| traceplot {bayesmeta} | R Documentation |
Illustrate conditional means of study-specific estimates as well as overall mean (or other linear combinations) as a function of heterogeneity.
Description
Generates a trace plot of study-specific (shrinkage) estimates as a
function of the heterogeneity (\tau), along with conditional
estimates of the overall mean or other linear combinations of
regression parameters.
The heterogeneity's posterior distribution is also shown at the bottom.
Usage
traceplot(x, ...)
## S3 method for class 'bayesmeta'
traceplot(x, mulim, taulim, ci=FALSE,
ylab="effect", prior=FALSE, infinity=FALSE,
rightmargin=8, col=rainbow(x$k), labcol=col,
meanlabel="overall mean", meancol="black",
meanlabcol=meancol, ...)
## S3 method for class 'bmr'
traceplot(x, mulim, taulim, ci=FALSE,
ylab="effect", prior=FALSE, infinity=FALSE,
rightmargin=8, col=rainbow(x$k), labcol=col,
X, Xlabels, Xcols="black", Xlabcols=Xcols, ...)
Arguments
x |
|
mulim, taulim |
(optional) ranges for the effect (mu) and
heterogeneity (tau) axes. If only one value is given for
|
ci |
a logical flag indicating whether to also show (conditional) confidence intervals. |
ylab |
a label for the effect (mu) axis. |
prior |
a logical flag indicating whether to show the (heterogeneity) prior density along with its posterior. |
infinity |
a logical flag indicating whether add an “infinity” tickmark to the heterogeneity (tau) axis and show the corresponding limiting values. |
rightmargin |
an additional margin to be added to the right side of the plot, in order to accomodate the estimates' labels. In case study labels still extend beyond the figure margin, try increasing this number. |
col |
colors to be used for plotting the ( |
labcol |
colors to be used for labelling the ( |
meanlabel |
a label for the overall mean estimate
( |
meancol |
colour specification for the overall mean estimate
( |
meanlabcol |
colour specification for the overall mean label
( |
X |
matrix (or vector) of coefficients defining linear combinations of
regression parameters to be shown ( |
Xlabels |
labels for the linear combinations ( |
Xcols |
colour specification for the linear combinations
( |
Xlabcols |
colour specification for the linear combination labels
( |
... |
other arguments passed on to the
|
Details
For a given heterogeneity (\tau) value, the conditional
posterior distributions of the overall effect (\mu) as well as
the study-specific parameters (\theta_i) are again
normal. The conditional normal moments (mean and variance) then vary
as functions of the heterogeneity; for large heterogeneity, the
shrinkage estimates approach the original data (y_i),
while the overall mean approaches an un-weighted overall average. For
small heterogeneity, both overall mean as well as study-specific
estimates are increasingly shrunk towards the
inverse-variance-weighted ‘common-effect’ estimate (Roever,
2020).
This trace plot illustrates the conditional (overall and study-specific) estimates along with the heterogeneity's posterior distribution (density) in a layout similar to that utilized by Rubin (1981).
Author(s)
Christian Roever christian.roever@med.uni-goettingen.de
References
C. Roever, D. Rindskopf, T. Friede. How trace plots help interpret meta-analysis results. (submitted for publication), 2023. https://arxiv.org/abs/2306.17043.
C. Roever. Bayesian random-effects meta-analysis using the bayesmeta R package. Journal of Statistical Software, 93(6):1-51, 2020. doi:10.18637/jss.v093.i06.
C. Roever, T. Friede. Using the bayesmeta R package for Bayesian random-effects meta-regression. Computer Methods and Programs in Biomedicine, 299:107303, 2023. doi:10.1016/j.cmpb.2022.107303.
D.B. Rubin. Estimation in parallel randomized experiments. Journal of Educational Statistics, 6(4):377-401, 1981. doi:10.3102/10769986006004377.
DuMouchel, W. H. (1994). Hierarchical Bayes linear models for meta-analysis. Technical Report 27, National Institute of Statistical Sciences (NISS); Research Triangle Park, NC, USA. https://www.niss.org/research/technical-reports/hierarchical-bayes-linear-models-meta-analysis-1994
See Also
Examples
## Not run:
########################
# SAT coaching example;
# load example data:
data("Rubin1981")
# perform meta-analysis:
bma01 <- bayesmeta(y=Rubin1981[,"effect"], sigma=Rubin1981[,"stderr"],
labels=Rubin1981[,"school"], tau.prior="uniform")
# show meta-analysis results:
forestplot(bma01)
# show trace plot:
traceplot(bma01)
##################################
# COPD (meta-regression) example;
# load example data,
# compute effect sizes (log-ORs):
data("KarnerEtAl2014")
karner.exa <- escalc(measure="OR",
ai=tiotropium.exa, n1i=tiotropium.total,
ci=placebo.exa, n2i=placebo.total,
slab=study, data=KarnerEtAl2014)
#################################
# perform "plain" meta-analysis:
bma02 <- bayesmeta(karner.exa,
tau.prior=function(t){dhalfnormal(t, scale=0.5)})
traceplot(bma02, ylab="log-OR",
prior=TRUE, infi=TRUE, taulim=0.53)
forestplot(bma02)
############################
# perform meta-regressions:
# 1st regression;
# specify regressor matrix
# (indicator variables, "short" vs. "long" study duration):
X1 <- cbind("short" = as.numeric(karner.exa$duration == "up to 1 year"),
"long" = as.numeric(karner.exa$duration == "1 year or longer"))
# perform meta-regression
# (two group means, common heterogeneity):
bmr01 <- bmr(karner.exa, X=X1,
tau.prior=function(t){dhalfnormal(t, scale=0.5)})
# show trace plot:
traceplot(bmr01, ylab="log-OR", prior=TRUE,
taulim=0.53, mulim=c(-1, 0.2),
X=rbind("short" = c(1,0),
"long" = c(0,1)))
# 2nd regression;
# specify regressor matrix
# (baseline FEV1, an indicator of disease severity):
X2 <- cbind("intercept" = 1,
"fev1" = karner.exa$baseline.fev1)
# perform meta-regression
# (linear effect of FEV1 on log-OR):
bmr02 <- bmr(karner.exa, X=X2,
tau.prior=function(t){dhalfnormal(t, scale=0.5)})
traceplot(bmr02, ylab="log-OR", prior=TRUE,
taulim=0.53, mulim=c(-1.0, 0.2),
X=rbind("FEV1 = 1.0"=c(1,1.0),
"FEV1 = 1.5"=c(1,1.5),
"FEV1 = 2.0"=c(1,2.0)))
## End(Not run)