meta-object: Description of R object of class "meta"
In meta: General Package for Meta-Analysis
meta-object R Documentation
Description of R object of class "meta"
Description
Detailed description of R objects of class "meta".
Details
The following R functions create an object of class "meta":
-
metabin, metacont,
metacor, metagen,
metainc, metamean,
metaprop, metarate,
metacr, metamerge,
trimfill
The following generic functions are available for an object of
class "meta":
-
as.data.frame.meta, labels.meta,
print.meta, print.summary.meta,
summary.meta, update.meta,
weights.meta
An object of class "meta" is a list containing the following
components.
studlab Study labels
sm Effect measure
null.effect Effect under the null hypothesis
TE Effect estimates (individual studies)
seTE Standard error of effect estimates (individual studies)
statistic Statistics for test of effect (individual studies)
pval P-values for test of effect (individual studies)
df Degrees of freedom (individual studies)
level Level of confidence intervals for individual studies
lower Lower confidence limits (individual studies)
upper Upper confidence limits (individual studies)
three.level Indicator variable for three-level
meta-analysis model
cluster Cluster variable (three-level meta-analysis
model)
rho Within-cluster correlation (three-level
meta-analysis model)
k Number of estimates combined in meta-analysis
k.study Number of studies combined in meta-analysis
k.all Number of all studies
k.TE Number of studies with estimable effects
overall Print meta-analysis results
overall.hetstat Print overall heterogeneity statistics
common Print results for common effect meta-analysis
random Print results for random effects meta-analysis
prediction Print prediction interval
backtransf Back transform results in printouts and plots
method Meta-analysis method (common effect model)
method.random Meta-analysis method (random effects model)
w.common Weights for common effect model (individual studies)
TE.common Estimated overall effect (common effect model)
seTE.common Standard error of overall effect (common effect model)
statistic.common Statistic for test of overall effect (common effect model)
pval.common P-value for test of overall effect (common effect model)
level.ma Level of confidence interval for meta-analysis
estimates
lower.common Lower confidence limit (common effect model)
upper.common Upper confidence limit (common effect model)
w.random Weight for random effects model (individual studies)
TE.random Estimated overall effect (random effects model)
seTE.random Standard error of overall effect (random effects model)
statistic.random Statistic for test of overall effect (random effects model)
pval.random P-value for test of overall effect (random effects model)
method.random.ci Confidence interval method (random effects model)
df.random Degrees of freedom (random effects model)
lower.random Lower confidence limit (random effects model)
upper.random Upper confidence limit (random effects model)
seTE.classic Standard error (classic random effects method)
adhoc.hakn.ci Ad hoc correction for
Hartung-Knapp method (confidence interval)
df.hakn.ci Degrees of freedom for Hartung-Knapp method
(if used in meta-analysis)
seTE.hakn.ci Standard error for Hartung-Knapp method
(not taking ad hoc variance correction into account)
seTE.hakn.adhoc.ci Standard error for Hartung-Knapp
method
(taking ad hoc variance correction into account)
df.kero Degrees of freedom for Kenward-Roger method
(if used in meta-analysis)
seTE.kero Standard error for Kenward-Roger
method
method.predict Method to calculate prediction interval
adhoc.hakn.pi Ad hoc correction for
Hartung-Knapp method (prediction interval)
df.hakn.ci Degrees of freedom for Hartung-Knapp method
(prediction interval)
seTE.predict Standard error used to calculate
prediction interval
df.predict Degrees of freedom for prediction interval
level.predict Level of prediction interval
lower.predict Lower limit of prediction interval
upper.predict Upper limit of prediction interval
seTE.hakn.pi Standard error for Hartung-Knapp method
(not taking ad hoc variance correction into account)
