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] -0.729435003 0.6503978 -1.9687503 -1.13586251 -0.771617778 -0.35746638
#> EM[3,1] -0.499647725 0.5783115 -1.7234890 -0.81462697 -0.476233039 -0.17373350
#> EM[4,1] 0.249373776 0.5314885 -0.6354726 -0.13960366 0.151178898 0.59793872
#> EM[5,1] -0.385859161 0.3268929 -1.1050041 -0.58772921 -0.373420563 -0.17260650
#> EM[6,1] 0.256669925 0.3804950 -0.4884441 0.03430182 0.247138996 0.48291713
#> EM[7,1] -0.196284766 0.2673986 -0.7698049 -0.36477101 -0.168228147 -0.01424160
#> EM[8,1] -0.396273716 0.2172388 -0.9145840 -0.51530506 -0.380928555 -0.25116335
#> EM[3,2] 0.229787278 0.7357732 -1.3351591 -0.16882263 0.236610329 0.66036895
#> EM[4,2] 0.978808779 0.7575542 -0.5133579 0.53032841 1.012990940 1.43064267
#> EM[5,2] 0.343575842 0.6997511 -1.1389424 -0.02441668 0.384969465 0.76334412
#> EM[6,2] 0.986104928 0.6697239 -0.2465141 0.58663881 0.995375900 1.36729122
#> EM[7,2] 0.533150237 0.6588538 -0.7432320 0.14320849 0.561853479 0.93294916
#> EM[8,2] 0.333161287 0.6409854 -0.9128291 -0.04485234 0.367312574 0.74607531
#> EM[4,3] 0.749021501 0.7341915 -0.5387650 0.26878950 0.690346545 1.19557714
#> EM[5,3] 0.113788563 0.6356688 -1.1884277 -0.22524774 0.118699275 0.45808291
#> EM[6,3] 0.756317649 0.6292665 -0.3936704 0.37628749 0.710663658 1.09843854
#> EM[7,3] 0.303362959 0.5978402 -0.8148756 -0.04888408 0.289451342 0.63105462
#> EM[8,3] 0.103374009 0.5759404 -0.9792351 -0.20925871 0.109778843 0.42816022
#> EM[5,4] -0.635232937 0.5961787 -2.0228468 -1.00417605 -0.537227580 -0.18181651
#> EM[6,4] 0.007296149 0.5540643 -1.1088495 -0.35146826 0.078631859 0.37507647
#> EM[7,4] -0.445658542 0.5205958 -1.5326042 -0.78133794 -0.354732170 -0.08354931
#> EM[8,4] -0.645647492 0.5420611 -1.7930329 -1.01905671 -0.528778757 -0.25615695
#> EM[6,5] 0.642529086 0.4902803 -0.2033832 0.30365174 0.607692948 0.89902809
#> EM[7,5] 0.189574396 0.3762033 -0.5385315 -0.03756531 0.174289333 0.40758274
#> EM[8,5] -0.010414554 0.3507139 -0.7081721 -0.22598988 -0.005159035 0.20396110
#> EM[7,6] -0.452954691 0.3934545 -1.3320143 -0.67759939 -0.417562044 -0.20368728
#> EM[8,6] -0.652943641 0.3836742 -1.4840857 -0.88406625 -0.625768729 -0.39291759
#> EM[8,7] -0.199988950 0.2789481 -0.7925469 -0.36563333 -0.184335951 -0.02270990
#> 97.5% Rhat n.eff
#> EM[2,1] 0.48604331 1.063367 50
#> EM[3,1] 0.54951419 1.016718 3000
#> EM[4,1] 1.32533409 1.752403 6
#> EM[5,1] 0.20555476 1.010263 500
#> EM[6,1] 1.05379125 1.027366 140
#> EM[7,1] 0.26580005 1.047246 48
#> EM[8,1] -0.02613156 1.026545 150
#> EM[3,2] 1.62286203 1.052629 59
#> EM[4,2] 2.45934599 1.199199 16
#> EM[5,2] 1.60667677 1.046607 71
#> EM[6,2] 2.38407565 1.036323 120
#> EM[7,2] 1.75516494 1.042505 72
#> EM[8,2] 1.46822040 1.048321 67
#> EM[4,3] 2.37965317 1.345067 10
#> EM[5,3] 1.38513785 1.015329 780
#> EM[6,3] 2.16486528 1.013661 260
#> EM[7,3] 1.52618647 1.026702 160
#> EM[8,3] 1.25675326 1.016605 520
#> EM[5,4] 0.24569161 1.483348 8
#> EM[6,4] 1.04167337 1.486034 8
#> EM[7,4] 0.41088328 1.502500 8
#> EM[8,4] 0.17668180 1.582709 7
#> EM[6,5] 1.77161764 1.004233 540
#> EM[7,5] 1.01319535 1.010200 240
#> EM[8,5] 0.71478441 1.000849 3000
#> EM[7,6] 0.24659626 1.009319 260
#> EM[8,6] 0.05420456 1.007780 540
#> EM[8,7] 0.31473915 1.014761 140
#>
#> $EM_pred
#> mean sd 2.5% 25% 50%
#> EM.pred[2,1] -0.730049187 0.6645377 -1.9894887 -1.13999081 -0.764601014
#> EM.pred[3,1] -0.497743371 0.5914367 -1.7383998 -0.82414591 -0.480715822
#> EM.pred[4,1] 0.251353868 0.5501307 -0.6740911 -0.13337572 0.166326033
#> EM.pred[5,1] -0.387390069 0.3594666 -1.1673583 -0.59498242 -0.369591662
#> EM.pred[6,1] 0.257191814 0.4026155 -0.6024316 0.03023935 0.253943737
#> EM.pred[7,1] -0.196438180 0.3008333 -0.8738566 -0.37278602 -0.162913991
#> EM.pred[8,1] -0.396485273 0.2586813 -1.0340368 -0.52564031 -0.369942060
#> EM.pred[3,2] 0.228752432 0.7479569 -1.3644515 -0.16875576 0.230585941
#> EM.pred[4,2] 0.981533782 0.7711601 -0.5164902 0.51410729 1.028994214
#> EM.pred[5,2] 0.343068614 0.7131618 -1.1821394 -0.03156844 0.368150696
#> EM.pred[6,2] 0.989710149 0.6878080 -0.2442415 0.57992537 0.991740389
#> EM.pred[7,2] 0.533952283 0.6717981 -0.7954658 0.14102066 0.554048445
#> EM.pred[8,2] 0.329857955 0.6535778 -0.9167624 -0.05506854 0.365657950
#> EM.pred[4,3] 0.748295723 0.7463144 -0.5694411 0.26837236 0.694636426
#> EM.pred[5,3] 0.116496351 0.6528435 -1.1628773 -0.24096988 0.118144859
#> EM.pred[6,3] 0.752202832 0.6457451 -0.4326600 0.36464884 0.713287469
#> EM.pred[7,3] 0.305415622 0.6154407 -0.8612226 -0.05887086 0.289863423
#> EM.pred[8,3] 0.099059333 0.5934675 -1.1231271 -0.22269134 0.101901905
#> EM.pred[5,4] -0.633591748 0.6093729 -2.0053989 -1.00564752 -0.539465114
#> EM.pred[6,4] 0.007746378 0.5691488 -1.1445653 -0.37527151 0.064251903
#> EM.pred[7,4] -0.441634698 0.5368390 -1.5412020 -0.79910366 -0.373547262
#> EM.pred[8,4] -0.646960295 0.5640642 -1.8408548 -1.04574057 -0.538264531
#> EM.pred[6,5] 0.649051456 0.5077806 -0.2338661 0.30640210 0.621930726
#> EM.pred[7,5] 0.186507875 0.4034124 -0.6084475 -0.05226965 0.174045495
#> EM.pred[8,5] -0.007523143 0.3765943 -0.7645108 -0.22775943 -0.007060713
#> EM.pred[7,6] -0.454166936 0.4157170 -1.3673168 -0.68936697 -0.413404297
#> EM.pred[8,6] -0.653521150 0.4077004 -1.5547260 -0.89074150 -0.630939570
#> EM.pred[8,7] -0.199525081 0.3152715 -0.8730581 -0.38131477 -0.180651521
#> 75% 97.5% Rhat n.eff
#> EM.pred[2,1] -0.343004931 0.50652046 1.060663 49
#> EM.pred[3,1] -0.157951436 0.58692819 1.012594 3000
#> EM.pred[4,1] 0.609335587 1.34859531 1.664446 6
#> EM.pred[5,1] -0.158921936 0.27899249 1.005454 570
#> EM.pred[6,1] 0.492383507 1.07956303 1.026058 150
#> EM.pred[7,1] 0.001793153 0.31945814 1.037242 61
#> EM.pred[8,1] -0.228074207 0.02292974 1.016640 310
#> EM.pred[3,2] 0.672851431 1.66265942 1.048272 61
#> EM.pred[4,2] 1.438863336 2.48326902 1.185544 17
#> EM.pred[5,2] 0.764436150 1.63888002 1.043009 76
#> EM.pred[6,2] 1.387231717 2.44064826 1.032273 120
#> EM.pred[7,2] 0.944020335 1.80743174 1.037229 81
#> EM.pred[8,2] 0.750587196 1.52578082 1.042844 74
#> EM.pred[4,3] 1.197357120 2.38840800 1.340116 10
#> EM.pred[5,3] 0.474044781 1.43956668 1.012272 1200
#> EM.pred[6,3] 1.102079224 2.20126187 1.011676 270
#> EM.pred[7,3] 0.650706205 1.60298470 1.022499 170
#> EM.pred[8,3] 0.423536601 1.29322085 1.011917 470
#> EM.pred[5,4] -0.186175407 0.31202304 1.457100 8
#> EM.pred[6,4] 0.391676006 1.06573094 1.445999 8
#> EM.pred[7,4] -0.067935455 0.49081691 1.460969 8
#> EM.pred[8,4] -0.244332340 0.24695693 1.512820 8
#> EM.pred[6,5] 0.915602841 1.79203683 1.003595 650
#> EM.pred[7,5] 0.429510063 1.04912701 1.008441 250
#> EM.pred[8,5] 0.227286155 0.76524814 1.001049 3000
#> EM.pred[7,6] -0.189866355 0.31484187 1.011234 270
#> EM.pred[8,6] -0.384213101 0.09864744 1.008138 650
#> EM.pred[8,7] -0.005789033 0.39034130 1.014241 200
#>
#> $tau
#> mean sd 2.5% 25% 50% 75%
#> 0.108497520 0.090433968 0.001108875 0.035241894 0.091310230 0.157859907
#> 97.5% Rhat n.eff
#> 0.331977064 1.393179206 10.000000000
#>
#> $delta
#> mean sd 2.5% 25% 50% 75%
#> delta[1,2] -0.03666252 0.3671547 -0.6985702 -0.2779142 -0.07863899 0.17381236
#> delta[2,2] -0.01995656 0.3513376 -0.6311290 -0.2573534 -0.07485724 0.18009893
#> delta[3,2] -0.40540396 0.1951379 -0.8575906 -0.5274179 -0.34316298 -0.28303923
#> delta[4,2] -0.42656192 0.1533086 -0.7654390 -0.5090336 -0.42754156 -0.33303933
#> delta[5,2] -0.38604907 0.2013007 -0.8825714 -0.4931159 -0.33219321 -0.26982550
#> delta[6,2] -0.31709022 0.1738810 -0.6948514 -0.4032870 -0.31635423 -0.22639496
#> delta[7,2] -0.42625570 0.1470359 -0.7557065 -0.5067459 -0.42847519 -0.33568011
#> delta[8,2] -0.35748003 0.1556953 -0.7050633 -0.4505636 -0.33067499 -0.26848170
