Network meta analysis of ThroMbolytics in Atrial fibrilation
Boya chandrasekhar
2022-12-22
Network meta analysis
NMA, an extension of the traditional pairwise meta-analysis, synthesizes, compares, and analyzes three or more interventions and treatment methods using both direct and indirect evidence to evaluate the effects or harms of each treatment.
The NMA includes multiple groups and is also called
‘multiple-treatment meta-analysis’. Moreover, NMA includes both direct
and indirect comparisons and is also called ‘mixed-treatment
comparison’.
##Running NMA in R
##Installing packages
## Loading required package: readxl## Loading required package: writexl## Loading required package: readr## Loading required package: dplyr##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## intersect, setdiff, setequal, union## Loading required package: ggplot2## Loading required package: Plotly## Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
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## [1] FALSE## Loading required package: multinma## For execution on a local, multicore CPU with excess RAM we recommend calling## options(mc.cores = parallel::detectCores())##
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## Type 'help("netmeta-package")' for a brief overview.
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## blocker, smoking## Loading required package: rjags## Linked to JAGS 4.3.1## Loaded modules: basemod,bugs## Loading required package: rstan## Loading required package: StanHeaders## rstan (Version 2.21.7, GitRev: 2e1f913d3ca3)## For execution on a local, multicore CPU with excess RAM we recommend calling
## options(mc.cores = parallel::detectCores()).
## To avoid recompilation of unchanged Stan programs, we recommend calling
## rstan_options(auto_write = TRUE)## Do not specify '-march=native' in 'LOCAL_CPPFLAGS' or a Makevars file##
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##Setting up the network
## A network with 50 AgD studies (arm-based).
##
## ------------------------------------------------------- AgD studies (arm-based) ----
## Study Treatment arms
## 1 3: SK | Acc t-PA | SK + t-PA
## 2 2: SK | t-PA
## 3 2: SK | t-PA
## 4 2: SK | t-PA
## 5 2: SK | t-PA
## 6 3: SK | ASPAC | t-PA
## 7 2: SK | t-PA
## 8 2: SK | t-PA
## 9 2: SK | t-PA
## 10 2: SK | SK + t-PA
## ... plus 40 more studies
##
## Outcome type: count
## ------------------------------------------------------------------------------------
## Total number of treatments: 9
## Total number of studies: 50
## Reference treatment is: SK
## Network is connected## A network with 50 AgD studies (arm-based).
##
## ------------------------------------------------------- AgD studies (arm-based) ----
## Study Treatment arms
## 1 3: SK | Acc t-PA | SK + t-PA
## 2 2: SK | t-PA
## 3 2: SK | t-PA
## 4 2: SK | t-PA
## 5 2: SK | t-PA
## 6 3: SK | ASPAC | t-PA
## 7 2: SK | t-PA
## 8 2: SK | t-PA
## 9 2: SK | t-PA
## 10 2: SK | SK + t-PA
## ... plus 40 more studies
##
## Outcome type: count
## ------------------------------------------------------------------------------------
## Total number of treatments: 9
## Total number of studies: 50
## Reference treatment is: SK
## Network is connected
##Fixed effect NMA
## A Normal prior distribution: location = 0, scale = 100.
## 50% of the prior density lies between -67.45 and 67.45.
## 95% of the prior density lies between -196 and 196.## A half-Normal prior distribution: location = 0, scale = 5.
## 50% of the prior density lies between 0 and 3.37.
## 95% of the prior density lies between 0 and 9.8.## Note: Setting "SK" as the network reference treatment.## A fixed effects NMA with a binomial likelihood (logit link).
