Meta-analysis models
We fit random effects (RE) models, firstly without any covariates,
and then with a meta-regression on the continuous covariate
latitude.
RE meta-analysis (no covariate)
We start by fitting a standard RE model without any covariates. We
use \(\mathrm{N}(0, 100^2)\) prior
distributions for the treatment effect \(d_\mathrm{Vaccine}\) and study-specific
intercepts \(\mu_j\), and a \(\textrm{half-N}(0, 5^2)\) prior
distribution 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.The model is fitted with the nma() function, with a
random effects model specified by
trt_effects = "random".
bcg_fit_unadj <- nma(bcg_net,
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:
bcg_fit_unadj
#> 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[Vaccinated] -0.75 0.01 0.22 -1.19 -0.89 -0.75 -0.62 -0.32 1351 1
#> lp__ -13453.93 0.13 4.43 -13463.15 -13456.86 -13453.65 -13450.88 -13446.15 1216 1
#> tau 0.69 0.01 0.21 0.40 0.54 0.65 0.79 1.19 1568 1
#>
#> Samples were drawn using NUTS(diag_e) at Tue May 23 11:41:41 2023.
#> 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 random effects \(\delta_j\) are hidden, but could be
examined by changing the pars argument:
# Not run
print(bcg_fit_unadj, pars = c("d", "mu", "delta", "tau"))The prior and posterior distributions can be compared visually using
the plot_prior_posterior() function:
plot_prior_posterior(bcg_fit_unadj, prior = c("trt", "het"))
RE meta-regression with covariate latitude
We now fit a RE meta-regression model, adjusting for
latitude. We use \(\mathrm{N}(0,
100^2)\) prior distributions for the treatment effect \(d_\mathrm{Vaccine}\), study-specific
intercepts \(\mu_j\), and regression
coefficient \(\beta\). We use a \(\text{half-N}(0, 5^2)\) prior distribution
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.Again, the model is fitted with the nma() function. The
regression formula ~ .trt:latitude means that
the interaction of latitude with treatment will be included; the
.trt special variable indicates treatment, and
latitude is in the original data set. We increase
adapt_delta to 0.99 to remove a small number of divergent
transition errors (the default for RE models is set to 0.95).
bcg_fit_lat <- nma(bcg_net,
trt_effects = "random",
regression = ~.trt:latitude,
prior_intercept = normal(scale = 100),
prior_trt = normal(scale = 100),
prior_reg = normal(scale = 100),
prior_het = half_normal(scale = 5),
adapt_delta = 0.99)#> 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.Basic parameter summaries are given by the print()
method:
bcg_fit_lat
#> A random effects NMA with a binomial likelihood (logit link).
#> Regression model: ~.trt:latitude.
#> Centred covariates at the following overall mean values:
#> latitude
#> 33.46154
#> 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%
#> beta[.trtVaccinated:latitude] -0.03 0.00 0.01 -0.05 -0.04 -0.03 -0.03
#> d[Vaccinated] -0.77 0.00 0.13 -1.05 -0.83 -0.76 -0.69
#> lp__ -13457.46 0.20 5.13 -13468.10 -13460.81 -13457.13 -13453.80
#> tau 0.29 0.01 0.18 0.02 0.16 0.26 0.39
#> 97.5% n_eff Rhat
#> beta[.trtVaccinated:latitude] -0.01 1498 1
#> d[Vaccinated] -0.53 1333 1
#> lp__ -13448.23 672 1
#> tau 0.74 722 1
#>
#> Samples were drawn using NUTS(diag_e) at Tue May 23 11:42:05 2023.
#> 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).Note that latitude has automatically been centered at
33.46, the mean value for the studies in the network.
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(bcg_fit_lat, pars = c("d", "beta", "mu", "delta", "tau"))The prior and posterior distributions can be compared visually using
the plot_prior_posterior() function:
plot_prior_posterior(bcg_fit_lat, prior = c("trt", "reg", "het"))
