Example: Diabetes

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Example: Diabetes

This vignette describes the analysis of data on the number of new cases of diabetes in 22 trials of 6 antihypertensive drugs (Elliott and Meyer 2007; Dias et al. 2011). The data are available in this package as diabetes:

Setting up the network

We begin by setting up the network. We have arm-level count data giving the number of new cases of diabetes (r) out of the total (n) in each arm, so we use the function set_agd_arm(). For computational efficiency, we let “Beta Blocker” be set as the network reference treatment by default. Elliott and Meyer (2007) and Dias et al. (2011) use “Diuretic” as the reference, but it is a simple matter to transform the results after fitting the NMA model.¹

db_net <- set_agd_arm(diabetes, 
                      study = studyc,
                      trt = trtc,
                      r = r, 
                      n = n)
db_net
#> A network with 22 AgD studies (arm-based).
#> 
#> ------------------------------------------------------- AgD studies (arm-based) ---- 
#>  Study  Treatment arms                       
#>  AASK   3: Beta Blocker | ACE Inhibitor | CCB
#>  ALLHAT 3: ACE Inhibitor | CCB | Diuretic    
#>  ALPINE 2: ARB | Diuretic                    
#>  ANBP-2 2: ACE Inhibitor | Diuretic          
#>  ASCOT  2: Beta Blocker | CCB                
#>  CAPPP  2: Beta Blocker | ACE Inhibitor      
#>  CHARM  2: ARB | Placebo                     
#>  DREAM  2: ACE Inhibitor | Placebo           
#>  EWPH   2: Diuretic | Placebo                
#>  FEVER  2: CCB | Placebo                     
#>  ... plus 12 more studies
#> 
#>  Outcome type: count
#> ------------------------------------------------------------------------------------
#> Total number of treatments: 6
#> Total number of studies: 22
#> Reference treatment is: Beta Blocker
#> Network is connected

We also have details of length of follow-up in years in each trial (time), which we will use as an offset with a cloglog link function to model the data as rates. We do not have to specify this in the function set_agd_arm(): any additional columns in the data (e.g. offsets or covariates, here the column time) will automatically be made available in the network.

Plot the network structure.

plot(db_net, weight_edges = TRUE, weight_nodes = TRUE)

Meta-analysis models

We fit both fixed effect (FE) and random effects (RE) models.

Fixed effect meta-analysis

First, we fit a fixed effect model using the nma() function with trt_effects = "fixed". We use \(\mathrm{N}(0, 100^2)\) prior distributions for the treatment effects \(d_k\) and study-specific intercepts \(\mu_j\). We can examine the range of parameter values implied by these prior distributions with the summary() method:

summary(normal(scale = 100))
#> 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.

The model is fitted using the nma() function. We specify that a cloglog link will be used with link = "cloglog" (the Binomial likelihood is the default for these data), and specify the log follow-up time offset using the regression formula regression = ~offset(log(time)).

db_fit_FE <- nma(db_net, 
                 trt_effects = "fixed",
                 link = "cloglog",
                 regression = ~offset(log(time)),
                 prior_intercept = normal(scale = 100),
                 prior_trt = normal(scale = 100))
#> Note: No treatment classes specified in network, any interactions in `regression` formula will be separate (independent) for each treatment.
#> Use set_*() argument `trt_class` and nma() argument `class_interactions` to change this.
#> Note: Setting "Beta Blocker" as the network reference treatment.

Basic parameter summaries are given by the print() method:

db_fit_FE
#> A fixed effects NMA with a binomial likelihood (cloglog link).
#> Regression model: ~offset(log(time)).
#> 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
#> d[ACE Inhibitor]     -0.30    0.00 0.05     -0.39     -0.33     -0.30     -0.27     -0.21  1441
#> d[ARB]               -0.39    0.00 0.05     -0.48     -0.43     -0.39     -0.36     -0.31  2116
#> d[CCB]               -0.20    0.00 0.03     -0.26     -0.22     -0.20     -0.17     -0.13  1884
#> d[Diuretic]           0.06    0.00 0.06     -0.05      0.02      0.06      0.10      0.17  1791
#> d[Placebo]           -0.19    0.00 0.05     -0.29     -0.22     -0.19     -0.16     -0.09  1429
#> lp__             -37970.32    0.09 3.58 -37978.17 -37972.59 -37969.93 -37967.70 -37964.34  1538
#>                  Rhat
#> d[ACE Inhibitor]    1
#> d[ARB]              1
#> d[CCB]              1
#> d[Diuretic]         1
#> d[Placebo]          1
#> lp__                1
#> 
#> Samples were drawn using NUTS(diag_e) at Mon Apr 29 16:39:01 2024.
#> 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).