seTE.hakn.adhoc.pi Standard error for Hartung-Knapp
method
(taking ad hoc variance correction into account)
Q Heterogeneity statistic
df.Q Degrees of freedom for heterogeneity statistic
Q
pval.Q P-value of heterogeneity test
method.tau Method to estimate between-study variance \tau^2
control Additional arguments for iterative estimation
of \tau^2
method.tau.ci Method for confidence interval of \tau^2
level.hetstat Level of confidence intervals for heterogeneity
statistics
tau2 Between-study variance \tau^2
se.tau2 Standard error of \tau^2
lower.tau2 Lower confidence limit (\tau^2)
upper.tau2 Upper confidence limit (\tau^2)
tau Square-root of between-study variance \tau
lower.tau Lower confidence limit (\tau)
upper.tau Upper confidence limit (\tau)
tau.preset Prespecified value for \tau
TE.tau Effect estimate used to estimate \tau^2
detail.tau Detail on between-study variance estimate
phi Multiplicative heterogeneity parameter phi in
penalised logistic regression
H Heterogeneity statistic H
lower.H Lower confidence limit (heterogeneity statistic H)
upper.H Upper confidence limit (heterogeneity statistic H)
I2 Heterogeneity statistic I^2
lower.I2 Lower confidence limit (heterogeneity statistic I^2)
upper.I2 Upper confidence limit (heterogeneity statistic I^2)
Rb Heterogeneity statistic R_b
lower.Rb Lower confidence limit (heterogeneity statistic R_b)
upper.Rb Upper confidence limit (heterogeneity statistic R_b)
method.bias Method to test for funnel plot asymmetry
text.common Label for common effect model
text.random Label for random effects model
text.predict Label for prediction interval
text.w.common Label for weights (common effect model)
text.w.random Label for weights (random effects model)
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
label.left Graph label on left side of forest plot
label.right Graph label on right side of forest plot
keepdata Keep original data
data Original data (set) used in function call (if
keepdata = TRUE)
subset Information on subset of original data used in
meta-analysis
(if keepdata = TRUE)
exclude Studies excluded from meta-analysis
warn Print warnings
call Function call
version Version of R package meta used to create
object
For subgroup analysis (argument subgroup), the following
additional components are added to the list.
subgroup Subgroup information (for individual studies)
subgroup.name Name of subgroup variable
print.subgroup.name Print name of subgroup variable
sep.subgroup Separator between name of subgroup variable and value
test.subgroup Print test for subgroup differences
prediction.subgroup Print prediction interval for subgroup(s)
tau.common Assumption of common between-study variance in subgroups
subgroup.levels Levels of grouping variable
k.w Number of estimates combined in subgroups
k.study.w Number of studies combined in subgroups
k.all.w Number of studies in subgroups
k.TE.w Number of studies with estimable effects
in subgroups
TE.common.w Estimated effect in subgroups (common
effect model)
seTE.common.w Standard error in subgroups (common effect model)
statistic.common.w Statistic for test of effect in subgroups (common effect model)
pval.common.w P-value for test of effect in subgroups (common effect model)
lower.common.w Lower confidence limit in subgroups (common effect model)
upper.common.w Upper confidence limit in subgroups (common effect model)
w.common.w Total weight in subgroups (common effect model)
TE.random.w Estimated effect in subgroups (random
effect model)
seTE.random.w Standard error in subgroups (random effects model)
statistic.random.w Statistic for test of effect in subgroups (random effects model)
pval.random.w P-value for test of effect in subgroups (random effects model)
df.random.w Degrees of freedom in subgroups (random effects model)
lower.random.w Lower confidence limit in subgroups (random effects model)
upper.random.w Upper confidence limit in subgroups (random effects model)
w.random.w Total weight in subgroups (random effects model)
seTE.classic.w Standard error (classic random effects method)
df.hakn.ci.w Degrees of freedom for Hartung-Knapp
method in subgroups
seTE.hakn.ci.w Standard error for Hartung-Knapp method in subgroups
