metabin {meta} | R Documentation |
Calculation of fixed and random effects estimates (relative risk, odds ratio, risk difference or arcsine difference) for meta-analyses with binary outcome data. Mantel-Haenszel, inverse variance and Peto method are available for pooling.
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)
event.e |
Number of events in experimental group. |
n.e |
Number of observations in experimental group. |
event.c |
Number of events in control group. |
n.c |
Number of observations in control group. |
studlab |
An optional vector with study labels. |
data |
An optional data frame containing the study information, i.e., event.e, n.e, event.c, and n.c. |
subset |
An optional vector specifying a subset of studies to be used. |
method |
A character string indicating which method is to be used
for pooling of studies. One of "Inverse" , "MH" , or
"Peto" , can be abbreviated. |
sm |
A character string indicating which summary measure
("RR" , "OR" , "RD" , or "AS" ) is to be used
for pooling of studies, see Details. |
incr |
Could be either a numerical value which is added to each cell
frequency for studies with a zero cell count or the character string
"TA" which stands for treatment arm continuity correction, see
Details. |
allincr |
A logical indicating if 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. |
addincr |
A logical indicating if incr is added to each cell
frequency of all studies irrespective of zero cell counts. |
allstudies |
A logical indicating if studies with zero or all
events in both groups are to be included in the meta-analysis
(applies only if sm = "RR" or "OR" ). |
MH.exact |
A logical indicating if 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. |
RR.cochrane |
A logical indicating if 2*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 in RevMan 5, the
Cochrane Collaboration's program for preparing and maintaining
Cochrane reviews. |
level |
The level used to calculate confidence intervals for individual studies. |
level.comb |
The level used to calculate confidence intervals for pooled estimates. |
comb.fixed |
A logical indicating whether a fixed effect meta-analysis should be conducted. |
comb.random |
A logical indicating whether a random effects meta-analysis should be conducted. |
title |
Title of meta-analysis / systematic review. |
complab |
Comparison label. |
outclab |
Outcome label. |
label.e |
Label for experimental group. |
label.c |
Label for control group. |
byvar |
An optional vector containing grouping information (must
be of same length as event.e ). |
bylab |
A character string with a label for the grouping variable. |
print.byvar |
A logical indicating whether the name of the grouping variable should be printed in front of the group labels. |
warn |
A logical indicating whether the addition of incr
to studies with zero cell frequencies should result in a warning. |
Treatment estimates and standard errors are calculated for each
study. The following measures of treatment effect are available:
relative risk (if 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.
An object of class c("metabin", "meta")
with corresponding
print
, summary
, plot
function. The object is a
list containing the following components:
event.e, n.e, event.c, n.c, studlab, |
|
sm, method, incr, allincr, addincr, |
|
allstudies, MH.exact, RR.cochrane, warn, |
|
level, level.comb, comb.fixed, comb.random, |
|
byvar, bylab, print.byvar |
As defined above. |
TE, seTE |
Estimated treatment effect and standard error of individual studies. |
w.fixed, w.random |
Weight of individual studies (in fixed and random effects model). |
TE.fixed, seTE.fixed |
Estimated overall treatment effect and standard error (fixed effect model). |
TE.random, seTE.random |
Estimated overall treatment effect and standard error (random effects model). |
k |
Number of studies combined in meta-analysis. |
Q |
Heterogeneity statistic Q. |
tau |
Square-root of between-study variance (moment estimator of DerSimonian-Laird). |
Q.CMH |
Cochrane-Mantel-Haenszel heterogeneity statistic. |
incr.e, incr.c |
Increment added to cells in the experimental and control group, respectively |
sparse |
Logical flag indicating if any study included in meta-analysis has any zero cell frequencies. |
call |
Function call. |
Guido Schwarzer sc@imbi.uni-freiburg.de
Cooper H & Hedges LV (1994), The Handbook of Research Synthesis. Newbury Park, CA: Russell Sage Foundation.
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.meta
metabin(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)