#> delta[9,2] -0.38196524 0.1655654 -0.7557501 -0.4852478 -0.33748545 -0.27846551
#> delta[10,2] 0.01918402 0.3321292 -0.5077400 -0.2275514 -0.03936241 0.23379499
#> delta[11,2] -0.08546597 0.2351902 -0.6334482 -0.1854344 -0.08777725 0.07333137
#> delta[12,2] -0.01031824 0.3254795 -0.5320622 -0.2426536 -0.06404836 0.17432771
#> delta[13,2] -0.94716172 0.4784875 -1.8278531 -1.2852386 -0.95394823 -0.70396411
#> delta[14,2] -0.10084250 0.1881128 -0.5263019 -0.2029342 -0.09484789 0.01769397
#> delta[15,2] -0.03417926 0.2348046 -0.5275008 -0.1802475 -0.04679128 0.13168386
#> delta[16,2] -0.34841372 0.1343871 -0.5922585 -0.4332615 -0.36619611 -0.26057055
#> delta[17,2] -0.43076907 0.2606703 -1.0082516 -0.5919781 -0.39453021 -0.25100028
#> delta[18,2] -0.44590175 0.2576942 -1.0714160 -0.5731167 -0.41638305 -0.29276769
#> delta[19,2] -0.43473977 0.2624816 -1.0734605 -0.5662827 -0.41363847 -0.27104078
#> delta[20,2] -0.35639088 0.1817216 -0.7385309 -0.4572196 -0.33064716 -0.26170713
#> delta[21,2] -0.49332789 0.1961291 -0.9632462 -0.5938534 -0.44665297 -0.37752486
#> delta[9,3] -0.47686950 0.1704262 -0.8855238 -0.5694134 -0.44055742 -0.37882920
#> delta[10,3] -0.40460159 0.2386957 -0.9448530 -0.5494874 -0.40690710 -0.26814281
#> delta[12,3] -0.34039359 0.2227411 -0.7802065 -0.4641617 -0.36016071 -0.19804722
#> delta[13,3] -0.70786562 0.3746683 -1.5569635 -0.9175235 -0.63705529 -0.49918661
#> delta[19,3] -0.40247564 0.2111696 -0.9416662 -0.5053247 -0.33828296 -0.27770649
#> delta[10,4] -0.38408028 0.1919511 -0.8377807 -0.4924685 -0.33371593 -0.27162555
#> delta[12,4] -0.31222668 0.1778883 -0.6665428 -0.3993491 -0.31371927 -0.22178013
#> delta[13,4] -0.12332463 0.2844940 -0.8256120 -0.2322746 -0.11383844 0.07278284
#> 97.5% Rhat n.eff
#> delta[1,2] 0.593325080 1.707167 6
#> delta[2,2] 0.595622704 1.764335 6
#> delta[3,2] -0.096783308 1.037293 60
#> delta[4,2] -0.134565926 1.053679 95
#> delta[5,2] -0.054857874 1.042353 66
#> delta[6,2] 0.032131124 1.010927 790
#> delta[7,2] -0.140375826 1.079237 49
#> delta[8,2] -0.079594806 1.031057 86
#> delta[9,2] -0.096753842 1.047425 62
#> delta[10,2] 0.601475942 1.788978 6
#> delta[11,2] 0.311966443 1.066593 73
#> delta[12,2] 0.589608060 1.797576 6
#> delta[13,2] 0.020328270 1.157857 23
#> delta[14,2] 0.236885663 1.037107 75
#> delta[15,2] 0.407918651 1.108528 26
#> delta[16,2] -0.066583018 1.188432 16
#> delta[17,2] 0.040419869 1.057398 59
#> delta[18,2] 0.009745178 1.127638 28
#> delta[19,2] 0.009004351 1.126696 28
#> delta[20,2] -0.016212477 1.079506 38
#> delta[21,2] -0.172104551 1.036298 140
#> delta[9,3] -0.180393056 1.042533 160
#> delta[10,3] 0.071918681 1.136710 27
#> delta[12,3] 0.105217355 1.105269 38
#> delta[13,3] -0.047446630 1.047107 130
#> delta[19,3] -0.084471694 1.078500 40
#> delta[10,4] -0.068387376 1.063073 42
#> delta[12,4] 0.070433845 1.021810 260
#> delta[13,4] 0.316615788 1.056904 110
#>
#> $beta_all
#> mean sd 2.5% 25% 50%
#> beta.all[2,1] 0.042799399 0.11350397 -0.15904580 -0.0101841057 0.0327067774
#> beta.all[3,1] 0.040502108 0.11234257 -0.17853450 -0.0109002716 0.0348735949
#> beta.all[4,1] 0.062074961 0.06161934 -0.03893218 0.0171467503 0.0550866708
#> beta.all[5,1] 0.004291932 0.07374653 -0.16114654 -0.0342057968 0.0109012189
#> beta.all[6,1] 0.073888145 0.07865226 -0.05353327 0.0183791267 0.0614074076
#> beta.all[7,1] 0.039202229 0.04447327 -0.04653872 0.0086341700 0.0398037019
#> beta.all[8,1] 0.011456614 0.03906117 -0.07388233 -0.0128829423 0.0139060880
#> beta.all[3,2] -0.002297291 0.13666427 -0.29496875 -0.0452258020 0.0005657622
#> beta.all[4,2] 0.019275562 0.11536323 -0.19819400 -0.0234880284 0.0083376838
#> beta.all[5,2] -0.038507467 0.12970364 -0.36087105 -0.0800090410 -0.0141446981
#> beta.all[6,2] 0.031088746 0.12365405 -0.19781610 -0.0164387412 0.0123455504
#> beta.all[7,2] -0.003597171 0.11475109 -0.24482670 -0.0406406047 0.0005503331
#> beta.all[8,2] -0.031342785 0.11364845 -0.28282767 -0.0742749894 -0.0132393267
#> beta.all[4,3] 0.021572853 0.11475398 -0.20835618 -0.0204705047 0.0091547793
#> beta.all[5,3] -0.036210176 0.12525849 -0.33937051 -0.0748174991 -0.0137417491
#> beta.all[6,3] 0.033386037 0.11787859 -0.18834239 -0.0163079126 0.0121605247
#> beta.all[7,3] -0.001299880 0.11343619 -0.23974800 -0.0402042825 0.0007024250
#> beta.all[8,3] -0.029045494 0.11402253 -0.29838917 -0.0705981928 -0.0130272298
#> beta.all[5,4] -0.057783029 0.09052665 -0.27916646 -0.1030367790 -0.0327432045
#> beta.all[6,4] 0.011813184 0.07380397 -0.13486725 -0.0240300069 0.0036294419
#> beta.all[7,4] -0.022872732 0.06253741 -0.16468554 -0.0556909704 -0.0111671203
#> beta.all[8,4] -0.050618347 0.06683955 -0.21360977 -0.0891991631 -0.0351208785
#> beta.all[6,5] 0.069596213 0.10758082 -0.08792114 0.0003751031 0.0400843192
#> beta.all[7,5] 0.034910297 0.07647225 -0.09642407 -0.0074365151 0.0168237418
#> beta.all[8,5] 0.007164682 0.07526842 -0.14461670 -0.0279100925 0.0013596947
#> beta.all[7,6] -0.034685916 0.07722660 -0.21864100 -0.0712323528 -0.0174616036
#> beta.all[8,6] -0.062431531 0.08084376 -0.25528347 -0.1101014297 -0.0441819568
#> beta.all[8,7] -0.027745614 0.04944595 -0.14204516 -0.0536344630 -0.0210261434
#> 75% 97.5% Rhat n.eff
#> beta.all[2,1] 0.090176126 0.27869028 1.074789 180
#> beta.all[3,1] 0.086245773 0.28438198 1.051986 220
#> beta.all[4,1] 0.102283958 0.19576931 1.265702 12
#> beta.all[5,1] 0.051021045 0.13515305 1.042187 70
#> beta.all[6,1] 0.119092092 0.26067667 1.077784 32
#> beta.all[7,1] 0.069037514 0.12340608 1.053976 45
#> beta.all[8,1] 0.038250270 0.08412452 1.062608 38
#> beta.all[3,2] 0.041677800 0.27987654 1.057677 840
#> beta.all[4,2] 0.061428124 0.25157626 1.096902 91
#> beta.all[5,2] 0.015820897 0.18031732 1.075551 250
#> beta.all[6,2] 0.074872491 0.31494668 1.065741 160
#> beta.all[7,2] 0.037118570 0.22047273 1.076866 3000
#> beta.all[8,2] 0.014758558 0.17153462 1.062837 1100
#> beta.all[4,3] 0.065844668 0.27611139 1.091467 48
#> beta.all[5,3] 0.017389330 0.17435947 1.049622 1300
#> beta.all[6,3] 0.073594247 0.31203460 1.063406 68
#> beta.all[7,3] 0.038380621 0.24473468 1.057552 680
#> beta.all[8,3] 0.016591394 0.18867553 1.037122 310
#> beta.all[5,4] 0.001789437 0.07796797 1.153417 21
#> beta.all[6,4] 0.042805616 0.18943113 1.014252 150
#> beta.all[7,4] 0.011830291 0.08532219 1.123430 23
#> beta.all[8,4] -0.002146577 0.04804405 1.083339 30
#> beta.all[6,5] 0.119733482 0.34363692 1.068204 37
#> beta.all[7,5] 0.073325841 0.22053227 1.040714 97
#> beta.all[8,5] 0.039544765 0.17632262 1.043281 60
#> beta.all[7,6] 0.009288612 0.09882743 1.042079 58
#> beta.all[8,6] -0.002823470 0.06294511 1.026711 90
#> beta.all[8,7] 0.002394307 0.05923014 1.009713 460
#>
#> $dev_o
#> mean sd 2.5% 25% 50% 75%
#> dev.o[1,1] 2.2214220 2.3698212 0.0062698751 0.46162257 1.4789441 3.2117888
#> dev.o[2,1] 0.8851547 1.2543520 0.0010607761 0.10371402 0.4268069 1.1662004
#> dev.o[3,1] 0.9970860 1.3807321 0.0011284473 0.10491616 0.4664472 1.3638745
#> dev.o[4,1] 0.7367308 1.0503955 0.0008928605 0.07623352 0.3440283 0.9860794
#> dev.o[5,1] 0.6519865 0.9123293 0.0006521370 0.07056914 0.2916027 0.8724345
#> dev.o[6,1] 1.0602205 1.3578373 0.0018975656 0.12674692 0.5568093 1.4679218
#> dev.o[7,1] 0.7720774 1.0912046 0.0007724435 0.08692880 0.3582633 1.0182024
#> dev.o[8,1] 0.7135708 0.9804396 0.0007249771 0.07379909 0.3266334 0.9843072
#> dev.o[9,1] 0.7517926 0.9722605 0.0010973285 0.09885384 0.3889992 1.0640587
#> dev.o[10,1] 0.5518217 0.7952398 0.0005860718 0.05762894 0.2511324 0.7071539
#> dev.o[11,1] 0.7834200 1.0848875 0.0011313150 0.08007218 0.3627560 1.0682785
#> dev.o[12,1] 1.0121390 1.2378190 0.0012230730 0.13386717 0.5577003 1.4558975
