -1.861 -0.938 -0.604 -0.231 0.830
#> EM[4,1] -0.106 0.467 -0.949 -0.403 -0.149 0.165 0.889
#> EM[5,1] -0.523 0.394 -1.373 -0.771 -0.512 -0.258 0.245
#> EM[6,1] 0.191 0.392 -0.526 -0.067 0.163 0.435 1.029
#> EM[7,1] -0.239 0.292 -0.781 -0.442 -0.242 -0.046 0.369
#> EM[8,1] -0.466 0.212 -0.939 -0.590 -0.444 -0.318 -0.105
#> EM[3,2] 0.238 0.766 -1.174 -0.210 0.185 0.669 1.988
#> EM[4,2] 0.713 0.760 -0.629 0.269 0.660 1.090 2.541
#> EM[5,2] 0.297 0.709 -1.195 -0.114 0.350 0.740 1.645
#> EM[6,2] 1.010 0.703 -0.185 0.586 0.935 1.375 2.676
#> EM[7,2] 0.581 0.646 -0.605 0.171 0.532 0.935 2.101
#> EM[8,2] 0.354 0.613 -0.838 -0.026 0.364 0.707 1.686
#> EM[4,3] 0.475 0.744 -0.925 0.031 0.454 0.841 2.209
#> EM[5,3] 0.059 0.732 -1.635 -0.326 0.122 0.516 1.361
#> EM[6,3] 0.772 0.704 -0.511 0.360 0.722 1.148 2.370
#> EM[7,3] 0.343 0.646 -0.935 -0.011 0.334 0.686 1.692
#> EM[8,3] 0.116 0.643 -1.349 -0.205 0.142 0.458 1.372
#> EM[5,4] -0.416 0.572 -1.680 -0.728 -0.345 -0.043 0.551
#> EM[6,4] 0.297 0.522 -0.807 -0.013 0.294 0.629 1.348
#> EM[7,4] -0.133 0.472 -1.045 -0.440 -0.140 0.195 0.798
#> EM[8,4] -0.360 0.457 -1.338 -0.634 -0.313 -0.049 0.399
#> EM[6,5] 0.713 0.546 -0.204 0.337 0.662 1.017 1.992
#> EM[7,5] 0.284 0.487 -0.596 -0.049 0.267 0.599 1.290
#> EM[8,5] 0.057 0.417 -0.755 -0.219 0.052 0.327 0.894
#> EM[7,6] -0.430 0.422 -1.310 -0.695 -0.417 -0.145 0.413
#> EM[8,6] -0.657 0.396 -1.557 -0.888 -0.608 -0.381 0.013
#> EM[8,7] -0.227 0.304 -0.875 -0.427 -0.216 -0.009 0.325
#> EM.pred[2,1] -0.822 0.641 -2.205 -1.183 -0.814 -0.432 0.439
#> EM.pred[3,1] -0.576 0.654 -1.911 -0.939 -0.595 -0.211 0.866
#> EM.pred[4,1] -0.110 0.494 -1.065 -0.415 -0.142 0.177 0.911
#> EM.pred[5,1] -0.523 0.426 -1.428 -0.778 -0.504 -0.243 0.287
#> EM.pred[6,1] 0.191 0.416 -0.590 -0.075 0.167 0.445 1.054
#> EM.pred[7,1] -0.241 0.326 -0.863 -0.454 -0.240 -0.027 0.404
#> EM.pred[8,1] -0.463 0.253 -1.064 -0.590 -0.434 -0.301 -0.041
#> EM.pred[3,2] 0.237 0.782 -1.244 -0.232 0.192 0.667 1.992
#> EM.pred[4,2] 0.715 0.774 -0.673 0.253 0.658 1.091 2.540
#> EM.pred[5,2] 0.295 0.727 -1.216 -0.139 0.348 0.737 1.693
#> EM.pred[6,2] 1.009 0.719 -0.229 0.563 0.938 1.390 2.659
#> EM.pred[7,2] 0.577 0.658 -0.612 0.153 0.517 0.949 2.109
#> EM.pred[8,2] 0.357 0.632 -0.868 -0.035 0.364 0.714 1.727
#> EM.pred[4,3] 0.479 0.758 -0.937 0.032 0.455 0.866 2.222
#> EM.pred[5,3] 0.054 0.748 -1.649 -0.344 0.108 0.521 1.429
#> EM.pred[6,3] 0.771 0.720 -0.559 0.343 0.724 1.173 2.410
#> EM.pred[7,3] 0.344 0.660 -0.979 -0.013 0.332 0.694 1.709
#> EM.pred[8,3] 0.119 0.657 -1.361 -0.208 0.144 0.467 1.406
#> EM.pred[5,4] -0.417 0.589 -1.743 -0.737 -0.349 -0.033 0.618
#> EM.pred[6,4] 0.301 0.543 -0.846 -0.026 0.297 0.640 1.396
#> EM.pred[7,4] -0.129 0.490 -1.080 -0.454 -0.145 0.216 0.824
#> EM.pred[8,4] -0.365 0.481 -1.442 -0.646 -0.308 -0.046 0.456
#> EM.pred[6,5] 0.716 0.564 -0.241 0.330 0.666 1.042 2.026
#> EM.pred[7,5] 0.282 0.510 -0.640 -0.073 0.269 0.615 1.306
#> EM.pred[8,5] 0.059 0.443 -0.858 -0.233 0.063 0.342 0.935
#> EM.pred[7,6] -0.432 0.453 -1.398 -0.706 -0.425 -0.126 0.461
#> EM.pred[8,6] -0.659 0.425 -1.644 -0.899 -0.605 -0.373 0.080
#> EM.pred[8,7] -0.227 0.344 -0.947 -0.447 -0.216 0.016 0.394
#> SUCRA[1] 0.222 0.166 0.000 0.143 0.143 0.286 0.571
#> SUCRA[2] 0.802 0.273 0.000 0.714 0.857 1.000 1.000
#> SUCRA[3] 0.690 0.304 0.000 0.429 0.857 1.000 1.000
#> SUCRA[4] 0.368 0.275 0.000 0.143 0.286 0.571 0.857
#> SUCRA[5] 0.658 0.263 0.143 0.429 0.714 0.857 1.000
#> SUCRA[6] 0.140 0.183 0.000 0.000 0.143 0.286 0.571
#> SUCRA[7] 0.458 0.224 0.000 0.286 0.429 0.571 0.857
#> SUCRA[8] 0.661 0.174 0.286 0.571 0.714 0.714 1.000
#> abs_risk[1] 0.390 0.000 0.390 0.390 0.390 0.390 0.390
#> abs_risk[2] 0.236 0.106 0.070 0.165 0.222 0.294 0.481
#> abs_risk[3] 0.278 0.119 0.090 0.200 0.259 0.337 0.594
#> abs_risk[4] 0.371 0.105 0.198 0.299 0.355 0.430 0.609
#> abs_risk[5] 0.282 0.076 0.139 0.228 0.277 0.331 0.450
#> abs_risk[6] 0.438 0.093 0.274 0.374 0.430 0.497 0.641
#> abs_risk[7] 0.338 0.065 0.227 0.291 0.334 0.379 0.480
#> abs_risk[8] 0.288 0.042 0.200 0.262 0.291 0.317 0.365
#> beta[1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> beta[2] 0.021 0.123 -0.253 -0.031 0.024 0.082 0.261
#> beta[3] 0.022 0.125 -0.244 -0.029 0.022 0.075 0.300
#> beta[4] 0.041 0.060 -0.072 0.003 0.038 0.078 0.167
#> beta[5] -0.020 0.090 -0.235 -0.065 -0.006 0.037 0.122
#> beta[6] 0.078 0.083 -0.059 0.022 0.068 0.126 0.266
#> beta[7] 0.038 0.045 -0.048 0.008 0.039 0.068 0.124
#> beta[8] 0.004 0.041 -0.084 -0.023 0.007 0.033 0.079
#> beta.all[2,1] 0.021 0.123 -0.253 -0.031 0.024 0.082 0.261
#> beta.all[3,1] 0.022 0.125 -0.244 -0.029 0.022 0.075 0.300
#> beta.all[4,1] 0.041 0.060 -0.072 0.003 0.038 0.078 0.167
#> beta.all[5,1] -0.020 0.090 -0.235 -0.065 -0.006 0.037 0.122
#> beta.all[6,1] 0.078 0.083 -0.059 0.022 0.068 0.126 0.266