## Inference for Stan model: binomial_1par.
## 4 chains, each with iter=2000; warmup=1000; thin=1;
## post-warmup draws per chain=1000, total post-warmup draws=4000.
##
## mean se_mean sd 2.5% 25% 50% 75%
## d[Acc t-PA] -0.18 0.00 0.04 -0.27 -0.21 -0.18 -0.15
## d[ASPAC] 0.02 0.00 0.04 -0.06 -0.01 0.02 0.04
## d[PTCA] -0.48 0.00 0.10 -0.67 -0.54 -0.48 -0.41
## d[r-PA] -0.12 0.00 0.06 -0.24 -0.16 -0.12 -0.08
## d[SK + t-PA] -0.05 0.00 0.05 -0.14 -0.08 -0.05 -0.02
## d[t-PA] 0.00 0.00 0.03 -0.06 -0.02 0.00 0.02
## d[TNK] -0.17 0.00 0.08 -0.32 -0.22 -0.17 -0.12
## d[UK] -0.20 0.00 0.22 -0.63 -0.35 -0.20 -0.05
## lp__ -43042.79 0.14 5.29 -43053.94 -43046.38 -43042.43 -43039.04
## 97.5% n_eff Rhat
## d[Acc t-PA] -0.09 2521 1
## d[ASPAC] 0.09 6766 1
## d[PTCA] -0.28 4151 1
## d[r-PA] 0.00 3532 1
## d[SK + t-PA] 0.04 5312 1
## d[t-PA] 0.06 5435 1
## d[TNK] -0.02 3903 1
## d[UK] 0.22 4602 1
## lp__ -43033.29 1524 1
##
## Samples were drawn using NUTS(diag_e) at Thu Dec 22 00:20:13 2022.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at
## convergence, Rhat=1).## mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS Tail_ESS Rhat
## d[Acc t-PA] 0.84 0.04 0.77 0.81 0.84 0.86 0.91 2543 3001 1
## d[ASPAC] 1.02 0.04 0.95 0.99 1.02 1.04 1.09 6558 3469 1
## d[PTCA] 0.62 0.06 0.51 0.58 0.62 0.66 0.76 4278 3455 1
## d[r-PA] 0.89 0.05 0.78 0.85 0.88 0.92 1.00 3578 3262 1
## d[SK + t-PA] 0.95 0.04 0.87 0.92 0.95 0.98 1.04 5518 3029 1
## d[t-PA] 1.00 0.03 0.95 0.98 1.00 1.02 1.06 5479 3337 1
## d[TNK] 0.84 0.07 0.72 0.80 0.84 0.89 0.98 3980 3208 1
## d[UK] 0.83 0.18 0.53 0.70 0.82 0.95 1.25 4628 3467 1
Random effects NMA
## Note: Setting "SK" as the network reference treatment.## Warning: There were 40 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.## Warning: Examine the pairs() plot to diagnose sampling problems## Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
## Running the chains for more iterations may help. See
## https://mc-stan.org/misc/warnings.html#bulk-ess## Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
## Running the chains for more iterations may help. See
## https://mc-stan.org/misc/warnings.html#tail-ess## mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS Tail_ESS Rhat
