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

dat.linde2015

Format

The data frame contains the following columns:

idintegerstudy ID
authorcharacterfirst author
yearintegeryear of publication
treatment1charactertreatment 1
treatment2charactertreatment 2
treatment3charactertreatment 3
n1integernumber of patients (arm 1)
resp1integernumber of early responder (arm 1)
remi1integernumber of early remissions (arm 1)
loss1integernumber of patients loss to follow-up (arm 1)
loss.ae1integernumber of patients loss to follow-up due to adverse events (arm 1)
ae1integernumber of patients with adverse events (arm 1)
n2integernumber of patients (arm 2)
resp2integernumber of early responder (arm 2)
remi2integernumber of early remissions (arm 2)
loss2integernumber of patients loss to follow-up (arm 2)
loss.ae2integernumber of patients loss to follow-up due to adverse events (arm 2)
ae2integernumber of patients with adverse events (arm 2)
n3integernumber of patients (arm 3)
resp3integernumber of early responder (arm 3)
remi3integernumber of early remissions (arm 3)
loss3integernumber of patients loss to follow-up (arm 3)
loss.ae3integernumber of patients loss to follow-up due to adverse events (arm 3)
ae3integernumber 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.

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

Concepts

medicine, psychiatry, odds ratios, network meta-analysis

Examples

### Show results from first three studies (including three-arm study
### Lecrubier 1997)
head(dat.linde2015, 3)
#>   id           author year treatment1 treatment2 treatment3 n1 resp1 remi1 loss1 loss.ae1 ae1 n2
#> 1  1        Lecrubier 1997        TCA       SNRI    Placebo 75    49    NA    23       10  NA 78
#> 2  4          Blashki 1971        TCA    Placebo            35    20    16     8        7  NA 23
#> 3  7 Barge-Schaapveld 2002        TCA    Placebo            32    16     9     9       NA  NA 31
#>   resp2 remi2 loss2 loss.ae2 ae2 n3 resp3 remi3 loss3 loss.ae3 ae3
#> 1    60    NA    23       11  NA 76    48    NA    19        4  NA
#> 2     8     6     5        4  NA NA    NA    NA    NA       NA  NA
#> 3    12     5     5       NA  NA NA    NA    NA    NA       NA  NA

# \dontrun{

### 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")
#> Warning: Comparisons with missing TE / seTE or zero seTE not considered in network meta-analysis.
#> Comparisons not considered in network meta-analysis:
#>  studlab treat1  treat2 TE seTE
#>       14    TCA    SSRI NA   NA
#>       18    TCA Placebo NA   NA
#>       21    TCA  rMAO-A NA   NA
#>       27   SSRI Placebo NA   NA
#>       51    TCA  rMAO-A NA   NA
#>      130    TCA   NaSSa NA   NA
#>      130    TCA Placebo NA   NA
#>      130  NaSSa Placebo NA   NA
#>      131   SSRI    SNRI NA   NA
#> 
net1
#> Number of studies: k = 59
#> Number of pairwise comparisons: m = 73
#> Number of observations: o = 12681
#> Number of treatments: n = 9
#> Number of designs: d = 21
#> 
#> Random effects model
#> 
#> Treatment estimate (sm = 'OR', comparison: other treatments vs 'Placebo'):
#>                 OR      95%-CI    z  p-value
#> TCA           1.72 (1.42-2.09) 5.56 < 0.0001
#> SSRI          1.68 (1.40-2.01) 5.68 < 0.0001
#> SNRI          1.74 (1.25-2.42) 3.27   0.0011
#> NRI           1.42 (0.84-2.40) 1.30   0.1938
#> Low-dose SARI 1.78 (1.18-2.70) 2.73   0.0064
#> NaSSa         1.14 (0.82-1.60) 0.77   0.4399
#> rMAO-A        1.05 (0.69-1.62) 0.24   0.8113
#> Hypericum     1.99 (1.58-2.49) 5.94 < 0.0001
#> Placebo          .           .    .        .
#> 
#> Quantifying heterogeneity / inconsistency:
#> tau^2 = 0.0352; tau = 0.1875; I^2 = 26.9% (0.0%-47.3%)
#> 
#> Tests of heterogeneity (within designs) and inconsistency (between designs):
#>                     Q d.f. p-value
#> Total           79.37   58  0.0327
#> Within designs  49.41   39  0.1226
#> Between designs 29.95   19  0.0524