#> beta.all[7,1] 0.038 0.045 -0.048 0.008 0.039 0.068 0.124
#> beta.all[8,1] 0.004 0.041 -0.084 -0.023 0.007 0.033 0.079
#> beta.all[3,2] 0.001 0.144 -0.320 -0.055 0.000 0.053 0.324
#> beta.all[4,2] 0.020 0.131 -0.224 -0.041 0.004 0.067 0.350
#> beta.all[5,2] -0.041 0.139 -0.376 -0.101 -0.019 0.022 0.214
#> beta.all[6,2] 0.057 0.141 -0.184 -0.014 0.029 0.110 0.407
#> beta.all[7,2] 0.017 0.123 -0.212 -0.036 0.007 0.061 0.304
#> beta.all[8,2] -0.016 0.124 -0.275 -0.074 -0.014 0.032 0.267
#> beta.all[4,3] 0.019 0.134 -0.244 -0.037 0.008 0.066 0.339
#> beta.all[5,3] -0.042 0.144 -0.410 -0.092 -0.018 0.023 0.203
#> beta.all[6,3] 0.056 0.141 -0.179 -0.014 0.027 0.110 0.423
#> beta.all[7,3] 0.016 0.125 -0.244 -0.035 0.007 0.064 0.300
#> beta.all[8,3] -0.017 0.126 -0.291 -0.069 -0.012 0.028 0.273
#> beta.all[5,4] -0.061 0.112 -0.336 -0.114 -0.034 0.007 0.109
#> beta.all[6,4] 0.037 0.089 -0.125 -0.014 0.023 0.084 0.236
#> beta.all[7,4] -0.003 0.063 -0.133 -0.040 -0.002 0.032 0.127
#> beta.all[8,4] -0.037 0.066 -0.185 -0.074 -0.028 0.005 0.080
#> beta.all[6,5] 0.098 0.128 -0.083 0.007 0.066 0.167 0.409
#> beta.all[7,5] 0.058 0.095 -0.085 -0.005 0.037 0.108 0.278
#> beta.all[8,5] 0.024 0.091 -0.129 -0.027 0.009 0.068 0.248
#> beta.all[7,6] -0.040 0.082 -0.228 -0.085 -0.025 0.010 0.102
#> beta.all[8,6] -0.073 0.087 -0.270 -0.127 -0.058 -0.007 0.058
#> beta.all[8,7] -0.033 0.056 -0.153 -0.067 -0.027 0.003 0.068
#> delta[1,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[2,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[3,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[4,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[5,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[6,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[7,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[8,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[9,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[10,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[11,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[12,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[13,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[14,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[15,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[16,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[17,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[18,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[19,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[20,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[21,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[1,2] -0.313 0.333 -1.018 -0.482 -0.322 -0.150 0.361
#> delta[2,2] -0.284 0.304 -0.886 -0.463 -0.311 -0.135 0.358
#> delta[3,2] -0.442 0.224 -0.882 -0.602 -0.464 -0.269 0.007
#> delta[4,2] -0.468 0.148 -0.782 -0.553 -0.458 -0.394 -0.175
#> delta[5,2] -0.405 0.230 -0.817 -0.577 -0.430 -0.252 0.039
#> delta[6,2] -0.352 0.217 -0.694 -0.539 -0.345 -0.219 0.084
#> delta[7,2] -0.469 0.147 -0.786 -0.558 -0.458 -0.392 -0.177
#> delta[8,2] -0.388 0.198 -0.696 -0.549 -0.401 -0.249 0.016
#> delta[9,2] -0.412 0.200 -0.752 -0.573 -0.432 -0.265 0.005
#> delta[10,2] -0.246 0.327 -0.860 -0.453 -0.266 -0.075 0.449
#> delta[11,2] -0.168 0.223 -0.592 -0.292 -0.167 -0.049 0.318
#> delta[12,2] -0.266 0.289 -0.818 -0.456 -0.280 -0.117 0.347
#> delta[13,2] -0.934 0.398 -1.729 -1.183 -0.920 -0.746 -0.172
#> delta[14,2] -0.095 0.199 -0.488 -0.224 -0.107 0.062 0.243
#> delta[15,2] -0.118 0.237 -0.542 -0.258 -0.127 0.000 0.417
#> delta[16,2] -0.374 0.156 -0.633 -0.468 -0.407 -0.289 -0.004
#> delta[17,2] -0.390 0.282 -1.055 -0.537 -0.356 -0.213 0.141
#> delta[18,2] -0.468 0.331 -1.017 -0.742 -0.514 -0.216 0.164
#> delta[19,2] -0.461 0.339 -1.021 -0.747 -0.506 -0.213 0.232
#> delta[20,2] -0.391 0.223 -0.771 -0.564 -0.406 -0.248 0.067
#> delta[21,2] -0.528 0.196 -0.987 -0.632 -0.493 -0.417 -0.200
#> delta[9,3] -0.517 0.164 -0.885 -0.618 -0.485 -0.415 -0.234
#> delta[10,3] -0.446 0.342 -1.038 -0.738 -0.474 -0.201 0.229
#> delta[12,3] -0.371 0.339 -0.878 -0.649 -0.393 -0.151 0.370
#> delta[13,3] -0.703 0.349 -1.445 -0.925 -0.705 -0.438 -0.055
#> delta[19,3] -0.438 0.248 -0.938 -0.605 -0.457 -0.261 0.026
#> delta[10,4] -0.430 0.250 -0.938 -0.602 -0.457 -0.259 0.052
#> delta[12,4] -0.348 0.233 -0.718 -0.537 -0.362 -0.215 0.124
#> delta[13,4] -0.201 0.264 -0.787 -0.344 -0.189 -0.054 0.305
#> dev.o[1,1] 2.084 2.271 0.006 0.390 1.322 3.039 8.106
#> dev.o[2,1] 0.929 1.349 0.001 0.091 0.408 1.185 4.928
#> dev.o[3,1] 0.972 1.308 0.001 0.108 0.468 1.318 4.669
#> dev.o[4,1] 0.745 1.038 0.000 0.070 0.336 0.985 3.643
#> dev.o[5,1] 0.680 0.937 0.001 0.076 0.323 0.897 3.273
#> dev.o[6,1] 1.102 1.429 0.002 0.131 0.553 1.525 5.088
#> dev.o[7,1] 0.736 1.067 0.001 0.069 0.320 0.959 3.772