## d[Acc t-PA] 0.80 0.08 0.61 0.76 0.81 0.85 0.92 430 305 1.01
## d[ASPAC] 1.04 0.10 0.87 0.99 1.03 1.08 1.29 2126 1457 1.00
## d[PTCA] 0.60 0.08 0.45 0.55 0.60 0.65 0.75 803 526 1.01
## d[r-PA] 0.85 0.10 0.62 0.80 0.86 0.91 1.02 772 430 1.01
## d[SK + t-PA] 0.94 0.10 0.72 0.89 0.94 0.99 1.15 1775 1046 1.00
## d[t-PA] 0.99 0.07 0.84 0.96 1.00 1.03 1.13 1985 1476 1.00
## d[TNK] 0.81 0.13 0.51 0.74 0.82 0.88 1.06 810 334 1.01
## d[UK] 0.82 0.20 0.50 0.68 0.80 0.94 1.27 2297 2133 1.00
## A random effects NMA with a binomial likelihood (logit link).
## Inference for Stan model: binomial_1par.
## 4 chains, each with iter=2000; warmup=1000; thin=1;
## post-warmup draws per chain=1000, total post-warmup draws=4000.
##
## mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
## d[Acc t-PA] -0.23 0.01 0.10 -0.50 -0.28 -0.21 -0.16 -0.08 299 1.01
## d[ASPAC] 0.04 0.00 0.09 -0.14 -0.01 0.03 0.08 0.25 1870 1.00
## d[PTCA] -0.52 0.01 0.13 -0.80 -0.60 -0.51 -0.43 -0.29 668 1.01
## d[r-PA] -0.17 0.01 0.13 -0.48 -0.22 -0.15 -0.09 0.02 407 1.01
## d[SK + t-PA] -0.07 0.00 0.11 -0.33 -0.12 -0.06 -0.01 0.14 1446 1.00
## d[t-PA] -0.01 0.00 0.07 -0.18 -0.04 0.00 0.03 0.12 1591 1.00
## d[TNK] -0.23 0.01 0.17 -0.68 -0.30 -0.20 -0.13 0.05 460 1.01
## d[UK] -0.23 0.00 0.24 -0.69 -0.39 -0.22 -0.07 0.24 2261 1.00
## mu[1] -2.54 0.00 0.03 -2.59 -2.56 -2.54 -2.52 -2.49 6432 1.00
## mu[2] -2.79 0.00 0.27 -3.35 -2.97 -2.78 -2.60 -2.29 6573 1.00
## mu[3] -2.86 0.01 0.41 -3.74 -3.12 -2.83 -2.58 -2.12 6329 1.00
## mu[4] -3.10 0.01 0.43 -4.01 -3.37 -3.08 -2.80 -2.32 6702 1.00
## mu[5] -2.36 0.00 0.03 -2.43 -2.38 -2.36 -2.34 -2.30 3824 1.00
## mu[6] -2.14 0.00 0.03 -2.19 -2.16 -2.14 -2.13 -2.09 4597 1.00
## mu[7] -2.71 0.00 0.33 -3.40 -2.91 -2.70 -2.49 -2.10 7837 1.00
## mu[8] -2.76 0.00 0.25 -3.27 -2.93 -2.76 -2.59 -2.29 6535 1.00
## mu[9] -2.85 0.00 0.29 -3.45 -3.04 -2.84 -2.65 -2.33 5107 1.00
## mu[10] -3.05 0.00 0.34 -3.76 -3.26 -3.03 -2.81 -2.44 5478 1.00
## mu[11] -2.23 0.00 0.06 -2.34 -2.27 -2.23 -2.19 -2.12 6659 1.00
## mu[12] -2.89 0.00 0.29 -3.49 -3.07 -2.88 -2.69 -2.34 5035 1.00
## mu[13] -3.08 0.01 0.54 -4.27 -3.37 -3.04 -2.71 -2.14 4761 1.00
## mu[14] -1.84 0.00 0.31 -2.46 -2.04 -1.83 -1.62 -1.25 7420 1.00
## mu[15] -3.94 0.01 0.80 -5.73 -4.38 -3.83 -3.38 -2.62 4013 1.00
## mu[16] -2.94 0.01 0.55 -4.14 -3.27 -2.89 -2.55 -1.97 5294 1.00
## mu[17] -1.93 0.00 0.25 -2.44 -2.09 -1.92 -1.76 -1.47 5717 1.00
## mu[18] -1.61 0.00 0.33 -2.31 -1.83 -1.59 -1.37 -1.00 5895 1.00
## mu[19] -2.17 0.00 0.13 -2.44 -2.26 -2.17 -2.08 -1.92 5922 1.00
## mu[20] -2.69 0.01 0.49 -3.79 -3.00 -2.65 -2.34 -1.81 6198 1.00
## mu[21] -2.85 0.00 0.26 -3.40 -3.02 -2.83 -2.67 -2.34 4313 1.00
## mu[22] -3.12 0.01 0.56 -4.32 -3.48 -3.08 -2.72 -2.13 5158 1.00
## mu[23] -3.04 0.01 0.56 -4.26 -3.40 -3.01 -2.64 -2.03 4712 1.00
## mu[24] -2.27 0.01 0.33 -2.94 -2.49 -2.27 -2.05 -1.64 2578 1.00
## mu[25] -2.33 0.01 0.19 -2.67 -2.45 -2.34 -2.23 -1.92 950 1.00
## mu[26] -2.89 0.01 0.50 -3.97 -3.19 -2.86 -2.55 -1.98 3834 1.00
## mu[27] -2.75 0.01 0.22 -3.16 -2.90 -2.76 -2.62 -2.30 1311 1.01
## mu[28] -2.49 0.01 0.40 -3.30 -2.75 -2.48 -2.22 -1.75 3580 1.00
## mu[29] -2.32 0.01 0.36 -3.04 -2.56 -2.31 -2.07 -1.62 3040 1.00
## mu[30] -2.22 0.01 0.18 -2.53 -2.33 -2.23 -2.12 -1.83 761 1.01
## mu[31] -2.80 0.01 0.42 -3.68 -3.07 -2.79 -2.52 -2.03 3794 1.00
## mu[32] -2.37 0.01 0.24 -2.83 -2.53 -2.37 -2.22 -1.90 1857 1.00
## mu[33] -1.83 0.01 0.32 -2.47 -2.05 -1.83 -1.62 -1.22 2339 1.00
## mu[34] -2.49 0.01 0.17 -2.76 -2.59 -2.52 -2.43 -2.07 381 1.01
## mu[35] -2.34 0.01 0.16 -2.57 -2.43 -2.36 -2.28 -1.91 431 1.01
## mu[36] -2.54 0.01 0.27 -3.07 -2.71 -2.54 -2.37 -1.99 1555 1.00
## mu[37] -3.05 0.00 0.28 -3.61 -3.22 -3.03 -2.86 -2.52 7091 1.00
## mu[38] -3.16 0.01 0.48 -4.20 -3.45 -3.13 -2.84 -2.31 5516 1.00
## mu[39] -3.00 0.00 0.36 -3.77 -3.22 -2.98 -2.75 -2.34 6664 1.00
## mu[40] -3.21 0.01 0.47 -4.25 -3.50 -3.18 -2.88 -2.37 5221 1.00
## mu[41] -2.70 0.00 0.22 -3.15 -2.85 -2.69 -2.54 -2.29 6333 1.00
## mu[42] -1.90 0.01 0.41 -2.74 -2.16 -1.89 -1.61 -1.13 4527 1.00
## mu[43] -2.84 0.01 0.27 -3.36 -3.02 -2.84 -2.67 -2.31 1893 1.00
## mu[44] -2.83 0.00 0.26 -3.34 -3.00 -2.83 -2.66 -2.34 3585 1.00
## mu[45] -2.78 0.00 0.29 -3.38 -2.96 -2.77 -2.58 -2.22 4817 1.00
## mu[46] -2.71 0.01 0.34 -3.39 -2.93 -2.70 -2.47 -2.09 4051 1.00
## mu[47] -2.91 0.00 0.30 -3.50 -3.11 -2.90 -2.71 -2.32 3682 1.00
## mu[48] -3.00 0.00 0.35 -3.72 -3.23 -2.99 -2.75 -2.34 5289 1.00
## mu[49] -2.61 0.00 0.26 -3.13 -2.78 -2.61 -2.45 -2.11 3855 1.00
## mu[50] -2.71 0.00 0.33 -3.38 -2.92 -2.70 -2.49 -2.09 5870 1.00
##
## Samples were drawn using NUTS(diag_e) at Thu Dec 22 00:22:17 2022.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at
## convergence, Rhat=1).prior and posterior distributions
## Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(density)` instead.
## ℹ The deprecated feature was likely used in the multinma package.
## Please report the issue at <]8;;https://github.com/dmphillippo/multinma/issueshttps://github.com/dmphillippo/multinma/issues]8;;>.
##interpretation of above pots
##Checking for inconsistency ##Unrelated mean effects model ###Fixed Effect model
