Stowe2010: Network meta-analysis of adjuvant treatments to levodopa...

Stowe2010R Documentation

Network meta-analysis of adjuvant treatments to levodopa therapy for Parkinson's disease

Description

This data set contains data from a Cochrane review assessing efficacy and safety of three drug classes as adjuvant treatment to levodopa therapy in patients with Parkinson’s disease and motor complications (Stowe et al., 2010). The authors conducted three pairwise meta-analyses comparing dopamine agonists, catechol-O-methyl transferase inhibitors (COMTIs), and monoamine oxidase type B inhibitors (MAOBIs), respectively, with placebo.

The primary outcome was the mean reduction of the time spent in a relatively immobile ’off’ phase (mean off-time), calculated in hours per day. Relative treatment effects were expressed as mean difference. Data on this outcome were available for 5,331 patients from 28 studies comparing an active treatment with placebo and one three-arm study comparing two active treatments with placebo.

Format

A data frame with the following columns:

study study label
id study id
t1 treatment 1
y1 treatment effect arm 1
sd1 Standard deviation arm 1
n1 Sample size arm 1
t2 treatment 2
y2 treatment effect arm 2
sd2 Standard deviation arm 2
n2 Sample size arm 2
t3 treatment 3
y3 treatment effect arm 3
sd3 Standard deviation arm 3
n3 Sample size arm 3

Source

Stowe R, Ives N, Clarke CE, Deane K, Hilten V, Wheatley K, et al. (2010): Evaluation of the efficacy and safety of adjuvant treatment to levodopa therapy in Parkinson's disease patients with motor complications. Cochrane Database of Systematic Reviews

See Also

pairwise, metacont, netmeta, netgraph.netmeta

Examples

data(Stowe2010)

# Transform data from arm-based format to contrast-based format
#
p1 <- pairwise(list(t1, t2, t3),
  n = list(n1, n2, n3),
  mean = list(y1, y2, y3), sd = list(sd1, sd2, sd3),
  data = Stowe2010, studlab = study)
p1

# Conduct network meta-analysis
net1 <- netmeta(p1, ref = "plac")
net1

netmeta documentation built on May 31, 2023, 5:45 p.m.