### 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")
#> Warning: Comparisons with missing TE / seTE or zero seTE not considered in network meta-analysis.
#> Comparisons not considered in network meta-analysis:
#>  studlab        treat1  treat2 TE seTE
#>        1           TCA    SNRI NA   NA
#>        1           TCA Placebo NA   NA
#>        1          SNRI Placebo NA   NA
#>       11           TCA    SSRI NA   NA
#>       11           TCA Placebo NA   NA
#>       11          SSRI Placebo NA   NA
#>       14           TCA    SSRI NA   NA
#>       18           TCA Placebo NA   NA
#>       20           TCA    SSRI NA   NA
#>       26          SSRI Placebo NA   NA
#>       53          SSRI   NaSSa NA   NA
#>       53          SSRI Placebo NA   NA
#>       53         NaSSa Placebo NA   NA
#>       56           TCA    SSRI NA   NA
#>       73     Hypericum Placebo NA   NA
#>       90           TCA    SSRI NA   NA
#>       96           TCA    SSRI NA   NA
#>      121 Low-dose SARI   NaSSa NA   NA
#>      130           TCA   NaSSa NA   NA
#>      130           TCA Placebo NA   NA
#>      130         NaSSa Placebo NA   NA
#> 
net2
#> Number of studies: k = 53
#> Number of pairwise comparisons: m = 61
#> Number of observations: o = 12220
#> Number of treatments: n = 9
#> Number of designs: d = 18
#> 
#> Random effects model
#> 
#> Treatment estimate (sm = 'OR', comparison: other treatments vs 'Placebo'):
#>                 OR      95%-CI    z  p-value
#> TCA           1.92 (1.54-2.41) 5.71 < 0.0001
#> SSRI          1.83 (1.48-2.27) 5.52 < 0.0001
#> SNRI          2.07 (1.51-2.84) 4.49 < 0.0001
#> NRI           1.83 (1.06-3.15) 2.18   0.0289
#> Low-dose SARI 1.91 (1.21-3.01) 2.76   0.0058
#> NaSSa         1.58 (1.02-2.45) 2.03   0.0424
#> rMAO-A        1.57 (1.06-2.33) 2.26   0.0237
#> Hypericum     2.04 (1.58-2.63) 5.43 < 0.0001
#> Placebo          .           .    .        .
#> 
#> Quantifying heterogeneity / inconsistency:
#> tau^2 = 0.0233; tau = 0.1528; I^2 = 19.3% (0.0%-43.6%)
#> 
#> Tests of heterogeneity (within designs) and inconsistency (between designs):
#>                     Q d.f. p-value
#> Total           60.71   49  0.1218
#> Within designs  36.83   36  0.4303
#> Between designs 23.88   13  0.0323

### Ranking of treatments
nr1 <- netrank(net1)
nr2 <- netrank(net2)
nr1
#>               P-score
#> Hypericum      0.8939
#> Low-dose SARI  0.7201
#> SNRI           0.6892
#> TCA            0.6802
#> SSRI           0.6164
#> NRI            0.4445
#> NaSSa          0.2128
#> rMAO-A         0.1521
#> Placebo        0.0908
nr2
#>               P-score
#> SNRI           0.7681
#> Hypericum      0.7453
#> TCA            0.6481
#> Low-dose SARI  0.6155
#> NRI            0.5513
#> SSRI           0.5111
#> NaSSa          0.3346
#> rMAO-A         0.3196
#> Placebo        0.0063

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


# }