Results from studies examining the effectiveness of histamine H2 antagonists (cimetidine or ranitidine) in treating patients with acute upper gastrointestinal hemorrhage.

dat.collins1985a

Format

The data frame contains the following columns:

idnumericstudy number
trialcharacterfirst author of trial
yearnumericyear of publication
refnumericreference number
trtcharacterC = cimetidine, R = ranitidine
ctrlcharacterP = placebo, AA = antacids, UT = usual treatment
ntinumericnumber of patients in treatment group
b.xtinumericnumber of patients in treatment group with persistent or recurrent bleedings
o.xtinumericnumber of patients in treatment group in need of operation
d.xtinumericnumber of patients in treatment group that died
ncinumericnumber of patients in control group
b.xcinumericnumber of patients in control group with persistent or recurrent bleedings
o.xcinumericnumber of patients in control group in need of operation
d.xcinumericnumber of patients in control group that died

Details

The data were obtained from Tables 1 and 2 in Collins and Langman (1985). The authors used Peto's (one-step) method for meta-analyzing the 27 trials. This approach is implemented in the rma.peto function. Using the same dataset, van Houwelingen, Zwinderman, and Stijnen (1993) describe some alternative approaches for analyzing these data, including fixed- and random-effects conditional logistic models. Those are implemented in the rma.glmm function.

Source

Collins, R., & Langman, M. (1985). Treatment with histamine H2 antagonists in acute upper gastrointestinal hemorrhage. New England Journal of Medicine, 313(11), 660--666. https://doi.org/10.1056/NEJM198509123131104

References

van Houwelingen, H. C., Zwinderman, K. H., & Stijnen, T. (1993). A bivariate approach to meta-analysis. Statistics in Medicine, 12(24), 2273--2284. https://doi.org/10.1002/sim.4780122405

Examples

### copy data into 'dat' and examine data
dat <- dat.collins1985a
dat
#>    id       trial year ref trt ctrl nti b.xti o.xti d.xti nci b.xci o.xci d.xci
#> 1   1       Hoare 1979   3   C    P  50     8     5     1  50    16    12     1
#> 2   2    La Brooy 1979   4   C    P  56    11     6     1  53    12     7     0
#> 3   3     Macklon 1979   5   C    P  18     5    NA    NA  12     1    NA    NA
#> 4   4        Nair 1979   6   C   AA  14     2     2     0  15     6     3     4
#> 5   5     Pickard 1979   7   C    P  33    12    NA    NA  36    10    NA    NA
#> 6   6     Siddiqi 1979   8   C   UT  58    NA     8     4  55    NA     8     5
#> 7   7  Carstensen 1980   9   C    P  40     9    15     4  48    11    16     5
#> 8   8        Foco 1980  10   C    P  20     7     2     0  20     8     3     2
#> 9   9    Galmiche 1980  11   C    P  46     5     5     2  47    12     6     5
#> 10 10     Gilsanz 1980  12   C    P  18     0    NA     0  19     3    NA     1
#> 11 11    Meredith 1980  13   C    P  45     9     6     1  43     3     1     0
#> 12 12       Teres 1980  14   C    P  21    10     0     6  19     8     0     4
#> 13 13   Zuckerman 1984  15   C    P 153    44    NA    10 132    30    NA     4
#> 14 14   Colecchia 1981  16   C   UT  14     0     2     1  14     0     5     0
#> 15 15 Arvanitakis 1982  17   C   AA  10     1    NA    NA   9     4    NA    NA
#> 16 16     Hostein 1982  18   C   AA  24     4     2     3  24     5     3     1
#> 17 17      Dawson 1982  19   R    P  78    14    11     5  80    21    15     4
#> 18 18       Nowak 1984  20   R   AA  75    NA     5     1  75    NA    17     4
#> 19 19       Barer 1983  21   C    P 259    50    34    20 260    51    38    35
#> 20 20       Brown 1983  NA   C    P  10     3     2     1  11     1     0     0
#> 21 21       Stiel 1984  22   C    P  31     6     4     0  29    12     5     0
#> 22 22  Carr-Locke 1984  23   C    P  51    16     7     0  54    15     6     2
#> 23 23      Birnie 1984  24   C    P 106    16     2     4 107    15     7     4
#> 24 24       Darle 1984  25   C    P  33     6     6     1  39     7     7     5
#> 25 25   Karlstrom 1981  26   C   UT  36    11    10     3  26     5     3     2
#> 26 26     Londong 1982  27   C    P  34     3     3     1  31    13     8     7
#> 27 27        Foco 1984  28   R    P  15     2     0     0  14     3     0     0

# \dontrun{

### load metafor package
library(metafor)

### meta-analysis of log ORs using Peto's method (outcome: persistent or recurrent bleedings)
res <- rma.peto(ai=b.xti, n1i=nti, ci=b.xci, n2i=nci, data=dat)
#> Warning: Tables with NAs omitted from model fitting.
#> Warning: Some yi/vi values are NA.
print(res, digits=2)
#> 
#> Equal-Effects Model (k = 25)
#> 
#> I^2 (total heterogeneity / total variability):  39.33%
#> H^2 (total variability / sampling variability): 1.65
#> 
#> Test for Heterogeneity: 
#> Q(df = 23) = 37.91, p-val = 0.03
#> 
#> Model Results (log scale):
#> 
#> estimate    se   zval  pval  ci.lb  ci.ub 
#>    -0.12  0.10  -1.22  0.22  -0.32   0.07 
#> 
#> Model Results (OR scale):
#> 
#> estimate  ci.lb  ci.ub 
#>     0.89   0.73   1.08 
#> 