#> dev.o[13,1] 1.2127593 1.5163832 0.0018930216 0.14481897 0.6232805 1.7273452
#> dev.o[14,1] 0.8196385 1.1755660 0.0006645356 0.08146076 0.3581395 1.1216390
#> dev.o[15,1] 0.7730625 1.1741824 0.0006936346 0.07994537 0.3480782 0.9624748
#> dev.o[16,1] 1.1611050 1.5357042 0.0015715411 0.12993090 0.5753501 1.5858845
#> dev.o[17,1] 1.8295376 2.0857015 0.0050541055 0.31276211 1.1029065 2.6070996
#> dev.o[18,1] 1.3323629 1.7366129 0.0022158169 0.17836689 0.6726152 1.8847664
#> dev.o[19,1] 1.9620689 1.9230671 0.0105966491 0.54873112 1.4271085 2.8352531
#> dev.o[20,1] 0.7846868 1.0605971 0.0006714862 0.08047914 0.3705285 1.0657893
#> dev.o[21,1] 1.4697381 1.7768939 0.0012057143 0.19852173 0.8025994 2.0929793
#> dev.o[1,2] 2.9156959 1.8274464 0.4900216680 1.56446624 2.5332250 3.8461025
#> dev.o[2,2] 0.8817099 1.2366640 0.0006383413 0.09449104 0.4106495 1.1983141
#> dev.o[3,2] 0.9679059 1.2979912 0.0011937717 0.10520160 0.4475943 1.3144840
#> dev.o[4,2] 0.7971286 1.1008413 0.0007025422 0.08456882 0.3767106 1.0756796
#> dev.o[5,2] 0.5657664 0.7646542 0.0005388765 0.06269112 0.2615280 0.7417538
#> dev.o[6,2] 1.1177573 1.4283180 0.0018343311 0.14894987 0.6119990 1.5254368
#> dev.o[7,2] 0.8453473 1.1735908 0.0008836089 0.08969180 0.4064889 1.0887424
#> dev.o[8,2] 0.6598539 0.9138559 0.0007221064 0.06753587 0.3006361 0.8848252
#> dev.o[9,2] 0.5793267 0.8634468 0.0004467595 0.04904967 0.2512690 0.7496885
#> dev.o[10,2] 1.7457183 1.9270327 0.0034920380 0.28581567 1.1056553 2.5368238
#> dev.o[11,2] 0.8618727 1.1870325 0.0009851915 0.09228321 0.3976239 1.1782580
#> dev.o[12,2] 1.3450177 1.8091453 0.0016278602 0.15431134 0.6564665 1.8231816
#> dev.o[13,2] 1.0183165 1.4131683 0.0010172846 0.10679413 0.4745439 1.3746830
#> dev.o[14,2] 0.6982708 1.0009092 0.0007578728 0.07129284 0.3212411 0.9332866
#> dev.o[15,2] 0.8187259 1.1285781 0.0006575196 0.08303286 0.3708326 1.1110285
#> dev.o[16,2] 1.2831329 1.7273247 0.0015829960 0.12894011 0.6438267 1.7464514
#> dev.o[17,2] 1.9451923 1.8656255 0.0078633336 0.50632339 1.4567549 2.8013188
#> dev.o[18,2] 1.3231498 1.6748932 0.0015156613 0.17265002 0.7057163 1.8404999
#> dev.o[19,2] 0.4339815 0.6327221 0.0004364867 0.04074738 0.1934889 0.5537370
#> dev.o[20,2] 0.7569258 1.0521804 0.0007729704 0.07752777 0.3569683 1.0000392
#> dev.o[21,2] 1.3362382 1.6172502 0.0016235305 0.19318759 0.7158192 1.9474032
#> dev.o[9,3] 0.8230240 1.1186751 0.0008370616 0.07873315 0.3864652 1.1357866
#> dev.o[10,3] 0.7998405 1.0907484 0.0008374860 0.08924996 0.3827147 1.0538803
#> dev.o[12,3] 1.3504422 1.6080470 0.0017837389 0.17423363 0.7540027 2.0000595
#> dev.o[13,3] 0.9738865 1.4177344 0.0009420259 0.09851998 0.4396986 1.2711752
#> dev.o[19,3] 1.7166830 1.3399792 0.0657454424 0.70272504 1.4044130 2.4051202
#> dev.o[10,4] 1.0817841 1.4214219 0.0018108541 0.13585731 0.5615902 1.4809173
#> dev.o[12,4] 1.0077731 1.3004726 0.0015635868 0.11882329 0.5274342 1.3723292
#> dev.o[13,4] 1.0572039 1.4350090 0.0009165133 0.11533966 0.4888516 1.4134509
#> 97.5% Rhat n.eff
#> dev.o[1,1] 8.505582 1.001145 3000
#> dev.o[2,1] 4.068134 1.004568 490
#> dev.o[3,1] 4.797714 1.001945 1900
#> dev.o[4,1] 3.518137 1.005404 410
#> dev.o[5,1] 3.139039 1.004810 940
#> dev.o[6,1] 4.895161 1.004174 870
#> dev.o[7,1] 3.918647 1.002570 970
#> dev.o[8,1] 3.427029 1.002024 1800
#> dev.o[9,1] 3.231570 1.000921 3000
#> dev.o[10,1] 2.732416 1.004201 540
#> dev.o[11,1] 3.859736 1.004723 480
#> dev.o[12,1] 4.515631 1.017383 120
#> dev.o[13,1] 5.518192 1.007933 270
#> dev.o[14,1] 3.933895 1.000923 3000
#> dev.o[15,1] 4.062150 1.003295 780
#> dev.o[16,1] 5.476054 1.013236 160
#> dev.o[17,1] 7.478078 1.011292 370
#> dev.o[18,1] 6.040120 1.010288 320
#> dev.o[19,1] 6.731106 1.006328 350
#> dev.o[20,1] 3.854885 1.001559 2700
#> dev.o[21,1] 6.597428 1.008264 410
#> dev.o[1,2] 7.509524 1.005417 420
#> dev.o[2,2] 4.167298 1.001401 2900
#> dev.o[3,2] 4.627518 1.000714 3000
#> dev.o[4,2] 3.797653 1.000542 3000
#> dev.o[5,2] 2.824957 1.000725 3000
#> dev.o[6,2] 5.357228 1.003940 840
#> dev.o[7,2] 4.256407 1.001312 2500
#> dev.o[8,2] 3.185609 1.001369 3000
#> dev.o[9,2] 2.940834 1.003000 800
#> dev.o[10,2] 6.941381 1.015431 150
#> dev.o[11,2] 4.179654 1.000790 3000
#> dev.o[12,2] 6.436374 1.073782 33
#> dev.o[13,2] 5.101005 1.009181 420
#> dev.o[14,2] 3.284667 1.004866 490
#> dev.o[15,2] 3.996256 1.001602 1800
#> dev.o[16,2] 6.038884 1.004511 500
#> dev.o[17,2] 6.688959 1.002322 1500
#> dev.o[18,2] 6.065805 1.004783 470
#> dev.o[19,2] 2.217057 1.000769 3000
#> dev.o[20,2] 3.703744 1.001048 3000
#> dev.o[21,2] 5.779020 1.007199 300
#> dev.o[9,3] 4.038421 1.008963 260
#> dev.o[10,3] 4.001643 1.000985 3000
#> dev.o[12,3] 5.640320 1.008813 240
#> dev.o[13,3] 4.754018 1.001016 3000
#> dev.o[19,3] 5.174523 1.003022 790
#> dev.o[10,4] 5.003209 1.001706 3000
#> dev.o[12,4] 4.677885 1.016455 140
#> dev.o[13,4] 5.168181 1.000764 3000
#>
#> $hat_par
#> mean sd 2.5% 25% 50%
#> hat.par[1,1] 1.645532 0.8042964 0.3706083 1.0297573 1.553599
#> hat.par[2,1] 50.213575 4.8553029 41.1553199 46.8844733 50.086022
#> hat.par[3,1] 44.357487 4.5008511 35.8131872 41.2900666 44.278777
#> hat.par[4,1] 42.124349 4.7549798 33.5372228 38.6816226 41.969015
#> hat.par[5,1] 17.315462 2.4455214 12.7325047 15.7055587 17.225086
#> hat.par[6,1] 44.310179 4.0612722 36.5888328 41.5980940 44.340784
#> hat.par[7,1] 157.651576 7.3123613 142.9403693 152.7211196 157.826395
#> hat.par[8,1] 68.178724 5.4747470 57.6761870 64.4086523 68.101056
#> hat.par[9,1] 88.952889 4.6156430 80.6010992 85.6546865 88.786447
#> hat.par[10,1] 78.628450 3.5703936 71.6793011 76.2926527 78.733302
#> hat.par[11,1] 73.820946 5.4481274 63.5315920 70.0091590 73.729462
#> hat.par[12,1] 76.555368 4.1658967 67.7690214 73.7898770 76.723772
#> hat.par[13,1] 49.501743 4.6358146 40.7599573 46.2445423 49.365998
#> hat.par[14,1] 34.819802 4.7956313 26.3354935 31.4484598 34.582057
#> hat.par[15,1] 34.853015 4.5985645 26.0302719 31.7600582 34.791935
#> hat.par[16,1] 303.547787 12.7869385 277.8679686 294.7634187 303.676652
#> hat.par[17,1] 10.931434 2.5326713 6.4840945 9.1730360 10.773055
#> hat.par[18,1] 21.313866 3.4025061 15.1915861 18.9243906 21.198141
#> hat.par[19,1] 3.729726 1.2353189 1.7259047 2.8406204 3.608087
#> hat.par[20,1] 23.519373 3.7347624 16.6927872 20.8788465 23.362276
#> hat.par[21,1] 31.180861 4.5804986 22.8956715 27.9837311 30.897075
#> hat.par[1,2] 1.291049 0.7186943 0.2412969 0.7452067 1.171432
#> hat.par[2,2] 45.612639 4.9795328 36.0670982 42.1379545 45.595770
#> hat.par[3,2] 30.593176 4.0424180 23.1254154 27.7159479 30.450738
#> hat.par[4,2] 43.700687 5.1781810 34.4376050 40.0902564 43.379986
#> hat.par[5,2] 11.670207 2.0713456 7.8428304 10.1786061 11.610687
#> hat.par[6,2] 34.667075 3.8400379 27.1933136 32.0967372 34.516940
#> hat.par[7,2] 196.964361 9.8664120 177.3284794 190.2832755 196.813563
#> hat.par[8,2] 51.931405 4.9270118 42.6222269 48.5892719 51.813052
#> hat.par[9,2] 81.897044 5.2099420 71.6860687 78.5046182 81.918181
#> hat.par[10,2] 72.474025 3.8511008 64.8451323 69.8919939 72.512681
#> hat.par[11,2] 118.257583 8.0135529 103.1272945 112.6335738 118.151909
#> hat.par[12,2] 82.853924 5.1701428 72.7476597 79.3285654 82.940637
#> hat.par[13,2] 25.969584 4.6128097 17.8611563 22.6311191 25.697597
#> hat.par[14,2] 30.941910 4.3303121 22.6469454 28.0206966 30.879254
#> hat.par[15,2] 34.012742 4.6069471 25.6547324 30.8263282 33.729955
#> hat.par[16,2] 247.428937 12.8026460 223.1496281 238.9299187 247.186545
#> hat.par[17,2] 6.915546 1.8806025 3.8133244 5.5296231 6.775397
#> hat.par[18,2] 13.623732 2.7429083 8.6882857 11.7002171 13.480012
#> hat.par[19,2] 2.524280 0.9642596 1.0397633 1.8434411 2.397829
#> hat.par[20,2] 20.448314 3.5661775 14.1702621 17.8737255 20.286419