#> dev.o[8,1] 0.710 1.021 0.001 0.075 0.315 0.921 3.479
#> dev.o[9,1] 0.754 0.984 0.001 0.085 0.376 1.030 3.493
#> dev.o[10,1] 0.616 0.858 0.001 0.064 0.296 0.812 3.019
#> dev.o[11,1] 0.815 1.173 0.001 0.083 0.370 1.045 4.197
#> dev.o[12,1] 1.376 1.618 0.002 0.211 0.827 1.989 5.770
#> dev.o[13,1] 1.313 1.580 0.002 0.157 0.699 1.968 5.666
#> dev.o[14,1] 0.829 1.196 0.001 0.085 0.374 1.096 4.305
#> dev.o[15,1] 0.878 1.177 0.001 0.102 0.421 1.157 4.223
#> dev.o[16,1] 1.333 1.870 0.001 0.135 0.633 1.804 6.691
#> dev.o[17,1] 1.829 2.043 0.002 0.321 1.123 2.703 7.358
#> dev.o[18,1] 1.162 1.643 0.001 0.113 0.510 1.527 5.923
#> dev.o[19,1] 1.793 1.777 0.006 0.454 1.251 2.577 6.446
#> dev.o[20,1] 0.774 1.058 0.001 0.084 0.366 1.030 3.796
#> dev.o[21,1] 1.336 1.682 0.002 0.171 0.725 1.856 6.079
#> dev.o[1,2] 2.890 1.852 0.505 1.554 2.521 3.704 7.424
#> dev.o[2,2] 0.913 1.294 0.001 0.108 0.429 1.175 4.385
#> dev.o[3,2] 0.952 1.268 0.001 0.109 0.443 1.337 4.573
#> dev.o[4,2] 0.787 1.143 0.001 0.079 0.348 1.040 3.811
#> dev.o[5,2] 0.558 0.815 0.001 0.055 0.263 0.734 2.784
#> dev.o[6,2] 1.202 1.519 0.002 0.153 0.654 1.695 5.402
#> dev.o[7,2] 0.884 1.266 0.001 0.085 0.409 1.192 4.281
#> dev.o[8,2] 0.699 0.984 0.001 0.064 0.320 0.956 3.489
#> dev.o[9,2] 0.685 0.934 0.001 0.068 0.309 0.930 3.449
#> dev.o[10,2] 1.743 1.932 0.002 0.277 1.083 2.557 6.973
#> dev.o[11,2] 0.884 1.233 0.001 0.090 0.427 1.197 4.330
#> dev.o[12,2] 0.875 1.208 0.001 0.085 0.389 1.183 4.196
#> dev.o[13,2] 1.042 1.441 0.001 0.106 0.451 1.416 4.992
#> dev.o[14,2] 0.816 1.116 0.001 0.098 0.384 1.105 4.128
#> dev.o[15,2] 0.926 1.266 0.001 0.091 0.437 1.277 4.428
#> dev.o[16,2] 1.472 1.896 0.002 0.194 0.751 2.039 6.564
#> dev.o[17,2] 2.068 1.927 0.013 0.591 1.537 2.994 7.066
#> dev.o[18,2] 1.047 1.413 0.001 0.109 0.495 1.425 4.984
#> dev.o[19,2] 0.402 0.613 0.000 0.040 0.171 0.515 2.117
#> dev.o[20,2] 0.730 1.024 0.001 0.074 0.321 0.996 3.680
#> dev.o[21,2] 1.293 1.562 0.002 0.186 0.733 1.842 5.786
#> dev.o[9,3] 0.911 1.200 0.001 0.100 0.461 1.251 4.406
#> dev.o[10,3] 0.799 1.099 0.001 0.075 0.359 1.080 3.984
#> dev.o[12,3] 1.281 1.584 0.002 0.155 0.678 1.836 5.653
#> dev.o[13,3] 0.992 1.359 0.002 0.099 0.478 1.311 4.990
#> dev.o[19,3] 1.788 1.501 0.039 0.698 1.420 2.462 5.486
#> dev.o[10,4] 1.108 1.409 0.001 0.135 0.565 1.582 5.026
#> dev.o[12,4] 0.827 1.083 0.001 0.092 0.395 1.138 3.956
#> dev.o[13,4] 1.101 1.453 0.001 0.117 0.548 1.530 5.081
#> effectiveness[1,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> effectiveness[2,1] 0.490 0.500 0.000 0.000 0.000 1.000 1.000
#> effectiveness[3,1] 0.254 0.435 0.000 0.000 0.000 1.000 1.000
#> effectiveness[4,1] 0.022 0.147 0.000 0.000 0.000 0.000 0.000
#> effectiveness[5,1] 0.163 0.369 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,1] 0.000 0.018 0.000 0.000 0.000 0.000 0.000
#> effectiveness[7,1] 0.013 0.115 0.000 0.000 0.000 0.000 0.000
#> effectiveness[8,1] 0.058 0.234 0.000 0.000 0.000 0.000 1.000
#> effectiveness[1,2] 0.001 0.032 0.000 0.000 0.000 0.000 0.000
#> effectiveness[2,2] 0.199 0.399 0.000 0.000 0.000 0.000 1.000
#> effectiveness[3,2] 0.265 0.441 0.000 0.000 0.000 1.000 1.000
#> effectiveness[4,2] 0.067 0.249 0.000 0.000 0.000 0.000 1.000
#> effectiveness[5,2] 0.221 0.415 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,2] 0.007 0.085 0.000 0.000 0.000 0.000 0.000
#> effectiveness[7,2] 0.057 0.232 0.000 0.000 0.000 0.000 1.000
#> effectiveness[8,2] 0.183 0.387 0.000 0.000 0.000 0.000 1.000
#> effectiveness[1,3] 0.008 0.091 0.000 0.000 0.000 0.000 0.000
#> effectiveness[2,3] 0.094 0.292 0.000 0.000 0.000 0.000 1.000
#> effectiveness[3,3] 0.134 0.341 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,3] 0.100 0.300 0.000 0.000 0.000 0.000 1.000
#> effectiveness[5,3] 0.197 0.398 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,3] 0.013 0.112 0.000 0.000 0.000 0.000 0.000
#> effectiveness[7,3] 0.154 0.361 0.000 0.000 0.000 0.000 1.000
#> effectiveness[8,3] 0.299 0.458 0.000 0.000 0.000 1.000 1.000
#> effectiveness[1,4] 0.052 0.222 0.000 0.000 0.000 0.000 1.000
#> effectiveness[2,4] 0.064 0.244 0.000 0.000 0.000 0.000 1.000
#> effectiveness[3,4] 0.095 0.293 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,4] 0.129 0.335 0.000 0.000 0.000 0.000 1.000
#> effectiveness[5,4] 0.135 0.341 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,4] 0.034 0.180 0.000 0.000 0.000 0.000 1.000
#> effectiveness[7,4] 0.201 0.401 0.000 0.000 0.000 0.000 1.000
#> effectiveness[8,4] 0.291 0.454 0.000 0.000 0.000 1.000 1.000
#> effectiveness[1,5] 0.138 0.345 0.000 0.000 0.000 0.000 1.000
#> effectiveness[2,5] 0.054 0.227 0.000 0.000 0.000 0.000 1.000
#> effectiveness[3,5] 0.083 0.275 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,5] 0.160 0.366 0.000 0.000 0.000 0.000 1.000
