Results from 9 studies examining the effects of diuretics in pregnancy on various outcomes.

dat.collins1985b

Format

The data frame contains the following columns:

idnumericstudy number
authorcharacterstudy author(s)
yearnumericpublication year
pre.ntinumericnumber of women in treatment group followed up for pre-eclampsia outcome
pre.ncinumericnumber of women in control/placebo group followed up for pre-eclampsia outcome
pre.xtinumericnumber of women in treatment group with any form of pre-eclampsia
pre.xcinumericnumber of women in control/placebo group with any form of pre-eclampsia
oedemanumericdummy variable indicating whether oedema was a diagnostic criterion
fup.ntinumericnumber of women in treatment group followed up for mortality outcomes
fup.ncinumericnumber of women in control/placebo group followed up for mortality outcomes
ped.xtinumericnumber of perinatal deaths in treatment group
ped.xcinumericnumber of perinatal deaths in control/placebo group
stb.xtinumericnumber of stillbirths in treatment group
stb.xcinumericnumber of stillbirths in control/placebo group
ned.xtinumericnumber of neonatal deaths in treatment group
ned.xcinumericnumber of neonatal deaths in control/placebo group

Details

The 9 studies in this dataset examined the effects of diuretics in pregnancy on various outcomes, including the presence of any form of pre-eclampsia, perinatal death, stillbirth, and neonatal death.

Source

Collins, R., Yusuf, S., & Peto, R. (1985). Overview of randomised trials of diuretics in pregnancy. British Medical Journal, 290(6461), 17--23. https://doi.org/10.1136/bmj.290.6461.17

Examples

### copy data into 'dat' dat <- dat.collins1985b ### calculate (log) odds ratio and sampling variance dat <- escalc(measure="OR", n1i=pre.nti, n2i=pre.nci, ai=pre.xti, ci=pre.xci, data=dat) summary(dat, digits=2, transf=exp)
#> id author year pre.nti pre.nci pre.xti pre.xci oedema #> 1 1 Weseley & Douglas 1962 131 136 14 14 0 #> 2 2 Flowers et al. 1962 385 134 21 17 0 #> 3 3 Menzies 1964 57 48 14 24 1 #> 4 4 Fallis et al. 1964 38 40 6 18 0 #> 5 5 Cuadros & Tatum 1964 1011 760 12 35 1 #> 6 6 Landesman et al. 1965 1370 1336 138 175 0 #> 7 7 Kraus et al. 1966 506 524 15 20 0 #> 8 8 Tervila & Vartiainen 1971 108 103 6 2 0 #> 9 9 Campbell & MacGillivray 1975 153 102 65 40 0 #> fup.nti fup.nci ped.xti ped.xci stb.xti stb.xci ned.xti ned.xci yi ci.lb #> 1 131 136 1 4 1 2 0 2 1.04 0.48 #> 2 335 110 6 3 3 2 3 1 0.40 0.20 #> 3 57 48 3 2 1 1 2 1 0.33 0.14 #> 4 34 40 1 3 0 1 1 2 0.23 0.08 #> 5 1011 760 14 13 6 5 8 8 0.25 0.13 #> 6 1370 1336 24 19 NA NA NA NA 0.74 0.59 #> 7 506 524 14 16 6 9 8 7 0.77 0.39 #> 8 108 103 0 0 0 0 0 0 2.97 0.59 #> 9 153 102 0 0 0 0 0 0 1.14 0.69 #> ci.ub #> 1 2.28 #> 2 0.78 #> 3 0.74 #> 4 0.67 #> 5 0.48 #> 6 0.94 #> 7 1.52 #> 8 15.07 #> 9 1.91
### meta-analysis using Peto's method for any form of pre-eclampsia rma.peto(n1i=pre.nti, n2i=pre.nci, ai=pre.xti, ci=pre.xci, data=dat, digits=2)
#> #> Fixed-Effects Model (k = 9) #> #> I^2 (total heterogeneity / total variability): 72.74% #> H^2 (total variability / sampling variability): 3.67 #> #> Test for Heterogeneity: #> Q(df = 8) = 29.34, p-val < .01 #> #> Model Results (log scale): #> #> estimate se zval pval ci.lb ci.ub #> -0.41 0.09 -4.65 <.01 -0.58 -0.24 #> #> Model Results (OR scale): #> #> estimate ci.lb ci.ub #> 0.66 0.56 0.79 #>
### meta-analysis including only studies where oedema was not a diagnostic criterion rma.peto(n1i=pre.nti, n2i=pre.nci, ai=pre.xti, ci=pre.xci, data=dat, digits=2, subset=(oedema==0))
#> #> Fixed-Effects Model (k = 7) #> #> I^2 (total heterogeneity / total variability): 60.84% #> H^2 (total variability / sampling variability): 2.55 #> #> Test for Heterogeneity: #> Q(df = 6) = 15.32, p-val = 0.02 #> #> Model Results (log scale): #> #> estimate se zval pval ci.lb ci.ub #> -0.28 0.09 -2.97 <.01 -0.47 -0.10 #> #> Model Results (OR scale): #> #> estimate ci.lb ci.ub #> 0.76 0.63 0.91 #>
### meta-analyses of mortality outcomes (perinatal deaths, stillbirths, and neonatal deaths) rma.peto(n1i=fup.nti, n2i=fup.nci, ai=ped.xti, ci=ped.xci, data=dat, digits=2)
#> Warning: Some yi/vi values are NA.
#> #> Fixed-Effects Model (k = 9) #> #> I^2 (total heterogeneity / total variability): 0.00% #> H^2 (total variability / sampling variability): 0.58 #> #> Test for Heterogeneity: #> Q(df = 6) = 3.49, p-val = 0.75 #> #> Model Results (log scale): #> #> estimate se zval pval ci.lb ci.ub #> -0.09 0.18 -0.50 0.62 -0.45 0.27 #> #> Model Results (OR scale): #> #> estimate ci.lb ci.ub #> 0.91 0.64 1.31 #>
rma.peto(n1i=fup.nti, n2i=fup.nci, ai=stb.xti, ci=stb.xci, data=dat, digits=2)
#> Warning: Tables with NAs omitted from model fitting.
#> Warning: Some yi/vi values are NA.
#> #> Fixed-Effects Model (k = 8) #> #> I^2 (total heterogeneity / total variability): 0.00% #> H^2 (total variability / sampling variability): 0.20 #> #> Test for Heterogeneity: #> Q(df = 5) = 0.99, p-val = 0.96 #> #> Model Results (log scale): #> #> estimate se zval pval ci.lb ci.ub #> -0.39 0.34 -1.16 0.25 -1.05 0.27 #> #> Model Results (OR scale): #> #> estimate ci.lb ci.ub #> 0.68 0.35 1.31 #>
rma.peto(n1i=fup.nti, n2i=fup.nci, ai=ned.xti, ci=ned.xci, data=dat, digits=2)
#> Warning: Tables with NAs omitted from model fitting.
#> Warning: Some yi/vi values are NA.
#> #> Fixed-Effects Model (k = 8) #> #> I^2 (total heterogeneity / total variability): 0.00% #> H^2 (total variability / sampling variability): 0.51 #> #> Test for Heterogeneity: #> Q(df = 5) = 2.54, p-val = 0.77 #> #> Model Results (log scale): #> #> estimate se zval pval ci.lb ci.ub #> -0.15 0.31 -0.47 0.64 -0.76 0.47 #> #> Model Results (OR scale): #> #> estimate ci.lb ci.ub #> 0.86 0.47 1.59 #>