#> hat.par[21,2] 22.644623 3.7786338 15.6203275 20.0180422 22.472302
#> hat.par[9,3] 80.028300 5.5118099 69.1843919 76.3873560 80.079723
#> hat.par[10,3] 69.192124 4.4345675 60.3229319 66.2134519 69.343748
#> hat.par[12,3] 66.485722 4.6476904 57.7405570 63.1721041 66.444795
#> hat.par[13,3] 34.957652 5.0764080 25.6520786 31.4721882 34.807435
#> hat.par[19,3] 2.780091 0.9763609 1.2296995 2.0673212 2.653697
#> hat.par[10,4] 66.615147 4.1026809 58.4273957 63.9485788 66.638357
#> hat.par[12,4] 61.716859 4.3958045 53.2456183 58.7376941 61.652106
#> hat.par[13,4] 41.162314 4.5918033 32.6303429 38.0365020 40.981381
#> 75% 97.5% Rhat n.eff
#> hat.par[1,1] 2.165492 3.417422 1.002672 1200
#> hat.par[2,1] 53.530542 59.688060 1.014653 160
#> hat.par[3,1] 47.356337 53.354388 1.003333 710
#> hat.par[4,1] 45.232578 51.810442 1.014103 220
#> hat.par[5,1] 18.951367 22.317415 1.003437 680
#> hat.par[6,1] 47.012432 52.334297 1.005819 390
#> hat.par[7,1] 162.422075 171.926179 1.012969 190
#> hat.par[8,1] 71.827350 78.670440 1.001313 2500
#> hat.par[9,1] 92.028532 98.269368 1.003764 1700
#> hat.par[10,1] 81.136445 85.488257 1.004370 910
#> hat.par[11,1] 77.382076 84.796859 1.023641 92
#> hat.par[12,1] 79.447279 84.435463 1.028511 76
#> hat.par[13,1] 52.549086 59.000681 1.008418 250
#> hat.par[14,1] 37.813879 44.963970 1.004055 890
#> hat.par[15,1] 37.792150 44.272782 1.005269 680
#> hat.par[16,1] 312.228448 328.314251 1.017235 130
#> hat.par[17,1] 12.507449 16.337324 1.003582 650
#> hat.par[18,1] 23.453599 28.375566 1.009078 280
#> hat.par[19,1] 4.447244 6.604263 1.014520 150
#> hat.par[20,1] 25.990567 31.026305 1.005398 410
#> hat.par[21,1] 34.037971 40.753374 1.020919 100
#> hat.par[1,2] 1.709377 2.996707 1.005696 410
#> hat.par[2,2] 48.945572 55.251230 1.007626 280
#> hat.par[3,2] 33.288401 38.757492 1.006139 360
#> hat.par[4,2] 47.031404 54.563025 1.021058 100
#> hat.par[5,2] 13.081433 15.987222 1.002312 1100
#> hat.par[6,2] 37.096962 42.347013 1.004509 500
#> hat.par[7,2] 203.689785 216.592742 1.003959 580
#> hat.par[8,2] 55.303736 61.656442 1.005630 390
#> hat.par[9,2] 85.373752 92.039267 1.014978 160
#> hat.par[10,2] 75.161813 79.806511 1.023089 91
#> hat.par[11,2] 123.731146 133.628032 1.005539 400
#> hat.par[12,2] 86.359017 92.898559 1.167660 17
#> hat.par[13,2] 28.867671 35.623577 1.019505 110
#> hat.par[14,2] 33.728089 39.836873 1.002776 3000
#> hat.par[15,2] 36.930220 43.681606 1.003651 960
#> hat.par[16,2] 255.758547 273.659799 1.012825 160
#> hat.par[17,2] 8.078909 10.979520 1.001743 1600
#> hat.par[18,2] 15.373285 19.418690 1.007794 320
#> hat.par[19,2] 3.073810 4.757148 1.000985 3000
#> hat.par[20,2] 22.734823 27.832757 1.015858 130
#> hat.par[21,2] 25.193412 30.366516 1.018476 120
#> hat.par[9,3] 83.706966 90.928564 1.009092 290
#> hat.par[10,3] 72.218786 77.841066 1.012903 170
#> hat.par[12,3] 69.684089 75.598894 1.024554 87
#> hat.par[13,3] 38.271589 45.333218 1.002980 1400
#> hat.par[19,3] 3.365702 5.056336 1.003910 590
#> hat.par[10,4] 69.302772 74.652831 1.003518 1200
#> hat.par[12,4] 64.627508 70.437080 1.033845 64
#> hat.par[13,4] 44.146272 50.571836 1.001264 2600
#>
#> $leverage_o
#> [1] 0.9512263 0.8628494 0.7492911 0.6968182 0.6061131 0.6320113 0.7347895
#> [8] 0.7128138 0.5231546 0.5459165 0.7486693 0.5946680 0.7809059 0.7744821
#> [15] 0.7469398 0.8585851 0.8710186 0.8529914 0.7029638 0.7722858 0.9073680
#> [22] 0.2204327 0.8686070 0.6717592 0.7512995 0.5107867 0.6574475 0.8361703
#> [29] 0.6597269 0.5791019 0.6800805 0.8411012 0.8795405 1.0182725 0.6659966
#> [36] 0.7838734 0.9564816 0.3590982 0.6330153 0.3118041 0.7449671 0.6326352
#> [43] 0.6305223 0.7470318 0.6930533 0.9738195 0.1446909 0.6183202 0.6476216
#> [50] 0.7045692
#>
#> $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] -0.46770528 0.4999260 -1.4646079 -0.7924920 -0.475565744 -0.1540572
#> phi[2] 0.02706911 0.9718686 -1.8933643 -0.6163584 -0.017167779 0.6838166
#> phi[3] -0.03892657 0.9221614 -1.8807181 -0.6555248 -0.005328918 0.5781910
#> phi[4] -0.71941124 0.9412443 -2.3217384 -1.3794474 -0.847026776 -0.1257983
#> phi[5] -0.63691556 0.7789958 -2.1510050 -1.1372134 -0.654635608 -0.1199179
#> phi[6] 0.82935369 0.7721105 -0.7683285 0.3274474 0.820369745 1.3687453
#> phi[7] -0.21498128 0.6667186 -1.5407708 -0.6378254 -0.205403080 0.2004087
#> phi[8] -0.10497462 0.9366665 -1.9148632 -0.7236395 -0.118958338 0.5148549
#> 97.5% Rhat n.eff
#> phi[1] 0.5901206 1.036076 88
#> phi[2] 2.0073829 1.078409 32
#> phi[3] 1.7046509 1.021576 110
#> phi[4] 1.2846678 1.502418 8
#> phi[5] 0.9516481 1.029527 86
#> phi[6] 2.2708177 1.044422 58
#> phi[7] 1.1348523 1.003755 630
#> phi[8] 1.7661186 1.060396 40
#>
#> $model_assessment
#> DIC pD dev
#> 1 89.23774 35.04769 54.19005
#>
#> $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. Running time = secs
#> mu.vect sd.vect 2.5% 25% 50% 75% 97.5%
#> EM[2,1] -0.729 0.650 -1.969 -1.136 -0.772 -0.357 0.486
#> EM[3,1] -0.500 0.578 -1.723 -0.815 -0.476 -0.174 0.550
#> EM[4,1] 0.249 0.531 -0.635 -0.140 0.151 0.598 1.325
#> EM[5,1] -0.386 0.327 -1.105 -0.588 -0.373 -0.173 0.206
#> EM[6,1] 0.257 0.380 -0.488 0.034 0.247 0.483 1.054
#> EM[7,1] -0.196 0.267 -0.770 -0.365 -0.168 -0.014 0.266
#> EM[8,1] -0.396 0.217 -0.915 -0.515 -0.381 -0.251 -0.026
#> EM[3,2] 0.230 0.736 -1.335 -0.169 0.237 0.660 1.623
#> EM[4,2] 0.979 0.758 -0.513 0.530 1.013 1.431 2.459
#> EM[5,2] 0.344 0.700 -1.139 -0.024 0.385 0.763 1.607
#> EM[6,2] 0.986 0.670 -0.247 0.587 0.995 1.367 2.384
#> EM[7,2] 0.533 0.659 -0.743 0.143 0.562 0.933 1.755
#> EM[8,2] 0.333 0.641 -0.913 -0.045 0.367 0.746 1.468
#> EM[4,3] 0.749 0.734 -0.539 0.269 0.690 1.196 2.380
#> EM[5,3] 0.114 0.636 -1.188 -0.225 0.119 0.458 1.385
#> EM[6,3] 0.756 0.629 -0.394 0.376 0.711 1.098 2.165
#> EM[7,3] 0.303 0.598 -0.815 -0.049 0.289 0.631 1.526
#> EM[8,3] 0.103 0.576 -0.979 -0.209 0.110 0.428 1.257
#> EM[5,4] -0.635 0.596 -2.023 -1.004 -0.537 -0.182 0.246
#> EM[6,4] 0.007 0.554 -1.109 -0.351 0.079 0.375 1.042
#> EM[7,4] -0.446 0.521 -1.533 -0.781 -0.355 -0.084 0.411
#> EM[8,4] -0.646 0.542 -1.793 -1.019 -0.529 -0.256 0.177
#> EM[6,5] 0.643 0.490 -0.203 0.304 0.608 0.899 1.772
#> EM[7,5] 0.190 0.376 -0.539 -0.038 0.174 0.408 1.013
#> EM[8,5] -0.010 0.351 -0.708 -0.226 -0.005 0.204 0.715
#> EM[7,6] -0.453 0.393 -1.332 -0.678 -0.418 -0.204 0.247
#> EM[8,6] -0.653 0.384 -1.484 -0.884 -0.626 -0.393 0.054
#> EM[8,7] -0.200 0.279 -0.793 -0.366 -0.184 -0.023 0.315
#> EM.pred[2,1] -0.730 0.665 -1.989 -1.140 -0.765 -0.343 0.507
#> EM.pred[3,1] -0.498 0.591 -1.738 -0.824 -0.481 -0.158 0.587
#> EM.pred[4,1] 0.251 0.550 -0.674 -0.133 0.166 0.609 1.349
#> EM.pred[5,1] -0.387 0.359 -1.167 -0.595 -0.370 -0.159 0.279
#> EM.pred[6,1] 0.257 0.403 -0.602 0.030 0.254 0.492 1.080
#> EM.pred[7,1] -0.196 0.301 -0.874 -0.373 -0.163 0.002 0.319
#> EM.pred[8,1] -0.396 0.259 -1.034 -0.526 -0.370 -0.228 0.023
#> EM.pred[3,2] 0.229 0.748 -1.364 -0.169 0.231 0.673 1.663
#> EM.pred[4,2] 0.982 0.771 -0.516 0.514 1.029 1.439 2.483
#> EM.pred[5,2] 0.343 0.713 -1.182 -0.032 0.368 0.764 1.639
#> EM.pred[6,2] 0.990 0.688 -0.244 0.580 0.992 1.387 2.441
#> EM.pred[7,2] 0.534 0.672 -0.795 0.141 0.554 0.944 1.807
#> EM.pred[8,2] 0.330 0.654 -0.917 -0.055 0.366 0.751 1.526
#> EM.pred[4,3] 0.748 0.746 -0.569 0.268 0.695 1.197 2.388
#> EM.pred[5,3] 0.116 0.653 -1.163 -0.241 0.118 0.474 1.440
#> EM.pred[6,3] 0.752 0.646 -0.433 0.365 0.713 1.102 2.201
#> EM.pred[7,3] 0.305 0.615 -0.861 -0.059 0.290 0.651 1.603
#> EM.pred[8,3] 0.099 0.593 -1.123 -0.223 0.102 0.424 1.293
#> EM.pred[5,4] -0.634 0.609 -2.005 -1.006 -0.539 -0.186 0.312
#> EM.pred[6,4] 0.008 0.569 -1.145 -0.375 0.064 0.392 1.066
#> EM.pred[7,4] -0.442 0.537 -1.541 -0.799 -0.374 -0.068 0.491
