Results from 13 studies on the relationship between maternal body mass index (BMI) and the risk of preeclampsia.

dat.obrien2003

Format

The data frame contains the following columns:

studynumericstudy id
authorcharacter(first) author of the study
yearnumericpublication year
refnumericreference number
chcharacterexclusion due to chronic hypertension (yes/no)
dmcharacterexclusion due to diabetes mellitus (yes/no)
mgcharacterexclusion due to multiple gestation (yes/no)
bmi.lbnumericlower bound of the BMI interval
bmi.ubnumericupper bound of the BMI interval
bminumericmidpoint of the BMI interval
casesnumericnumber of preeclampsia cases in the BMI group
totalnumericnumber of individuals in the BMI group

Details

The dataset includes the results from 13 studies examining the relationship between maternal body mass index (BMI) and the risk of preeclampsia. For each study, results are given in terms of the number of preeclampsia cases within two or more groups defined by the lower and upper BMI bounds as shown in the dataset (NA means that the interval is either open to the left or right). The bmi variable is the interval midpoint as defined by O'Brien et al. (2003).

Source

O'Brien, T. E., Ray, J. G., & Chan, W.-S. (2003). Maternal body mass index and the risk of preeclampsia: A systematic overview. Epidemiology, 14(3), 368--374. https://doi.org/10.1097/00001648-200305000-00020

Examples

### copy data into 'dat' and examine data
dat <- dat.obrien2003
dat
#>    study        author year ref  ch  dm  mg grp bmi.lb bmi.ub   bmi cases  total
#> 1      1       Edwards 1996   8  no  no yes   1   19.8   26.0 22.90    28    660
#> 2      1       Edwards 1996   8  no  no yes   2   29.0     NA 29.10    68    683
#> 3      2         Sibai 1997   9 yes yes yes   1     NA   19.8 19.70    18    414
#> 4      2         Sibai 1997   9 yes yes yes   2   19.8   25.9 22.85   142   2253
#> 5      2         Sibai 1997   9 yes yes yes   3   26.0   34.9 30.45   118   1283
#> 6      2         Sibai 1997   9 yes yes yes   4   35.0     NA 35.10    48    360
#> 7      3      Ogunyemi 1998  10  no  no yes   1     NA   19.8 19.70     3     78
#> 8      3      Ogunyemi 1998  10  no  no yes   2   19.8   26.0 22.90     5    334
#> 9      3      Ogunyemi 1998  10  no  no yes   3   26.1   29.0 27.55    13     78
#> 10     3      Ogunyemi 1998  10  no  no yes   4   29.0     NA 29.10    25    203
#> 11     4           Ros 1998  16  no  no  no   1     NA   19.8 19.70    11    350
#> 12     4           Ros 1998  16  no  no  no   2   19.8   26.0 22.90    78   1720
#> 13     4           Ros 1998  16  no  no  no   3   26.1   29.0 27.55    19    208
#> 14     4           Ros 1998  16  no  no  no   4   29.0     NA 29.10    18    140
#> 15     5        Bianco 1998  11  no  no yes   1   19.0   27.0 23.00   357  11313
#> 16     5        Bianco 1998  11  no  no yes   2   35.0     NA 35.10    85    613
#> 17     6        Knuist 1998  17 yes yes yes   1     NA   19.8 19.70     7    422
#> 18     6        Knuist 1998  17 yes yes yes   2   19.8   26.0 22.90    16   1406
#> 19     6        Knuist 1998  17 yes yes yes   3   26.0     NA 26.10     5    252
#> 20     7      Thadhani 1999  13 yes  no  no   1     NA   21.0 20.90    22   5605
#> 21     7      Thadhani 1999  13 yes  no  no   2   21.0   22.9 21.95    22   4463
#> 22     7      Thadhani 1999  13 yes  no  no   3   23.0   24.9 23.95    17   3182
#> 23     7      Thadhani 1999  13 yes  no  no   4   25.0   29.9 27.45    12   2906
#> 24     7      Thadhani 1999  13 yes  no  no   5   30.0     NA 30.10    10   1055
#> 25     8        Bowers 1999  12 yes yes yes   1     NA   26.0 25.90     7    135
#> 26     8        Bowers 1999  12 yes yes yes   2   26.0   29.0 27.50     2     63
#> 27     8        Bowers 1999  12 yes yes yes   3   29.0     NA 29.10     5     85
#> 28     9           Lee 2000  19 yes yes  no   1     NA   19.8 19.70    80   9879
#> 29     9           Lee 2000  19 yes yes  no   2   19.8   24.2 22.00   261  17750
#> 30     9           Lee 2000  19 yes yes  no   3   24.2     NA 24.30    74   2106
#> 31    10 Conde-Agudelo 2000  18  no  no  no   1     NA   19.8 19.70  2901  86924
#> 32    10 Conde-Agudelo 2000  18  no  no  no   2   19.8   26.0 22.90 16885 362073
#> 33    10 Conde-Agudelo 2000  18  no  no  no   3   26.1   29.0 27.55  4264  61601
#> 34    10 Conde-Agudelo 2000  18  no  no  no   4   29.0     NA 29.10  4749  51172
#> 35    11     Steinfeld 2000  14  no  no yes   1     NA   29.0 28.90   114   2256
#> 36    11     Steinfeld 2000  14  no  no yes   2   29.0     NA 29.10    15    168
#> 37    12        Sebire 2001  20  no  no yes   1   20.0   24.9 22.45  1238 176923
#> 38    12        Sebire 2001  20  no  no yes   2   25.0   29.9 27.45   766  79014
#> 39    12        Sebire 2001  20  no  no yes   3   30.0     NA 30.10   447  31276
#> 40    13        Baeten 2001  15  no  no yes   1     NA   20.0 19.90   731  18893
#> 41    13        Baeten 2001  15  no  no yes   2   20.0   24.9 22.45  2866  50212
#> 42    13        Baeten 2001  15  no  no yes   3   25.0   29.9 27.45  1594  17501
#> 43    13        Baeten 2001  15  no  no yes   4   30.0     NA 30.10  1321   9778

