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:

 study numeric study number source character source of data n1i numeric number of patients under specialized care m1i numeric mean length of stay (in days) under specialized care sd1i numeric standard deviation of the length of stay under specialized care n2i numeric number of patients under routine care m2i numeric mean length of stay (in days) under routine care sd2i numeric standard 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

## Author

Wolfgang Viechtbauer, wvb@metafor-project.org, https://www.metafor-project.org

## 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{

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

### 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
#>

# }