#> EM.pred[8,4] -0.647 0.564 -1.841 -1.046 -0.538 -0.244 0.247
#> EM.pred[6,5] 0.649 0.508 -0.234 0.306 0.622 0.916 1.792
#> EM.pred[7,5] 0.187 0.403 -0.608 -0.052 0.174 0.430 1.049
#> EM.pred[8,5] -0.008 0.377 -0.765 -0.228 -0.007 0.227 0.765
#> EM.pred[7,6] -0.454 0.416 -1.367 -0.689 -0.413 -0.190 0.315
#> EM.pred[8,6] -0.654 0.408 -1.555 -0.891 -0.631 -0.384 0.099
#> EM.pred[8,7] -0.200 0.315 -0.873 -0.381 -0.181 -0.006 0.390
#> SUCRA[1] 0.293 0.174 0.000 0.143 0.286 0.429 0.714
#> SUCRA[2] 0.809 0.287 0.000 0.714 1.000 1.000 1.000
#> SUCRA[3] 0.697 0.285 0.000 0.571 0.857 0.857 1.000
#> SUCRA[4] 0.222 0.240 0.000 0.000 0.143 0.429 0.857
#> SUCRA[5] 0.653 0.237 0.143 0.429 0.714 0.857 1.000
#> SUCRA[6] 0.143 0.175 0.000 0.000 0.143 0.143 0.571
#> SUCRA[7] 0.494 0.216 0.143 0.286 0.429 0.571 0.857
#> SUCRA[8] 0.690 0.173 0.286 0.571 0.714 0.857 1.000
#> abs_risk[1] 0.390 0.000 0.390 0.390 0.390 0.390 0.390
#> abs_risk[2] 0.252 0.116 0.082 0.170 0.228 0.309 0.510
#> abs_risk[3] 0.292 0.110 0.102 0.221 0.284 0.350 0.526
#> abs_risk[4] 0.452 0.125 0.253 0.357 0.427 0.538 0.706
#> abs_risk[5] 0.307 0.067 0.175 0.262 0.306 0.350 0.440
#> abs_risk[6] 0.454 0.091 0.282 0.398 0.450 0.509 0.647
#> abs_risk[7] 0.347 0.059 0.228 0.307 0.351 0.387 0.455
#> abs_risk[8] 0.303 0.044 0.204 0.276 0.304 0.332 0.384
#> beta[1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> beta[2] 0.043 0.114 -0.159 -0.010 0.033 0.090 0.279
#> beta[3] 0.041 0.112 -0.179 -0.011 0.035 0.086 0.284
#> beta[4] 0.062 0.062 -0.039 0.017 0.055 0.102 0.196
#> beta[5] 0.004 0.074 -0.161 -0.034 0.011 0.051 0.135
#> beta[6] 0.074 0.079 -0.054 0.018 0.061 0.119 0.261
#> beta[7] 0.039 0.044 -0.047 0.009 0.040 0.069 0.123
#> beta[8] 0.011 0.039 -0.074 -0.013 0.014 0.038 0.084
#> beta.all[2,1] 0.043 0.114 -0.159 -0.010 0.033 0.090 0.279
#> beta.all[3,1] 0.041 0.112 -0.179 -0.011 0.035 0.086 0.284
#> beta.all[4,1] 0.062 0.062 -0.039 0.017 0.055 0.102 0.196
#> beta.all[5,1] 0.004 0.074 -0.161 -0.034 0.011 0.051 0.135
#> beta.all[6,1] 0.074 0.079 -0.054 0.018 0.061 0.119 0.261
#> beta.all[7,1] 0.039 0.044 -0.047 0.009 0.040 0.069 0.123
#> beta.all[8,1] 0.011 0.039 -0.074 -0.013 0.014 0.038 0.084
#> beta.all[3,2] -0.002 0.137 -0.295 -0.045 0.001 0.042 0.280
#> beta.all[4,2] 0.019 0.115 -0.198 -0.023 0.008 0.061 0.252
#> beta.all[5,2] -0.039 0.130 -0.361 -0.080 -0.014 0.016 0.180
#> beta.all[6,2] 0.031 0.124 -0.198 -0.016 0.012 0.075 0.315
#> beta.all[7,2] -0.004 0.115 -0.245 -0.041 0.001 0.037 0.220
#> beta.all[8,2] -0.031 0.114 -0.283 -0.074 -0.013 0.015 0.172
#> beta.all[4,3] 0.022 0.115 -0.208 -0.020 0.009 0.066 0.276
#> beta.all[5,3] -0.036 0.125 -0.339 -0.075 -0.014 0.017 0.174
#> beta.all[6,3] 0.033 0.118 -0.188 -0.016 0.012 0.074 0.312
#> beta.all[7,3] -0.001 0.113 -0.240 -0.040 0.001 0.038 0.245
#> beta.all[8,3] -0.029 0.114 -0.298 -0.071 -0.013 0.017 0.189
#> beta.all[5,4] -0.058 0.091 -0.279 -0.103 -0.033 0.002 0.078
#> beta.all[6,4] 0.012 0.074 -0.135 -0.024 0.004 0.043 0.189
#> beta.all[7,4] -0.023 0.063 -0.165 -0.056 -0.011 0.012 0.085
#> beta.all[8,4] -0.051 0.067 -0.214 -0.089 -0.035 -0.002 0.048
#> beta.all[6,5] 0.070 0.108 -0.088 0.000 0.040 0.120 0.344
#> beta.all[7,5] 0.035 0.076 -0.096 -0.007 0.017 0.073 0.221
#> beta.all[8,5] 0.007 0.075 -0.145 -0.028 0.001 0.040 0.176
#> beta.all[7,6] -0.035 0.077 -0.219 -0.071 -0.017 0.009 0.099
#> beta.all[8,6] -0.062 0.081 -0.255 -0.110 -0.044 -0.003 0.063
#> beta.all[8,7] -0.028 0.049 -0.142 -0.054 -0.021 0.002 0.059
#> 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] -0.037 0.367 -0.699 -0.278 -0.079 0.174 0.593
#> delta[2,2] -0.020 0.351 -0.631 -0.257 -0.075 0.180 0.596
#> delta[3,2] -0.405 0.195 -0.858 -0.527 -0.343 -0.283 -0.097
#> delta[4,2] -0.427 0.153 -0.765 -0.509 -0.428 -0.333 -0.135
#> delta[5,2] -0.386 0.201 -0.883 -0.493 -0.332 -0.270 -0.055
#> delta[6,2] -0.317 0.174 -0.695 -0.403 -0.316 -0.226 0.032
#> delta[7,2] -0.426 0.147 -0.756 -0.507 -0.428 -0.336 -0.140
#> delta[8,2] -0.357 0.156 -0.705 -0.451 -0.331 -0.268 -0.080
#> delta[9,2] -0.382 0.166 -0.756 -0.485 -0.337 -0.278 -0.097
#> delta[10,2] 0.019 0.332 -0.508 -0.228 -0.039 0.234 0.601
#> delta[11,2] -0.085 0.235 -0.633 -0.185 -0.088 0.073 0.312
#> delta[12,2] -0.010 0.325 -0.532 -0.243 -0.064 0.174 0.590
#> delta[13,2] -0.947 0.478 -1.828 -1.285 -0.954 -0.704 0.020
#> delta[14,2] -0.101 0.188 -0.526 -0.203 -0.095 0.018 0.237
#> delta[15,2] -0.034 0.235 -0.528 -0.180 -0.047 0.132 0.408
#> delta[16,2] -0.348 0.134 -0.592 -0.433 -0.366 -0.261 -0.067
#> delta[17,2] -0.431 0.261 -1.008 -0.592 -0.395 -0.251 0.040
#> delta[18,2] -0.446 0.258 -1.071 -0.573 -0.416 -0.293 0.010
#> delta[19,2] -0.435 0.262 -1.073 -0.566 -0.414 -0.271 0.009
#> delta[20,2] -0.356 0.182 -0.739 -0.457 -0.331 -0.262 -0.016
#> delta[21,2] -0.493 0.196 -0.963 -0.594 -0.447 -0.378 -0.172
#> delta[9,3] -0.477 0.170 -0.886 -0.569 -0.441 -0.379 -0.180
#> delta[10,3] -0.405 0.239 -0.945 -0.549 -0.407 -0.268 0.072
#> delta[12,3] -0.340 0.223 -0.780 -0.464 -0.360 -0.198 0.105
#> delta[13,3] -0.708 0.375 -1.557 -0.918 -0.637 -0.499 -0.047
#> delta[19,3] -0.402 0.211 -0.942 -0.505 -0.338 -0.278 -0.084
#> delta[10,4] -0.384 0.192 -0.838 -0.492 -0.334 -0.272 -0.068
#> delta[12,4] -0.312 0.178 -0.667 -0.399 -0.314 -0.222 0.070
#> delta[13,4] -0.123 0.284 -0.826 -0.232 -0.114 0.073 0.317
#> dev.o[1,1] 2.221 2.370 0.006 0.462 1.479 3.212 8.506
#> dev.o[2,1] 0.885 1.254 0.001 0.104 0.427 1.166 4.068
#> dev.o[3,1] 0.997 1.381 0.001 0.105 0.466 1.364 4.798
#> dev.o[4,1] 0.737 1.050 0.001 0.076 0.344 0.986 3.518
#> dev.o[5,1] 0.652 0.912 0.001 0.071 0.292 0.872 3.139
#> dev.o[6,1] 1.060 1.358 0.002 0.127 0.557 1.468 4.895
#> dev.o[7,1] 0.772 1.091 0.001 0.087 0.358 1.018 3.919
#> dev.o[8,1] 0.714 0.980 0.001 0.074 0.327 0.984 3.427
#> dev.o[9,1] 0.752 0.972 0.001 0.099 0.389 1.064 3.232
#> dev.o[10,1] 0.552 0.795 0.001 0.058 0.251 0.707 2.732
#> dev.o[11,1] 0.783 1.085 0.001 0.080 0.363 1.068 3.860
#> dev.o[12,1] 1.012 1.238 0.001 0.134 0.558 1.456 4.516
#> dev.o[13,1] 1.213 1.516 0.002 0.145 0.623 1.727 5.518
#> dev.o[14,1] 0.820 1.176 0.001 0.081 0.358 1.122 3.934
#> dev.o[15,1] 0.773 1.174 0.001 0.080 0.348 0.962 4.062
#> dev.o[16,1] 1.161 1.536 0.002 0.130 0.575 1.586 5.476
#> dev.o[17,1] 1.830 2.086 0.005 0.313 1.103 2.607 7.478
#> dev.o[18,1] 1.332 1.737 0.002 0.178 0.673 1.885 6.040
#> dev.o[19,1] 1.962 1.923 0.011 0.549 1.427 2.835 6.731
#> dev.o[20,1] 0.785 1.061 0.001 0.080 0.371 1.066 3.855
#> dev.o[21,1] 1.470 1.777 0.001 0.199 0.803 2.093 6.597
#> dev.o[1,2] 2.916 1.827 0.490 1.564 2.533 3.846 7.510
#> dev.o[2,2] 0.882 1.237 0.001 0.094 0.411 1.198 4.167
#> dev.o[3,2] 0.968 1.298 0.001 0.105 0.448 1.314 4.628
#> dev.o[4,2] 0.797 1.101 0.001 0.085 0.377 1.076 3.798
#> dev.o[5,2] 0.566 0.765 0.001 0.063 0.262 0.742 2.825
#> dev.o[6,2] 1.118 1.428 0.002 0.149 0.612 1.525 5.357
#> dev.o[7,2] 0.845 1.174 0.001 0.090 0.406 1.089 4.256
#> dev.o[8,2] 0.660 0.914 0.001 0.068 0.301 0.885 3.186
#> dev.o[9,2] 0.579 0.863 0.000 0.049 0.251 0.750 2.941
#> dev.o[10,2] 1.746 1.927 0.003 0.286 1.106 2.537 6.941
#> dev.o[11,2] 0.862 1.187 0.001 0.092 0.398 1.178 4.180
#> dev.o[12,2] 1.345 1.809 0.002 0.154 0.656 1.823 6.436
#> dev.o[13,2] 1.018 1.413 0.001 0.107 0.475 1.375 5.101
#> dev.o[14,2] 0.698 1.001 0.001 0.071 0.321 0.933 3.285
#> dev.o[15,2] 0.819 1.129 0.001 0.083 0.371 1.111 3.996
#> dev.o[16,2] 1.283 1.727 0.002 0.129 0.644 1.746 6.039
#> dev.o[17,2] 1.945 1.866 0.008 0.506 1.457 2.801 6.689
#> dev.o[18,2] 1.323 1.675 0.002 0.173 0.706 1.841 6.066
#> dev.o[19,2] 0.434 0.633 0.000 0.041 0.193 0.554 2.217
#> dev.o[20,2] 0.757 1.052 0.001 0.078 0.357 1.000 3.704
#> dev.o[21,2] 1.336 1.617 0.002 0.193 0.716 1.947 5.779