# \dontrun{

### load metafor package
library(metafor)

### restructure the data into a wide format
dat2 <- to.wide(dat, study="study", grp="grp", ref=1, grpvars=c("bmi","cases","total"),
                addid=FALSE, adddesign=FALSE, postfix=c(1,2))
dat2[1:10, -c(2:3)]
#>    study ref  ch  dm  mg grp1 bmi.lb bmi.ub  bmi1 cases1 total1 grp2 bmi2 cases2 total2 comp
#> 1      1   8  no  no yes    2   29.0     NA 29.10     68    683    1 22.9     28    660  2-1
#> 2      2   9 yes yes yes    2   19.8   25.9 22.85    142   2253    1 19.7     18    414  2-1
#> 3      2   9 yes yes yes    3   26.0   34.9 30.45    118   1283    1 19.7     18    414  3-1
#> 4      2   9 yes yes yes    4   35.0     NA 35.10     48    360    1 19.7     18    414  4-1
#> 5      3  10  no  no yes    2   19.8   26.0 22.90      5    334    1 19.7      3     78  2-1
#> 6      3  10  no  no yes    3   26.1   29.0 27.55     13     78    1 19.7      3     78  3-1
#> 7      3  10  no  no yes    4   29.0     NA 29.10     25    203    1 19.7      3     78  4-1
#> 8      4  16  no  no  no    2   19.8   26.0 22.90     78   1720    1 19.7     11    350  2-1
#> 9      4  16  no  no  no    3   26.1   29.0 27.55     19    208    1 19.7     11    350  3-1
#> 10     4  16  no  no  no    4   29.0     NA 29.10     18    140    1 19.7     11    350  4-1

### calculate log risk ratios and corresponding sampling variances
dat2 <- escalc(measure="RR", ai=cases1, n1i=total1, ci=cases2, n2i=total2, data=dat2)
dat2[1:10, -c(2:7)]
#> 
#>    study grp1 bmi.lb bmi.ub  bmi1 cases1 total1 grp2 bmi2 cases2 total2 comp      yi     vi 
#> 1      1    2   29.0     NA 29.10     68    683    1 22.9     28    660  2-1  0.8530 0.0474 
#> 2      2    2   19.8   25.9 22.85    142   2253    1 19.7     18    414  2-1  0.3713 0.0597 
#> 3      2    3   26.0   34.9 30.45    118   1283    1 19.7     18    414  3-1  0.7492 0.0608 
#> 4      2    4   35.0     NA 35.10     48    360    1 19.7     18    414  4-1  1.1206 0.0712 
#> 5      3    2   19.8   26.0 22.90      5    334    1 19.7      3     78  2-1 -0.9436 0.5175 
#> 6      3    3   26.1   29.0 27.55     13     78    1 19.7      3     78  3-1  1.4663 0.3846 
#> 7      3    4   29.0     NA 29.10     25    203    1 19.7      3     78  4-1  1.1638 0.3556 
#> 8      4    2   19.8   26.0 22.90     78   1720    1 19.7     11    350  2-1  0.3667 0.1003 
#> 9      4    3   26.1   29.0 27.55     19    208    1 19.7     11    350  3-1  1.0669 0.1359 
#> 10     4    4   29.0     NA 29.10     18    140    1 19.7     11    350  4-1  1.4088 0.1365 
#> 

### forest plot of the risk ratios
dd <- c(0,diff(dat2$study))
dd[dd > 0] <- 1
rows <- (1:nrow(dat2)) + cumsum(dd)
rows <- 1 + max(rows) - rows
slabs <- mapply(function(x,y,z) as.expression(bquote(.(x)^.(y)~.(z))),
                dat2$author, dat2$ref, dat2$year)
with(dat2, forest(yi, vi, header=TRUE, slab=slabs, xlim=c(-7,5.5), fonts="mono", cex=0.8,
   psize=1, pch=19, efac=0, rows=rows, ylim=c(0,max(rows)+3), yaxs="i",
   atransf=exp, at=log(c(.05,0.1,0.2,0.5,1,2,5,10,20)), ilab=comp, ilab.xpos=-4, ilab.pos=4))
text(-4.4, max(rows)+2, "Comparison", font=2, cex=0.8, pos=4)