By default, summaries of the study-specific intercepts \(\mu_j\) are hidden, but could be examined by changing the pars argument:

# Not run
print(db_fit_FE, pars = c("d", "mu"))

The prior and posterior distributions can be compared visually using the plot_prior_posterior() function:

plot_prior_posterior(db_fit_FE)

Random effects meta-analysis

We now fit a random effects model using the nma() function with trt_effects = "random". Again, we use \(\mathrm{N}(0, 100^2)\) prior distributions for the treatment effects \(d_k\) and study-specific intercepts \(\mu_j\), and we additionally use a \(\textrm{half-N}(5^2)\) prior for the heterogeneity standard deviation \(\tau\). We can examine the range of parameter values implied by these prior distributions with the summary() method:

summary(normal(scale = 100))
#> 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.
summary(half_normal(scale = 5))
#> 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.

Fitting the RE model

db_fit_RE <- nma(db_net, 
                 trt_effects = "random",
                 link = "cloglog",
                 regression = ~offset(log(time)),
                 prior_intercept = normal(scale = 10),
                 prior_trt = normal(scale = 10),
                 prior_het = half_normal(scale = 5),
                 init_r = 0.5)
#> Note: No treatment classes specified in network, any interactions in `regression` formula will be separate (independent) for each treatment.
#> Use set_*() argument `trt_class` and nma() argument `class_interactions` to change this.
#> Note: Setting "Beta Blocker" as the network reference treatment.

Basic parameter summaries are given by the print() method:

db_fit_RE
#> A random effects NMA with a binomial likelihood (cloglog link).
#> Regression model: ~offset(log(time)).
#> 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
#> d[ACE Inhibitor]     -0.33    0.00 0.08     -0.49     -0.38     -0.33     -0.28     -0.18  2166
#> d[ARB]               -0.40    0.00 0.10     -0.59     -0.46     -0.40     -0.34     -0.22  2355
#> d[CCB]               -0.17    0.00 0.07     -0.30     -0.21     -0.17     -0.13     -0.04  2131
#> d[Diuretic]           0.07    0.00 0.09     -0.10      0.02      0.07      0.13      0.25  2205
#> d[Placebo]           -0.22    0.00 0.09     -0.40     -0.27     -0.21     -0.16     -0.05  1782
#> lp__             -37981.26    0.24 6.96 -37996.03 -37985.56 -37980.85 -37976.46 -37968.78   845
#> tau                   0.13    0.00 0.04      0.05      0.10      0.12      0.15      0.23   924
#>                  Rhat
#> d[ACE Inhibitor]    1
#> d[ARB]              1
#> d[CCB]              1
#> d[Diuretic]         1
#> d[Placebo]          1
#> lp__                1
#> tau                 1
#> 
#> Samples were drawn using NUTS(diag_e) at Mon Apr 29 16:39:24 2024.
#> 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).

By default, summaries of the study-specific intercepts \(\mu_j\) and study-specific relative effects \(\delta_{jk}\) are hidden, but could be examined by changing the pars argument:

# Not run
print(db_fit_RE, pars = c("d", "mu", "delta"))

The prior and posterior distributions can be compared visually using the plot_prior_posterior() function:

plot_prior_posterior(db_fit_RE, prior = c("trt", "het"))

Model comparison

Model fit can be checked using the dic() function:

(dic_FE <- dic(db_fit_FE))
#> Residual deviance: 78.2 (on 48 data points)
#>                pD: 27
#>               DIC: 105.3

(dic_RE <- dic(db_fit_RE))
#> Residual deviance: 54 (on 48 data points)
#>                pD: 38.3
#>               DIC: 92.3

The FE model is a very poor fit to the data, with a residual deviance much higher than the number of data points. The RE model fits the data better, and has a much lower DIC; we prefer the RE model.

We can also examine the residual deviance contributions with the corresponding plot() method.

plot(dic_FE)

plot(dic_RE)

Further results

For comparison with Elliott and Meyer (2007) and Dias et al. (2011), we can produce relative effects against “Diuretic” using the relative_effects() function with trt_ref = "Diuretic":

(db_releff_FE <- relative_effects(db_fit_FE, trt_ref = "Diuretic"))
#>                   mean   sd  2.5%   25%   50%   75% 97.5% Bulk_ESS Tail_ESS Rhat
#> d[Beta Blocker]  -0.06 0.06 -0.17 -0.10 -0.06 -0.02  0.05     1801     2541    1
#> d[ACE Inhibitor] -0.36 0.05 -0.46 -0.39 -0.36 -0.32 -0.25     4373     3227    1
#> d[ARB]           -0.45 0.06 -0.57 -0.49 -0.45 -0.41 -0.33     3366     3057    1
#> d[CCB]           -0.25 0.05 -0.36 -0.29 -0.25 -0.21 -0.15     2922     2896    1
#> d[Placebo]       -0.25 0.06 -0.36 -0.28 -0.25 -0.21 -0.13     4119     3051    1
plot(db_releff_FE, ref_line = 0)