## Note: Setting "SK" as the network reference treatment.## A fixed effects NMA with a binomial likelihood (logit link).
## An inconsistency model ('ume') was fitted.
## Inference for Stan model: binomial_1par.
## 4 chains, each with iter=2000; warmup=1000; thin=1;
## post-warmup draws per chain=1000, total post-warmup draws=4000.
##
## mean se_mean sd 2.5% 25% 50%
## d[Acc t-PA vs. SK] -0.16 0.00 0.05 -0.25 -0.19 -0.16
## d[ASPAC vs. SK] 0.01 0.00 0.04 -0.07 -0.02 0.01
## d[PTCA vs. SK] -0.66 0.00 0.19 -1.04 -0.79 -0.66
## d[r-PA vs. SK] -0.06 0.00 0.09 -0.24 -0.12 -0.06
## d[SK + t-PA vs. SK] -0.04 0.00 0.05 -0.14 -0.08 -0.04
## d[t-PA vs. SK] 0.00 0.00 0.03 -0.06 -0.02 0.00
## d[UK vs. SK] -0.37 0.01 0.51 -1.39 -0.71 -0.36
## d[ASPAC vs. Acc t-PA] 1.40 0.01 0.42 0.63 1.11 1.38
## d[PTCA vs. Acc t-PA] -0.22 0.00 0.12 -0.46 -0.30 -0.22
## d[r-PA vs. Acc t-PA] 0.02 0.00 0.07 -0.11 -0.03 0.02
## d[TNK vs. Acc t-PA] 0.00 0.00 0.07 -0.13 -0.04 0.00
## d[UK vs. Acc t-PA] 0.14 0.01 0.35 -0.56 -0.09 0.15
## d[t-PA vs. ASPAC] 0.30 0.01 0.36 -0.39 0.06 0.30
## d[t-PA vs. PTCA] 0.53 0.01 0.42 -0.29 0.24 0.53
## d[UK vs. t-PA] -0.29 0.00 0.35 -0.96 -0.53 -0.29
## lp__ -43040.06 0.14 5.78 -43051.96 -43043.84 -43039.73
## 75% 97.5% n_eff Rhat
## d[Acc t-PA vs. SK] -0.12 -0.06 5960 1
## d[ASPAC vs. SK] 0.03 0.08 4428 1
## d[PTCA vs. SK] -0.53 -0.30 5343 1
## d[r-PA vs. SK] 0.00 0.13 6046 1
## d[SK + t-PA vs. SK] -0.01 0.05 6417 1
## d[t-PA vs. SK] 0.02 0.06 3738 1
## d[UK vs. SK] -0.02 0.63 5325 1
## d[ASPAC vs. Acc t-PA] 1.67 2.26 3653 1
## d[PTCA vs. Acc t-PA] -0.14 0.02 4732 1
## d[r-PA vs. Acc t-PA] 0.07 0.15 5283 1
## d[TNK vs. Acc t-PA] 0.05 0.13 5332 1
## d[UK vs. Acc t-PA] 0.38 0.84 4219 1
## d[t-PA vs. ASPAC] 0.53 1.01 4316 1
## d[t-PA vs. PTCA] 0.81 1.37 4147 1
## d[UK vs. t-PA] -0.05 0.38 5649 1
## lp__ -43036.05 -43029.53 1694 1
##
## Samples were drawn using NUTS(diag_e) at Thu Dec 22 00:23:35 2022.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at
## convergence, Rhat=1).###Random effect model
## Note: Setting "SK" as the network reference treatment.## Warning: There were 80 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.## Warning: Examine the pairs() plot to diagnose sampling problems## Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
## Running the chains for more iterations may help. See
## https://mc-stan.org/misc/warnings.html#bulk-ess## A random effects NMA with a binomial likelihood (logit link).
## An inconsistency model ('ume') was fitted.
## Inference for Stan model: binomial_1par.
## 4 chains, each with iter=2000; warmup=1000; thin=1;
## post-warmup draws per chain=1000, total post-warmup draws=4000.
##
## mean se_mean sd 2.5% 25% 50%
## d[Acc t-PA vs. SK] -0.15 0.00 0.15 -0.47 -0.22 -0.16
## d[ASPAC vs. SK] -0.01 0.00 0.11 -0.24 -0.07 -0.01
## d[PTCA vs. SK] -0.69 0.00 0.20 -1.07 -0.82 -0.69
## d[r-PA vs. SK] -0.06 0.00 0.16 -0.37 -0.15 -0.06
## d[SK + t-PA vs. SK] -0.03 0.00 0.14 -0.30 -0.10 -0.03
## d[t-PA vs. SK] -0.05 0.00 0.10 -0.30 -0.09 -0.03
## d[UK vs. SK] -0.37 0.01 0.52 -1.43 -0.73 -0.37
## d[ASPAC vs. Acc t-PA] 1.42 0.01 0.43 0.65 1.13 1.41
## d[PTCA vs. Acc t-PA] -0.23 0.00 0.14 -0.50 -0.32 -0.23
## d[r-PA vs. Acc t-PA] -0.02 0.00 0.14 -0.35 -0.09 -0.01
## d[TNK vs. Acc t-PA] 0.00 0.00 0.15 -0.31 -0.08 0.00
## d[UK vs. Acc t-PA] 0.16 0.01 0.37 -0.56 -0.10 0.16
## d[t-PA vs. ASPAC] 0.29 0.01 0.37 -0.41 0.04 0.29
## d[t-PA vs. PTCA] 0.53 0.01 0.44 -0.31 0.21 