In softloud/adpain: Antidepressants for the treatment of chronic pain in adults

knitr::opts_chunk$set(echo = TRUE)

library(tidyverse)
library(targets)
library(multinma)

# set up data
dat <- 
  tar_read(mod_dat) %>% 
  filter(outcome == "mood", timepoint == "change_score", study != "creed 2003") %>% 
  select(
    study, intervention, arm, mean, sd, se, n, type
  ) %>% 
  mutate(
    intervention = fct_relevel(intervention, "placebo", "cbt", "cbt and milnacipran")
  ) %>% 
  arrange(study, intervention) %>% 
  group_by(study) %>% 
  # Does it matter which is designated the treatment reference?
  mutate(ref = first(arm))

write_rds(dat, "example-smd-dat.rds")

# import example dat
dat <- read_rds("example-smd-dat.rds")

How to compute standardised mean difference using multinma?

dat <- dat %>% 
  mutate(arm = 1:n(),
         mi = sum(n) - n(),
         cmi = exp(lgamma(mi/2) - log(sqrt(mi/2)) - lgamma((mi - 1)/2)),
         sdpool = sqrt(weighted.mean(sd^2, n - 1)),
         smd = if_else(arm == 1, NA_real_, (mean - first(mean)) / sdpool * cmi),
         se_smd = if_else(arm == 1,
                          se / sdpool * cmi,
                          sqrt((n + first(n))/(n * first(n)) + smd^2 / (2 * (n + first(n))))))

# set network
net <- 
  set_agd_contrast(
    data = dat,
    study = study,
    trt = intervention,
    y = smd,
    se = se_smd,
    sample_size = n,
    trt_ref = "placebo",
    trt_class = type
  ) 



# network plots fine!
plot(net, weight_nodes = TRUE, weight_edges = TRUE, show_trt_class = TRUE) + ggplot2::theme(legend.position = "bottom", legend.box = "vertical")

# model is empty?
mod <- 
  nma(
    net,
    trt_effects = "random"
  )

mod