(db_releff_RE <- relative_effects(db_fit_RE, trt_ref = "Diuretic"))
#>                   mean   sd  2.5%   25%   50%   75% 97.5% Bulk_ESS Tail_ESS Rhat
#> d[Beta Blocker]  -0.07 0.09 -0.25 -0.13 -0.07 -0.02  0.10     2263     2536    1
#> d[ACE Inhibitor] -0.40 0.09 -0.58 -0.46 -0.40 -0.34 -0.24     4739     2870    1
#> d[ARB]           -0.47 0.11 -0.71 -0.54 -0.47 -0.40 -0.26     4152     2646    1
#> d[CCB]           -0.24 0.08 -0.41 -0.29 -0.24 -0.19 -0.08     5016     3221    1
#> d[Placebo]       -0.29 0.09 -0.47 -0.34 -0.29 -0.23 -0.12     4421     3111    1
plot(db_releff_RE, ref_line = 0)

Dias et al. (2011) produce absolute predictions of the probability of developing diabetes after three years, assuming a Normal distribution on the baseline cloglog probability of developing diabetes on diuretic treatment with mean \(-4.2\) and precision \(1.11\). We can replicate these results using the predict() method. We specify a data frame of newdata, containing the time offset(s) at which to produce predictions (here only 3 years). The baseline argument takes a distr() distribution object with which we specify the corresponding Normal distribution on the baseline cloglog probability, and we set baseline_trt = "Diuretic" to indicate that the baseline distribution corresponds to “Diuretic” rather than the network reference “Beta Blocker”. We set type = "response" to produce predicted event probabilities (type = "link" would produce predicted cloglog probabilities).

db_pred_FE <- predict(db_fit_FE, 
                      newdata = data.frame(time = 3),
                      baseline = distr(qnorm, mean = -4.2, sd = 1.11^-0.5), 
                      baseline_trt = "Diuretic",
                      type = "response")
db_pred_FE
#> ------------------------------------------------------------------ Study: New 1 ---- 
#> 
#>                            mean   sd 2.5%  25%  50%  75% 97.5% Bulk_ESS Tail_ESS Rhat
#> pred[New 1: Beta Blocker]  0.06 0.07 0.01 0.02 0.04 0.08  0.24     3758     3959    1
#> pred[New 1: ACE Inhibitor] 0.05 0.05 0.01 0.02 0.03 0.06  0.19     3755     4098    1
#> pred[New 1: ARB]           0.04 0.05 0.00 0.02 0.03 0.05  0.17     3767     4097    1
#> pred[New 1: CCB]           0.05 0.06 0.01 0.02 0.03 0.06  0.20     3760     4046    1
#> pred[New 1: Diuretic]      0.07 0.07 0.01 0.02 0.04 0.08  0.26     3755     3924    1
#> pred[New 1: Placebo]       0.05 0.06 0.01 0.02 0.03 0.06  0.21     3767     3917    1
plot(db_pred_FE)

db_pred_RE <- predict(db_fit_RE, 
                      newdata = data.frame(time = 3),
                      baseline = distr(qnorm, mean = -4.2, sd = 1.11^-0.5), 
                      baseline_trt = "Diuretic",
                      type = "response")
db_pred_RE
#> ------------------------------------------------------------------ Study: New 1 ---- 
#> 
#>                            mean   sd 2.5%  25%  50%  75% 97.5% Bulk_ESS Tail_ESS Rhat
#> pred[New 1: Beta Blocker]  0.06 0.06 0.01 0.02 0.04 0.07  0.24     3991     3930    1
#> pred[New 1: ACE Inhibitor] 0.04 0.05 0.00 0.02 0.03 0.05  0.18     4017     4015    1
#> pred[New 1: ARB]           0.04 0.05 0.00 0.01 0.03 0.05  0.16     4001     3932    1
#> pred[New 1: CCB]           0.05 0.06 0.01 0.02 0.03 0.06  0.20     4049     3974    1
#> pred[New 1: Diuretic]      0.06 0.07 0.01 0.02 0.04 0.08  0.25     3989     3892    1
#> pred[New 1: Placebo]       0.05 0.05 0.01 0.02 0.03 0.06  0.19     4002     4015    1
plot(db_pred_RE)

If the baseline and newdata arguments are omitted, predicted probabilities will be produced for every study in the network based on their follow-up times and estimated baseline cloglog probabilities \(\mu_j\):