(not taking ad hoc variance correction into account)
seTE.hakn.adhoc.ci.w Standard error for Hartung-Knapp
method in subgroups
df.kero.w Degrees of freedom for Kenward-Roger method in subgroups
seTE.kero.w Standard error for Kenward-Roger
method in subgroups
seTE.predict.w Standard error for prediction interval in subgroups
df.predict.w Degrees of freedom for prediction interval in subgroups
lower.predict.w Lower limit of prediction interval in subgroups
upper.predict.w Upper limit of prediction interval in subgroups
seTE.hakn.pi.w Standard error for Hartung-Knapp method in subgroups (prediction intervals)
(not taking ad hoc variance correction into account)
seTE.hakn.adhoc.pi.w Standard error for Hartung-Knapp
method in subgroups (prediction intervals)
Q.w Heterogeneity statistic Q in subgroups
pval.Q.w P-value for test of heterogeneity in subgroups
tau2.w Between-study variance \tau^2 in subgroups
tau.w Square-root of between-study variance \tau in subgroups
H.w Heterogeneity statistic H in subgroups
lower.H.w Lower confidence limit for H in subgroups
upper.H.w Upper confidence limit for H in subgroups
I2.w Heterogeneity statistic I^2 in subgroups
lower.I2.w Lower confidence limit for I^2 in subgroups
upper.I2.w Upper confidence limit for I^2 in subgroups
Rb.w Heterogeneity statistic R_b in subgroups
lower.Rb.w Lower confidence limit for R_b in subgroups
upper.Rb.w Upper confidence limit for R_b in subgroups
Q.w.common Within-group heterogeneity statistic Q
(common effect model)
Q.w.random Within-group heterogeneity statistic
Q (random effects model)
(only calculated if argument tau.common = TRUE)
df.Q.w Degrees of freedom for Q.w.common and Q.w.random
pval.Q.w.common P-value of test for residual
heterogeneity (common effect model)
pval.Q.w.random P-value of test for residual
heterogeneity (random effects model)
Q.b.common Between-groups heterogeneity statistic
Q (common effect model)
df.Q.b.common Degrees of freedom for Q.b.common
pval.Q.b.common P-value of test for subgroup
differences (common effect model)
Q.b.random Between-groups heterogeneity statistic
Q (random effects model)
df.Q.b.random Degrees of freedom for Q.b.random
pval.Q.b.random P-value of test for subgroup
differences (random effects model)
An object created with metabin has the additional
class "metabin" and the following components.
event.e Events in experimental group (individual
studies)
n.e Sample size in experimental group (individual
studies)
event.e Events in control group (individual studies)
n.e Sample size in control group (individual studies)
incr Increment added to zero cells
method.incr Continuity correction method
sparse Continuity correction applied
allstudies Include studies with double zeros
doublezeros Indicator for studies with double zeros
MH.exact Exact Mantel-Haenszel method
RR.Cochrane Cochrane method to calculate risk ratio
Q.Cochrane Cochrane method to calculate \tau^2
Q.CMH Cochran-Mantel-Haenszel statistic
df.Q.CMH Degrees of freedom for Q.CMH
pval.Q.CMH P-value of Cochran-Mantel-Haenszel test
print.CMH Print results for Cochran-Mantel-Haenszel
statistic
incr.e Continuity correction in experimental group
(individual studies)
incr.c Continuity correction in control group
(individual studies)
k.MH Number of studies (Mantel-Haenszel method)
An object created with metacont has the additional
class "metacont" and the following components.
n.e Sample size in experimental group (individual
studies)
mean.e Estimated mean in experimental group (individual
studies)
sd.e Standard deviation in experimental group
(individual studies)
n.c Sample size in control group (individual studies)
mean.c Estimated mean in control group (individual
studies)
sd.c Standard deviation in control group (individual
studies)
pooledvar Use pooled variance for mean difference
method.smd Method for standardised mean difference
(SMD)
sd.glass Denominator in Glass' method
exact.smd Use exact formulae for SMD
method.ci Method to calculate confidence limits
method.mean Method to approximate mean
method.sd Method to approximate standard deviation
An object created with metacor has the additional
class "metacor" and the following components.