#> effectiveness[5,5] 0.136 0.342 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,5] 0.077 0.267 0.000 0.000 0.000 0.000 1.000
#> effectiveness[7,5] 0.226 0.418 0.000 0.000 0.000 0.000 1.000
#> effectiveness[8,5] 0.127 0.333 0.000 0.000 0.000 0.000 1.000
#> effectiveness[1,6] 0.282 0.450 0.000 0.000 0.000 1.000 1.000
#> effectiveness[2,6] 0.038 0.191 0.000 0.000 0.000 0.000 1.000
#> effectiveness[3,6] 0.058 0.234 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,6] 0.176 0.381 0.000 0.000 0.000 0.000 1.000
#> effectiveness[5,6] 0.078 0.269 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,6] 0.132 0.339 0.000 0.000 0.000 0.000 1.000
#> effectiveness[7,6] 0.200 0.400 0.000 0.000 0.000 0.000 1.000
#> effectiveness[8,6] 0.036 0.187 0.000 0.000 0.000 0.000 1.000
#> effectiveness[1,7] 0.322 0.467 0.000 0.000 0.000 1.000 1.000
#> effectiveness[2,7] 0.031 0.174 0.000 0.000 0.000 0.000 1.000
#> effectiveness[3,7] 0.053 0.225 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,7] 0.178 0.382 0.000 0.000 0.000 0.000 1.000
#> effectiveness[5,7] 0.053 0.225 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,7] 0.240 0.427 0.000 0.000 0.000 0.000 1.000
#> effectiveness[7,7] 0.117 0.321 0.000 0.000 0.000 0.000 1.000
#> effectiveness[8,7] 0.005 0.073 0.000 0.000 0.000 0.000 0.000
#> effectiveness[1,8] 0.197 0.398 0.000 0.000 0.000 0.000 1.000
#> effectiveness[2,8] 0.030 0.171 0.000 0.000 0.000 0.000 1.000
#> effectiveness[3,8] 0.058 0.234 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,8] 0.169 0.375 0.000 0.000 0.000 0.000 1.000
#> effectiveness[5,8] 0.017 0.129 0.000 0.000 0.000 0.000 0.000
#> effectiveness[6,8] 0.497 0.500 0.000 0.000 0.000 1.000 1.000
#> effectiveness[7,8] 0.031 0.174 0.000 0.000 0.000 0.000 1.000
#> effectiveness[8,8] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> hat.par[1,1] 1.710 0.834 0.398 1.070 1.624 2.243 3.542
#> hat.par[2,1] 51.410 5.040 41.683 47.958 51.254 54.671 61.851
#> hat.par[3,1] 44.615 4.580 35.951 41.510 44.433 47.743 53.935
#> hat.par[4,1] 42.420 4.713 33.538 39.151 42.233 45.524 52.010
#> hat.par[5,1] 17.270 2.485 12.613 15.530 17.212 18.991 22.130
#> hat.par[6,1] 44.469 4.065 36.576 41.744 44.432 47.179 52.533
#> hat.par[7,1] 156.966 7.259 142.950 152.252 156.810 161.835 171.402
#> hat.par[8,1] 68.266 5.456 57.621 64.606 68.130 71.899 78.979
#> hat.par[9,1] 89.064 4.696 80.148 85.794 89.043 92.134 98.414
#> hat.par[10,1] 78.607 3.759 71.356 75.971 78.627 81.150 86.127
#> hat.par[11,1] 74.364 5.652 63.104 70.535 74.353 78.080 85.654
#> hat.par[12,1] 77.777 4.195 69.572 75.032 77.872 80.671 85.860
#> hat.par[13,1] 48.832 4.368 40.598 45.688 48.873 51.741 57.617
#> hat.par[14,1] 34.644 4.748 25.842 31.360 34.511 37.793 44.338
#> hat.par[15,1] 35.253 4.853 26.127 31.818 35.268 38.409 44.998
#> hat.par[16,1] 304.489 13.469 278.325 295.444 304.365 313.563 330.912
#> hat.par[17,1] 10.963 2.581 6.531 9.091 10.767 12.512 16.661
#> hat.par[18,1] 22.027 3.560 15.177 19.558 22.002 24.446 29.028
#> hat.par[19,1] 3.873 1.307 1.783 2.957 3.750 4.606 6.819
#> hat.par[20,1] 23.843 3.785 16.954 21.155 23.650 26.338 31.753
#> hat.par[21,1] 31.518 4.521 23.353 28.407 31.166 34.379 41.144
#> hat.par[1,2] 1.280 0.721 0.249 0.740 1.166 1.653 2.970
#> hat.par[2,2] 44.452 5.022 34.719 40.911 44.330 47.861 54.447
#> hat.par[3,2] 30.499 4.044 22.720 27.760 30.355 33.233 38.719
#> hat.par[4,2] 43.956 5.204 34.195 40.512 43.727 47.338 54.668
#> hat.par[5,2] 11.578 2.077 7.856 10.105 11.518 12.953 15.787
#> hat.par[6,2] 34.510 3.938 27.091 31.826 34.282 37.106 42.429
#> hat.par[7,2] 197.130 10.103 177.463 190.358 197.314 203.933 216.689
#> hat.par[8,2] 51.390 5.003 41.905 47.961 51.418 54.748 61.370
#> hat.par[9,2] 81.723 5.657 70.638 77.729 81.914 85.394 92.841
#> hat.par[10,2] 72.526 3.905 64.816 69.868 72.538 75.268 80.123
#> hat.par[11,2] 117.728 8.170 102.341 112.126 117.557 123.439 133.719
#> hat.par[12,2] 81.076 4.823 70.946 78.006 81.109 84.442 90.091
#> hat.par[13,2] 26.437 4.669 17.709 23.245 26.258 29.455 36.087
#> hat.par[14,2] 31.394 4.649 22.784 28.058 31.301 34.378 41.233
#> hat.par[15,2] 33.537 4.751 25.029 30.227 33.290 36.433 43.978
#> hat.par[16,2] 245.832 12.995 221.834 236.910 245.642 254.296 272.119
#> hat.par[17,2] 7.035 1.919 3.723 5.664 6.861 8.246 11.227
#> hat.par[18,2] 12.947 2.708 8.153 10.986 12.773 14.720 18.594
#> hat.par[19,2] 2.379 0.956 0.944 1.685 2.245 2.888 4.639
#> hat.par[20,2] 20.238 3.506 14.110 17.803 20.037 22.405 27.686
#> hat.par[21,2] 22.383 3.855 15.017 19.822 22.296 24.909 30.316
#> hat.par[9,3] 80.264 5.752 68.347 76.575 80.376 84.150 91.297
#> hat.par[10,3] 69.064 4.469 59.830 66.080 69.004 72.240 77.719
#> hat.par[12,3] 66.883 4.779 57.732 63.549 66.817 70.120 76.320
#> hat.par[13,3] 35.482 5.147 25.618 31.837 35.338 38.958 45.784
#> hat.par[19,3] 2.819 1.065 1.173 2.063 2.666 3.404 5.233