db_pred_RE_studies <- predict(db_fit_RE, type = "response")
db_pred_RE_studies
#> ------------------------------------------------------------------- Study: AASK ---- 
#> 
#>                           mean   sd 2.5%  25%  50%  75% 97.5% Bulk_ESS Tail_ESS Rhat
#> pred[AASK: Beta Blocker]  0.17 0.02 0.14 0.16 0.17 0.18  0.20     5589     2823    1
#> pred[AASK: ACE Inhibitor] 0.12 0.01 0.10 0.12 0.12 0.13  0.15     4599     2711    1
#> pred[AASK: ARB]           0.12 0.01 0.09 0.11 0.12 0.13  0.15     4401     2946    1
#> pred[AASK: CCB]           0.14 0.01 0.12 0.13 0.14 0.15  0.18     5196     2850    1
#> pred[AASK: Diuretic]      0.18 0.02 0.14 0.17 0.18 0.19  0.22     4210     2956    1
#> pred[AASK: Placebo]       0.14 0.02 0.11 0.13 0.14 0.15  0.17     3677     2770    1
#> 
#> ----------------------------------------------------------------- Study: ALLHAT ---- 
#> 
#>                             mean   sd 2.5%  25%  50%  75% 97.5% Bulk_ESS Tail_ESS Rhat
#> pred[ALLHAT: Beta Blocker]  0.04 0.01 0.03 0.04 0.04 0.05  0.06     2678     2340    1
#> pred[ALLHAT: ACE Inhibitor] 0.03 0.00 0.02 0.03 0.03 0.03  0.04     4297     2350    1
#> pred[ALLHAT: ARB]           0.03 0.00 0.02 0.03 0.03 0.03  0.04     3918     2452    1
#> pred[ALLHAT: CCB]           0.04 0.00 0.03 0.03 0.04 0.04  0.05     4199     2190    1
#> pred[ALLHAT: Diuretic]      0.05 0.01 0.04 0.04 0.05 0.05  0.06     4544     2710    1
#> pred[ALLHAT: Placebo]       0.03 0.00 0.03 0.03 0.03 0.04  0.04     3729     2838    1
#> 
#> ----------------------------------------------------------------- Study: ALPINE ---- 
#> 
#>                             mean   sd 2.5%  25%  50%  75% 97.5% Bulk_ESS Tail_ESS Rhat
#> pred[ALPINE: Beta Blocker]  0.03 0.01 0.01 0.02 0.03 0.03  0.05     6749     3136    1
#> pred[ALPINE: ACE Inhibitor] 0.02 0.01 0.01 0.01 0.02 0.02  0.04     7455     3173    1
#> pred[ALPINE: ARB]           0.02 0.01 0.01 0.01 0.02 0.02  0.03     7520     3382    1
#> pred[ALPINE: CCB]           0.02 0.01 0.01 0.02 0.02 0.03  0.04     7470     3346    1
#> pred[ALPINE: Diuretic]      0.03 0.01 0.01 0.02 0.03 0.03  0.05     7744     3343    1
#> pred[ALPINE: Placebo]       0.02 0.01 0.01 0.02 0.02 0.03  0.04     7705     3296    1
#> 
#> ----------------------------------------------------------------- Study: ANBP-2 ---- 
#> 
#>                             mean   sd 2.5%  25%  50%  75% 97.5% Bulk_ESS Tail_ESS Rhat
#> pred[ANBP-2: Beta Blocker]  0.07 0.01 0.05 0.06 0.07 0.07  0.09     3624     2292    1
#> pred[ANBP-2: ACE Inhibitor] 0.05 0.01 0.04 0.04 0.05 0.05  0.06     5435     2763    1
#> pred[ANBP-2: ARB]           0.05 0.01 0.03 0.04 0.05 0.05  0.06     5110     2884    1
#> pred[ANBP-2: CCB]           0.06 0.01 0.04 0.05 0.06 0.06  0.08     5140     3016    1
#> pred[ANBP-2: Diuretic]      0.07 0.01 0.06 0.07 0.07 0.08  0.09     5670     2938    1
#> pred[ANBP-2: Placebo]       0.05 0.01 0.04 0.05 0.05 0.06  0.07     5632     3088    1
#> 
#> ------------------------------------------------------------------ Study: ASCOT ---- 
#> 
#>                            mean   sd 2.5%  25%  50%  75% 97.5% Bulk_ESS Tail_ESS Rhat
#> pred[ASCOT: Beta Blocker]  0.11 0.00 0.10 0.11 0.11 0.11  0.12     5348     2746    1
#> pred[ASCOT: ACE Inhibitor] 0.08 0.01 0.07 0.08 0.08 0.09  0.10     2684     2859    1
#> pred[ASCOT: ARB]           0.08 0.01 0.06 0.07 0.08 0.08  0.09     2879     2828    1
#> pred[ASCOT: CCB]           0.10 0.01 0.08 0.09 0.09 0.10  0.11     2513     2732    1
#> pred[ASCOT: Diuretic]      0.12 0.01 0.10 0.11 0.12 0.13  0.14     2542     2777    1
#> pred[ASCOT: Placebo]       0.09 0.01 0.08 0.09 0.09 0.10  0.11     2132     2485    1
#> 
#> ------------------------------------------------------------------ Study: CAPPP ---- 
#> 
#>                            mean   sd 2.5%  25%  50%  75% 97.5% Bulk_ESS Tail_ESS Rhat
#> pred[CAPPP: Beta Blocker]  0.07 0.00 0.07 0.07 0.07 0.08  0.08     5261     2742    1
#> pred[CAPPP: ACE Inhibitor] 0.05 0.00 0.05 0.05 0.05 0.06  0.06     2480     2616    1
#> pred[CAPPP: ARB]           0.05 0.01 0.04 0.05 0.05 0.05  0.06     2693     2747    1
#> pred[CAPPP: CCB]           0.06 0.00 0.05 0.06 0.06 0.07  0.07     3060     2835    1
#> pred[CAPPP: Diuretic]      0.08 0.01 0.07 0.08 0.08 0.08  0.10     2808     2656    1
#> pred[CAPPP: Placebo]       0.06 0.01 0.05 0.06 0.06 0.06  0.07     2061     2446    1
#> 
#> ------------------------------------------------------------------ Study: CHARM ---- 
#> 
#>                            mean   sd 2.5%  25%  50%  75% 97.5% Bulk_ESS Tail_ESS Rhat
#> pred[CHARM: Beta Blocker]  0.09 0.01 0.07 0.08 0.09 0.10  0.12     3076     2496    1
#> pred[CHARM: ACE Inhibitor] 0.07 0.01 0.05 0.06 0.07 0.07  0.09     4908     2727    1
#> pred[CHARM: ARB]           0.06 0.01 0.05 0.06 0.06 0.07  0.08     5553     2948    1
#> pred[CHARM: CCB]           0.08 0.01 0.06 0.07 0.08 0.08  0.10     4237     2548    1
#> pred[CHARM: Diuretic]      0.10 0.01 0.07 0.09 0.10 0.11  0.13     4547     2741    1
#> pred[CHARM: Placebo]       0.07 0.01 0.06 0.07 0.07 0.08  0.10     5334     2774    1
#> 
#> ------------------------------------------------------------------ Study: DREAM ---- 
#> 
#>                            mean   sd 2.5%  25%  50%  75% 97.5% Bulk_ESS Tail_ESS Rhat
