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added Roberge et al. example data

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# Aspirin example data
#
# Roberge et al.
# The role of aspirin dose on the prevention of preeclampsia
# and fetal growth restriction: systematic review and meta-analysis
# American Journal of Obstetrics and Gynecology 216(2):110-120, 2017.
# https://doi.org/10.1016/j.ajog.2016.09.076
#
# data from Table 1
# as well as Supplemental Figures 1 and 4 (preeclapsia; PE)
# and Supplemental Figures 3 and 6 (fetal growth restriction; FGR)
# FGR total number for "CLASP (1994)" study from original reference, Fig.3
RobergeEtAl2017 <- cbind.data.frame("study" = c("Tulppala (1997)", "Benigni (1989)", "Caritis (1998a)",
"Sibai (1993a)", "Golding (1998a)", "Ebrashi (2005)",
"Zhao (2012)", "Odibo (2015)", "Porreco (1993)",
"Jamal (2012)", "Mesdaghinia (2011)", "August (1994)",
"Azar (1990)", "Bakhti (2011)", "Chiaffarino (2004)",
"Dasari (1998)", "Hermida (1997)", "Ayala (2013)",
"Michael (1992)", "Villa (2013)", "Beaufils (1985)",
"Zimmermann (1997)", "Caritis (1998b)", "CLASP (1994)",
"ECPPA (1996)", "Ferrier (1996)", "Golding (1998b)",
"Hauth (1993)", "Sibai (1993b)", "Kim (1997)",
"Wallenburg (1986)", "Wallenburg (1991)", "Byaruhanga (1998)",
"Davies (1995)", "McParland (1990)", "Rotchell (1998)",
"Wang (1996)", "Rogers (1999)", "Schrocksnadel (1992)",
"Grab (2000)", "Omrani (1992)", "Gallery (1997)",
"McCowan (1999)", "Morris (1996)", "Newnham (1995)",
"Schiff (1989)", "Trudinger (1988)", "Yu (2003)"),
"year" = c(1997, 1989, 1998, 1993, 1998, 2005, 2012, 2015, 1993,
2012, 2011, 1994, 1990, 2011, 2004, 1998, 1997, 2013,
1992, 2013, 1985, 1997, 1998, 1994, 1996, 1996, 1998,
1993, 1993, 1997, 1986, 1991, 1998, 1995, 1990, 1998,
1996, 1999, 1992, 2000, 1992, 1997, 1999, 1996, 1995,
1989, 1988, 2003),
"N" = c( 66, 33, 652, 644, 1997, 136, 237, 30, 90,
70, 80, 54, 91, 84, 35, 50, 107, 350,
110, 121, 93, 26, 1851, 2492, 606, 43, 4292,
606, 2340, 70, 46, 36, 230, 118, 100, 1485,
84, 193, 41, 43, 40, 120, 99, 102, 51,
65, 46, 554),
"onset" = factor(rep(c("up to wk 16", "after wk 16"), times=c(21,27)),
levels=c("up to wk 16", "after wk 16")),
"dose" = c(50,60,60,60,60, 75,75, 80,80,80,80, 100,100,100,100,100,100,
100,100,100, 150, 50, 60,60,60,60,60,60,60,60,60,60,
75,75,75,75,75, 80,80, 100,100,100,100,100,100,100, 150,150),
"control" = factor(c("placebo", "no treatment", "unclear")[c(1,1,1,1,1,2,1,1,1,2,2,1,2,2,2,1,1,1,1,1,2,2,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,3,1,1,1,1,1,1,1,1,1,1)],
levels=c("placebo", "no treatment", "unclear")),
############################################################################
"asp.PE.events" = c( 1, 0, 68, 22, 43, 25, 22,
3, 6, 2, 1,
3, 1, 1, NA, NA, 3, 11, 1, 8, 0,
4, 163, 91, 16, 1, 83, 5, 47, NA, 0,
NA, 17, 5, 1, 10, NA, 3, 0,
3, 2, NA, 12, 4, NA, 1, NA, 49),
"asp.PE.total" = c(33, 17, 313, 320, 1009, 73, 118,
16, 48, 35, 40,
24, 46, 82, NA, NA, 50, 176, 55, 61, 48,
13, 941, 1259, 284, 23, 2139, 302, 1165, NA, 23,
NA, 113, 58, 48, 739, NA, 118, 22,
22, 21, NA, 49, 52, NA, 34, NA, 276),
"cont.PE.events" = c( 3, 0, 84, 24, 40, 40, 69,
3, 9, 4, 9,
5, 4, 9, NA, NA, 7, 22, 5, 11, 6,
2, 170, 80, 22, 1, 68, 17, 70, NA, 7,
NA, 23, 7, 10, 12, NA, 7, 6,
2, 7, NA, 9, 7, NA, 7, NA, 52),
"cont.PE.total" = c(33, 16, 339, 324, 988, 63, 119,
14, 42, 35, 40,
25, 45, 82, NA, NA, 50, 174, 55, 60, 45,
