dat.tian2009: Studies on the Effect of Rosiglitazone on the Risk of...

In metadat: Meta-Analysis Datasets

Studies on the Effect of Rosiglitazone on the Risk of Myocardial Infarction and Death from Cardiovascular Causes

Description

Results from 48 trials examining the effect of rosiglitazone on the risk of myocardial infarction and death from cardiovascular causes.

Usage

dat.tian2009

Format

The data frame contains the following columns:

study	character	Study ID
group	character	Treatment group (rosiglitazone or control)
intervention	character	Intervention group, i.e., combination of drug classes
detailed.intervention	character	Intervention group with drug names instead of classes
phase	integer	Phase of clinical trial
duration	integer	Duration of the trial (in weeks)
n.all	integer	Number of patients
n	integer	Number of patients with information on outcomes
deaths	integer	Number of deaths
infarcts	integer	Number of myocardial infarctions
type	character	Study types as in Table 1 from Nissen and Wolski (2007)
population	character	Details on the trial population
period	character	Trial period
age	numeric	Mean age (in years)
male	numeric	Percentage of males
hemoglobin	numeric	Mean baseline glycated hemoglobin level

Details

Nissen and Wolski (2007) performed a meta-analysis of trials examining the effect of rosiglitazone on the risk of myocardial infarction and death from cardiovascular causes. The present dataset by Tian et al. (2009) is based on this meta-analysis, but includes six additional trials where no event was observed in either group for both outcomes. The data set is in long arm-based format. See dat.nissen2007 for the original dataset.

Concepts

medicine, cardiology, odds ratios, Peto's method, generalized linear models

Author(s)

Guido Schwarzer, [e-mail], https://github.com/guido-s/

Source

Tian, L., Cai, T., Pfeffer, M. A., Piankov, N., Cremieux, P.-Y., & Wei, L. J. (2009). Exact and efficient inference procedure for meta-analysis and its application to the analysis of independent 2 x 2 tables with all available data but without artificial continuity correction. Biostatistics, 10(2), 275–281. ⁠https://doi.org/10.1093/biostatistics/kxn034⁠

References

Nissen, S. E., & Wolski, K. (2007). Effect of Rosiglitazone on the risk of myocardial infarction and death from cardiovascular causes. New England Journal of Medicine, 356(24), 2457-2471. ⁠https://doi.org/10.1056/NEJMoa072761⁠

See Also

dat.nissen2007

Examples

### Show first 6 rows / 3 trials of the dataset
head(dat.tian2009)

## Not run: 
### Load meta package
suppressPackageStartupMessages(library(meta))

### Print odds ratios and confidence limits with two digits and define
### labels shown in forest plots
oldset <- settings.meta(digits = 2,
  label.e = "Rosiglitazone", label.c = "Control",
  label.left = "Favors rosiglitazome",
  label.right = "Favors control")

### Transform data from long arm-based format to contrast-based
### format. 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.
pw <- pairwise(treat = group, event = deaths, n = n, studlab = study,
  data = dat.tian2009, sm = "OR", varnames = c("lnOR", "selnOR"))

### Fit the inverse variance model
mod_iv <- metabin(pw,
  text.common = "IV method (common)",
  text.random = "IV method (random)")

### Fit the Mantel-Haenszel model
mod_mh <- update(mod_iv, method = "MH", random = FALSE,
  method.incr = gs("method.incr"),
  text.common = "MH method")

### Fit the Peto model
mod_peto <- update(mod_mh, method = "Peto",
  text.common = "Peto method")

### Fit generalized linear mixed models (GLMM)
mod_glmm <- update(mod_mh, method = "glmm", model = "CM.AL",
  random = TRUE,
  text.common = "GLMM (common)",
  text.random = "GLMM (random)")

if (requireNamespace("brglm2", quietly = TRUE)) {
### Fit the logistic regression model with penalized likelihood (LRP)
mod_plma <- update(mod_glmm, method = "LRP",
  text.common = "LRP method (common)",
  text.random = "LRP method (random)")

### Fit the logistic regression model after excluding double zero studies
mod_plma1 <- metabin(event1, n1, event2, n2, studlab = study,
  data = subset(pw, !is.na(lnOR)), sm = "OR", method = "LRP",
  text.common = "LRP method, exclude zeros (common)",
  text.random = "LRP method, exclude zeros (random)")
}

### Create forest plot with all results
mm <- metaadd(mod_iv, data = mod_mh)
mm <- metaadd(mm, data = mod_peto)
mm <- metaadd(mm, data = mod_glmm)

if (requireNamespace("brglm2", quietly = TRUE)) {
mm <- metaadd(mm, data = mod_plma)
mm <- metaadd(mm, data = mod_plma1)
}

fname <- tempfile(pattern = "forest", fileext = ".pdf")
forest(mm, hetstat = FALSE, file = fname, width = 10, rows.gr = 1)

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

## End(Not run)

metadat documentation built on April 29, 2026, 5:10 p.m.