#> pred[DREAM: Beta Blocker]  0.23 0.03 0.18 0.21 0.23 0.24  0.29     2691     2182    1
#> pred[DREAM: ACE Inhibitor] 0.17 0.02 0.13 0.16 0.17 0.18  0.21     4867     2944    1
#> pred[DREAM: ARB]           0.16 0.02 0.12 0.14 0.16 0.17  0.21     4700     3176    1
#> pred[DREAM: CCB]           0.20 0.02 0.15 0.18 0.19 0.21  0.25     4052     2737    1
#> pred[DREAM: Diuretic]      0.24 0.03 0.19 0.22 0.24 0.26  0.31     4468     2471    1
#> pred[DREAM: Placebo]       0.19 0.02 0.15 0.18 0.19 0.20  0.23     5260     3192    1
#> 
#> ------------------------------------------------------------------- Study: EWPH ---- 
#> 
#>                           mean   sd 2.5%  25%  50%  75% 97.5% Bulk_ESS Tail_ESS Rhat
#> pred[EWPH: Beta Blocker]  0.06 0.01 0.04 0.05 0.06 0.07  0.09     4260     3014    1
#> pred[EWPH: ACE Inhibitor] 0.05 0.01 0.03 0.04 0.04 0.05  0.06     5766     2489    1
#> pred[EWPH: ARB]           0.04 0.01 0.03 0.04 0.04 0.05  0.06     5411     2930    1
#> pred[EWPH: CCB]           0.05 0.01 0.04 0.05 0.05 0.06  0.08     5420     3027    1
#> pred[EWPH: Diuretic]      0.07 0.01 0.05 0.06 0.07 0.07  0.09     6018     3048    1
#> pred[EWPH: Placebo]       0.05 0.01 0.03 0.04 0.05 0.06  0.07     5746     3125    1
#> 
#> ------------------------------------------------------------------ Study: FEVER ---- 
#> 
#>                            mean   sd 2.5%  25%  50%  75% 97.5% Bulk_ESS Tail_ESS Rhat
#> pred[FEVER: Beta Blocker]  0.04 0.01 0.03 0.04 0.04 0.04  0.05     3545     2549    1
#> pred[FEVER: ACE Inhibitor] 0.03 0.00 0.02 0.03 0.03 0.03  0.04     5031     2991    1
#> pred[FEVER: ARB]           0.03 0.00 0.02 0.03 0.03 0.03  0.04     4868     3001    1
#> pred[FEVER: CCB]           0.04 0.00 0.03 0.03 0.03 0.04  0.05     4917     2911    1
#> pred[FEVER: Diuretic]      0.04 0.01 0.03 0.04 0.04 0.05  0.06     5000     2952    1
#> pred[FEVER: Placebo]       0.03 0.00 0.03 0.03 0.03 0.04  0.04     4947     2951    1
#> 
#> ----------------------------------------------------------------- Study: HAPPHY ---- 
#> 
#>                             mean sd 2.5%  25%  50%  75% 97.5% Bulk_ESS Tail_ESS Rhat
#> pred[HAPPHY: Beta Blocker]  0.02  0 0.02 0.02 0.02 0.03  0.03     6034     3112    1
#> pred[HAPPHY: ACE Inhibitor] 0.02  0 0.01 0.02 0.02 0.02  0.02     4671     3258    1
#> pred[HAPPHY: ARB]           0.02  0 0.01 0.02 0.02 0.02  0.02     4466     2589    1
#> pred[HAPPHY: CCB]           0.02  0 0.02 0.02 0.02 0.02  0.03     4620     3066    1
#> pred[HAPPHY: Diuretic]      0.03  0 0.02 0.02 0.03 0.03  0.03     3741     2856    1
#> pred[HAPPHY: Placebo]       0.02  0 0.02 0.02 0.02 0.02  0.03     4043     2595    1
#> 
#> ------------------------------------------------------------------- Study: HOPE ---- 
#> 
#>                           mean   sd 2.5%  25%  50%  75% 97.5% Bulk_ESS Tail_ESS Rhat
#> pred[HOPE: Beta Blocker]  0.06 0.01 0.04 0.05 0.06 0.06  0.08     3592     3033    1
#> pred[HOPE: ACE Inhibitor] 0.04 0.01 0.03 0.04 0.04 0.05  0.05     5488     2938    1
#> pred[HOPE: ARB]           0.04 0.01 0.03 0.04 0.04 0.04  0.05     5239     3146    1
#> pred[HOPE: CCB]           0.05 0.01 0.04 0.04 0.05 0.05  0.07     4921     2944    1
#> pred[HOPE: Diuretic]      0.06 0.01 0.05 0.06 0.06 0.07  0.08     5345     2844    1
#> pred[HOPE: Placebo]       0.05 0.01 0.04 0.04 0.05 0.05  0.06     6036     2557    1
#> 
#> ---------------------------------------------------------------- Study: INSIGHT ---- 
#> 
#>                              mean   sd 2.5%  25%  50%  75% 97.5% Bulk_ESS Tail_ESS Rhat
#> pred[INSIGHT: Beta Blocker]  0.07 0.01 0.05 0.06 0.06 0.07  0.09     3795     3054    1
#> pred[INSIGHT: ACE Inhibitor] 0.05 0.01 0.03 0.04 0.05 0.05  0.06     5173     3064    1
#> pred[INSIGHT: ARB]           0.04 0.01 0.03 0.04 0.04 0.05  0.06     4761     2929    1
#> pred[INSIGHT: CCB]           0.06 0.01 0.04 0.05 0.05 0.06  0.07     5268     3091    1
#> pred[INSIGHT: Diuretic]      0.07 0.01 0.05 0.06 0.07 0.07  0.09     5693     3062    1
#> pred[INSIGHT: Placebo]       0.05 0.01 0.04 0.05 0.05 0.06  0.07     4825     2422    1
#> 
#> ----------------------------------------------------------------- Study: INVEST ---- 
#> 
#>                             mean   sd 2.5%  25%  50%  75% 97.5% Bulk_ESS Tail_ESS Rhat
#> pred[INVEST: Beta Blocker]  0.08 0.00 0.08 0.08 0.08 0.08  0.09     7467     3150    1
#> pred[INVEST: ACE Inhibitor] 0.06 0.01 0.05 0.06 0.06 0.06  0.07     2655     2586    1
#> pred[INVEST: ARB]           0.06 0.01 0.05 0.05 0.06 0.06  0.07     2826     2663    1
#> pred[INVEST: CCB]           0.07 0.00 0.06 0.07 0.07 0.07  0.08     2641     2777    1
#> pred[INVEST: Diuretic]      0.09 0.01 0.07 0.08 0.09 0.09  0.10     2722     2513    1
#> pred[INVEST: Placebo]       0.07 0.01 0.06 0.06 0.07 0.07  0.08     2204     2307    1
#> 
#> ------------------------------------------------------------------- Study: LIFE ---- 
#> 
#>                           mean   sd 2.5%  25%  50%  75% 97.5% Bulk_ESS Tail_ESS Rhat
#> pred[LIFE: Beta Blocker]  0.08 0.00 0.07 0.08 0.08 0.08  0.09     7431     2608    1
#> pred[LIFE: ACE Inhibitor] 0.06 0.01 0.05 0.06 0.06 0.06  0.07     2849     2562    1
#> pred[LIFE: ARB]           0.06 0.01 0.05 0.05 0.06 0.06  0.07     2675     2349    1