cor Correlation (individual studies)
n Sample size (individual studies)
An object created with metainc has the additional
class "metainc" and the following components.
event.e Events in experimental group (individual
studies)
time.e Person time in experimental group (individual
studies)
n.e Sample size in experimental group (individual
studies)
event.c Events in control group (individual studies)
time.c Person time in control group (individual studies)
n.c Sample size in control group (individual studies)
incr Increment added to zero cells
method.incr Continuity correction method
sparse Continuity correction applied
incr.event Continuity correction (individual studies)
k.MH Number of studies (Mantel-Haenszel method)
An object created with metamean has the additional
class "metamean" and the following components.
n Sample size (individual studies)
mean Estimated mean (individual studies)
sd Standard deviation (individual studies)
method.ci Method to calculate confidence limits
method.mean Method to approximate mean
method.sd Method to approximate standard deviation
An object created with metaprop has the additional
class "metaprop" and the following components.
event Events (individual studies)
n Sample size (individual studies)
incr Increment added to zero cells
method.incr Continuity correction method
sparse Continuity correction applied
method.ci Method to calculate confidence limits
incr.event Continuity correction (individual studies)
An object created with metarate has the additional
class "metarate" and the following components.
event Events (individual studies)
time Person time (individual studies)
n Sample size (individual studies)
incr Increment added to zero cells
method.incr Continuity correction method
sparse Continuity correction applied
method.ci Method to calculate confidence limits
incr.event Continuity correction (individual studies)
An object created with trimfill has the additional
classes "trimfill" and "metagen" and the following
components.
k0 Number of added studies
left Studies missing on left side
ma.common Use common effect or random effects model to
estimate
number of missing studies
type Method to estimate missing studies
n.iter.max Maximum number of iterations
n.iter Number of iterations
trimfill Filled studies (individual studies)
class.x Primary class of meta-analysis object
An object created with metamerge has the additional
class "metamerge". Furthermore, the following components
have a different meaning:
k Vector with number of estimates
k.study Vector with number of studies
k.all Vector with total number of studies
k.TE Vector with number of studies with estimable
effects
k.MH Vector with number of studies combined with
Mantel-Haenszel method
TE.common Vector with common effect estimates
seTE.common Vector with standard errors of common effect
estimates
lower.common Vector with lower confidence limits (common
effect model)
upper.common Vector with upper confidence limits (common
effect model)
statistic.common Vector with test statistics for test of
overall effect (common effect model)
pval.common Vector with p-value of test for overall effect
(common effect model)
TE.random Vector with random effects estimates
seTE.random Vector with standard errors of random effects
estimates
lower.random Vector with lower confidence limits (random
effects model)
upper.random Vector with upper confidence limits (random
effects model)
statistic.random Vector with test statistics for test of
overall effect (random effects model)
pval.random Vector with p-value of test for overall effect
(random effects model)
w.common Vector or matrix with common effect weights
w.random Vector or matrix with random effects weights
Author(s)
Guido Schwarzer guido.schwarzer@uniklinik-freiburg.de
See Also
meta-package, meta-sm,
print.meta, summary.meta,
forest.meta
meta documentation built on Oct. 31, 2024, 5:07 p.m.
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| meta-object | R Documentation |
Description of R object of class "meta"
Description
Detailed description of R objects of class "meta".