#> hat.par[10,4] 66.589 4.214 58.075 63.935 66.617 69.435 74.655
#> hat.par[12,4] 62.618 4.361 54.146 59.580 62.601 65.605 71.185
#> hat.par[13,4] 41.297 4.641 32.366 38.102 41.178 44.451 50.451
#> phi[1] -0.280 0.535 -1.438 -0.607 -0.251 0.051 0.783
#> phi[2] 0.130 1.019 -1.961 -0.493 0.145 0.808 2.003
#> phi[3] 0.084 0.950 -1.905 -0.512 0.111 0.731 1.856
#> phi[4] -1.175 0.737 -2.619 -1.648 -1.169 -0.720 0.394
#> phi[5] -0.464 0.947 -2.318 -1.106 -0.474 0.182 1.391
#> phi[6] 0.739 0.824 -1.019 0.257 0.766 1.256 2.284
#> phi[7] -0.104 0.767 -1.597 -0.642 -0.085 0.451 1.280
#> phi[8] -0.211 0.942 -2.057 -0.851 -0.183 0.446 1.618
#> tau 0.111 0.100 0.004 0.023 0.088 0.172 0.362
#> totresdev.o 54.443 8.991 37.998 48.345 53.983 60.178 73.134
#> deviance 582.045 13.335 557.777 572.498 581.311 591.047 608.492
#> Rhat n.eff
#> EM[2,1] 1.100 27
#> EM[3,1] 1.017 610
#> EM[4,1] 1.184 16
#> EM[5,1] 1.082 32
#> EM[6,1] 1.054 50
#> EM[7,1] 1.048 79
#> EM[8,1] 1.009 370
#> EM[3,2] 1.036 62
#> EM[4,2] 1.147 19
#> EM[5,2] 1.100 29
#> EM[6,2] 1.043 67
#> EM[7,2] 1.084 29
#> EM[8,2] 1.087 30
#> EM[4,3] 1.071 38
#> EM[5,3] 1.035 120
#> EM[6,3] 1.010 390
#> EM[7,3] 1.012 240
#> EM[8,3] 1.014 1700
#> EM[5,4] 1.033 110
#> EM[6,4] 1.089 27
#> EM[7,4] 1.310 10
#> EM[8,4] 1.174 16
#> EM[6,5] 1.020 120
#> EM[7,5] 1.115 23
#> EM[8,5] 1.060 42
#> EM[7,6] 1.078 31
#> EM[8,6] 1.027 78
#> EM[8,7] 1.043 66
#> EM.pred[2,1] 1.094 28
#> EM.pred[3,1] 1.016 570
#> EM.pred[4,1] 1.152 18
#> EM.pred[5,1] 1.059 44
#> EM.pred[6,1] 1.043 64
#> EM.pred[7,1] 1.034 110
#> EM.pred[8,1] 1.004 570
#> EM.pred[3,2] 1.034 65
#> EM.pred[4,2] 1.139 20
#> EM.pred[5,2] 1.099 29
#> EM.pred[6,2] 1.038 78
#> EM.pred[7,2] 1.078 31
#> EM.pred[8,2] 1.079 33
#> EM.pred[4,3] 1.070 38
#> EM.pred[5,3] 1.034 110
#> EM.pred[6,3] 1.009 380
#> EM.pred[7,3] 1.010 270
#> EM.pred[8,3] 1.014 1800
#> EM.pred[5,4] 1.031 110
#> EM.pred[6,4] 1.083 29
#> EM.pred[7,4] 1.285 11
#> EM.pred[8,4] 1.159 17
#> EM.pred[6,5] 1.017 150
#> EM.pred[7,5] 1.106 25
#> EM.pred[8,5] 1.053 48
#> EM.pred[7,6] 1.068 34
#> EM.pred[8,6] 1.022 97
#> EM.pred[8,7] 1.030 88
#> SUCRA[1] 1.058 46
#> SUCRA[2] 1.194 18
#> SUCRA[3] 1.012 190
#> SUCRA[4] 1.199 15
#> SUCRA[5] 1.121 23
#> SUCRA[6] 1.025 130
#> SUCRA[7] 1.167 16
#> SUCRA[8] 1.039 81
#> abs_risk[1] 1.000 1
#> abs_risk[2] 1.087 30
#> abs_risk[3] 1.011 800
#> abs_risk[4] 1.181 16
#> abs_risk[5] 1.071 36
#> abs_risk[6] 1.067 44
#> abs_risk[7] 1.046 77
#> abs_risk[8] 1.009 350
#> beta[1] 1.000 1
#> beta[2] 1.009 1400
#> beta[3] 1.035 310
#> beta[4] 1.046 56
#> beta[5] 1.061 46
#> beta[6] 1.011 340
#> beta[7] 1.021 100
#> beta[8] 1.035 68
#> beta.all[2,1] 1.009 1400
#> beta.all[3,1] 1.035 310
#> beta.all[4,1] 1.046 56
#> beta.all[5,1] 1.061 46
#> beta.all[6,1] 1.011 340
#> beta.all[7,1] 1.021 100
#> beta.all[8,1] 1.035 68
#> beta.all[3,2] 1.011 790
#> beta.all[4,2] 1.008 530
#> beta.all[5,2] 1.043 70
#> beta.all[6,2] 1.007 350
#> beta.all[7,2] 1.013 310
#> beta.all[8,2] 1.016 230
#> beta.all[4,3] 1.017 3000
#> beta.all[5,3] 1.066 59
#> beta.all[6,3] 1.013 220
#> beta.all[7,3] 1.027 230
#> beta.all[8,3] 1.038 150
#> beta.all[5,4] 1.088 32
#> beta.all[6,4] 1.031 70
#> beta.all[7,4] 1.054 46
#> beta.all[8,4] 1.082 34
#> beta.all[6,5] 1.019 190
#> beta.all[7,5] 1.039 82
#> beta.all[8,5] 1.032 110
#> beta.all[7,6] 1.001 2400
#> beta.all[8,6] 1.001 3000
#> beta.all[8,7] 1.004 530
#> delta[1,1] 1.000 1
#> delta[2,1] 1.000 1
#> delta[3,1] 1.000 1
#> delta[4,1] 1.000 1
#> delta[5,1] 1.000 1
#> delta[6,1] 1.000 1
#> delta[7,1] 1.000 1
#> delta[8,1] 1.000 1
#> delta[9,1] 1.000 1
#> delta[10,1] 1.000 1
#> delta[11,1] 1.000 1
#> delta[12,1] 1.000 1
#> delta[13,1] 1.000 1
#> delta[14,1] 1.000 1
#> delta[15,1] 1.000 1
#> delta[16,1] 1.000 1
#> delta[17,1] 1.000 1
#> delta[18,1] 1.000 1
#> delta[19,1] 1.000 1
#> delta[20,1] 1.000 1
#> delta[21,1] 1.000 1
#> delta[1,2] 1.182 16
#> delta[2,2] 1.211 14
#> delta[3,2] 1.065 53
#> delta[4,2] 1.027 140
#> delta[5,2] 1.052 60
#> delta[6,2] 1.066 58
#> delta[7,2] 1.039 59
#> delta[8,2] 1.081 39
#> delta[9,2] 1.069 49
#> delta[10,2] 1.254 12
#> delta[11,2] 1.180 19
#> delta[12,2] 1.295 11
#> delta[13,2] 1.352 10
#> delta[14,2] 1.200 15
#> delta[15,2] 1.118 24
#> delta[16,2] 1.051 51
#> delta[17,2] 1.060 49
#> delta[18,2] 1.399 9
#> delta[19,2] 1.387 9
#> delta[20,2] 1.076 40
#> delta[21,2] 1.054 54
#> delta[9,3] 1.039 76
#> delta[10,3] 1.448 8
#> delta[12,3] 1.421 8
#> delta[13,3] 1.014 180
#> delta[19,3] 1.057 56
#> delta[10,4] 1.023 150
#> delta[12,4] 1.057 79
#> delta[13,4] 1.170 18
#> dev.o[1,1] 1.002 1900
#> dev.o[2,1] 1.001 3000
#> dev.o[3,1] 1.005 480
#> dev.o[4,1] 1.006 380
#> dev.o[5,1] 1.005 770
#> dev.o[6,1] 1.001 3000
#> dev.o[7,1] 1.002 1900
#> dev.o[8,1] 1.003 780
#> dev.o[9,1] 1.004 580
#> dev.o[10,1] 1.003 770