#> pred[LIFE: CCB]           0.07 0.01 0.06 0.07 0.07 0.07  0.08     3157     2710    1
#> pred[LIFE: Diuretic]      0.09 0.01 0.07 0.08 0.09 0.09  0.10     2868     2940    1
#> pred[LIFE: Placebo]       0.07 0.01 0.05 0.06 0.07 0.07  0.08     2250     2594    1
#> 
#> ------------------------------------------------------------------ Study: MRC-E ---- 
#> 
#>                            mean sd 2.5%  25%  50%  75% 97.5% Bulk_ESS Tail_ESS Rhat
#> pred[MRC-E: Beta Blocker]  0.03  0 0.02 0.03 0.03 0.03  0.04     4227     3157    1
#> pred[MRC-E: ACE Inhibitor] 0.02  0 0.02 0.02 0.02 0.02  0.03     6049     3455    1
#> pred[MRC-E: ARB]           0.02  0 0.01 0.02 0.02 0.02  0.03     5343     2976    1
#> pred[MRC-E: CCB]           0.03  0 0.02 0.02 0.02 0.03  0.03     4825     3577    1
#> pred[MRC-E: Diuretic]      0.03  0 0.02 0.03 0.03 0.03  0.04     3999     3323    1
#> pred[MRC-E: Placebo]       0.02  0 0.02 0.02 0.02 0.03  0.03     5412     3398    1
#> 
#> ----------------------------------------------------------------- Study: NORDIL ---- 
#> 
#>                             mean   sd 2.5%  25%  50%  75% 97.5% Bulk_ESS Tail_ESS Rhat
#> pred[NORDIL: Beta Blocker]  0.05 0.00 0.04 0.05 0.05 0.05  0.06     7013     2923    1
#> pred[NORDIL: ACE Inhibitor] 0.04 0.00 0.03 0.03 0.04 0.04  0.04     3022     3151    1
#> pred[NORDIL: ARB]           0.03 0.00 0.03 0.03 0.03 0.04  0.04     3199     2692    1
#> pred[NORDIL: CCB]           0.04 0.00 0.04 0.04 0.04 0.04  0.05     3396     3113    1
#> pred[NORDIL: Diuretic]      0.05 0.01 0.04 0.05 0.05 0.06  0.06     3168     2982    1
#> pred[NORDIL: Placebo]       0.04 0.00 0.03 0.04 0.04 0.04  0.05     2528     2796    1
#> 
#> ------------------------------------------------------------------ Study: PEACE ---- 
#> 
#>                            mean   sd 2.5%  25%  50%  75% 97.5% Bulk_ESS Tail_ESS Rhat
#> pred[PEACE: Beta Blocker]  0.14 0.02 0.10 0.13 0.14 0.15  0.18     2722     2645    1
#> pred[PEACE: ACE Inhibitor] 0.10 0.01 0.08 0.09 0.10 0.11  0.13     4689     2814    1
#> pred[PEACE: ARB]           0.09 0.01 0.07 0.09 0.09 0.10  0.13     4733     2853    1
#> pred[PEACE: CCB]           0.12 0.02 0.09 0.11 0.12 0.13  0.15     3962     2102    1
#> pred[PEACE: Diuretic]      0.15 0.02 0.11 0.13 0.15 0.16  0.19     4935     2962    1
#> pred[PEACE: Placebo]       0.11 0.01 0.09 0.10 0.11 0.12  0.14     5109     2688    1
#> 
#> ------------------------------------------------------------------ Study: SCOPE ---- 
#> 
#>                            mean   sd 2.5%  25%  50%  75% 97.5% Bulk_ESS Tail_ESS Rhat
#> pred[SCOPE: Beta Blocker]  0.06 0.01 0.05 0.06 0.06 0.07  0.09     3424     2888    1
#> pred[SCOPE: ACE Inhibitor] 0.05 0.01 0.03 0.04 0.05 0.05  0.06     5295     2823    1
#> pred[SCOPE: ARB]           0.04 0.01 0.03 0.04 0.04 0.05  0.06     5749     3175    1
#> pred[SCOPE: CCB]           0.06 0.01 0.04 0.05 0.05 0.06  0.07     4868     2709    1
#> pred[SCOPE: Diuretic]      0.07 0.01 0.05 0.06 0.07 0.08  0.09     5243     2620    1
#> pred[SCOPE: Placebo]       0.05 0.01 0.04 0.05 0.05 0.06  0.07     5835     2557    1
#> 
#> ------------------------------------------------------------------- Study: SHEP ---- 
#> 
#>                           mean   sd 2.5%  25%  50%  75% 97.5% Bulk_ESS Tail_ESS Rhat
#> pred[SHEP: Beta Blocker]  0.09 0.01 0.06 0.08 0.09 0.09  0.11     3186     2674    1
#> pred[SHEP: ACE Inhibitor] 0.06 0.01 0.05 0.06 0.06 0.07  0.08     4693     2678    1
#> pred[SHEP: ARB]           0.06 0.01 0.04 0.05 0.06 0.06  0.08     4607     2675    1
#> pred[SHEP: CCB]           0.07 0.01 0.05 0.07 0.07 0.08  0.10     4501     2903    1
#> pred[SHEP: Diuretic]      0.09 0.01 0.07 0.08 0.09 0.10  0.12     4989     3001    1
#> pred[SHEP: Placebo]       0.07 0.01 0.05 0.06 0.07 0.08  0.09     5346     2818    1
#> 
#> ----------------------------------------------------------------- Study: STOP-2 ---- 
#> 
#>                             mean   sd 2.5%  25%  50%  75% 97.5% Bulk_ESS Tail_ESS Rhat
#> pred[STOP-2: Beta Blocker]  0.05 0.00 0.05 0.05 0.05 0.06  0.06     4443     3221    1
#> pred[STOP-2: ACE Inhibitor] 0.04 0.00 0.03 0.04 0.04 0.04  0.05     3230     3131    1
#> pred[STOP-2: ARB]           0.04 0.00 0.03 0.03 0.04 0.04  0.05     3260     2961    1
#> pred[STOP-2: CCB]           0.05 0.00 0.04 0.04 0.05 0.05  0.05     4151     2922    1
#> pred[STOP-2: Diuretic]      0.06 0.01 0.05 0.05 0.06 0.06  0.07     3857     3132    1
#> pred[STOP-2: Placebo]       0.04 0.00 0.03 0.04 0.04 0.05  0.05     2904     2851    1
#> 
#> ------------------------------------------------------------------ Study: VALUE ---- 
#> 
#>                            mean   sd 2.5%  25%  50%  75% 97.5% Bulk_ESS Tail_ESS Rhat
#> pred[VALUE: Beta Blocker]  0.20 0.02 0.15 0.18 0.19 0.21  0.25     3053     2187    1
#> pred[VALUE: ACE Inhibitor] 0.15 0.02 0.11 0.13 0.14 0.16  0.19     4654     2519    1
#> pred[VALUE: ARB]           0.14 0.02 0.10 0.13 0.14 0.14  0.17     4783     2673    1
#> pred[VALUE: CCB]           0.17 0.02 0.13 0.16 0.17 0.18  0.21     4538     2131    1
#> pred[VALUE: Diuretic]      0.21 0.03 0.16 0.19 0.21 0.22  0.27     4774     2679    1
#> pred[VALUE: Placebo]       0.16 0.02 0.12 0.15 0.16 0.17  0.21     4816     2530    1
plot(db_pred_RE_studies)