0.53
## d[UK vs. t-PA] -0.30 0.01 0.35 -0.98 -0.54 -0.30
## lp__ -43066.57 0.25 8.17 -43083.82 -43071.86 -43066.11
## tau 0.11 0.00 0.08 0.01 0.04 0.09
## 75% 97.5% n_eff Rhat
## d[Acc t-PA vs. SK] -0.08 0.17 893 1.00
## d[ASPAC vs. SK] 0.05 0.21 1444 1.00
## d[PTCA vs. SK] -0.56 -0.31 3303 1.00
## d[r-PA vs. SK] 0.03 0.29 1585 1.00
## d[SK + t-PA vs. SK] 0.03 0.31 1109 1.00
## d[t-PA vs. SK] 0.01 0.10 756 1.00
## d[UK vs. SK] -0.02 0.64 3159 1.00
## d[ASPAC vs. Acc t-PA] 1.70 2.31 2229 1.00
## d[PTCA vs. Acc t-PA] -0.14 0.03 2085 1.00
## d[r-PA vs. Acc t-PA] 0.06 0.23 1409 1.00
## d[TNK vs. Acc t-PA] 0.08 0.32 1346 1.00
## d[UK vs. Acc t-PA] 0.41 0.90 2363 1.00
## d[t-PA vs. ASPAC] 0.54 1.02 2857 1.00
## d[t-PA vs. PTCA] 0.82 1.41 2637 1.00
## d[UK vs. t-PA] -0.06 0.38 2821 1.00
## lp__ -43061.08 -43051.17 1084 1.01
## tau 0.16 0.32 393 1.02
##
## Samples were drawn using NUTS(diag_e) at Thu Dec 22 00:24:52 2022.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at
## convergence, Rhat=1).## Residual deviance: 105.7 (on 102 data points)
## pD: 58.6
## DIC: 164.3## Warning in ggplot2::geom_point(...): Ignoring unknown parameters: `point_alpha`
## and `interval_alpha`
## Warning in ggplot2::geom_point(...): Ignoring unknown parameters: `point_alpha`
## and `interval_alpha`
##Node-splitting
###Fixed effects model
## Fitting model 1 of 15, node-split: Acc t-PA vs. SK
## Note: Setting "SK" as the network reference treatment.
## Fitting model 2 of 15, node-split: ASPAC vs. SK
## Note: Setting "SK" as the network reference treatment.
## Fitting model 3 of 15, node-split: PTCA vs. SK
## Note: Setting "SK" as the network reference treatment.
## Fitting model 4 of 15, node-split: r-PA vs. SK
## Note: Setting "SK" as the network reference treatment.
## Fitting model 5 of 15, node-split: t-PA vs. SK
## Note: Setting "SK" as the network reference treatment.
## Fitting model 6 of 15, node-split: UK vs. SK
## Note: Setting "SK" as the network reference treatment.
## Fitting model 7 of 15, node-split: ASPAC vs. Acc t-PA
## Note: Setting "SK" as the network reference treatment.
## Fitting model 8 of 15, node-split: PTCA vs. Acc t-PA
## Note: Setting "SK" as the network reference treatment.
## Fitting model 9 of 15, node-split: r-PA vs. Acc t-PA
## Note: Setting "SK" as the network reference treatment.
## Fitting model 10 of 15, node-split: SK + t-PA vs. Acc t-PA
## Note: Setting "SK" as the network reference treatment.
## Fitting model 11 of 15, node-split: UK vs. Acc t-PA
## Note: Setting "SK" as the network reference treatment.
## Fitting model 12 of 15, node-split: t-PA vs. ASPAC
## Note: Setting "SK" as the network reference treatment.
## Fitting model 13 of 15, node-split: t-PA vs. PTCA
## Note: Setting "SK" as the network reference treatment.
## Fitting model 14 of 15, node-split: UK vs. t-PA
## Note: Setting "SK" as the network reference treatment.
## Fitting model 15 of 15, consistency model
## Note: Setting "SK" as the network reference treatment.Random effects model
## Fitting model 1 of 15, node-split: Acc t-PA vs. SK
## Note: Setting "SK" as the network reference treatment.## Warning: There were 10 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.## Warning: Examine the pairs() plot to diagnose sampling problems## Fitting model 2 of 15, node-split: ASPAC vs. SK
## Note: Setting "SK" as the network reference treatment.## Warning: There were 6 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.