Details
The following R functions create an object of class "meta":
-
metabin,metacont,metacor,metagen,metainc,metamean,metaprop,metarate,metacr,metamerge,trimfill
The following generic functions are available for an object of
class "meta":
-
as.data.frame.meta,labels.meta,print.meta,print.summary.meta,summary.meta,update.meta,weights.meta
An object of class "meta" is a list containing the following
components.
studlab | Study labels |
sm | Effect measure |
null.effect | Effect under the null hypothesis |
TE | Effect estimates (individual studies) |
seTE | Standard error of effect estimates (individual studies) |
statistic | Statistics for test of effect (individual studies) |
pval | P-values for test of effect (individual studies) |
df | Degrees of freedom (individual studies) |
level | Level of confidence intervals for individual studies |
lower | Lower confidence limits (individual studies) |
upper | Upper confidence limits (individual studies) |
three.level | Indicator variable for three-level meta-analysis model |
cluster | Cluster variable (three-level meta-analysis model) |
rho | Within-cluster correlation (three-level meta-analysis model) |
k | Number of estimates combined in meta-analysis |
k.study | Number of studies combined in meta-analysis |
k.all | Number of all studies |
k.TE | Number of studies with estimable effects |
overall | Print meta-analysis results |
overall.hetstat | Print overall heterogeneity statistics |
common | Print results for common effect meta-analysis |
random | Print results for random effects meta-analysis |
prediction | Print prediction interval |
backtransf | Back transform results in printouts and plots |
method | Meta-analysis method (common effect model) |
method.random | Meta-analysis method (random effects model) |
w.common | Weights for common effect model (individual studies) |
TE.common | Estimated overall effect (common effect model) |
seTE.common | Standard error of overall effect (common effect model) |
statistic.common | Statistic for test of overall effect (common effect model) |
pval.common | P-value for test of overall effect (common effect model) |
level.ma | Level of confidence interval for meta-analysis estimates |
lower.common | Lower confidence limit (common effect model) |
upper.common | Upper confidence limit (common effect model) |
w.random | Weight for random effects model (individual studies) |
TE.random | Estimated overall effect (random effects model) |
seTE.random | Standard error of overall effect (random effects model) |
statistic.random | Statistic for test of overall effect (random effects model) |
pval.random | P-value for test of overall effect (random effects model) |
method.random.ci | Confidence interval method (random effects model) |
df.random | Degrees of freedom (random effects model) |
lower.random | Lower confidence limit (random effects model) |
upper.random | Upper confidence limit (random effects model) |
seTE.classic | Standard error (classic random effects method) |
adhoc.hakn.ci | Ad hoc correction for Hartung-Knapp method (confidence interval) |
df.hakn.ci | Degrees of freedom for Hartung-Knapp method |
| (if used in meta-analysis) | |
seTE.hakn.ci | Standard error for Hartung-Knapp method |
| (not taking ad hoc variance correction into account) | |
seTE.hakn.adhoc.ci | Standard error for Hartung-Knapp method |
| (taking ad hoc variance correction into account) | |
df.kero | Degrees of freedom for Kenward-Roger method |
| (if used in meta-analysis) | |
seTE.kero | Standard error for Kenward-Roger method |
method.predict | Method to calculate prediction interval |
adhoc.hakn.pi | Ad hoc correction for Hartung-Knapp method (prediction interval) |
df.hakn.ci | Degrees of freedom for Hartung-Knapp method |
| (prediction interval) | |
seTE.predict | Standard error used to calculate prediction interval |
df.predict | Degrees of freedom for prediction interval |
level.predict | Level of prediction interval |
lower.predict | Lower limit of prediction interval |
upper.predict | Upper limit of prediction interval |
seTE.hakn.pi | Standard error for Hartung-Knapp method |