We can also produce treatment rankings, rank probabilities, and cumulative rank probabilities.

(db_ranks <- posterior_ranks(db_fit_RE))
#>                     mean   sd 2.5% 25% 50% 75% 97.5% Bulk_ESS Tail_ESS Rhat
#> rank[Beta Blocker]  5.18 0.42    5   5   5   5     6     2241       NA    1
#> rank[ACE Inhibitor] 1.84 0.52    1   2   2   2     3     3781     3436    1
#> rank[ARB]           1.26 0.51    1   1   1   1     3     3592     2866    1
#> rank[CCB]           3.71 0.51    3   3   4   4     4     4111     2970    1
#> rank[Diuretic]      5.80 0.41    5   6   6   6     6     2597       NA    1
#> rank[Placebo]       3.20 0.59    2   3   3   4     4     3144     2813    1
plot(db_ranks)

(db_rankprobs <- posterior_rank_probs(db_fit_RE))
#>                  p_rank[1] p_rank[2] p_rank[3] p_rank[4] p_rank[5] p_rank[6]
#> d[Beta Blocker]       0.00      0.00      0.00      0.01      0.79       0.2
#> d[ACE Inhibitor]      0.22      0.72      0.06      0.00      0.00       0.0
#> d[ARB]                0.77      0.20      0.03      0.00      0.00       0.0
#> d[CCB]                0.00      0.02      0.26      0.71      0.01       0.0
#> d[Diuretic]           0.00      0.00      0.00      0.00      0.20       0.8
#> d[Placebo]            0.01      0.06      0.66      0.27      0.01       0.0
plot(db_rankprobs)