13, 910, 1233, 322, 20, 2153, 302, 1175, NA, 23,
NA, 117, 60, 52, 746, NA, 75, 19,
21, 19, NA, 50, 50, NA, 31, NA, 278),
############################################################################
"asp.FGR.events" = c( 3, 2, 22, 16, NA, 13, 16, 1, NA, 1, 0,
0, NA, 0, 2, 1, 1, 16, NA, 2, 4,
2, 111, 93, 28, NA, NA, 17, 61, 5, 4,
NA, 18, 6, 7, NA, 3,
NA, 1, NA, NA, NA, 37, 14, 25, 2, NA, 61),
"asp.FGR.total" = c(23, 17, 329, 318, NA, 73, 118, 16, NA, 35, 40,
24, NA, 82, 16, 25, 50, 176, NA, 61, 48,
13, 1255, 1321, 286, NA, NA, 302, 1165, 32, 23,
NA, 114, 58, 48, NA, 40,
NA, 22, NA, NA, NA, 49, 52, 29, 34, NA, 276),
"cont.FGR.events"= c( 3, 6, 28, 21, NA, 21, 36, 1, NA, 2, 0,
1, NA, 1, 5, 5, 2, 32, NA, 6, 13,
1, 85, 87, 42, NA, NA, 19, 76, 5, 6,
NA, 20, 6, 7, NA, 12,
NA, 2, NA, NA, NA, 39, 11, 27, 6, NA, 68),
"cont.FGR.total" = c(23, 16, 374, 324, NA, 63, 119, 14, NA, 35, 40,
25, NA, 82, 19, 25, 50, 174, NA, 60, 45,
13, 1182, 1301, 329, NA, NA, 302, 1180, 38, 23,
NA, 122, 60, 52, NA, 44,
NA, 19, NA, NA, NA, 50, 50, 30, 31, NA, 278),
stringsAsFactors=FALSE)
# check for missings:
nodata <- which(!(complete.cases(RobergeEtAl2017[,7:10]) | (complete.cases(RobergeEtAl2017[,11:14]))))
# drop missings:
RobergeEtAl2017 <- RobergeEtAl2017[-nodata,]
rm(list="nodata")
# re-assign row names (-numbers):
rownames(RobergeEtAl2017) <- sprintf("%02d", 1:nrow(RobergeEtAl2017))
man/RobergeEtAl2017.Rd 0 → 100644
+ 102
− 0
View file @ 1966fb3c
\name{RobergeEtAl2017}
\docType{data}
\alias{RobergeEtAl2017}
\title{Aspirin during pregnancy example data}
\description{Numbers of cases (patients) and events (preeclampsia (PE) or
fetal growth restriction (FGR)) in experimental and control groups of
45 studies.}
\usage{data("RobergeEtAl2017")}
\format{The data frame contains the following columns:
\tabular{lll}{
\bold{study} \tab \code{character} \tab publication identifier (first author and publication year) \cr
\bold{year} \tab \code{numeric} \tab publication year \cr
\bold{N} \tab \code{numeric} \tab number of patients \cr
\bold{onset} \tab \code{factor} \tab treatment onset (up to 16 weeks' gestation or later) \cr
\bold{dose} \tab \code{numeric} \tab dose (mg) \cr
\bold{control} \tab \code{factor} \tab type of control group \cr
\bold{asp.PE.events} \tab \code{numeric} \tab number of PE events in aspirin group \cr
\bold{asp.PE.total} \tab \code{numeric} \tab number of PE cases in aspirin group \cr
\bold{cont.PE.events} \tab \code{numeric} \tab number of PE events in control group \cr
\bold{cont.PE.total} \tab \code{numeric} \tab number of PE cases in control group \cr
\bold{asp.FGR.events} \tab \code{numeric} \tab number of FGR events in aspirin group \cr
\bold{asp.FGR.total} \tab \code{numeric} \tab number of FGR cases in aspirin group \cr
\bold{cont.FGR.events} \tab \code{numeric} \tab number of FGR events in control group \cr
\bold{cont.FGR.total} \tab \code{numeric} \tab number of FGR cases in control group \cr
}
}
\details{A systematic literature review was performed in order to
investigate effects of aspirin administered during pregnancy. Of
particular interest were the occurrence of \emph{preeclampsia (PE)}
and \emph{fetal growth restriction (FGR)}. A total of 45 relevant
studies were found, out of which 40 reported on PE, and 30 reported on
FGR. Besides event rates, the mode of administration (treatment onset
(early vs. late) and dose (in mg)) was recorded.
