Berkey98 | R Documentation |
The data set includes five published trials, reported by Berkey et al. (1998), comparing surgical and non-surgical treatments for medium-severity periodontal disease, one year after treatment.
data(Berkey98)
The variables are:
Trial number
Publication year
Number of patients
Patient improvements (mm) in probing depth
Patient improvements (mm) in attachment level
Sampling variance of PD
Sampling covariance between PD and AD
Sampling variance of AL
Berkey, C. S., Hoaglin, D. C., Antczak-Bouckoms, A., Mosteller, F, & Colditz, G. A. (1998). Meta-analysis of multiple outcomes by regression with random effects. Statistics in Medicine, 17, 2537-2550.
data(Berkey98)
#### ML estimation method
## Multivariate meta-analysis
x <- meta(y=cbind(PD, AL), v=cbind(var_PD, cov_PD_AL, var_AL), data=Berkey98)
x <- rerun(x)
summary(x)
plot(x)
## Plot individual studies proportional to the weights
plot(x, study.weight.plot=TRUE)
## Include forest plot from the metafor package
library(metafor)
plot(x, diag.panel=TRUE, main="Multivariate meta-analysis",
axis.label=c("PD", "AL"))
forest( rma(yi=PD, vi=var_PD, data=Berkey98) )
title("Forest plot of PD")
forest( rma(yi=AL, vi=var_AL, data=Berkey98) )
title("Forest plot of AL")
## Multivariate meta-analysis with "publication year-1979" as the predictor
summary( meta(y=cbind(PD, AL), v=cbind(var_PD, cov_PD_AL, var_AL),
x=scale(pub_year, center=1979), data=Berkey98,
RE.lbound=NA) )
## Multivariate meta-analysis with equality constraint on the regression coefficients
summary( meta(y=cbind(PD, AL), v=cbind(var_PD, cov_PD_AL, var_AL),
x=scale(pub_year, center=1979), data=Berkey98,
coef.constraints=matrix(c("0.3*Eq_slope", "0.3*Eq_slope"),
nrow=2)) )
#### REML estimation method
## Multivariate meta-analysis
summary( reml(y=cbind(PD, AL), v=cbind(var_PD, cov_PD_AL, var_AL),
data=Berkey98,
model.name="Multivariate meta analysis with REML") )
## Multivariate meta-analysis with "publication year-1979" as the predictor
## Diagonal structure for the variance component
summary( reml(y=cbind(PD, AL), v=cbind(var_PD, cov_PD_AL, var_AL),
RE.constraints=Diag(c("1e-5*Tau2_1_1", "1e-5*Tau2_2_2")),
x=scale(pub_year, center=1979), data=Berkey98) )
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