(db_cumrankprobs <- posterior_rank_probs(db_fit_RE, cumulative = TRUE))
#>                  p_rank[1] p_rank[2] p_rank[3] p_rank[4] p_rank[5] p_rank[6]
#> d[Beta Blocker]       0.00      0.00      0.00      0.01       0.8         1
#> d[ACE Inhibitor]      0.22      0.94      1.00      1.00       1.0         1
#> d[ARB]                0.77      0.97      1.00      1.00       1.0         1
#> d[CCB]                0.00      0.02      0.28      0.99       1.0         1
#> d[Diuretic]           0.00      0.00      0.00      0.00       0.2         1
#> d[Placebo]            0.01      0.07      0.73      0.99       1.0         1
plot(db_cumrankprobs)

References

Dias, S., N. J. Welton, A. J. Sutton, and A. E. Ades. 2011. “NICE DSU Technical Support Document 2: A Generalised Linear Modelling Framework for Pair-Wise and Network Meta-Analysis of Randomised Controlled Trials.” National Institute for Health and Care Excellence. https://www.sheffield.ac.uk/nice-dsu.

Elliott, W. J., and P. M. Meyer. 2007. “Incident Diabetes in Clinical Trials of Antihypertensive Drugs: A Network Meta-Analysis.” The Lancet 369 (9557): 201–7. https://doi.org/10.1016/s0140-6736(07)60108-1.

The gain in efficiency here from using “Beta Blocker” as the network reference treatment instead of “Diuretic” is considerable - around 4-8 times, in terms of effective samples per second. The functions in this package will always attempt to choose a default network reference treatment that maximises computational efficiency and stability. If you have chosen an alternative network reference treatment and the model runs very slowly or has low effective sample size, this is a likely cause.↩︎

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.