}
\source{S. Roberge, K. Nicolaides, S. Demers, J. Hyett, N. Chaillet, E. Bujold.
\href{https://doi.org/10.1016/j.ajog.2016.09.076}{The role of aspirin
dose on the prevention of preeclampsia and fetal growth restriction:
systematic review and meta-analysis}.
\emph{American Journal of Obstetrics & Gynecology}, \bold{216}(2):110-120, 2017.
}
\seealso{
\code{\link{bmr}}, \code{\link[metafor]{escalc}},
\code{\link[stats]{model.matrix}}.
}
\examples{
# load data:
data("RobergeEtAl2017")
str(RobergeEtAl2017)
head(RobergeEtAl2017)
# compute effect sizes (log odds ratios) from count data
# (using the "metafor" package's "escalc()" function);
# preeclampsia (PE):
es.pe <- escalc(measure="OR",
ai=asp.PE.events, n1i=asp.PE.total,
ci=cont.PE.events, n2i=cont.PE.total,
slab=study, data=RobergeEtAl2017,
subset=complete.cases(RobergeEtAl2017[,7:10]))
# show forest plot:
forestplot(es.pe, title="preeclampsia (PE)")
# show "bubble plot":
plot(es.pe$dose, es.pe$yi, cex=1/sqrt(es.pe$vi),
col=c("blue","red")[as.numeric(es.pe$onset)],
xlab="dose", ylab="log-OR (PE)", main="Roberge et al. (2017)")
legend("topright", col=c("blue","red"), c("early onset", "late onset"), pch=1)
# fetal growth restriction (FGR):
es.fgr <- escalc(measure="OR",
ai=asp.FGR.events, n1i=asp.FGR.total,
ci=cont.FGR.events, n2i=cont.FGR.total,
slab=study, data=RobergeEtAl2017,
subset=complete.cases(RobergeEtAl2017[,11:14]))
# show forest plot:
forestplot(es.fgr, title="fetal growth restriction (FGR)")
# show "bubble plot":
plot(es.fgr$dose, es.fgr$yi, cex=1/sqrt(es.fgr$vi),
col=c("blue","red")[as.numeric(es.fgr$onset)],
xlab="dose", ylab="log-OR (FGR)", main="Roberge et al. (2017)")
legend("topright", col=c("blue","red"), c("early onset", "late onset"), pch=1)
\dontrun{
# set up regressor matrix (common intercept and slope):
X01 <- model.matrix(~ dose, data=es.pe)
colnames(X01) <- c("intercept", "slope")
print(X01)
# perform regression:
bmr01 <- bmr(es.pe, X=X01)
bmr01$summary
# set up alternative regressor matrix
# (individual intercepts and slopes for two subgroups):
X02 <- model.matrix(~ -1 + onset + onset:dose, data=es.pe)
colnames(X02) <- c("intEarly", "intLate", "slopeEarly", "slopeLate")
print(X02)
# perform regression:
bmr02 <- bmr(es.pe, X=X02)
bmr02$summary
}
}
\keyword{datasets}
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