| (not taking ad hoc variance correction into account) | |
seTE.hakn.adhoc.pi | Standard error for Hartung-Knapp method |
| (taking ad hoc variance correction into account) | |
Q | Heterogeneity statistic |
df.Q | Degrees of freedom for heterogeneity statistic
Q |
pval.Q | P-value of heterogeneity test |
method.tau | Method to estimate between-study variance \tau^2 |
control | Additional arguments for iterative estimation
of \tau^2 |
method.tau.ci | Method for confidence interval of \tau^2 |
level.hetstat | Level of confidence intervals for heterogeneity statistics |
tau2 | Between-study variance \tau^2 |
se.tau2 | Standard error of \tau^2 |
lower.tau2 | Lower confidence limit (\tau^2) |
upper.tau2 | Upper confidence limit (\tau^2) |
tau | Square-root of between-study variance \tau |
lower.tau | Lower confidence limit (\tau) |
upper.tau | Upper confidence limit (\tau) |
tau.preset | Prespecified value for \tau |
TE.tau | Effect estimate used to estimate \tau^2 |
detail.tau | Detail on between-study variance estimate |
phi | Multiplicative heterogeneity parameter phi in
penalised logistic regression |
H | Heterogeneity statistic H |
lower.H | Lower confidence limit (heterogeneity statistic H) |
upper.H | Upper confidence limit (heterogeneity statistic H) |
I2 | Heterogeneity statistic I^2 |
lower.I2 | Lower confidence limit (heterogeneity statistic I^2) |
upper.I2 | Upper confidence limit (heterogeneity statistic I^2) |
Rb | Heterogeneity statistic R_b |
lower.Rb | Lower confidence limit (heterogeneity statistic R_b) |
upper.Rb | Upper confidence limit (heterogeneity statistic R_b) |
method.bias | Method to test for funnel plot asymmetry |
text.common | Label for common effect model |
text.random | Label for random effects model |
text.predict | Label for prediction interval |
text.w.common | Label for weights (common effect model) |
text.w.random | Label for weights (random effects model) |
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 |
label.left | Graph label on left side of forest plot |
label.right | Graph label on right side of forest plot |
keepdata | Keep original data |
data | Original data (set) used in function call (if
keepdata = TRUE) |
subset | Information on subset of original data used in meta-analysis |
(if keepdata = TRUE) |
|
exclude | Studies excluded from meta-analysis |
warn | Print warnings |
call | Function call |
version | Version of R package meta used to create object |
For subgroup analysis (argument subgroup), the following
additional components are added to the list.
subgroup | Subgroup information (for individual studies) |
subgroup.name | Name of subgroup variable |
print.subgroup.name | Print name of subgroup variable |
sep.subgroup | Separator between name of subgroup variable and value |
test.subgroup | Print test for subgroup differences |
prediction.subgroup | Print prediction interval for subgroup(s) |
tau.common | Assumption of common between-study variance in subgroups |
subgroup.levels | Levels of grouping variable |
k.w | Number of estimates combined in subgroups |
k.study.w | Number of studies combined in subgroups |
k.all.w | Number of studies in subgroups |
k.TE.w | Number of studies with estimable effects in subgroups |
TE.common.w | Estimated effect in subgroups (common effect model) |
seTE.common.w | Standard error in subgroups (common effect model) |
statistic.common.w | Statistic for test of effect in subgroups (common effect model) |
pval.common.w | P-value for test of effect in subgroups (common effect model) |
lower.common.w | Lower confidence limit in subgroups (common effect model) |
upper.common.w | Upper confidence limit in subgroups (common effect model) |
w.common.w | Total weight in subgroups (common effect model) |
TE.random.w | Estimated effect in subgroups (random effect model) |
seTE.random.w | Standard error in subgroups (random effects model) |
statistic.random.w | Statistic for test of effect in subgroups (random effects model) |
pval.random.w | P-value for test of effect in subgroups (random effects model) |