#> dev.o[9,3] 0.823 1.119 0.001 0.079 0.386 1.136 4.038
#> dev.o[10,3] 0.800 1.091 0.001 0.089 0.383 1.054 4.002
#> dev.o[12,3] 1.350 1.608 0.002 0.174 0.754 2.000 5.640
#> dev.o[13,3] 0.974 1.418 0.001 0.099 0.440 1.271 4.754
#> dev.o[19,3] 1.717 1.340 0.066 0.703 1.404 2.405 5.175
#> dev.o[10,4] 1.082 1.421 0.002 0.136 0.562 1.481 5.003
#> dev.o[12,4] 1.008 1.300 0.002 0.119 0.527 1.372 4.678
#> dev.o[13,4] 1.057 1.435 0.001 0.115 0.489 1.413 5.168
#> effectiveness[1,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> effectiveness[2,1] 0.549 0.498 0.000 0.000 1.000 1.000 1.000
#> effectiveness[3,1] 0.217 0.412 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,1] 0.004 0.066 0.000 0.000 0.000 0.000 0.000
#> effectiveness[5,1] 0.121 0.327 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,1] 0.001 0.036 0.000 0.000 0.000 0.000 0.000
#> effectiveness[7,1] 0.021 0.143 0.000 0.000 0.000 0.000 0.000
#> effectiveness[8,1] 0.086 0.280 0.000 0.000 0.000 0.000 1.000
#> effectiveness[1,2] 0.006 0.077 0.000 0.000 0.000 0.000 0.000
#> effectiveness[2,2] 0.169 0.375 0.000 0.000 0.000 0.000 1.000
#> effectiveness[3,2] 0.307 0.461 0.000 0.000 0.000 1.000 1.000
#> effectiveness[4,2] 0.023 0.151 0.000 0.000 0.000 0.000 0.000
#> effectiveness[5,2] 0.196 0.397 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,2] 0.008 0.089 0.000 0.000 0.000 0.000 0.000
#> effectiveness[7,2] 0.085 0.279 0.000 0.000 0.000 0.000 1.000
#> effectiveness[8,2] 0.206 0.404 0.000 0.000 0.000 0.000 1.000
#> effectiveness[1,3] 0.021 0.142 0.000 0.000 0.000 0.000 0.000
#> effectiveness[2,3] 0.066 0.248 0.000 0.000 0.000 0.000 1.000
#> effectiveness[3,3] 0.143 0.350 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,3] 0.050 0.218 0.000 0.000 0.000 0.000 1.000
#> effectiveness[5,3] 0.251 0.434 0.000 0.000 0.000 1.000 1.000
#> effectiveness[6,3] 0.014 0.118 0.000 0.000 0.000 0.000 0.000
#> effectiveness[7,3] 0.136 0.342 0.000 0.000 0.000 0.000 1.000
#> effectiveness[8,3] 0.320 0.466 0.000 0.000 0.000 1.000 1.000
#> effectiveness[1,4] 0.086 0.280 0.000 0.000 0.000 0.000 1.000
#> effectiveness[2,4] 0.054 0.227 0.000 0.000 0.000 0.000 1.000
#> effectiveness[3,4] 0.098 0.297 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,4] 0.073 0.261 0.000 0.000 0.000 0.000 1.000
#> effectiveness[5,4] 0.181 0.385 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,4] 0.026 0.158 0.000 0.000 0.000 0.000 1.000
#> effectiveness[7,4] 0.218 0.413 0.000 0.000 0.000 0.000 1.000
#> effectiveness[8,4] 0.265 0.441 0.000 0.000 0.000 1.000 1.000
#> effectiveness[1,5] 0.221 0.415 0.000 0.000 0.000 0.000 1.000
#> effectiveness[2,5] 0.043 0.202 0.000 0.000 0.000 0.000 1.000
#> effectiveness[3,5] 0.073 0.260 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,5] 0.105 0.307 0.000 0.000 0.000 0.000 1.000
#> effectiveness[5,5] 0.121 0.327 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,5] 0.062 0.241 0.000 0.000 0.000 0.000 1.000
#> effectiveness[7,5] 0.282 0.450 0.000 0.000 0.000 1.000 1.000
#> effectiveness[8,5] 0.092 0.290 0.000 0.000 0.000 0.000 1.000
#> effectiveness[1,6] 0.343 0.475 0.000 0.000 0.000 1.000 1.000
#> effectiveness[2,6] 0.047 0.211 0.000 0.000 0.000 0.000 1.000
#> effectiveness[3,6] 0.069 0.253 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,6] 0.146 0.353 0.000 0.000 0.000 0.000 1.000
#> effectiveness[5,6] 0.082 0.274 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,6] 0.117 0.321 0.000 0.000 0.000 0.000 1.000
#> effectiveness[7,6] 0.170 0.376 0.000 0.000 0.000 0.000 1.000
#> effectiveness[8,6] 0.027 0.161 0.000 0.000 0.000 0.000 1.000
#> effectiveness[1,7] 0.217 0.412 0.000 0.000 0.000 0.000 1.000
#> effectiveness[2,7] 0.039 0.194 0.000 0.000 0.000 0.000 1.000
#> effectiveness[3,7] 0.059 0.235 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,7] 0.231 0.422 0.000 0.000 0.000 0.000 1.000
#> effectiveness[5,7] 0.037 0.189 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,7] 0.348 0.477 0.000 0.000 0.000 1.000 1.000
#> effectiveness[7,7] 0.064 0.245 0.000 0.000 0.000 0.000 1.000
#> effectiveness[8,7] 0.005 0.068 0.000 0.000 0.000 0.000 0.000
#> effectiveness[1,8] 0.106 0.308 0.000 0.000 0.000 0.000 1.000
#> effectiveness[2,8] 0.034 0.181 0.000 0.000 0.000 0.000 1.000
#> effectiveness[3,8] 0.035 0.184 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,8] 0.367 0.482 0.000 0.000 0.000 1.000 1.000
#> effectiveness[5,8] 0.011 0.103 0.000 0.000 0.000 0.000 0.000
#> effectiveness[6,8] 0.424 0.494 0.000 0.000 0.000 1.000 1.000
#> effectiveness[7,8] 0.023 0.151 0.000 0.000 0.000 0.000 0.000
#> effectiveness[8,8] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> hat.par[1,1] 1.646 0.804 0.371 1.030 1.554 2.165 3.417
#> hat.par[2,1] 50.214 4.855 41.155 46.884 50.086 53.531 59.688
#> hat.par[3,1] 44.357 4.501 35.813 41.290 44.279 47.356 53.354
#> hat.par[4,1] 42.124 4.755 33.537 38.682 41.969 45.233 51.810
#> hat.par[5,1] 17.315 2.446 12.733 15.706 17.225 18.951 22.317
#> hat.par[6,1] 44.310 4.061 36.589 41.598 44.341 47.012 52.334
#> hat.par[7,1] 157.652 7.312 142.940 152.721 157.826 162.422 171.926
#> hat.par[8,1] 68.179 5.475 57.676 64.409 68.101 71.827 78.670
#> hat.par[9,1] 88.953 4.616 80.601 85.655 88.786 92.029 98.269
#> hat.par[10,1] 78.628 3.570 71.679 76.293 78.733 81.136 85.488
#> hat.par[11,1] 73.821 5.448 63.532 70.009 73.729 77.382 84.797
#> hat.par[12,1] 76.555 4.166 67.769 73.790 76.724 79.447 84.435
#> hat.par[13,1] 49.502 4.636 40.760 46.245 49.366 52.549 59.001
#> hat.par[14,1] 34.820 4.796 26.335 31.448 34.582 37.814 44.964
#> hat.par[15,1] 34.853 4.599 26.030 31.760 34.792 37.792 44.273
#> hat.par[16,1] 303.548 12.787 277.868 294.763 303.677 312.228 328.314
#> hat.par[17,1] 10.931 2.533 6.484 9.173 10.773 12.507 16.337
#> hat.par[18,1] 21.314 3.403 15.192 18.924 21.198 23.454 28.376
#> hat.par[19,1] 3.730 1.235 1.726 2.841 3.608 4.447 6.604
#> hat.par[20,1] 23.519 3.735 16.693 20.879 23.362 25.991 31.026
#> hat.par[21,1] 31.181 4.580 22.896 27.984 30.897 34.038 40.753
#> hat.par[1,2] 1.291 0.719 0.241 0.745 1.171 1.709 2.997
#> hat.par[2,2] 45.613 4.980 36.067 42.138 45.596 48.946 55.251
#> hat.par[3,2] 30.593 4.042 23.125 27.716 30.451 33.288 38.757
#> hat.par[4,2] 43.701 5.178 34.438 40.090 43.380 47.031 54.563
#> hat.par[5,2] 11.670 2.071 7.843 10.179 11.611 13.081 15.987
#> hat.par[6,2] 34.667 3.840 27.193 32.097 34.517 37.097 42.347
#> hat.par[7,2] 196.964 9.866 177.328 190.283 196.814 203.690 216.593
#> hat.par[8,2] 51.931 4.927 42.622 48.589 51.813 55.304 61.656
#> hat.par[9,2] 81.897 5.210 71.686 78.505 81.918 85.374 92.039
#> hat.par[10,2] 72.474 3.851 64.845 69.892 72.513 75.162 79.807
#> hat.par[11,2] 118.258 8.014 103.127 112.634 118.152 123.731 133.628
#> hat.par[12,2] 82.854 5.170 72.748 79.329 82.941 86.359 92.899
#> hat.par[13,2] 25.970 4.613 17.861 22.631 25.698 28.868 35.624
#> hat.par[14,2] 30.942 4.330 22.647 28.021 30.879 33.728 39.837
#> hat.par[15,2] 34.013 4.607 25.655 30.826 33.730 36.930 43.682
#> hat.par[16,2] 247.429 12.803 223.150 238.930 247.187 255.759 273.660
#> hat.par[17,2] 6.916 1.881 3.813 5.530 6.775 8.079 10.980
#> hat.par[18,2] 13.624 2.743 8.688 11.700 13.480 15.373 19.419
#> hat.par[19,2] 2.524 0.964 1.040 1.843 2.398 3.074 4.757
#> hat.par[20,2] 20.448 3.566 14.170 17.874 20.286 22.735 27.833
#> hat.par[21,2] 22.645 3.779 15.620 20.018 22.472 25.193 30.367
#> hat.par[9,3] 80.028 5.512 69.184 76.387 80.080 83.707 90.929
#> hat.par[10,3] 69.192 4.435 60.323 66.213 69.344 72.219 77.841
#> hat.par[12,3] 66.486 4.648 57.741 63.172 66.445 69.684 75.599
#> hat.par[13,3] 34.958 5.076 25.652 31.472 34.807 38.272 45.333
#> hat.par[19,3] 2.780 0.976 1.230 2.067 2.654 3.366 5.056
