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

dat.linde2015R Documentation

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)

Details

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

Concepts

medicine, psychiatry, odds ratios, network meta-analysis

Author(s)

Guido Schwarzer, sc@imbi.uni-freiburg.de, https://github.com/guido-s/

Source

Linde, K., Kriston, L., Rücker, G., Jamil, S., Schumann, I., Meissner, K., Sigterman, K., & Schneider, A. (2015). Efficacy and acceptability of pharmacological treatments for depressive disorders in primary care: Systematic review and network meta-analysis. Annals of Family Medicine, 13(1), 69–79. https://doi.org/10.1370/afm.1687

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

See Also

pairwise, metabin, netmeta, netrank

Examples

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

## Not run: 

### Load netmeta package
suppressPackageStartupMessages(library(netmeta))

### Print odds ratios and confidence limits with two digits
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)


## End(Not run)

metadat documentation built on April 6, 2022, 5:08 p.m.