metainc(event.e, time.e, event.c, time.c, studlab,
data=NULL, subset=NULL, method="MH",
sm="IRR",
incr=0.5, allincr=FALSE, addincr=FALSE,
level=0.95, level.comb=level,
comb.fixed=TRUE, comb.random=TRUE,
hakn=FALSE,
method.tau="DL", tau.preset=NULL, TE.tau=NULL,
tau.common=FALSE,
prediction=FALSE, level.predict=level,
method.bias="linreg",
n.e=NULL, n.c=NULL,
title="", complab="", outclab="",
label.e="Experimental", label.c="Control",
label.left="", label.right="",
byvar, bylab, print.byvar=TRUE,
keepdata=TRUE,
warn=TRUE)"MH", "Inverse", or
"Cochran", can be abbreviated."IRR" or "IRD") is to be used for pooling of
studies, see Details.incr is added to each
cell frequency of all studies if at least one study has a zero cell
count. If FALSE (default), 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."DL", "REML", "ML", "HS", "SJ",
"HE", or "EB""linreg" or
"rank", can be abbreviated.event.e).incr is added to studies with zero cell
frequencies).c("metainc", "meta") with corresponding
print, summary, plot function. The object is a
list containing the following components:hakn=TRUE).keepdata=TRUE).keepdata=TRUE).sm="IRR")sm="IRD") For studies with a zero cell count, by default, 0.5 is added to
all cell frequencies of these studies (argument incr).
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 (Greenland & Robbins, 1985); if method is
"Inverse", inverse variance weighting is used for pooling;
finally, if method is "Cochran", the Cochran method is
used for pooling (Bayne-Jones, 1964, Chapter 8). By default, the
DerSimonian-Laird estimate is used in the random effects model (see
paragraph on argument method.tau).
For Mantel-Haenszel and Cochran method, nothing is added to zero
cell counts. Accordingly, Mantel-Haenszel and Cochran estimate are
not defined if the number of events is zero in all studies either in
the experimental or control group.
A prediction interval for treatment effect of a new study is
calculated (Higgins et al., 2009) if arguments prediction and
comb.random are TRUE.
R function update.meta can be used to redo the
meta-analysis of an existing metainc object by only specifying
arguments which should be changed.
If R package metafor (Viechtbauer 2010) is installed, the following statistical methods are also available.
For the random effects model (argument comb.random=TRUE), the
method by Hartung and Knapp (Hartung, Knapp 2001; Knapp, Hartung
2003) is used to adjust test statistics and confidence intervals if
argument hakn=TRUE (internally R function rma.uni of R
package metafor is called).
Several methods are available to estimate the between-study variance
$\tau^2$ (argument method.tau):
method.tau="DL") (default)method.tau="REML")method.tau="ML")method.tau="HS")method.tau="SJ")method.tau="HE")method.tau="EB").rma.uni of R package metafor is called internally. See help
page of R function rma.uni for more details on the various
methods to estimate between-study variance $\tau^2$.Greenland S & Robins JM (1985), Estimation of a common effect parameter from sparse follow-up data. Biometrics, 41, 55--68.
Hartung J & Knapp G (2001), A Refined Method for the Meta-analysis of Controlled Clinical Trials with Binary Outcome. Statistics in Medicine, 20, 3875--89. Higgins JPT, Thompson SG, Spiegelhalter DJ (2009), A re-evaluation of random-effects meta-analysis. Journal of the Royal Statistical Society: Series A, 172, 137-159.
Knapp G & Hartung J (2003), Improved Tests for a Random Effects Meta-regression with a Single Covariate. Statistics in Medicine, 22, 2693-710, doi: 10.1002/sim.1482 .
Bayne-Jones S et al. (1964),
Smoking and Health: Report of the Advisory Committee to the Surgeon
General of the United States. U-23 Department of Health, Education,
and Welfare. Public Health Service Publication No. 1103.
Viechtbauer W (2010), Conducting Meta-Analyses in R with the Metafor Package. Journal of Statistical Software, 36, 1--48.
metabin, update.meta, print.metadata(smoking)
m1 <- metainc(d.smokers, py.smokers,
d.nonsmokers, py.nonsmokers,
data=smoking, studlab=study)
print(m1, digits=2)
m2 <- metainc(d.smokers, py.smokers,
d.nonsmokers, py.nonsmokers,
data=smoking, studlab=study,
method="Cochran")
print(m2, digits=2)
data(lungcancer)
m3 <- metainc(d.smokers, py.smokers,
d.nonsmokers, py.nonsmokers,
data=lungcancer, studlab=study)
print(m3, digits=2)Run the code above in your browser using DataLab