Results from 9 studies on the length of the hospital stay of stroke patients under specialized care and under conventional/routine (non-specialist) care.

dat.normand1999

Format

The data frame contains the following columns:

studynumericstudy number
sourcecharactersource of data
n1inumericnumber of patients under specialized care
m1inumericmean length of stay (in days) under specialized care
sd1inumericstandard deviation of the length of stay under specialized care
n2inumericnumber of patients under routine care
m2inumericmean length of stay (in days) under routine care
sd2inumericstandard deviation of the length of stay under routine care

Details

The 9 studies provide data in terms of the mean length of the hospital stay (in days) of stroke patients under specialized care and under conventional/routine (non-specialist) care. The goal of the meta-analysis was to examine the hypothesis whether specialist stroke unit care will result in a shorter length of hospitalization compared to routine management.

Source

Normand, S. T. (1999). Meta-analysis: Formulating, evaluating, combining, and reporting. Statistics in Medicine, 18(3), 321--359. https://doi.org/10.1002/(sici)1097-0258(19990215)18:3<321::aid-sim28>3.0.co;2-p

Examples

### copy data into 'dat' and examine data
dat <- dat.normand1999
dat
#>   study             source n1i m1i sd1i n2i m2i sd2i
#> 1     1          Edinburgh 155  55   47 156  75   64
#> 2     2     Orpington-Mild  31  27    7  32  29    4
#> 3     3 Orpington-Moderate  75  64   17  71 119   29
#> 4     4   Orpington-Severe  18  66   20  18 137   48
#> 5     5      Montreal-Home   8  14    8  13  18   11
#> 6     6  Montreal-Transfer  57  19    7  52  18    4
#> 7     7          Newcastle  34  52   45  33  41   34
#> 8     8               Umea 110  21   16 183  31   27
#> 9     9            Uppsala  60  30   27  52  23   20

# \dontrun{

### load metafor package
library(metafor)

### calculate mean differences and corresponding sampling variances
dat <- escalc(measure="MD", m1i=m1i, sd1i=sd1i, n1i=n1i, m2i=m2i, sd2i=sd2i, n2i=n2i, data=dat)
dat
#> 
#>   study             source n1i m1i sd1i n2i m2i sd2i       yi       vi 
#> 1     1          Edinburgh 155  55   47 156  75   64 -20.0000  40.5080 
#> 2     2     Orpington-Mild  31  27    7  32  29    4  -2.0000   2.0806 
#> 3     3 Orpington-Moderate  75  64   17  71 119   29 -55.0000  15.6984 
#> 4     4   Orpington-Severe  18  66   20  18 137   48 -71.0000 150.2222 
#> 5     5      Montreal-Home   8  14    8  13  18   11  -4.0000  17.3077 
#> 6     6  Montreal-Transfer  57  19    7  52  18    4   1.0000   1.1673 
#> 7     7          Newcastle  34  52   45  33  41   34  11.0000  94.5891 
#> 8     8               Umea 110  21   16 183  31   27 -10.0000   6.3109 
#> 9     9            Uppsala  60  30   27  52  23   20   7.0000  19.8423 
#> 

### meta-analysis of mean differences using a random-effects model
res <- rma(yi, vi, data=dat)
res
#> 
#> Random-Effects Model (k = 9; tau^2 estimator: REML)
#> 
#> tau^2 (estimated amount of total heterogeneity): 684.6462 (SE = 359.7541)
#> tau (square root of estimated tau^2 value):      26.1657
#> I^2 (total heterogeneity / total variability):   98.97%
#> H^2 (total variability / sampling variability):  97.21
#> 
#> Test for Heterogeneity:
#> Q(df = 8) = 238.9158, p-val < .0001
#> 
#> Model Results:
#> 
#> estimate      se     zval    pval     ci.lb   ci.ub   ​ 
#> -15.1060  8.9466  -1.6885  0.0913  -32.6409  2.4289  . 
#> 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 

### meta-analysis of standardized mean differences using a random-effects model
res <- rma(measure="SMD", m1i=m1i, sd1i=sd1i, n1i=n1i, m2i=m2i, sd2i=sd2i, n2i=n2i,
           data=dat, slab=source)
res
#> 
#> Random-Effects Model (k = 9; tau^2 estimator: REML)
#> 
#> tau^2 (estimated amount of total heterogeneity): 0.7908 (SE = 0.4281)
#> tau (square root of estimated tau^2 value):      0.8893
#> I^2 (total heterogeneity / total variability):   95.49%
#> H^2 (total variability / sampling variability):  22.20
#> 
#> Test for Heterogeneity:
#> Q(df = 8) = 123.7293, p-val < .0001
#> 
#> Model Results:
#> 
#> estimate      se     zval    pval    ci.lb   ci.ub   ​ 
#>  -0.5371  0.3087  -1.7401  0.0818  -1.1421  0.0679  . 
#> 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 

### draw forest plot
forest(res, xlim=c(-7,5), alim=c(-3,1), header="Study/Source")


### calculate (log transformed) ratios of means and corresponding sampling variances
dat <- escalc(measure="ROM", m1i=m1i, sd1i=sd1i, n1i=n1i, m2i=m2i, sd2i=sd2i, n2i=n2i, data=dat)
dat
#> 
#>   study             source n1i m1i sd1i n2i m2i sd2i      yi     vi 
#> 1     1          Edinburgh 155  55   47 156  75   64 -0.3102 0.0094 
#> 2     2     Orpington-Mild  31  27    7  32  29    4 -0.0715 0.0028 
#> 3     3 Orpington-Moderate  75  64   17  71 119   29 -0.6202 0.0018 
#> 4     4   Orpington-Severe  18  66   20  18 137   48 -0.7303 0.0119 
#> 5     5      Montreal-Home   8  14    8  13  18   11 -0.2513 0.0695 
#> 6     6  Montreal-Transfer  57  19    7  52  18    4  0.0541 0.0033 
#> 7     7          Newcastle  34  52   45  33  41   34  0.2377 0.0429 
#> 8     8               Umea 110  21   16 183  31   27 -0.3895 0.0094 
#> 9     9            Uppsala  60  30   27  52  23   20  0.2657 0.0280 
#> 

### meta-analysis of the (log transformed) ratios of means using a random-effects model
res <- rma(yi, vi, data=dat)
res
#> 
#> Random-Effects Model (k = 9; tau^2 estimator: REML)
#> 
#> tau^2 (estimated amount of total heterogeneity): 0.1080 (SE = 0.0620)
#> tau (square root of estimated tau^2 value):      0.3287
#> I^2 (total heterogeneity / total variability):   94.35%
#> H^2 (total variability / sampling variability):  17.71
#> 
#> Test for Heterogeneity:
#> Q(df = 8) = 148.8186, p-val < .0001
#> 
#> Model Results:
#> 
#> estimate      se     zval    pval    ci.lb   ci.ub   ​ 
#>  -0.2184  0.1178  -1.8541  0.0637  -0.4492  0.0125  . 
#> 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
predict(res, transf=exp, digits=2)
#> 
#>  pred ci.lb ci.ub pi.lb pi.ub 
#>  0.80  0.64  1.01  0.41  1.59 
#> 

# }