### meta-analysis of log ORs using a conditional logistic regression model (FE model)
res <- rma.glmm(measure="OR", ai=b.xti, n1i=nti, ci=b.xci, n2i=nci, data=dat,
                model="CM.EL", method="FE")
#> Warning: Studies with NAs omitted from model fitting.
#> Warning: Some yi/vi values are NA.
summary(res)
#> 
#> Fixed-Effects Model (k = 24)
#> Model Type: Conditional Model with Exact Likelihood
#> 
#>   logLik  deviance       AIC       BIC      AICc  ​ 
#> -53.6789   40.3423  109.3579  110.5359  109.5397   
#> 
#> Tests for Heterogeneity:
#> Wld(df = 23) = 32.5527, p-val = 0.0892
#> LRT(df = 23) = 40.3423, p-val = 0.0141
#> 
#> Model Results:
#> 
#> estimate      se     zval    pval    ci.lb   ci.ub   ​ 
#>  -0.1216  0.0996  -1.2211  0.2220  -0.3167  0.0736    
#> 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
predict(res, transf=exp, digits=2)
#> 
#>  pred ci.lb ci.ub 
#>  0.89  0.73  1.08 
#> 

### plot the likelihoods of the odds ratios
llplot(measure="OR", ai=b.xti, n1i=nti, ci=b.xci, n2i=nci, data=dat,
       lwd=1, refline=NA, xlim=c(-4,4), drop00=FALSE)
#> Warning: Studies with NAs omitted from plotting.


### meta-analysis of log odds ratios using a conditional logistic regression model (RE model)
res <- rma.glmm(measure="OR", ai=b.xti, n1i=nti, ci=b.xci, n2i=nci, data=dat,
                model="CM.EL", method="ML")
#> Warning: Studies with NAs omitted from model fitting.
#> Warning: Some yi/vi values are NA.
summary(res)
#> 
#> Random-Effects Model (k = 24; tau^2 estimator: ML)
#> Model Type: Conditional Model with Exact Likelihood
#> 
#>   logLik  deviance       AIC       BIC      AICc  ​ 
#> -52.9901   38.9647  109.9802  112.3363  110.5516   
#> 
#> tau^2 (estimated amount of total heterogeneity): 0.1192 (SE = 0.1396)
#> tau (square root of estimated tau^2 value):      0.3453
#> I^2 (total heterogeneity / total variability):   30.8590%
#> H^2 (total variability / sampling variability):  1.4463
#> 
#> Tests for Heterogeneity:
#> Wld(df = 23) = 32.5527, p-val = 0.0892
#> LRT(df = 23) = 40.3423, p-val = 0.0141
#> 
#> Model Results:
#> 
#> estimate      se     zval    pval    ci.lb   ci.ub   ​ 
#>  -0.1744  0.1364  -1.2786  0.2010  -0.4418  0.0929    
#> 
#> ---
#> 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 
#>  0.84  0.64  1.10  0.41  1.74 
#> 

### meta-analysis of log ORs using Peto's method (outcome: need for surgery)
res <- rma.peto(ai=o.xti, n1i=nti, ci=o.xci, n2i=nci, data=dat)
#> Warning: Tables with NAs omitted from model fitting.
#> Warning: Some yi/vi values are NA.
print(res, digits=2)
#> 
#> Equal-Effects Model (k = 22)
#> 
#> I^2 (total heterogeneity / total variability):  25.38%
#> H^2 (total variability / sampling variability): 1.34
#> 
#> Test for Heterogeneity: 
#> Q(df = 19) = 25.46, p-val = 0.15
#> 
#> Model Results (log scale):
#> 
#> estimate    se   zval  pval  ci.lb  ci.ub 
#>    -0.25  0.12  -1.97  0.05  -0.49  -0.00 
#> 
#> Model Results (OR scale):
#> 
#> estimate  ci.lb  ci.ub 
#>     0.78   0.61   1.00 
#> 

### meta-analysis of log ORs using Peto's method (outcome: death)
res <- rma.peto(ai=d.xti, n1i=nti, ci=d.xci, n2i=nci, data=dat)
#> Warning: Tables with NAs omitted from model fitting.
#> Warning: Some yi/vi values are NA.
print(res, digits=2)
#> 
#> Equal-Effects Model (k = 24)
#> 
#> I^2 (total heterogeneity / total variability):  23.17%
#> H^2 (total variability / sampling variability): 1.30
#> 
#> Test for Heterogeneity: 
#> Q(df = 21) = 27.33, p-val = 0.16
#> 
#> Model Results (log scale):
#> 
#> estimate    se   zval  pval  ci.lb  ci.ub 
#>    -0.36  0.16  -2.21  0.03  -0.68  -0.04 
#> 
#> Model Results (OR scale):
#> 
#> estimate  ci.lb  ci.ub 
#>     0.70   0.51   0.96 
#> 

# }