df.random.w | Degrees of freedom in subgroups (random effects model) |
lower.random.w | Lower confidence limit in subgroups (random effects model) |
upper.random.w | Upper confidence limit in subgroups (random effects model) |
w.random.w | Total weight in subgroups (random effects model) |
seTE.classic.w | Standard error (classic random effects method) |
df.hakn.ci.w | Degrees of freedom for Hartung-Knapp method in subgroups |
seTE.hakn.ci.w | Standard error for Hartung-Knapp method in subgroups |
| (not taking ad hoc variance correction into account) | |
seTE.hakn.adhoc.ci.w | Standard error for Hartung-Knapp method in subgroups |
df.kero.w | Degrees of freedom for Kenward-Roger method in subgroups |
seTE.kero.w | Standard error for Kenward-Roger method in subgroups |
seTE.predict.w | Standard error for prediction interval in subgroups |
df.predict.w | Degrees of freedom for prediction interval in subgroups |
lower.predict.w | Lower limit of prediction interval in subgroups |
upper.predict.w | Upper limit of prediction interval in subgroups |
seTE.hakn.pi.w | Standard error for Hartung-Knapp method in subgroups (prediction intervals) |
| (not taking ad hoc variance correction into account) | |
seTE.hakn.adhoc.pi.w | Standard error for Hartung-Knapp method in subgroups (prediction intervals) |
Q.w | Heterogeneity statistic Q in subgroups |
pval.Q.w | P-value for test of heterogeneity in subgroups |
tau2.w | Between-study variance \tau^2 in subgroups |
tau.w | Square-root of between-study variance \tau in subgroups |
H.w | Heterogeneity statistic H in subgroups |
lower.H.w | Lower confidence limit for H in subgroups |
upper.H.w | Upper confidence limit for H in subgroups |
I2.w | Heterogeneity statistic I^2 in subgroups |
lower.I2.w | Lower confidence limit for I^2 in subgroups |
upper.I2.w | Upper confidence limit for I^2 in subgroups |
Rb.w | Heterogeneity statistic R_b in subgroups |
lower.Rb.w | Lower confidence limit for R_b in subgroups |
upper.Rb.w | Upper confidence limit for R_b in subgroups |
Q.w.common | Within-group heterogeneity statistic Q (common effect model) |
Q.w.random | Within-group heterogeneity statistic Q (random effects model) |
(only calculated if argument tau.common = TRUE) |
|
df.Q.w | Degrees of freedom for Q.w.common and Q.w.random |
pval.Q.w.common | P-value of test for residual heterogeneity (common effect model) |
pval.Q.w.random | P-value of test for residual heterogeneity (random effects model) |
Q.b.common | Between-groups heterogeneity statistic Q (common effect model) |
df.Q.b.common | Degrees of freedom for Q.b.common
|
pval.Q.b.common | P-value of test for subgroup differences (common effect model) |
Q.b.random | Between-groups heterogeneity statistic Q (random effects model) |
df.Q.b.random | Degrees of freedom for Q.b.random
|
pval.Q.b.random | P-value of test for subgroup differences (random effects model) |
An object created with metabin has the additional
class "metabin" and the following components.
event.e | Events in experimental group (individual studies) |
n.e | Sample size in experimental group (individual studies) |
event.e | Events in control group (individual studies) |
n.e | Sample size in control group (individual studies) |
incr | Increment added to zero cells |
method.incr | Continuity correction method |
sparse | Continuity correction applied |
allstudies | Include studies with double zeros |
doublezeros | Indicator for studies with double zeros |
MH.exact | Exact Mantel-Haenszel method |
RR.Cochrane | Cochrane method to calculate risk ratio |
Q.Cochrane | Cochrane method to calculate \tau^2
|
Q.CMH | Cochran-Mantel-Haenszel statistic |
df.Q.CMH | Degrees of freedom for Q.CMH |
pval.Q.CMH | P-value of Cochran-Mantel-Haenszel test |
print.CMH | Print results for Cochran-Mantel-Haenszel statistic |
incr.e | Continuity correction in experimental group (individual studies) |
incr.c | Continuity correction in control group (individual studies) |
k.MH | Number of studies (Mantel-Haenszel method) |
An object created with metacont has the additional
class "metacont" and the following components.