#> dev.o[11,1] 1.005 450
#> dev.o[12,1] 1.050 53
#> dev.o[13,1] 1.030 72
#> dev.o[14,1] 1.002 1200
#> dev.o[15,1] 1.003 1400
#> dev.o[16,1] 1.001 2400
#> dev.o[17,1] 1.008 330
#> dev.o[18,1] 1.005 460
#> dev.o[19,1] 1.003 1300
#> dev.o[20,1] 1.002 1600
#> dev.o[21,1] 1.006 380
#> dev.o[1,2] 1.004 1300
#> dev.o[2,2] 1.002 1300
#> dev.o[3,2] 1.014 180
#> dev.o[4,2] 1.004 790
#> dev.o[5,2] 1.002 1700
#> dev.o[6,2] 1.003 910
#> dev.o[7,2] 1.001 3000
#> dev.o[8,2] 1.002 1400
#> dev.o[9,2] 1.002 1300
#> dev.o[10,2] 1.003 1300
#> dev.o[11,2] 1.002 2400
#> dev.o[12,2] 1.011 250
#> dev.o[13,2] 1.004 660
#> dev.o[14,2] 1.005 480
#> dev.o[15,2] 1.003 940
#> dev.o[16,2] 1.001 3000
#> dev.o[17,2] 1.015 250
#> dev.o[18,2] 1.012 230
#> dev.o[19,2] 1.010 240
#> dev.o[20,2] 1.001 3000
#> dev.o[21,2] 1.002 1900
#> dev.o[9,3] 1.012 210
#> dev.o[10,3] 1.003 840
#> dev.o[12,3] 1.002 1400
#> dev.o[13,3] 1.002 3000
#> dev.o[19,3] 1.032 100
#> dev.o[10,4] 1.001 2400
#> dev.o[12,4] 1.001 3000
#> dev.o[13,4] 1.003 920
#> effectiveness[1,1] 1.000 1
#> effectiveness[2,1] 1.073 32
#> effectiveness[3,1] 1.008 330
#> effectiveness[4,1] 1.147 130
#> effectiveness[5,1] 1.121 32
#> effectiveness[6,1] 1.291 3000
#> effectiveness[7,1] 1.114 280
#> effectiveness[8,1] 1.036 240
#> effectiveness[1,2] 1.292 1000
#> effectiveness[2,2] 1.003 940
#> effectiveness[3,2] 1.018 150
#> effectiveness[4,2] 1.095 78
#> effectiveness[5,2] 1.017 160
#> effectiveness[6,2] 1.142 390
#> effectiveness[7,2] 1.075 120
#> effectiveness[8,2] 1.014 230
#> effectiveness[1,3] 1.073 740
#> effectiveness[2,3] 1.001 3000
#> effectiveness[3,3] 1.007 580
#> effectiveness[4,3] 1.092 58
#> effectiveness[5,3] 1.030 110
#> effectiveness[6,3] 1.081 440
#> effectiveness[7,3] 1.117 35
#> effectiveness[8,3] 1.003 890
#> effectiveness[1,4] 1.015 650
#> effectiveness[2,4] 1.041 190
#> effectiveness[3,4] 1.008 680
#> effectiveness[4,4] 1.069 63
#> effectiveness[5,4] 1.008 500
#> effectiveness[6,4] 1.004 3000
#> effectiveness[7,4] 1.013 230
#> effectiveness[8,4] 1.007 320
#> effectiveness[1,5] 1.041 100
#> effectiveness[2,5] 1.120 74
#> effectiveness[3,5] 1.039 160
#> effectiveness[4,5] 1.001 2600
#> effectiveness[5,5] 1.044 96
#> effectiveness[6,5] 1.003 2100
#> effectiveness[7,5] 1.010 280
#> effectiveness[8,5] 1.020 230
#> effectiveness[1,6] 1.016 150
#> effectiveness[2,6] 1.091 140
#> effectiveness[3,6] 1.024 370
#> effectiveness[4,6] 1.020 170
#> effectiveness[5,6] 1.079 85
#> effectiveness[6,6] 1.007 540
#> effectiveness[7,6] 1.061 52
#> effectiveness[8,6] 1.069 190
#> effectiveness[1,7] 1.035 69
#> effectiveness[2,7] 1.142 98
#> effectiveness[3,7] 1.001 3000
#> effectiveness[4,7] 1.080 45
#> effectiveness[5,7] 1.076 120
#> effectiveness[6,7] 1.009 300
#> effectiveness[7,7] 1.080 60
#> effectiveness[8,7] 1.127 610
#> effectiveness[1,8] 1.077 44
#> effectiveness[2,8] 1.172 82
#> effectiveness[3,8] 1.015 570
#> effectiveness[4,8] 1.049 74
#> effectiveness[5,8] 1.100 260
#> effectiveness[6,8] 1.022 97
#> effectiveness[7,8] 1.046 340
#> effectiveness[8,8] 1.000 1
#> hat.par[1,1] 1.007 640
#> hat.par[2,1] 1.010 200
#> hat.par[3,1] 1.017 130
#> hat.par[4,1] 1.017 160
#> hat.par[5,1] 1.002 3000
#> hat.par[6,1] 1.003 940
#> hat.par[7,1] 1.008 320
#> hat.par[8,1] 1.013 160
#> hat.par[9,1] 1.009 280
#> hat.par[10,1] 1.020 110
#> hat.par[11,1] 1.014 310
#> hat.par[12,1] 1.058 39
#> hat.par[13,1] 1.068 36
#> hat.par[14,1] 1.011 190
#> hat.par[15,1] 1.009 230
#> hat.par[16,1] 1.004 940
#> hat.par[17,1] 1.008 280
#> hat.par[18,1] 1.016 140
#> hat.par[19,1] 1.003 950
#> hat.par[20,1] 1.009 280
#> hat.par[21,1] 1.006 410
#> hat.par[1,2] 1.004 1200
#> hat.par[2,2] 1.015 140
#> hat.par[3,2] 1.021 100
#> hat.par[4,2] 1.005 460
#> hat.par[5,2] 1.006 350
#> hat.par[6,2] 1.006 400
#> hat.par[7,2] 1.004 540
#> hat.par[8,2] 1.018 120
#> hat.par[9,2] 1.054 43
#> hat.par[10,2] 1.003 1200
#> hat.par[11,2] 1.003 700
#> hat.par[12,2] 1.029 73
#> hat.par[13,2] 1.064 36
#> hat.par[14,2] 1.011 190
#> hat.par[15,2] 1.014 160
#> hat.par[16,2] 1.001 3000
#> hat.par[17,2] 1.009 230
#> hat.par[18,2] 1.023 93
#> hat.par[19,2] 1.030 74
#> hat.par[20,2] 1.003 680
#> hat.par[21,2] 1.001 3000
#> hat.par[9,3] 1.012 180
#> hat.par[10,3] 1.017 120
#> hat.par[12,3] 1.003 810
#> hat.par[13,3] 1.014 150
#> hat.par[19,3] 1.029 75
#> hat.par[10,4] 1.004 560
#> hat.par[12,4] 1.003 960
#> hat.par[13,4] 1.005 400
#> phi[1] 1.095 31
#> phi[2] 1.154 19
#> phi[3] 1.018 120
#> phi[4] 1.058 40
#> phi[5] 1.174 16
#> phi[6] 1.052 50
#> phi[7] 1.265 12
#> phi[8] 1.001 3000
#> tau 1.086 49
#> totresdev.o 1.006 350
#> deviance 1.006 350
#>
#> 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).
#>
#> DIC info (using the rule, pD = var(deviance)/2)
#> pD = 88.5 and DIC = 670.5
#> DIC is an estimate of expected predictive error (lower deviance is better).
