Results from 12 trials examining the effectiveness of a reduced versus standard rehydration solution for the prevention of unscheduled intravenous infusion in children with diarrhoea.

dat.hahn2001

Format

The data frame contains the following columns:

studycharactertrial name and year
ainumericnumber of children requiring unscheduled intravenous infusion in the reduced rehydration solution group
n1inumericnumber of children in the reduced rehydration solution group
cinumericnumber of children requiring unscheduled intravenous infusion in the standard rehydration solution group
n2inumericnumber of children in the standard rehydration solution group

Details

The dataset includes the results from 12 randomized clinical trials that examined the effectiveness of a reduced osmolarity oral rehydration solution (total osmolarity <250 mmol/l with reduced sodium) with a standard WHO oral rehydration solution (sodium 90 mmol/l, glucose 111mmol/l, total osmolarity 311 mmol/l) for the prevention of unscheduled intravenous infusion in children with diarrhoea.

Source

Hahn, S., Kim, Y., & Garner, P. (2001). Reduced osmolarity oral rehydration solution for treating dehydration due to diarrhoea in children: Systematic review. British Medical Journal, 323(7304), 81–85. https://doi.org/10.1136/bmj.323.7304.81

Concepts

medicine, odds ratios, Mantel-Haenszel method

Examples

### copy data into 'dat' and examine data
dat <- dat.hahn2001
dat
#>               study ai n1i ci n2i
#> 1  Banhladesh 1995a  4  19  5  19
#> 2  Banhladesh 1996a  0  18  0  18
#> 3       CHOICE 2001 34 341 50 334
#> 4     Colombia 2000  7  71 16  69
#> 5       Egypt 1886a  6  45  5  44
#> 6       Egypt 1996b  1  94  8  96
#> 7       India 1984a  0  22  0  22
#> 8       India 2000b 11  88 12  82
#> 9      Mexico 1990a  2  82  7  84
#> 10      Panama 1982  0  33  0  30
#> 11         USA 1982  0  15  1  20
#> 12         WHO 1995 33 221 43 218

# \dontrun{

### load metafor package
library(metafor)

### meta-analysis of (log) odds rations using the Mantel-Haenszel method
res <- rma.mh(measure="OR", ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat, digits=2, slab=study)
#> Warning: Some yi/vi values are NA.
res
#> 
#> Equal-Effects Model (k = 12)
#> 
#> I^2 (total heterogeneity / total variability):  0.00%
#> H^2 (total variability / sampling variability): 0.82
#> 
#> Test for Heterogeneity: 
#> Q(df = 8) = 6.53, p-val = 0.59
#> 
#> Model Results (log scale):
#> 
#> estimate    se   zval  pval  ci.lb  ci.ub 
#>    -0.49  0.14  -3.51  <.01  -0.77  -0.22 
#> 
#> Model Results (OR scale):
#> 
#> estimate  ci.lb  ci.ub 
#>     0.61   0.46   0.80 
#> 
#> Cochran-Mantel-Haenszel Test:    CMH = 12.00, df = 1, p-val < 0.01
#> Tarone's Test for Heterogeneity: X^2 =  7.58, df = 8, p-val = 0.48
#> 

### forest plot (also show studies that were excluded from the analysis)
options(na.action="na.pass")
forest(res, atransf=exp, at=log(c(.01, .1, 1, 10, 100)), header=TRUE)

options(na.action="na.omit")

# }