### within-study mean center the BMI variable
dat$bmicent <- dat$bmi - ave(dat$bmi, dat$study)

### compute the proportion of preeclampsia cases and corresponding sampling variances
dat <- escalc(measure="PR", xi=cases, ni=total, data=dat)

### convert the proportions to percentages (and convert the variances accordingly)
dat$yi <- dat$yi*100
dat$vi <- dat$vi*100^2
dat[1:10, -c(2:3)]
#> 
#>    study ref  ch  dm  mg grp bmi.lb bmi.ub   bmi cases total bmicent      yi      vi 
#> 1      1   8  no  no yes   1   19.8   26.0 22.90    28   660 -3.1000  4.2424  0.6155 
#> 2      1   8  no  no yes   2   29.0     NA 29.10    68   683  3.1000  9.9561  1.3126 
#> 3      2   9 yes yes yes   1     NA   19.8 19.70    18   414 -7.3250  4.3478  1.0045 
#> 4      2   9 yes yes yes   2   19.8   25.9 22.85   142  2253 -4.1750  6.3027  0.2621 
#> 5      2   9 yes yes yes   3   26.0   34.9 30.45   118  1283  3.4250  9.1972  0.6509 
#> 6      2   9 yes yes yes   4   35.0     NA 35.10    48   360  8.0750 13.3333  3.2099 
#> 7      3  10  no  no yes   1     NA   19.8 19.70     3    78 -5.1125  3.8462  4.7413 
#> 8      3  10  no  no yes   2   19.8   26.0 22.90     5   334 -1.9125  1.4970  0.4415 
#> 9      3  10  no  no yes   3   26.1   29.0 27.55    13    78  2.7375 16.6667 17.8063 
#> 10     3  10  no  no yes   4   29.0     NA 29.10    25   203  4.2875 12.3153  5.3195 
#> 

### fit multilevel meta-regression model to examine the relationship between the
### (centered) BMI variable and the risk of preeclampsia
res <- rma.mv(yi, vi, mods = ~ bmicent, random = ~ 1 | study/grp, data=dat)
res
#> 
#> Multivariate Meta-Analysis Model (k = 43; method: REML)
#> 
#> Variance Components:
#> 
#>             estim    sqrt  nlvls  fixed     factor 
#> sigma^2.1  7.2715  2.6966     13     no      study 
#> sigma^2.2  2.5192  1.5872     43     no  study/grp 
#> 
#> Test for Residual Heterogeneity:
#> QE(df = 41) = 20152.6718, p-val < .0001
#> 
#> Test of Moderators (coefficient 2):
#> QM(df = 1) = 57.0360, p-val < .0001
#> 
#> Model Results:
#> 
#>          estimate      se    zval    pval   ci.lb   ci.ub     ​ 
#> intrcpt    5.0125  0.8068  6.2129  <.0001  3.4312  6.5938  *** 
#> bmicent    0.5749  0.0761  7.5522  <.0001  0.4257  0.7241  *** 
#> 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 

### draw scatterplot with regression line
res$slab <- dat$ref
regplot(res, xlab=expression("Within-Study Mean Centered BMI"~(kg/m^2)),
        ylab="Preeclampsia Prevalence (%)", las=1, bty="l",
        at=seq(0,18,by=2), olim=c(0,100), psize=2, bg="gray90",
        label=TRUE, offset=0, labsize=0.6)


### fit model using a random slope for bmicent
res <- rma.mv(yi, vi, mods = ~ bmicent, random = ~ bmicent | study, struct="GEN", data=dat)
res
#> 
#> Multivariate Meta-Analysis Model (k = 43; method: REML)
#> 
#> Variance Components:
#> 
#> outer factor: study    (nlvls = 13)
#> inner term:   ~bmicent (nlvls = 33)
#> 
#>           estim    sqrt  fixed  rho:  intr    bmcn 
#> intrcpt  8.2128  2.8658     no           -  0.8723 
#> bmicent  0.0903  0.3006     no          no       - 
#> 
#> Test for Residual Heterogeneity:
#> QE(df = 41) = 20152.6718, p-val < .0001
#> 
#> Test of Moderators (coefficient 2):
#> QM(df = 1) = 28.6345, p-val < .0001
#> 
#> Model Results:
#> 
#>          estimate      se    zval    pval   ci.lb   ci.ub     ​ 
#> intrcpt    4.7340  0.8085  5.8553  <.0001  3.1494  6.3186  *** 
#> bmicent    0.4887  0.0913  5.3511  <.0001  0.3097  0.6677  *** 
#> 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 

# }