Trials on Haloperidol in Schizophrenia — dat.higgins2008 • metadat

Results on clinical improvement after therapy from 17 trials evaluating haloperidol in patients with schizophrenia.

dat.higgins2008

Format

The data frame contains the following columns:

author	`character`	study label
resp.halo	`integer`	number of responders (haloperidol group)
fail.halo	`integer`	number of failures (haloperidol group)
miss.halo	`integer`	number of missing observations (haloperidol group)
resp.plac	`integer`	number of responders (placebo group)
fail.plac	`integer`	number of failures (placebo group)
miss.plac	`integer`	number of missing observations (placebo group)

Details

Higgins et al. (2008) suggested several imputation methods for the meta-analysis of binary outcomes with missing data. The example data set with 17 trials comes originally from a Cochrane review comparing haloperidol with placebo for the treatment of schizophrenia. While the antipsychotic benefits of haloperidol were identified in the 1950s, trials in this patient population are prone to high proportions of missing outcome data, often due to insufficient compliance with randomized controlled trial protocols.

The outcome is clinical improvement after therapy. For each study, the number of responders, failures, and missing observations are available.

Source

Higgins, J. P. T., White, I. R., & Wood, A. M. (2008). Imputation methods for missing outcome data in meta-analysis of clinical trials. Clinical Trials, 5(3), 225–239. https://doi.org/10.1177/1740774508091600

Author

Guido Schwarzer, guido.schwarzer@uniklinik-freiburg.de, https://github.com/guido-s/

Concepts

psychiatry, odds ratios, missing data

Examples

### Show first five studies
head(dat.higgins2008, 5)
#>      author resp.halo fail.halo miss.halo resp.plac fail.plac miss.plac
#> 1  Avanitis        25        25         2        18        33         0
#> 2   Beasley        29        18        22        20        14        34
#> 3  Bechelli        12        17         1         2        28         1
#> 4   Borison         3         9         0         0        12         0
#> 5 Chouinard        10        11         0         3        19         0

### Load metasens package
suppressPackageStartupMessages(library(metasens))

### Print odds ratios and confidence limits with three digits
oldset <- settings.meta(digits = 3)

### Conduct common effect meta-analysis of available data
m <- metabin(resp.halo, resp.halo + fail.halo,
  resp.plac, resp.plac + fail.plac,
  data = dat.higgins2008, studlab = author,
  sm = "OR", method = "Inverse", random = FALSE,
  label.e = "Haloperidol", label.c = "Placebo",
  label.left = "Favours placebo",
  label.right = "Favours haloperidol")

### Best case scenario for haloperidol
m.b <- metamiss(m, miss.halo, miss.plac,
  method.miss = "b", small.values = "undesirable")

### Worst case scenario for haloperidol
m.w <- metamiss(m, miss.halo, miss.plac,
  method.miss = "w", small.values = "undesirable")

### Forest plot
m.sens <- metamerge(m, m.b, text.pooled2 = "Best case scenario")
m.sens <- metamerge(m.sens, m.w, text.pooled2 = "Worst case scenario")
forest(m.sens)