Studies on Potassium Supplementation to Reduce Diastolic Blood Pressure

Results from 21 cross-over studies evaluating the effect of potassium supplementation to reduce diastolic blood pressure.

dat.curtin2002

Format

The data frame contains the following columns:

author	character	first author
year	character	year of publication
N	integer	total sample size
mean	numeric	mean difference in diastolic blood pressure
SE	numeric	standard error
corr	numeric	within-patient correlation

Details

Results from 21 cross-over studies evaluating the effect of potassium supplementation to reduce diastolic blood pressure (Curtin et al., 2002, Table II).

This data set is used as an example in Schwarzer et al. (2015), Chapter 2.

Source

Curtin, F., Altman, D. G., & Elbourne, D. (2002). Meta-analysis combining parallel and cross-over clinical trials. I: Continuous outcomes. Statistics in Medicine, 21(15), 2131–2144. https://doi.org/10.1002/sim.1205

References

Schwarzer, G., Carpenter, J. R., & Rücker, G. (2015). Meta-analysis with R. Cham, Switzerland: Springer.

Author

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

Concepts

raw mean differences

Examples

### Show first five studies
head(dat.curtin2002, 5)
#>             author  year  N mean  SE corr
#> 1   Skrabal et al. 1981a 20 -4.5 2.1 0.49
#> 2   Skrabal et al. 1981b 20 -0.5 1.7 0.54
#> 3 MacGregor et al.  1982 23 -4.0 1.9 0.41
#> 4    Khaw and Thom  1982 20 -2.4 1.1 0.83
#> 5  Richards et al.  1984 12 -1.0 3.4 0.50

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

### Use DerSimonian-Laird estimator (which was the default in meta in the year 2015).
### Furthermore, print meta-analysis results with two digits.
oldset <- settings.meta(method.tau = "DL", digits = 2)

### Conduct meta-analysis
mg2 <- metagen(mean, SE, studlab = paste(author, year),
               data = dat.curtin2002, sm = "MD")
mg2
#> Number of studies: k = 21
#> 
#>                         MD         95%-CI      z  p-value
#> Common effect model  -3.71 [-4.32; -3.11] -12.03 < 0.0001
#> Random effects model -2.38 [-4.76; -0.01]  -1.96   0.0495
#> 
#> Quantifying heterogeneity:
#>  tau^2 = 27.0262 [8.5264; 41.2225]; tau = 5.1987 [2.9200; 6.4205]
#>  I^2 = 92.5% [89.9%; 94.5%]; H = 3.66 [3.14; 4.25]
#> 
#> Test of heterogeneity:
#>       Q d.f.  p-value
#>  267.24   20 < 0.0001
#> 
#> Details on meta-analytical method:
#> - Inverse variance method
#> - DerSimonian-Laird estimator for tau^2
#> - Jackson method for confidence interval of tau^2 and tau

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