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' dat <- dat.normand1999 ### 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), cex=.8, header="Study/Source")