---
title: "Perform network meta-analysis"
author: Loukia M. Spineli
date: "Latest version: `r Sys.Date()`"
source: vignettes/perform_network_meta-analysis.Rmd
output: 
  rmarkdown::html_vignette:
    toc: true
    toc_depth: 3
params:
  EVAL: !r identical(Sys.getenv("NOT_CRAN"), "true")
vignette: >
  %\VignetteIndexEntry{Perform network meta-analysis}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup, include = FALSE}
library(rnmamod)
```

## Introduction

This vignette illustrates how to perform a one-stage Bayesian random-effects 
network meta-analysis with consistency equation using the `run_model` function. 
This function includes arguments to handle aggregate missing participant outcome 
data (MOD) in each arm of every trial via the pattern-mixture model.
 
## Example on a binary outcome 

We will use the network on pharmacologic interventions for chronic obstructive 
pulmonary disease (COPD) from the systematic review of 
[Baker et al. (2009)](https://pubmed.ncbi.nlm.nih.gov/19637942/). This network 
comprises 21 trials comparing seven pharmacological interventions with each 
other and placebo. The exacerbation of COPD (harmful outcome) is the analysed 
binary outcome (see `?nma.baker2009`).

```{r, echo = FALSE, results = "hide", fig.width = 5, fig.height = 5, fig.align = "center"}
netplot(data = nma.baker2009, drug_names = c("placebo", "budesonide", "budesonide plus \n formoterol", "fluticasone", "fluticasone plus \n salmeterol", "formoterol", "salmeterol", "tiotropium"),
        adjust.figsizex = 1.35, adjust.figsizey = 1.15)
```

## Perform Bayesian random-effects network meta-analysis

### Run the model

`run_model` calls the `jags` function from the 
[**R2jags**](https://CRAN.R-project.org/package=R2jags) package to perform the
Bayesian analysis using the BUGS code of Dias and colleagues (2013). 

```{r eval = FALSE}
run_model(data = nma.baker2009,
          measure = "OR",
          heter_prior = list("halfnormal", 0, 1),
          D = 0,
          n_chains = 3,
          n_iter = 10000,
          n_burnin = 1000,
          n_thin = 1)
```

With only 
the minimum required arguments, the function adjusts MOD under the
missing-at-random assumption (MAR; by default) via the informative missingness 
odds ratio (IMOR) in the logarithmic scale (White et al. (2008)):
The minimum required arguments of `run_model` include specifying:

- the dataset (a data-frame with one-trial-per-row format) in `data` (see
`?data_preparation`);
- the effect measure in `measure` (see 'Arguments' in `?run_model`):
- the prior distribution for the heterogeneity parameter in `heter_prior` (see
`?heterogeneity_param_prior`);
- the direction of the outcome in `D` (here, `D = 0` because the outcome is 
harmful; see, 'Arguments' in `?run_model`)
- the number of chains in `n_chains` (see 'Arguments' in `?run_model` -- also 
for the subsequent arguments);
- the number of iterations in `n_iter`;
- the number of burn-in in `n_burnin`, and 
- the thinning in `n_thin`.

### Using all arguments

Suppose we decide to use an *empirically-based prior distribution for the 
between-trial variance* that aligns with the outcome and interventions under 
investigation. We also consider a *hierarchical structure for the prior 
normal distribution of the log IMOR that is specific to the interventions* in 
the network (`assumption = "HIE-ARM"`) (Turner et al., 2015a; Spineli, 2019). We 
still assume MAR on average with variance of log IMOR equal to 1 
(`var_misspar = 1`) which is also the default argument. In this case, 
`run_model` must be specified as follows:

```{r eval = FALSE}
run_model(data = nma.baker2009,
          measure = "OR",
          model = "RE",
          assumption = "HIE-ARM",
          heter_prior = list("lognormal", -2.06, 0.438),
          mean_misspar = c(0, 0),
          var_misspar = 1,
          D = 0,
          n_chains = 3,
          n_iter = 10000,
          n_burnin = 1000,
          n_thin = 1)
```

The argument `model = "RE"` refers to the random-effects model. For the 
fixed-effect model, use `model = "FE"`.

`heter_prior = list("lognormal", -2.06, 0.438)` refers to 'symptoms reflecting 
the continuation of condition' for the 'pharmacological versus placebo' 
comparison-type as elicited by Turner and colleagues (2015b). 

In the argument `mean_misspar = c(0, 0)`, the first and second element of the
vector refers to the mean log IMOR in the non-reference interventions and the
reference intervention of the network, respectively -- the latter is always the 
intervention with identifier equal to 1. Hence, for all non-reference 
interventions we can consider **the same** mean log IMOR. See 'Details' in 
`?missingness_param_prior`

### The output 

`run_model` returns **a list of R2jags output** on the summaries of the 
posterior distribution, and the Gelman-Rubin convergence diagnostic of the 
monitored parameters (see 'Value' in `?run_model`). The output is used as an S3 
object by other functions of the package to be processed further and provide an 
end-user-ready output. See, for example, the function `?league_heatmap` that 
creates the league table with the effect sizes of all possible comparisons in 
the network.


### No or partially extracted missing participant outcome data

`run_model` can also handle a dataset where MOD have not be extracted or MOD 
have been extracted for some trials or trial-arms. For illustrative purposes, we
removed the item `m` from `nma.baker2009` to indicate that MOD were not 
extracted for this outcome:

```{r echo = FALSE, comment = NA}
head(nma.baker2009[, c(-10, -11, -12, -13)])
```

Using the minimum required arguments, `run_model` will run and provide results. 

`run_model` calls the `data_preparation` function. The latter creates a 
pseudo-data-frame for the item `m` (see 'Value' in `?data_preparation`) that 
assigns `NA` to all trial-arms. `data_preparation` also creates the 
pseudo-data-frame `I` that has the same dimension with the other items in the 
dataset, and assigns the zero value to all trial-arms to indicate that no MOD 
have been extracted. Both pseudo-data-frames aim to retain in the dataset the 
trials without information on MOD; otherwise, these trials would have been 
excluded from the analysis. See 'Details' in `?data_preparation` and 
`?run_model`.

## References

Dias S, Sutton AJ, Ades AE, Welton NJ. Evidence synthesis for decision making 2:
a generalized linear modeling framework for pairwise and network meta-analysis 
of randomized controlled trials. *Med Decis Making* 2013;**33**(5):607--617. 
[doi: 10.1177/0272989X12458724](https://pubmed.ncbi.nlm.nih.gov/23104435/)

White IR, Higgins JP, Wood AM. Allowing for uncertainty due to missing data in 
meta-analysis--part 1: two-stage methods. *Stat Med* 2008;**27**(5):711--27.
[doi: 10.1002/sim.3008](https://pubmed.ncbi.nlm.nih.gov/17703496/)

Turner NL, Dias S, Ades AE, Welton NJ. A Bayesian framework to account for 
uncertainty due to missing binary outcome data in pairwise meta-analysis. 
*Stat Med* 2015a;**34**(12):2062--80.
[doi: 10.1002/sim.6475](https://pubmed.ncbi.nlm.nih.gov/25809313/)

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](https://pubmed.ncbi.nlm.nih.gov/31018836/)

Turner RM, Jackson D, Wei Y, Thompson SG, Higgins JPT. Predictive distributions 
for between-study heterogeneity and simple methods for their application in 
Bayesian meta-analysis. *Stat Med* 2015b;**34**(6):984--98.
[doi: 10.1002/sim.6381](https://pubmed.ncbi.nlm.nih.gov/25475839/)