#>
#> $data
#> study t1 t2 t3 t4 r1 r2 r3 r4 m1 m2 m3 m4 n1 n2 n3 n4
#> 1 Llewellyn-Jones, 1996 1 4 NA NA 3 0 NA NA 1 0 NA NA 8 8 NA NA
#> 2 Paggiaro, 1998 1 4 NA NA 51 45 NA NA 27 19 NA NA 139 142 NA NA
#> 3 Mahler, 1999 1 7 NA NA 47 28 NA NA 23 9 NA NA 143 135 NA NA
#> 4 Casaburi, 2000 1 8 NA NA 41 45 NA NA 18 12 NA NA 191 279 NA NA
#> 5 van Noord, 2000 1 7 NA NA 18 11 NA NA 8 7 NA NA 50 47 NA NA
#> 6 Rennard, 2001 1 7 NA NA 41 38 NA NA 29 22 NA NA 135 132 NA NA
#> 7 Casaburi, 2002 1 8 NA NA 156 198 NA NA 77 66 NA NA 371 550 NA NA
#> 8 Chapman, 2002 1 7 NA NA 68 52 NA NA 28 20 NA NA 207 201 NA NA
#> 9 Donohue, 2002 1 7 8 NA 92 82 77 NA 37 20 10 NA 201 213 209 NA
#> 10 Mahler, 2002 1 4 7 5 79 77 63 68 69 68 45 52 181 168 160 165
#> 11 Rossi, 2002 1 6 NA NA 75 117 NA NA 59 92 NA NA 220 425 NA NA
#> 12 Hanania, 2003 1 4 7 5 73 79 65 71 59 49 57 53 185 183 177 178
#> 13 Szafranski, 2003 1 2 6 3 53 26 38 35 90 62 64 59 205 198 201 208
#> 14 Briggs, 2005 8 7 NA NA 30 36 NA NA 29 41 NA NA 328 325 NA NA
#> 15 Campbell, 2005 1 6 NA NA 34 35 NA NA 39 30 NA NA 217 215 NA NA
#> 16 Niewoehner, 2005 1 8 NA NA 296 255 NA NA 111 75 NA NA 915 914 NA NA
#> 17 van Noord, 2005 8 6 NA NA 4 14 NA NA 1 1 NA NA 70 69 NA NA
#> 18 Barnes, 2006 1 5 NA NA 24 11 NA NA 4 8 NA NA 73 67 NA NA
#> 19 O Donnell, 2006 1 7 5 NA 6 1 2 NA 5 1 3 NA 64 59 62 NA
#> 20 Baumgartner, 2007 1 7 NA NA 24 20 NA NA 32 26 NA NA 143 144 NA NA
#> 21 Freeman, 2007 1 8 NA NA 35 19 NA NA 33 18 NA NA 195 200 NA NA
#>
#> $n_chains
#> [1] 3
#>
#> $n_iter
#> [1] 1000
#>
#> $n_burnin
#> [1] 100
#>
#> $n_thin
#> [1] 1
#>
#> $abs_risk
#> mean sd 2.5% 25% 50% 75%
#> abs_risk[1] 0.3900000 0.00000000 0.39000000 0.3900000 0.3900000 0.3900000
#> abs_risk[2] 0.2364882 0.10615640 0.07004784 0.1652156 0.2219984 0.2937088
#> abs_risk[3] 0.2783026 0.11947639 0.09041635 0.2002359 0.2588948 0.3367435
#> abs_risk[4] 0.3708181 0.10502001 0.19842692 0.2994036 0.3551987 0.4298906
#> abs_risk[5] 0.2815707 0.07638675 0.13934277 0.2282077 0.2771107 0.3307284
#> abs_risk[6] 0.4381650 0.09320515 0.27413936 0.3742147 0.4295181 0.4968689
#> abs_risk[7] 0.3378500 0.06456983 0.22650470 0.2912404 0.3342427 0.3791830
#> abs_risk[8] 0.2883392 0.04210265 0.20001389 0.2615990 0.2907464 0.3174078
#> 97.5% Rhat n.eff
#> abs_risk[1] 0.3900000 1.000000 1
#> abs_risk[2] 0.4814064 1.087219 30
#> abs_risk[3] 0.5944199 1.010830 800
#> abs_risk[4] 0.6085447 1.180558 16
#> abs_risk[5] 0.4496711 1.071147 36
#> abs_risk[6] 0.6414777 1.066895 44
#> abs_risk[7] 0.4803750 1.045527 77
#> abs_risk[8] 0.3652665 1.009140 350
#>
#> $SUCRA
#> mean sd 2.5% 25% 50% 75% 97.5%
#> SUCRA[1] 0.2221905 0.1659064 0.0000000 0.1428571 0.1428571 0.2857143 0.5714286
#> SUCRA[2] 0.8023333 0.2730410 0.0000000 0.7142857 0.8571429 1.0000000 1.0000000
#> SUCRA[3] 0.6902381 0.3035449 0.0000000 0.4285714 0.8571429 1.0000000 1.0000000
#> SUCRA[4] 0.3683333 0.2753761 0.0000000 0.1428571 0.2857143 0.5714286 0.8571429
#> SUCRA[5] 0.6581905 0.2626244 0.1428571 0.4285714 0.7142857 0.8571429 1.0000000
#> SUCRA[6] 0.1398571 0.1828593 0.0000000 0.0000000 0.1428571 0.2857143 0.5714286
#> SUCRA[7] 0.4580952 0.2237333 0.0000000 0.2857143 0.4285714 0.5714286 0.8571429
#> SUCRA[8] 0.6607619 0.1741913 0.2857143 0.5714286 0.7142857 0.7142857 1.0000000
#> Rhat n.eff
#> SUCRA[1] 1.057585 46
#> SUCRA[2] 1.193886 18
#> SUCRA[3] 1.012383 190
#> SUCRA[4] 1.199407 15
#> SUCRA[5] 1.121497 23
#> SUCRA[6] 1.025162 130
#> SUCRA[7] 1.166870 16
#> SUCRA[8] 1.039356 81
#>
#> $effectiveness
#> mean sd 2.5% 25% 50% 75% 97.5% Rhat
#> effectiveness[1,1] 0.0000000000 0.00000000 0 0 0 0 0 1.000000
#> effectiveness[2,1] 0.4896666667 0.49997655 0 0 0 1 1 1.073429
#> effectiveness[3,1] 0.2536666667 0.43518159 0 0 0 1 1 1.007606
#> effectiveness[4,1] 0.0220000000 0.14670779 0 0 0 0 0 1.147031
#> effectiveness[5,1] 0.1626666667 0.36912280 0 0 0 0 1 1.120826
#> effectiveness[6,1] 0.0003333333 0.01825742 0 0 0 0 0 1.290904
#> effectiveness[7,1] 0.0133333333 0.11471679 0 0 0 0 0 1.114242
#> effectiveness[8,1] 0.0583333333 0.23441176 0 0 0 0 1 1.036245
#> effectiveness[1,2] 0.0010000000 0.03161223 0 0 0 0 0 1.292018
#> effectiveness[2,2] 0.1986666667 0.39906304 0 0 0 0 1 1.002847
#> effectiveness[3,2] 0.2646666667 0.44122910 0 0 0 1 1 1.017524
#> effectiveness[4,2] 0.0666666667 0.24948541 0 0 0 0 1 1.095104
#> effectiveness[5,2] 0.2213333333 0.41521363 0 0 0 0 1 1.017469
#> effectiveness[6,2] 0.0073333333 0.08533454 0 0 0 0 0 1.141583
#> effectiveness[7,2] 0.0570000000 0.23188127 0 0 0 0 1 1.075026
#> effectiveness[8,2] 0.1833333333 0.38700406 0 0 0 0 1 1.014091
#> effectiveness[1,3] 0.0083333333 0.09092109 0 0 0 0 0 1.072603
#> effectiveness[2,3] 0.0943333333 0.29234063 0 0 0 0 1 1.001149