n.e | Sample size in experimental group (individual studies) |
mean.e | Estimated mean in experimental group (individual studies) |
sd.e | Standard deviation in experimental group (individual studies) |
n.c | Sample size in control group (individual studies) |
mean.c | Estimated mean in control group (individual studies) |
sd.c | Standard deviation in control group (individual studies) |
pooledvar | Use pooled variance for mean difference |
method.smd | Method for standardised mean difference (SMD) |
sd.glass | Denominator in Glass' method |
exact.smd | Use exact formulae for SMD |
method.ci | Method to calculate confidence limits |
method.mean | Method to approximate mean |
method.sd | Method to approximate standard deviation |
An object created with metacor has the additional
class "metacor" and the following components.
cor | Correlation (individual studies) |
n | Sample size (individual studies) |
An object created with metainc has the additional
class "metainc" and the following components.
event.e | Events in experimental group (individual studies) |
time.e | Person time in experimental group (individual studies) |
n.e | Sample size in experimental group (individual studies) |
event.c | Events in control group (individual studies) |
time.c | Person time in control group (individual studies) |
n.c | Sample size in control group (individual studies) |
incr | Increment added to zero cells |
method.incr | Continuity correction method |
sparse | Continuity correction applied |
incr.event | Continuity correction (individual studies) |
k.MH | Number of studies (Mantel-Haenszel method) |
An object created with metamean has the additional
class "metamean" and the following components.
n | Sample size (individual studies) |
mean | Estimated mean (individual studies) |
sd | Standard deviation (individual studies) |
method.ci | Method to calculate confidence limits |
method.mean | Method to approximate mean |
method.sd | Method to approximate standard deviation |
An object created with metaprop has the additional
class "metaprop" and the following components.
event | Events (individual studies) |
n | Sample size (individual studies) |
incr | Increment added to zero cells |
method.incr | Continuity correction method |
sparse | Continuity correction applied |
method.ci | Method to calculate confidence limits |
incr.event | Continuity correction (individual studies) |
An object created with metarate has the additional
class "metarate" and the following components.
event | Events (individual studies) |
time | Person time (individual studies) |
n | Sample size (individual studies) |
incr | Increment added to zero cells |
method.incr | Continuity correction method |
sparse | Continuity correction applied |
method.ci | Method to calculate confidence limits |
incr.event | Continuity correction (individual studies) |
An object created with trimfill has the additional
classes "trimfill" and "metagen" and the following
components.
k0 | Number of added studies |
left | Studies missing on left side |
ma.common | Use common effect or random effects model to estimate |
| number of missing studies | |
type | Method to estimate missing studies |
n.iter.max | Maximum number of iterations |
n.iter | Number of iterations |
trimfill | Filled studies (individual studies) |
class.x | Primary class of meta-analysis object |
An object created with metamerge has the additional
class "metamerge". Furthermore, the following components
have a different meaning:
k | Vector with number of estimates |
k.study | Vector with number of studies |
k.all | Vector with total number of studies |
k.TE | Vector with number of studies with estimable effects |
k.MH | Vector with number of studies combined with Mantel-Haenszel method |
TE.common | Vector with common effect estimates |
seTE.common | Vector with standard errors of common effect estimates |
lower.common | Vector with lower confidence limits (common effect model) |
upper.common | Vector with upper confidence limits (common effect model) |
statistic.common | Vector with test statistics for test of overall effect (common effect model) |
pval.common | Vector with p-value of test for overall effect (common effect model) |
TE.random | Vector with random effects estimates |
seTE.random | Vector with standard errors of random effects estimates |
lower.random | Vector with lower confidence limits (random effects model) |
upper.random | Vector with upper confidence limits (random effects model) |
statistic.random | Vector with test statistics for test of overall effect (random effects model) |
pval.random | Vector with p-value of test for overall effect (random effects model) |
w.common | Vector or matrix with common effect weights |
w.random | Vector or matrix with random effects weights |
Author(s)
Guido Schwarzer guido.schwarzer@uniklinik-freiburg.de
See Also
meta-package, meta-sm,
print.meta, summary.meta,
forest.meta
R Package Documentation
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