#> hat.par[10,4] 66.615 4.103 58.427 63.949 66.638 69.303 74.653
#> hat.par[12,4] 61.717 4.396 53.246 58.738 61.652 64.628 70.437
#> hat.par[13,4] 41.162 4.592 32.630 38.037 40.981 44.146 50.572
#> phi[1] -0.468 0.500 -1.465 -0.792 -0.476 -0.154 0.590
#> phi[2] 0.027 0.972 -1.893 -0.616 -0.017 0.684 2.007
#> phi[3] -0.039 0.922 -1.881 -0.656 -0.005 0.578 1.705
#> phi[4] -0.719 0.941 -2.322 -1.379 -0.847 -0.126 1.285
#> phi[5] -0.637 0.779 -2.151 -1.137 -0.655 -0.120 0.952
#> phi[6] 0.829 0.772 -0.768 0.327 0.820 1.369 2.271
#> phi[7] -0.215 0.667 -1.541 -0.638 -0.205 0.200 1.135
#> phi[8] -0.105 0.937 -1.915 -0.724 -0.119 0.515 1.766
#> tau 0.108 0.090 0.001 0.035 0.091 0.158 0.332
#> totresdev.o 54.190 9.058 38.007 47.920 53.659 59.823 73.165
#> deviance 581.881 13.395 557.695 572.835 581.101 590.552 609.777
#> Rhat n.eff
#> EM[2,1] 1.063 50
#> EM[3,1] 1.017 3000
#> EM[4,1] 1.752 6
#> EM[5,1] 1.010 500
#> EM[6,1] 1.027 140
#> EM[7,1] 1.047 48
#> EM[8,1] 1.027 150
#> EM[3,2] 1.053 59
#> EM[4,2] 1.199 16
#> EM[5,2] 1.047 71
#> EM[6,2] 1.036 120
#> EM[7,2] 1.043 72
#> EM[8,2] 1.048 67
#> EM[4,3] 1.345 10
#> EM[5,3] 1.015 780
#> EM[6,3] 1.014 260
#> EM[7,3] 1.027 160
#> EM[8,3] 1.017 520
#> EM[5,4] 1.483 8
#> EM[6,4] 1.486 8
#> EM[7,4] 1.502 8
#> EM[8,4] 1.583 7
#> EM[6,5] 1.004 540
#> EM[7,5] 1.010 240
#> EM[8,5] 1.001 3000
#> EM[7,6] 1.009 260
#> EM[8,6] 1.008 540
#> EM[8,7] 1.015 140
#> EM.pred[2,1] 1.061 49
#> EM.pred[3,1] 1.013 3000
#> EM.pred[4,1] 1.664 6
#> EM.pred[5,1] 1.005 570
#> EM.pred[6,1] 1.026 150
#> EM.pred[7,1] 1.037 61
#> EM.pred[8,1] 1.017 310
#> EM.pred[3,2] 1.048 61
#> EM.pred[4,2] 1.186 17
#> EM.pred[5,2] 1.043 76
#> EM.pred[6,2] 1.032 120
#> EM.pred[7,2] 1.037 81
#> EM.pred[8,2] 1.043 74
#> EM.pred[4,3] 1.340 10
#> EM.pred[5,3] 1.012 1200
#> EM.pred[6,3] 1.012 270
#> EM.pred[7,3] 1.022 170
#> EM.pred[8,3] 1.012 470
#> EM.pred[5,4] 1.457 8
#> EM.pred[6,4] 1.446 8
#> EM.pred[7,4] 1.461 8
#> EM.pred[8,4] 1.513 8
#> EM.pred[6,5] 1.004 650
#> EM.pred[7,5] 1.008 250
#> EM.pred[8,5] 1.001 3000
#> EM.pred[7,6] 1.011 270
#> EM.pred[8,6] 1.008 650
#> EM.pred[8,7] 1.014 200
#> SUCRA[1] 1.092 28
#> SUCRA[2] 1.076 41
#> SUCRA[3] 1.016 170
#> SUCRA[4] 1.333 10
#> SUCRA[5] 1.005 1700
#> SUCRA[6] 1.035 150
#> SUCRA[7] 1.011 240
#> SUCRA[8] 1.022 120
#> abs_risk[1] 1.000 1
#> abs_risk[2] 1.052 58
#> abs_risk[3] 1.022 1700
#> abs_risk[4] 1.674 6
#> abs_risk[5] 1.011 620
#> abs_risk[6] 1.031 110
#> abs_risk[7] 1.047 49
#> abs_risk[8] 1.030 140
#> beta[1] 1.000 1
#> beta[2] 1.075 180
#> beta[3] 1.052 220
#> beta[4] 1.266 12
#> beta[5] 1.042 70
#> beta[6] 1.078 32
#> beta[7] 1.054 45
#> beta[8] 1.063 38
#> beta.all[2,1] 1.075 180
#> beta.all[3,1] 1.052 220
#> beta.all[4,1] 1.266 12
#> beta.all[5,1] 1.042 70
#> beta.all[6,1] 1.078 32
#> beta.all[7,1] 1.054 45
#> beta.all[8,1] 1.063 38
#> beta.all[3,2] 1.058 840
#> beta.all[4,2] 1.097 91
#> beta.all[5,2] 1.076 250
#> beta.all[6,2] 1.066 160
#> beta.all[7,2] 1.077 3000
#> beta.all[8,2] 1.063 1100
#> beta.all[4,3] 1.091 48
#> beta.all[5,3] 1.050 1300
#> beta.all[6,3] 1.063 68
#> beta.all[7,3] 1.058 680
#> beta.all[8,3] 1.037 310
#> beta.all[5,4] 1.153 21
#> beta.all[6,4] 1.014 150
#> beta.all[7,4] 1.123 23
#> beta.all[8,4] 1.083 30
#> beta.all[6,5] 1.068 37
#> beta.all[7,5] 1.041 97
#> beta.all[8,5] 1.043 60
#> beta.all[7,6] 1.042 58
#> beta.all[8,6] 1.027 90
#> beta.all[8,7] 1.010 460
#> 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] 1.707 6
#> delta[2,2] 1.764 6
#> delta[3,2] 1.037 60
#> delta[4,2] 1.054 95
#> delta[5,2] 1.042 66
#> delta[6,2] 1.011 790
#> delta[7,2] 1.079 49
#> delta[8,2] 1.031 86
#> delta[9,2] 1.047 62
#> delta[10,2] 1.789 6
#> delta[11,2] 1.067 73
#> delta[12,2] 1.798 6
#> delta[13,2] 1.158 23
#> delta[14,2] 1.037 75
#> delta[15,2] 1.109 26
#> delta[16,2] 1.188 16
#> delta[17,2] 1.057 59
#> delta[18,2] 1.128 28
#> delta[19,2] 1.127 28
#> delta[20,2] 1.080 38
#> delta[21,2] 1.036 140
#> delta[9,3] 1.043 160
#> delta[10,3] 1.137 27
#> delta[12,3] 1.105 38
#> delta[13,3] 1.047 130
#> delta[19,3] 1.078 40
#> delta[10,4] 1.063 42
#> delta[12,4] 1.022 260
#> delta[13,4] 1.057 110
#> dev.o[1,1] 1.001 3000
#> dev.o[2,1] 1.005 490
#> dev.o[3,1] 1.002 1900
#> dev.o[4,1] 1.005 410
#> dev.o[5,1] 1.005 940
#> dev.o[6,1] 1.004 870
#> dev.o[7,1] 1.003 970
#> dev.o[8,1] 1.002 1800
#> dev.o[9,1] 1.001 3000
#> dev.o[10,1] 1.004 540
#> dev.o[11,1] 1.005 480
#> dev.o[12,1] 1.017 120
#> dev.o[13,1] 1.008 270
#> dev.o[14,1] 1.001 3000
#> dev.o[15,1] 1.003 780
#> dev.o[16,1] 1.013 160
#> dev.o[17,1] 1.011 370
#> dev.o[18,1] 1.010 320
#> dev.o[19,1] 1.006 350
#> dev.o[20,1] 1.002 2700
#> dev.o[21,1] 1.008 410
#> dev.o[1,2] 1.005 420
#> dev.o[2,2] 1.001 2900
#> dev.o[3,2] 1.001 3000
#> dev.o[4,2] 1.001 3000
#> dev.o[5,2] 1.001 3000
#> dev.o[6,2] 1.004 840
#> dev.o[7,2] 1.001 2500
#> dev.o[8,2] 1.001 3000
#> dev.o[9,2] 1.003 800
#> dev.o[10,2] 1.015 150
#> dev.o[11,2] 1.001 3000
#> dev.o[12,2] 1.074 33
#> dev.o[13,2] 1.009 420
#> dev.o[14,2] 1.005 490
#> dev.o[15,2] 1.002 1800
#> dev.o[16,2] 1.005 500
#> dev.o[17,2] 1.002 1500
#> dev.o[18,2] 1.005 470
#> dev.o[19,2] 1.001 3000
#> dev.o[20,2] 1.001 3000
#> dev.o[21,2] 1.007 300
#> dev.o[9,3] 1.009 260
#> dev.o[10,3] 1.001 3000
#> dev.o[12,3] 1.009 240
#> dev.o[13,3] 1.001 3000
#> dev.o[19,3] 1.003 790
#> dev.o[10,4] 1.002 3000
#> dev.o[12,4] 1.016 140
#> dev.o[13,4] 1.001 3000
#> effectiveness[1,1] 1.000 1
#> effectiveness[2,1] 1.058 39
#> effectiveness[3,1] 1.028 100
#> effectiveness[4,1] 1.298 230
#> effectiveness[5,1] 1.042 110
#> effectiveness[6,1] 1.166 1700
#> effectiveness[7,1] 1.001 3000
#> effectiveness[8,1] 1.024 260
#> effectiveness[1,2] 1.145 460
#> effectiveness[2,2] 1.012 290
#> effectiveness[3,2] 1.013 180
#> effectiveness[4,2] 1.198 86
#> effectiveness[5,2] 1.002 1100
#> effectiveness[6,2] 1.234 190
#> effectiveness[7,2] 1.064 97
#> effectiveness[8,2] 1.002 1000
#> effectiveness[1,3] 1.062 370
#> effectiveness[2,3] 1.019 410
#> effectiveness[3,3] 1.005 710
#> effectiveness[4,3] 1.148 63
#> effectiveness[5,3] 1.009 300
#> effectiveness[6,3] 1.051 660
#> effectiveness[7,3] 1.001 2400
#> effectiveness[8,3] 1.001 3000
#> effectiveness[1,4] 1.015 410
#> effectiveness[2,4] 1.045 200
#> effectiveness[3,4] 1.001 3000
#> effectiveness[4,4] 1.097 71
#> effectiveness[5,4] 1.002 1200
#> effectiveness[6,4] 1.001 3000
#> effectiveness[7,4] 1.002 1300
#> effectiveness[8,4] 1.004 600
#> effectiveness[1,5] 1.025 120
#> effectiveness[2,5] 1.017 670
#> effectiveness[3,5] 1.004 1800
#> effectiveness[4,5] 1.033 150
#> effectiveness[5,5] 1.001 3000
#> effectiveness[6,5] 1.007 1200
#> effectiveness[7,5] 1.003 670
#> effectiveness[8,5] 1.020 280
#> effectiveness[1,6] 1.013 170
#> effectiveness[2,6] 1.021 500
#> effectiveness[3,6] 1.004 1800
#> effectiveness[4,6] 1.068 58
#> effectiveness[5,6] 1.001 3000
#> effectiveness[6,6] 1.006 700
#> effectiveness[7,6] 1.002 1600
#> effectiveness[8,6] 1.095 180
#> effectiveness[1,7] 1.047 65
#> effectiveness[2,7] 1.085 140
#> effectiveness[3,7] 1.052 170
#> effectiveness[4,7] 1.034 84
#> effectiveness[5,7] 1.012 1100
#> effectiveness[6,7] 1.146 19
#> effectiveness[7,7] 1.003 2400
#> effectiveness[8,7] 1.017 3000
#> effectiveness[1,8] 1.161 32
#> effectiveness[2,8] 1.088 160
#> effectiveness[3,8] 1.030 470
#> effectiveness[4,8] 1.424 9
#> effectiveness[5,8] 1.034 1300
#> effectiveness[6,8] 1.132 20
#> effectiveness[7,8] 1.005 3000
#> effectiveness[8,8] 1.000 1
#> hat.par[1,1] 1.003 1200
#> hat.par[2,1] 1.015 160
#> hat.par[3,1] 1.003 710
#> hat.par[4,1] 1.014 220
#> hat.par[5,1] 1.003 680
#> hat.par[6,1] 1.006 390
#> hat.par[7,1] 1.013 190
#> hat.par[8,1] 1.001 2500
#> hat.par[9,1] 1.004 1700
#> hat.par[10,1] 1.004 910