#> effectiveness[3,3] 0.1340000000 0.34070911 0 0 0 0 1 1.006916
#> effectiveness[4,3] 0.1003333333 0.30049402 0 0 0 0 1 1.092364
#> effectiveness[5,3] 0.1970000000 0.39779863 0 0 0 0 1 1.029560
#> effectiveness[6,3] 0.0126666667 0.11184987 0 0 0 0 0 1.081377
#> effectiveness[7,3] 0.1543333333 0.36132821 0 0 0 0 1 1.116986
#> effectiveness[8,3] 0.2990000000 0.45789616 0 0 0 1 1 1.002763
#> effectiveness[1,4] 0.0520000000 0.22206404 0 0 0 0 1 1.014909
#> effectiveness[2,4] 0.0636666667 0.24419889 0 0 0 0 1 1.040950
#> effectiveness[3,4] 0.0950000000 0.29326382 0 0 0 0 1 1.007957
#> effectiveness[4,4] 0.1286666667 0.33488646 0 0 0 0 1 1.069410
#> effectiveness[5,4] 0.1346666667 0.34142409 0 0 0 0 1 1.008137
#> effectiveness[6,4] 0.0336666667 0.18039975 0 0 0 0 1 1.004245
#> effectiveness[7,4] 0.2013333333 0.40106339 0 0 0 0 1 1.012934
#> effectiveness[8,4] 0.2910000000 0.45429924 0 0 0 1 1 1.007169
#> effectiveness[1,5] 0.1380000000 0.34495748 0 0 0 0 1 1.040906
#> effectiveness[2,5] 0.0543333333 0.22671205 0 0 0 0 1 1.119684
#> effectiveness[3,5] 0.0826666667 0.27542363 0 0 0 0 1 1.039477
#> effectiveness[4,5] 0.1596666667 0.36635770 0 0 0 0 1 1.001275
#> effectiveness[5,5] 0.1356666667 0.34249135 0 0 0 0 1 1.044299
#> effectiveness[6,5] 0.0770000000 0.26663589 0 0 0 0 1 1.002893
#> effectiveness[7,5] 0.2260000000 0.41830889 0 0 0 0 1 1.009767
#> effectiveness[8,5] 0.1266666667 0.33265464 0 0 0 0 1 1.019679
#> effectiveness[1,6] 0.2816666667 0.44988668 0 0 0 1 1 1.016409
#> effectiveness[2,6] 0.0380000000 0.19122811 0 0 0 0 1 1.090778
#> effectiveness[3,6] 0.0583333333 0.23441176 0 0 0 0 1 1.023519
#> effectiveness[4,6] 0.1756666667 0.38059976 0 0 0 0 1 1.020128
#> effectiveness[5,6] 0.0783333333 0.26874020 0 0 0 0 1 1.078614
#> effectiveness[6,6] 0.1320000000 0.33854720 0 0 0 0 1 1.007464
#> effectiveness[7,6] 0.1996666667 0.39981642 0 0 0 0 1 1.061308
#> effectiveness[8,6] 0.0363333333 0.18714940 0 0 0 0 1 1.069129
#> effectiveness[1,7] 0.3223333333 0.46744774 0 0 0 1 1 1.035304
#> effectiveness[2,7] 0.0313333333 0.17424602 0 0 0 0 1 1.142401
#> effectiveness[3,7] 0.0533333333 0.22473479 0 0 0 0 1 1.000727
#> effectiveness[4,7] 0.1776666667 0.38229562 0 0 0 0 1 1.080420
#> effectiveness[5,7] 0.0533333333 0.22473479 0 0 0 0 1 1.075927
#> effectiveness[6,7] 0.2396666667 0.42695119 0 0 0 0 1 1.008781
#> effectiveness[7,7] 0.1170000000 0.32147387 0 0 0 0 1 1.079941
#> effectiveness[8,7] 0.0053333333 0.07284681 0 0 0 0 0 1.127291
#> effectiveness[1,8] 0.1966666667 0.39754442 0 0 0 0 1 1.076807
#> effectiveness[2,8] 0.0300000000 0.17061566 0 0 0 0 1 1.172320
#> effectiveness[3,8] 0.0583333333 0.23441176 0 0 0 0 1 1.015155
#> effectiveness[4,8] 0.1693333333 0.37510859 0 0 0 0 1 1.048750
#> effectiveness[5,8] 0.0170000000 0.12929258 0 0 0 0 0 1.099927
#> effectiveness[6,8] 0.4973333333 0.50007624 0 0 0 1 1 1.021509
#> effectiveness[7,8] 0.0313333333 0.17424602 0 0 0 0 1 1.045812
#> effectiveness[8,8] 0.0000000000 0.00000000 0 0 0 0 0 1.000000
#> n.eff
#> effectiveness[1,1] 1
#> effectiveness[2,1] 32
#> effectiveness[3,1] 330
#> effectiveness[4,1] 130
#> effectiveness[5,1] 32
#> effectiveness[6,1] 3000
#> effectiveness[7,1] 280
#> effectiveness[8,1] 240
#> effectiveness[1,2] 1000
#> effectiveness[2,2] 940
#> effectiveness[3,2] 150
#> effectiveness[4,2] 78
#> effectiveness[5,2] 160
#> effectiveness[6,2] 390
#> effectiveness[7,2] 120
#> effectiveness[8,2] 230
#> effectiveness[1,3] 740
#> effectiveness[2,3] 3000
#> effectiveness[3,3] 580
#> effectiveness[4,3] 58
#> effectiveness[5,3] 110
#> effectiveness[6,3] 440
#> effectiveness[7,3] 35
#> effectiveness[8,3] 890
#> effectiveness[1,4] 650
#> effectiveness[2,4] 190
#> effectiveness[3,4] 680
#> effectiveness[4,4] 63
#> effectiveness[5,4] 500
#> effectiveness[6,4] 3000
#> effectiveness[7,4] 230
#> effectiveness[8,4] 320
#> effectiveness[1,5] 100
#> effectiveness[2,5] 74
#> effectiveness[3,5] 160
#> effectiveness[4,5] 2600
#> effectiveness[5,5] 96
#> effectiveness[6,5] 2100
#> effectiveness[7,5] 280
#> effectiveness[8,5] 230
#> effectiveness[1,6] 150
#> effectiveness[2,6] 140
#> effectiveness[3,6] 370
#> effectiveness[4,6] 170
#> effectiveness[5,6] 85
#> effectiveness[6,6] 540
#> effectiveness[7,6] 52
#> effectiveness[8,6] 190
#> effectiveness[1,7] 69
#> effectiveness[2,7] 98
#> effectiveness[3,7] 3000
#> effectiveness[4,7] 45
#> effectiveness[5,7] 120
#> effectiveness[6,7] 300
#> effectiveness[7,7] 60
#> effectiveness[8,7] 610
#> effectiveness[1,8] 44
#> effectiveness[2,8] 82
#> effectiveness[3,8] 570
#> effectiveness[4,8] 74
#> effectiveness[5,8] 260
#> effectiveness[6,8] 97
#> effectiveness[7,8] 340
#> effectiveness[8,8] 1
#>
#> $D
#> [1] 0
#>
#> attr(,"class")
#> [1] "run_metareg"
# }