Studies on Dopamine Agonists to Reduce “Off-Time” in Patients with Advanced Parkinson Disease

Results from 7 trials examining the effectiveness of four dopamine agonists and placebo to reduce “off-time” in patients with advanced Parkinson disease.

dat.franchini2012

Format

The data frame contains the following columns:

Studycharacterstudy label
Treatment1charactertreatment 1
y1numerictreatment effect arm 1
sd1numericstandard deviation arm 2
n1integersample size arm 1
Treatment2charactertreatment 2
y2numerictreatment effect arm 2
sd2numericstandard deviation arm 2
n2integersample size arm 1
Treatment3charactertreatment 3
y3numerictreatment effect arm 3
sd3numericstandard deviation arm 2
n3integersample size arm 1

Details

This network meta-analysis compared the effectiveness of four active treatments and placebo in patients with advanced Parkinson disease (Franchini et al., 2012). The outcome is mean lost work-time reduction in patients given dopamine agonists as adjunct therapy. The data are given as sample size, mean, and standard deviation in each trial arm.

This dataset was 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.

Source

Dias, S., Sutton, A. J., Ades, A. E., & Welton, N. J. (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(5), 607–617. https://doi.org/10.1177/0272989X12458724

Franchini, A. J., Dias, S., Ades, A. E., Jansen, J. P., & Welton, N. J. (2012). Accounting for correlation in network meta-analysis with multi-arm trials. Research Synthesis Methods, 3(2), 142–160. https://doi.org/10.1002/jrsm.1049

Concepts

medicine, raw mean differences, network meta-analysis

Examples

### Show results from first three studies; third study is a three-arm
### study
head(dat.franchini2012, 3)
#>            Study Treatment1    y1 sd1  n1  Treatment2    y2  sd2  n2    Treatment3   y3 sd3 n3
#> 1 Lieberman 1998    Placebo -1.22 3.7  54  Ropinirole -1.53 4.28  95          <NA>   NA  NA NA
#> 2 Lieberman 1997    Placebo -0.70 3.7 172 Pramipexole -2.40 3.40 173          <NA>   NA  NA NA
#> 3   Guttman 1997    Placebo -0.30 4.4  76 Pramipexole -2.60 4.30  71 Bromocriptine -1.2 4.3 81

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

### Print mean differences with two digits
oldset <- settings.meta(digits = 2)

### Transform data from wide arm-based format to contrast-based
### format. Argument 'sm' must not be provided as the mean difference
### is the default in R function metacont() called internally.
pw <- pairwise(list(Treatment1, Treatment2, Treatment3),
  n = list(n1, n2, n3),
  mean = list(y1, y2, y3),
  sd = list(sd1, sd2, sd3),
  data = dat.franchini2012, studlab = Study, sm = "MD")

### Show calculated mean differences (TE) for first three studies
pw[1:5, c(3:7, 10, 1)]
#>          studlab      treat1        treat2  n1 mean1 mean2    TE
#> 1 Lieberman 1998     Placebo    Ropinirole  54 -1.22 -1.53  0.31
#> 2 Lieberman 1997     Placebo   Pramipexole 172 -0.70 -2.40  1.70
#> 3   Guttman 1997     Placebo   Pramipexole  76 -0.30 -2.60  2.30
#> 4   Guttman 1997     Placebo Bromocriptine  76 -0.30 -1.20  0.90
#> 5   Guttman 1997 Pramipexole Bromocriptine  71 -2.60 -1.20 -1.40

### Conduct network meta-analysis
net <- netmeta(pw)
net
#> Number of studies: k = 7
#> Number of pairwise comparisons: m = 9
#> Number of observations: o = 1613
#> Number of treatments: n = 5
#> Number of designs: d = 5
#> 
#> Common effects model
#> 
#> Treatment estimate (sm = 'MD', comparison: other treatments vs 'Bromocriptine'):
#>                  MD         95%-CI     z p-value
#> Bromocriptine     .              .     .       .
#> Cabergoline   -0.30 [-0.71;  0.11] -1.44  0.1497
#> Placebo        0.52 [-0.41;  1.46]  1.09  0.2736
#> Pramipexole   -1.29 [-2.31; -0.26] -2.47  0.0137
#> Ropinirole     0.05 [-0.59;  0.68]  0.14  0.8871
#> 
#> Random effects model
#> 
#> Treatment estimate (sm = 'MD', comparison: other treatments vs 'Bromocriptine'):
#>                  MD         95%-CI     z p-value
#> Bromocriptine     .              .     .       .
#> Cabergoline   -0.30 [-0.71;  0.11] -1.44  0.1497
#> Placebo        0.52 [-0.41;  1.46]  1.09  0.2736
#> Pramipexole   -1.29 [-2.31; -0.26] -2.47  0.0137
#> Ropinirole     0.05 [-0.59;  0.68]  0.14  0.8871
#> 
#> Quantifying heterogeneity / inconsistency:
#> tau^2 = 0; tau = 0; I^2 = 0% [0.0%; 79.2%]
#> 
#> Tests of heterogeneity (within designs) and inconsistency (between designs):
#>                    Q d.f. p-value
#> Total           2.29    4  0.6830
#> Within designs  1.61    2  0.4473
#> Between designs 0.68    2  0.7121

### Draw network graph
netgraph(net, points = TRUE, cex.points = 3, cex = 1.5,
  plastic = TRUE, thickness = "se.fixed",
  iterate = TRUE, start = "eigen")


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