Network meta-analysis comparing the effects of a number of treatments for Parkinson's disease.
The data are the mean lost work-time reduction in patients given dopamine agonists as adjunct therapy in Parkinson’s disease (Franchini et al. 2012). The data are given as sample size, mean and standard deviation in each trial arm. Treatments are placebo and four active drugs. These data are used as an example in the supplemental material of Dias et al. (2013) where placebo is coded as 1 and the four active drugs as 2 to 5.
A data frame with the following columns:
|y1||treatment effect arm 1|
|sd1||Standard deviation arm 1|
|n1||Sample size arm 1|
|y2||treatment effect arm 2|
|sd2||Standard deviation arm 2|
|n2||Sample size arm 2|
|y3||treatment effect arm 3|
|sd3||Standard deviation arm 3|
|n3||Sample size arm 3|
Dias S, Sutton AJ, Ades AE and Welton NJ (2013): Evidence synthesis for decision making 2: A generalized linear modeling framework for pairwise and network meta-analysis of randomized controlled trials. Medical Decision Making, 33, 607–17
Franchini AJ, Dias S, Ades AE, Jansen JP, Welton NJ (2012): Accounting for correlation in network meta-analysis with multi-arm trials. Research Synthesis Methods, 3, 142–60
data(Franchini2012) # Transform data from arm-based format to contrast-based format # p1 <- pairwise(list(Treatment1, Treatment2, Treatment3), n = list(n1, n2, n3), mean = list(y1, y2, y3), sd = list(sd1, sd2, sd3), data = Franchini2012, studlab = Study) p1 # Conduct network meta-analysis net1 <- netmeta(p1) net1 # Draw network graphs netgraph(net1, points = TRUE, cex.points = 3, cex = 1.5, thickness = "se.common") netgraph(net1, points = TRUE, cex.points = 3, cex = 1.5, thickness = "se.common", iterate = TRUE, plastic = TRUE) netgraph(net1, points = TRUE, cex.points = 3, cex = 1.5, thickness = "se.common", iterate = TRUE, plastic = TRUE, start = "eigen")
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