## Warning: Examine the pairs() plot to diagnose sampling problems## Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
## Running the chains for more iterations may help. See
## https://mc-stan.org/misc/warnings.html#bulk-ess## Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
## Running the chains for more iterations may help. See
## https://mc-stan.org/misc/warnings.html#tail-ess## Fitting model 3 of 15, node-split: PTCA vs. SK
## Note: Setting "SK" as the network reference treatment.## Warning: There were 29 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.## Warning: Examine the pairs() plot to diagnose sampling problems## Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
## Running the chains for more iterations may help. See
## https://mc-stan.org/misc/warnings.html#bulk-ess## Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
## Running the chains for more iterations may help. See
## https://mc-stan.org/misc/warnings.html#tail-ess## Fitting model 4 of 15, node-split: r-PA vs. SK
## Note: Setting "SK" as the network reference treatment.## Warning: There were 23 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.## Warning: Examine the pairs() plot to diagnose sampling problems## Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
## Running the chains for more iterations may help. See
## https://mc-stan.org/misc/warnings.html#bulk-ess## Fitting model 5 of 15, node-split: t-PA vs. SK
## Note: Setting "SK" as the network reference treatment.## Warning: There were 5 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.## Warning: Examine the pairs() plot to diagnose sampling problems## Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
## Running the chains for more iterations may help. See
## https://mc-stan.org/misc/warnings.html#bulk-ess## Fitting model 6 of 15, node-split: UK vs. SK
## Note: Setting "SK" as the network reference treatment.## Warning: There were 15 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.## Warning: Examine the pairs() plot to diagnose sampling problems## Fitting model 7 of 15, node-split: ASPAC vs. Acc t-PA
## Note: Setting "SK" as the network reference treatment.## Warning: There were 6 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.
## Warning: Examine the pairs() plot to diagnose sampling problems## Fitting model 8 of 15, node-split: PTCA vs. Acc t-PA
## Note: Setting "SK" as the network reference treatment.## Warning: There were 49 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.
## Warning: Examine the pairs() plot to diagnose sampling problems## Fitting model 9 of 15, node-split: r-PA vs. Acc t-PA
## Note: Setting "SK" as the network reference treatment.## Warning: There were 8 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.
## Warning: Examine the pairs() plot to diagnose sampling problems## Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
## Running the chains for more iterations may help. See
## https://mc-stan.org/misc/warnings.html#bulk-ess## Fitting model 10 of 15, node-split: SK + t-PA vs. Acc t-PA
## Note: Setting "SK" as the network reference treatment.## Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
## Running the chains for more iterations may help. See
## https://mc-stan.org/misc/warnings.html#bulk-ess## Fitting model 11 of 15, node-split: UK vs. Acc t-PA
## Note: Setting "SK" as the network reference treatment.## Warning: There were 41 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.## Warning: Examine the pairs() plot to diagnose sampling problems## Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
## Running the chains for more iterations may help. See
## https://mc-stan.org/misc/warnings.html#bulk-ess## Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
## Running the chains for more iterations may help. See
## https://mc-stan.org/misc/warnings.html#tail-ess## Fitting model 12 of 15, node-split: t-PA vs. ASPAC
## Note: Setting "SK" as the network reference treatment.## Warning: There were 7 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.## Warning: Examine the pairs() plot to diagnose sampling problems## Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
## Running the chains for more iterations may help. See
## https://mc-stan.org/misc/warnings.html#bulk-ess## Fitting model 13 of 15, node-split: t-PA vs. PTCA
## Note: Setting "SK" as the network reference treatment.## Warning: There were 20 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.## Warning: Examine the pairs() plot to diagnose sampling problems## Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
## Running the chains for more iterations may help. See
## https://mc-stan.org/misc/warnings.html#bulk-ess## Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
## Running the chains for more iterations may help. See
## https://mc-stan.org/misc/warnings.html#tail-ess## Fitting model 14 of 15, node-split: UK vs. t-PA
## Note: Setting "SK" as the network reference treatment.## Warning: There were 65 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.## Warning: Examine the pairs() plot to diagnose sampling problems## Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
## Running the chains for more iterations may help. See
## https://mc-stan.org/misc/warnings.html#bulk-ess## Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
## Running the chains for more iterations may help. See
## https://mc-stan.org/misc/warnings.html#tail-ess## Fitting model 15 of 15, consistency model
## Note: Setting "SK" as the network reference treatment.## Warning: There were 114 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.## Warning: Examine the pairs() plot to diagnose sampling problems## Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
## Running the chains for more iterations may help. See
## https://mc-stan.org/misc/warnings.html#bulk-ess## Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
## Running the chains for more iterations may help. See
## https://mc-stan.org/misc/warnings.html#tail-essNode split Plots
Fixed effect model
Random effects model
Ranks
Cumulative ranks