#> hat.par[11,1] 1.024 92
#> hat.par[12,1] 1.029 76
#> hat.par[13,1] 1.008 250
#> hat.par[14,1] 1.004 890
#> hat.par[15,1] 1.005 680
#> hat.par[16,1] 1.017 130
#> hat.par[17,1] 1.004 650
#> hat.par[18,1] 1.009 280
#> hat.par[19,1] 1.015 150
#> hat.par[20,1] 1.005 410
#> hat.par[21,1] 1.021 100
#> hat.par[1,2] 1.006 410
#> hat.par[2,2] 1.008 280
#> hat.par[3,2] 1.006 360
#> hat.par[4,2] 1.021 100
#> hat.par[5,2] 1.002 1100
#> hat.par[6,2] 1.005 500
#> hat.par[7,2] 1.004 580
#> hat.par[8,2] 1.006 390
#> hat.par[9,2] 1.015 160
#> hat.par[10,2] 1.023 91
#> hat.par[11,2] 1.006 400
#> hat.par[12,2] 1.168 17
#> hat.par[13,2] 1.020 110
#> hat.par[14,2] 1.003 3000
#> hat.par[15,2] 1.004 960
#> hat.par[16,2] 1.013 160
#> hat.par[17,2] 1.002 1600
#> hat.par[18,2] 1.008 320
#> hat.par[19,2] 1.001 3000
#> hat.par[20,2] 1.016 130
#> hat.par[21,2] 1.018 120
#> hat.par[9,3] 1.009 290
#> hat.par[10,3] 1.013 170
#> hat.par[12,3] 1.025 87
#> hat.par[13,3] 1.003 1400
#> hat.par[19,3] 1.004 590
#> hat.par[10,4] 1.004 1200
#> hat.par[12,4] 1.034 64
#> hat.par[13,4] 1.001 2600
#> phi[1] 1.036 88
#> phi[2] 1.078 32
#> phi[3] 1.022 110
#> phi[4] 1.502 8
#> phi[5] 1.030 86
#> phi[6] 1.044 58
#> phi[7] 1.004 630
#> phi[8] 1.060 40
#> tau 1.393 10
#> totresdev.o 1.039 57
#> deviance 1.028 76
#>
#> 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: pV = var(deviance)/2)
#> pV = 87.4 and DIC = 669.3
#> 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] 0.2518042 0.11611437 0.08195602 0.1703463 0.2281248 0.3090043
#> abs_risk[3] 0.2918572 0.10974141 0.10240336 0.2206403 0.2842361 0.3495437
#> abs_risk[4] 0.4524276 0.12547246 0.25297936 0.3573428 0.4265030 0.5375856
#> abs_risk[5] 0.3073281 0.06674530 0.17475197 0.2621079 0.3056081 0.3497999
#> abs_risk[6] 0.4539580 0.09068393 0.28175834 0.3981905 0.4501231 0.5089003
#> abs_risk[7] 0.3469780 0.05855693 0.22844419 0.3074468 0.3507964 0.3866173
#> abs_risk[8] 0.3027778 0.04400385 0.20393230 0.2763545 0.3040172 0.3321503
#> 97.5% Rhat n.eff
#> abs_risk[1] 0.3900000 1.000000 1
#> abs_risk[2] 0.5096816 1.052232 58
#> abs_risk[3] 0.5255282 1.022257 1700
#> abs_risk[4] 0.7064120 1.674098 6
#> abs_risk[5] 0.4398533 1.010856 620
#> abs_risk[6] 0.6471372 1.031084 110
#> abs_risk[7] 0.4547461 1.046622 49
#> abs_risk[8] 0.3838015 1.029957 140
#>
#> $SUCRA
#> mean sd 2.5% 25% 50% 75% 97.5%
#> SUCRA[1] 0.2926667 0.1741526 0.0000000 0.1428571 0.2857143 0.4285714 0.7142857
#> SUCRA[2] 0.8087143 0.2874268 0.0000000 0.7142857 1.0000000 1.0000000 1.0000000
#> SUCRA[3] 0.6973333 0.2851153 0.0000000 0.5714286 0.8571429 0.8571429 1.0000000
#> SUCRA[4] 0.2218095 0.2402735 0.0000000 0.0000000 0.1428571 0.4285714 0.8571429
#> SUCRA[5] 0.6525714 0.2372588 0.1428571 0.4285714 0.7142857 0.8571429 1.0000000
#> SUCRA[6] 0.1425238 0.1745056 0.0000000 0.0000000 0.1428571 0.1428571 0.5714286
#> SUCRA[7] 0.4943810 0.2162080 0.1428571 0.2857143 0.4285714 0.5714286 0.8571429
#> SUCRA[8] 0.6900000 0.1732202 0.2857143 0.5714286 0.7142857 0.8571429 1.0000000
#> Rhat n.eff
#> SUCRA[1] 1.091660 28
#> SUCRA[2] 1.076468 41
#> SUCRA[3] 1.016139 170
#> SUCRA[4] 1.333219 10
#> SUCRA[5] 1.004659 1700
#> SUCRA[6] 1.035011 150
#> SUCRA[7] 1.011073 240
#> SUCRA[8] 1.022327 120
#>
#> $effectiveness
#> mean sd 2.5% 25% 50% 75% 97.5% Rhat n.eff
#> effectiveness[1,1] 0.000000000 0.00000000 0 0 0 0 0 1.000000 1
#> effectiveness[2,1] 0.549000000 0.49767616 0 0 1 1 1 1.058306 39
#> effectiveness[3,1] 0.217000000 0.41227134 0 0 0 0 1 1.028147 100
#> effectiveness[4,1] 0.004333333 0.06569623 0 0 0 0 0 1.297669 230
#> effectiveness[5,1] 0.121333333 0.32656868 0 0 0 0 1 1.041614 110
#> effectiveness[6,1] 0.001333333 0.03649657 0 0 0 0 0 1.165939 1700
#> effectiveness[7,1] 0.021000000 0.14340800 0 0 0 0 0 1.001073 3000
#> effectiveness[8,1] 0.086000000 0.28041079 0 0 0 0 1 1.024145 260
#> effectiveness[1,2] 0.006000000 0.07723981 0 0 0 0 0 1.145043 460
#> effectiveness[2,2] 0.168666667 0.37451966 0 0 0 0 1 1.011763 290
#> effectiveness[3,2] 0.306666667 0.46118664 0 0 0 1 1 1.012775 180
#> effectiveness[4,2] 0.023333333 0.15098506 0 0 0 0 0 1.198325 86
#> effectiveness[5,2] 0.196000000 0.39703469 0 0 0 0 1 1.002416 1100
#> effectiveness[6,2] 0.008000000 0.08909908 0 0 0 0 0 1.233584 190
#> effectiveness[7,2] 0.085333333 0.27942366 0 0 0 0 1 1.064372 97
#> effectiveness[8,2] 0.206000000 0.40449789 0 0 0 0 1 1.002487 1000
#> effectiveness[1,3] 0.020666667 0.14228951 0 0 0 0 0 1.061866 370
#> effectiveness[2,3] 0.065666667 0.24773981 0 0 0 0 1 1.018987 410
#> effectiveness[3,3] 0.143333333 0.35047087 0 0 0 0 1 1.005188 710
#> effectiveness[4,3] 0.050000000 0.21798128 0 0 0 0 1 1.147957 63
#> effectiveness[5,3] 0.251000000 0.43366080 0 0 0 1 1 1.008556 300
#> effectiveness[6,3] 0.014000000 0.11751001 0 0 0 0 0 1.050846 660
#> effectiveness[7,3] 0.135666667 0.34249135 0 0 0 0 1 1.001348 2400
#> effectiveness[8,3] 0.319666667 0.46642513 0 0 0 1 1 1.000910 3000
#> effectiveness[1,4] 0.085666667 0.27991786 0 0 0 0 1 1.014992 410
#> effectiveness[2,4] 0.054333333 0.22671205 0 0 0 0 1 1.045098 200
#> effectiveness[3,4] 0.097666667 0.29691291 0 0 0 0 1 1.000924 3000
#> effectiveness[4,4] 0.073333333 0.26072632 0 0 0 0 1 1.096917 71
#> effectiveness[5,4] 0.180666667 0.38480590 0 0 0 0 1 1.002452 1200
#> effectiveness[6,4] 0.025666667 0.15816519 0 0 0 0 1 1.000653 3000
#> effectiveness[7,4] 0.218000000 0.41295623 0 0 0 0 1 1.002076 1300
#> effectiveness[8,4] 0.264666667 0.44122910 0 0 0 1 1 1.003836 600
#> effectiveness[1,5] 0.221000000 0.41498965 0 0 0 0 1 1.024606 120
#> effectiveness[2,5] 0.042666667 0.20213818 0 0 0 0 1 1.017451 670
#> effectiveness[3,5] 0.073000000 0.26017987 0 0 0 0 1 1.003549 1800
#> effectiveness[4,5] 0.105333333 0.30703362 0 0 0 0 1 1.033377 150
#> effectiveness[5,5] 0.121333333 0.32656868 0 0 0 0 1 1.000763 3000
#> effectiveness[6,5] 0.062000000 0.24119575 0 0 0 0 1 1.006769 1200
#> effectiveness[7,5] 0.282333333 0.45020971 0 0 0 1 1 1.003479 670
#> effectiveness[8,5] 0.092333333 0.28954418 0 0 0 0 1 1.020376 280
#> effectiveness[1,6] 0.343333333 0.47490076 0 0 0 1 1 1.013245 170
#> effectiveness[2,6] 0.046666667 0.21095906 0 0 0 0 1 1.021197 500
#> effectiveness[3,6] 0.068666667 0.25292861 0 0 0 0 1 1.003721 1800
#> effectiveness[4,6] 0.146000000 0.35316508 0 0 0 0 1 1.068111 58
#> effectiveness[5,6] 0.082000000 0.27441046 0 0 0 0 1 1.001464 3000
#> effectiveness[6,6] 0.116666667 0.32107619 0 0 0 0 1 1.006408 700
#> effectiveness[7,6] 0.170000000 0.37569542 0 0 0 0 1 1.001780 1600
#> effectiveness[8,6] 0.026666667 0.16113414 0 0 0 0 1 1.094696 180
#> effectiveness[1,7] 0.217000000 0.41227134 0 0 0 0 1 1.046611 65
#> effectiveness[2,7] 0.039000000 0.19362721 0 0 0 0 1 1.085120 140
#> effectiveness[3,7] 0.058666667 0.23503894 0 0 0 0 1 1.051734 170
#> effectiveness[4,7] 0.231000000 0.42154268 0 0 0 0 1 1.033903 84
#> effectiveness[5,7] 0.037000000 0.18879322 0 0 0 0 1 1.012071 1100
#> effectiveness[6,7] 0.348333333 0.47652168 0 0 0 1 1 1.146154 19
#> effectiveness[7,7] 0.064333333 0.24538669 0 0 0 0 1 1.002902 2400
#> effectiveness[8,7] 0.004666667 0.06816478 0 0 0 0 0 1.017049 3000
#> effectiveness[1,8] 0.106333333 0.30831517 0 0 0 0 1 1.160916 32
#> effectiveness[2,8] 0.034000000 0.18125935 0 0 0 0 1 1.088295 160
#> effectiveness[3,8] 0.035000000 0.18381040 0 0 0 0 1 1.029968 470
#> effectiveness[4,8] 0.366666667 0.48197475 0 0 0 1 1 1.423861 9
#> effectiveness[5,8] 0.010666667 0.10274438 0 0 0 0 0 1.034076 1300
#> effectiveness[6,8] 0.424000000 0.49427263 0 0 0 1 1 1.131529 20
#> effectiveness[7,8] 0.023333333 0.15098506 0 0 0 0 0 1.005026 3000
#> effectiveness[8,8] 0.000000000 0.00000000 0 0 0 0 0 1.000000 1
#>
#> $D
#> [1] 0
#>
#> attr(,"class")
#> [1] "run_metareg"
# }