metadat (version 1.4-0)

dat.linde2015: Studies on Classes of Antidepressants for the Primary Care Setting

Description

Results from 66 trials examining eight classes of antidepressants and placebo for the primary care setting.

Usage

dat.linde2015

Format

The data frame contains the following columns:

id	integer	study ID
author	character	first author
year	integer	year of publication
treatment1	character	treatment 1
treatment2	character	treatment 2
treatment3	character	treatment 3
n1	integer	number of patients (arm 1)
resp1	integer	number of early responder (arm 1)
remi1	integer	number of early remissions (arm 1)
loss1	integer	number of patients loss to follow-up (arm 1)
loss.ae1	integer	number of patients loss to follow-up due to adverse events (arm 1)
ae1	integer	number of patients with adverse events (arm 1)
n2	integer	number of patients (arm 2)
resp2	integer	number of early responder (arm 2)
remi2	integer	number of early remissions (arm 2)
loss2	integer	number of patients loss to follow-up (arm 2)
loss.ae2	integer	number of patients loss to follow-up due to adverse events (arm 2)
ae2	integer	number of patients with adverse events (arm 2)
n3	integer	number of patients (arm 3)
resp3	integer	number of early responder (arm 3)
remi3	integer	number of early remissions (arm 3)
loss3	integer	number of patients loss to follow-up (arm 3)
loss.ae3	integer	number of patients loss to follow-up due to adverse events (arm 3)
ae3	integer	number of patients with adverse events (arm 3)

Author

Guido Schwarzer, , https://github.com/guido-s/

Concepts

medicine, psychiatry, odds ratios, network meta-analysis

Details

This dataset comes from a systematic review of 8 pharmacological treatments of depression and placebo in primary care with 66 studies (8 of which were 3-arm studies) including 14,785 patients.

The primary outcome is early response, defined as at least a 50% score reduction on a depression scale after completion of treatment. Secondary outcomes (also measured as dichotomous) were early remission (defined as having a symptom score below a fixed threshold after completion of treatment), lost to follow-up, lost to follow-up due to adverse events, and any adverse event. The odds ratio was used as effect measure.

This dataset was used as an example in Rücker and Schwarzer (2017) who introduced methods to resolve conflicting rankings of outcomes in network meta-analysis.

References

Rücker, G., & Schwarzer, G. (2017). Resolve conflicting rankings of outcomes in network meta-analysis: Partial ordering of treatments. Research Synthesis Methods, 8(4), 526--536. https://doi.org/10.1002/jrsm.1270

Examples

### Show results from first three studies (including three-arm study
### Lecrubier 1997)
head(dat.linde2015, 3)

if (FALSE) {
### Load netmeta package
suppressPackageStartupMessages(library("netmeta"))

### Print odds ratios and confidence limits with two digits
oldset <- settings.meta(digits = 2)

### Change appearance of confidence intervals
cilayout("(", "-")

### Define order of treatments in printouts
trts <- c("TCA", "SSRI", "SNRI", "NRI", "Low-dose SARI",
 "NaSSa", "rMAO-A", "Hypericum", "Placebo")

### Transform data from wide arm-based format to contrast-based format
### (outcome: early response). Argument 'sm' has to be used for odds
### ratio as summary measure; by default the risk ratio is used in the
### metabin function called internally.
pw1 <- pairwise(list(treatment1, treatment2, treatment3),
  event = list(resp1, resp2, resp3),
  n = list(n1, n2, n3),
  studlab = id, data = dat.linde2015, sm = "OR")

### Conduct random effects network meta-analysis for primary outcome
### (early response); small number of early responses is bad (argument
### small.values)
net1 <- netmeta(pw1, fixed = FALSE, reference = "Placebo", seq = trts,
  small.values = "bad")
net1

### Random effects NMA for early remission
pw2 <- pairwise(treat = list(treatment1, treatment2, treatment3),
  event = list(remi1, remi2, remi3),
  n = list(n1, n2, n3),
  studlab = id, data = dat.linde2015, sm = "OR")
net2 <- netmeta(pw2, fixed = FALSE,
   seq = trts, ref = "Placebo", small.values = "bad")
net2

### Ranking of treatments
nr1 <- netrank(net1)
nr2 <- netrank(net2)
nr1
nr2

### Partial order of treatment rankings (two outcomes)
outcomes <- c("Early response", "Early remission")
po12 <- netposet(nr1, nr2, outcomes = outcomes)
plot(po12)

### Use previous settings
settings.meta(oldset)
}