metabin(event.e, n.e, event.c, n.c, studlab,
data = NULL, subset = NULL, method = "MH",
sm = ifelse(!is.na(charmatch(method, c("Peto", "peto"), nomatch = NA)), "OR", "RR"),
incr = 0.5, allincr = FALSE, addincr = FALSE, allstudies = FALSE,
MH.exact = FALSE, RR.cochrane = FALSE,
level = 0.95, level.comb = level,
comb.fixed=TRUE, comb.random=TRUE,
title="", complab="", outclab="",
label.e="Experimental", label.c="Control",
byvar, bylab, print.byvar=TRUE,
warn = TRUE)"Inverse", "MH", or
"Peto", can be abbreviated."RR", "OR", "RD", or "AS") is to be used
for pooling of studies, see Details."TA" which stands for treatment arm continuity correction, see
Details.incr is added to each
cell frequency of all studies if at least one study has a zero cell
count. If false, incr is added only to each cell frequency of
studies with a zero cell count.incr is added to each cell
frequency of all studies irrespective of zero cell counts."RR" or "OR").incr is not to be added
to all cell frequencies for studies with a zero cell count to
calculate the pooled estimate based on the Mantel-Haenszel method.incr instead of
1*incr is to be added to n.e and n.c in the
calculation of the relative risk (i.e., sm="RR") for studies
with a zero cell. This is used event.e).incr
to studies with zero cell frequencies should result in a warning.c("metabin", "meta") with corresponding
print, summary, plot function. The object is a
list containing the following components:sm="RR"), odds ratio (sm="OR"), risk
difference (sm="RD"), and arcsine difference (sm="AS"). For studies with a zero cell count, by default, 0.5 is added to
all cell frequencies of these studies; if incr is
"TA" a treatment arm continuity correction is used instead
(Sweeting et al., 2004; Diamond et al., 2007). Treatment estimates
and standard errors are only calculated for studies with zero or all
events in both groups if allstudies is TRUE.
Internally, both fixed effect and random effects models are calculated
regardless of values choosen for arguments comb.fixed and
comb.random. Accordingly, the estimate for the random effects
model can be extracted from component TE.random of an object
of class "meta" even if comb.random=FALSE. However, all
functions in R package meta will adequately consider the values
for comb.fixed and comb.random. E.g. function
print.meta will not print results for the random effects
model if comb.random=FALSE.
By default, both fixed effect and random effects models are considered
(arguments comb.fixed=TRUE and comb.random=TRUE). If
method is "MH" (default), the Mantel-Haenszel method is
used to calculate the fixed effect estimate; if method is
"Inverse", inverse variance weighting is used for
pooling; finally, if method is "Peto", the Peto method
is used for pooling. The DerSimonian-Laird estimate is used in the
random effects model. For the Peto method, Peto's log odds ratio,
i.e. (O-E)/V and its standard error sqrt(1/V) with
O-E and V denoting "Observed minus Expected" and
"V", are utilised in the random effects model. Accordingly, results of
a random effects model using sm="Peto" can be (slightly)
different to results from a random effects model using sm="MH" or
sm="Inverse".
For the Mantel-Haenszel method, by default (if MH.exact is
FALSE), 0.5 is added to all cell frequencies of a study with a zero cell
count in the calculation of the pooled estimate. This approach is also
used in other software, e.g. RevMan 5 and the Stata procedure metan.
According to Fleiss (in Cooper & Hedges, 1994), there is no need to
add 0.5 to a cell frequency of zero to calculate the Mantel-Haenszel
estimate and he advocates the exact method
(MH.exact=TRUE). Note, the estimate based on the exact method
is not defined if the number of events is zero in all studies either
in the experimental or control group.
Diamond GA, Bax L, Kaul S (2007, Uncertain Effects of Rosiglitazone on the Risk for Myocardial Infarction and Cardiovascular Death. Annals of Internal Medicine, 147, 578--581.
DerSimonian R & Laird N (1986), Meta-analysis in clinical trials. Controlled Clinical Trials, 7, 177--188.
Fleiss JL (1993), The statistical basis of meta-analysis. Statistical Methods in Medical Research, 2, 121--145.
Greenland S & Robins JM (1985), Estimation of a common effect parameter from sparse follow-up data. Biometrics, 41, 55--68. Review Manager (RevMan) [Computer program]. Version 5.0. The Nordic Cochrane Centre, The Cochrane Collaboration, 2008. Ruecker G, Schwarzer G, Carpenter JR (2008) Arcsine test for publication bias in meta-analyses with binary outcomes. Statistics in Medicine, 27,746--763. StataCorp. 2009. Stata Statistical Software: Release 11. College Station, TX: Stata Corporation.
Sweeting MJ, Sutton AJ, Lambert PC (2004), What to add to nothing? Use and avoidance of continuity corrections in meta-analysis of sparse data. Statistics in Medicine, 23, 1351--1375.
funnel, metabias, metacont, metagen, print.metametabin(10, 20, 15, 20, sm="OR")
##
## Different results:
##
metabin(0, 10, 0, 10, sm="OR")
metabin(0, 10, 0, 10, sm="OR", allstudies=TRUE)
data(Olkin95)
meta1 <- metabin(event.e, n.e, event.c, n.c,
data=Olkin95, subset=c(41,47,51,59),
sm="RR", meth="I")
summary(meta1)
funnel(meta1)
meta2 <- metabin(event.e, n.e, event.c, n.c,
data=Olkin95, subset=Olkin95$year<1970,
sm="RR", meth="I")
summary(meta2)Run the code above in your browser using DataLab