```
library(multinma)
options(mc.cores = parallel::detectCores())
```

This vignette describes the analysis of 6 trials comparing transfusion of granulocytes (white blood cells) to control for preventing mortality in patients with neutropenia or neutrophil dysfunction (Stanworth et al. 2005; Turner et al. 2012). The data are available in this package as `transfusion`

:

```
head(transfusion)
#> studyc trtc r n
#> 1 Bow 1984 Transfusion 5 13
#> 2 Bow 1984 Control 4 11
#> 3 Herzig 1977 Transfusion 1 13
#> 4 Herzig 1977 Control 3 14
#> 5 Higby 1975 Transfusion 2 17
#> 6 Higby 1975 Control 14 19
```

Turner et al. (2012) previously used this dataset to demonstrate the application of informative priors for heterogeneity, an analysis which we recreate here.

We begin by setting up the network - here just a pairwise meta-analysis. We have arm-level count data giving the number of deaths (`r`

) out of the total (`n`

) in each arm, so we use the function `set_agd_arm()`

. We set “Control” as the reference treatment.

```
<- set_agd_arm(transfusion,
tr_net study = studyc,
trt = trtc,
r = r,
n = n,
trt_ref = "Control")
tr_net#> A network with 6 AgD studies (arm-based).
#>
#> ------------------------------------------------------- AgD studies (arm-based) ----
#> Study Treatments
#> Bow 1984 2: Transfusion | Control
#> Herzig 1977 2: Transfusion | Control
#> Higby 1975 2: Transfusion | Control
#> Scali 1978 2: Transfusion | Control
#> Vogler 1977 2: Transfusion | Control
#> Winston 1982a 2: Transfusion | Control
#>
#> Outcome type: count
#> ------------------------------------------------------------------------------------
#> Total number of treatments: 2
#> Total number of studies: 6
#> Reference treatment is: Control
#> Network is connected
```

We fit two random effects models, first with a non-informative prior for the heterogeneity, then using the informative prior described by Turner et al. (2012).

We fit a random effects model using the `nma()`

function with `trt_effects = "random"`

. We use \(\mathrm{N}(0, 100^2)\) prior distributions for the treatment effects \(d_k\) and study-specific intercepts \(\mu_j\), and a non-informative \(\textrm{half-N}(5^2)\) 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

```
<- nma(tr_net,
tr_fit_RE_noninf trt_effects = "random",
prior_intercept = normal(scale = 100),
prior_trt = normal(scale = 100),
prior_het = half_normal(scale = 5))
```

Basic parameter summaries are given by the `print()`

method:

```
tr_fit_RE_noninf#> 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[Transfusion] -1.13 0.03 0.96 -3.23 -1.64 -1.10 -0.55 0.73 1195 1
#> lp__ -134.26 0.09 2.96 -141.17 -136.06 -133.91 -132.11 -129.30 1103 1
#> tau 1.84 0.03 1.03 0.54 1.15 1.61 2.27 4.55 1348 1
#>
#> Samples were drawn using NUTS(diag_e) at Tue Jan 18 09:41:11 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).
```

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(tr_fit_RE_noninf, pars = c("d", "mu", "delta"))
```

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

function:

`plot_prior_posterior(tr_fit_RE_noninf, prior = "het")`

The posterior distribution for the heterogeneity variance \(\tau^2\) is summarised by

```
<- as.array(tr_fit_RE_noninf, pars = "tau")
noninf_tau <- noninf_tau^2
noninf_tausq names(noninf_tausq) <- "tausq"
summary(noninf_tausq)
#> mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS Tail_ESS Rhat
#> tausq 4.47 6.2 0.29 1.33 2.59 5.16 20.66 1368 1765 1
```

Keeping the rest of the model setup the same, we now use an informative \(\textrm{log-N}(-3.93, 1.51^2)\) prior for the heterogeneity variance \(\tau^2\). We can examine the range of parameter values implied by this prior distribution with the `summary()`

method:

```
summary(log_normal(-3.93, 1.51))
#> A log-Normal prior distribution: location = -3.93, scale = 1.51.
#> 50% of the prior density lies between 0.01 and 0.05.
#> 95% of the prior density lies between 0 and 0.38.
```

Fitting the RE model, we specify the `log_normal`

prior distribution in the `prior_het`

argument, and set `prior_het_type = "var"`

to indicate that this prior distribution is on the variance scale (instead of the standard deviation, the default).

```
<- nma(tr_net,
tr_fit_RE_inf trt_effects = "random",
prior_intercept = normal(scale = 100),
prior_trt = normal(scale = 100),
prior_het = log_normal(-3.93, 1.51),
prior_het_type = "var")
```

Basic parameter summaries are given by the `print()`

method:

```
tr_fit_RE_inf#> 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[Transfusion] -0.77 0.01 0.44 -1.72 -1.02 -0.73 -0.47 0.01 2405 1
#> lp__ -140.27 0.07 2.75 -146.58 -142.00 -139.95 -138.26 -135.85 1377 1
#> tau 0.47 0.01 0.35 0.05 0.20 0.40 0.67 1.32 1605 1
#>
#> Samples were drawn using NUTS(diag_e) at Tue Jan 18 09:41:25 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).
```

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(tr_fit_RE_inf, pars = c("d", "mu", "delta"))
```

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

function:

`plot_prior_posterior(tr_fit_RE_inf, prior = "het")`

Note:The heterogeneityvariance\(\tau^2\) is plotted here since the prior was specified on \(\tau^2\).

The posterior distribution for the heterogeneity variance \(\tau^2\) is summarised by

```
<- as.array(tr_fit_RE_inf, pars = "tau")
inf_tau <- inf_tau^2
inf_tausq names(inf_tausq) <- "tausq"
summary(inf_tausq)
#> mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS Tail_ESS Rhat
#> tausq 0.34 0.5 0 0.04 0.16 0.45 1.73 1490 3055 1
```

Stanworth, S., E. Massey, C. Hyde, S. J. Brunskill, C. Navarette, G. Lucas, D. Marks, and U. Paulus. 2005. “Granulocyte Transfusions for Treating Infections in Patients with Neutropenia or Neutrophil Dysfunction.” *Cochrane Database of Systematic Reviews*, no. 3. https://doi.org/10.1002/14651858.CD005339.

Turner, R. M., J. Davey, M. J. Clarke, S. G. Thompson, and J. P. T. Higgins. 2012. “Predicting the Extent of Heterogeneity in Meta-Analysis, Using Empirical Data from the Cochrane Database of Systematic Reviews.” *International Journal of Epidemiology* 41 (3): 818–27. https://doi.org/10.1093/ije/dys041.