metafor (version 2.4-0)

dat.fine1993: Studies on Radiation Therapy with or without Adjuvant Chemotherapy in Patients with Malignant Gliomas

Description

Results from 17 trials comparing post-operative radiation therapy with and without adjuvant chemotherapy in patients with malignant gliomas.

Usage

dat.fine1993

Format

The data frame contains the following columns:

study	numeric	study number
nei	numeric	sample size in the experimental group receiving radiotherapy plus adjuvant chemotherapy
nci	numeric	sample size in the control group receiving radiotherapy alone
e1i	numeric	number of survivors at 6 months in the experimental group
c1i	numeric	number of survivors at 6 months in the control group
e2i	numeric	number of survivors at 12 months in the experimental group
c2i	numeric	number of survivors at 12 months in the control group
e3i	numeric	number of survivors at 18 months in the experimental group
c3i	numeric	number of survivors at 18 months in the control group
e4i	numeric	number of survivors at 24 months in the experimental group

Details

The 17 trials report the post-operative survival of patients with malignant gliomas receiving either radiation therapy with adjuvant chemotherapy or radiation therapy alone. Survival was assessed at 6, 12, 18, and 24 months in all but one study (which assessed survival only at 12 and at 24 months).

The data were reconstructed by Trikalinos and Olkin (2012) based on Table 2 in Fine et al. (1993) and Table 3 in Dear (1994). The data can be used to illustrate how a meta-analysis can be conducted of effect sizes reported at multiple time points using a multivariate model.

References

Fine, H. A., Dear, K. B., Loeffler, J. S., Black, P. M., & Canellos, G. P. (1993). Meta-analysis of radiation therapy with and without adjuvant chemotherapy for malignant gliomas in adults. Cancer, 71, 2585--2597.

Examples

# NOT RUN {
### copy data into 'dat' and examine data
dat <- dat.fine1993
dat

### calculate log(ORs) and sampling variances for each time point
dat <- escalc(measure="OR", ai=e1i, n1i=nei, ci=c1i, n2i=nci, data=dat, var.names=c("y1i","v1i"))
dat <- escalc(measure="OR", ai=e2i, n1i=nei, ci=c2i, n2i=nci, data=dat, var.names=c("y2i","v2i"))
dat <- escalc(measure="OR", ai=e3i, n1i=nei, ci=c3i, n2i=nci, data=dat, var.names=c("y3i","v3i"))
dat <- escalc(measure="OR", ai=e4i, n1i=nei, ci=c4i, n2i=nci, data=dat, var.names=c("y4i","v4i"))

### calculate the covariances (equations in Appendix of Trikalinos & Olkin, 2012)
dat$v12i <- with(dat, nei / (e1i * (nei - e2i)) + nci / (c1i * (nci - c2i)))
dat$v13i <- with(dat, nei / (e1i * (nei - e3i)) + nci / (c1i * (nci - c3i)))
dat$v14i <- with(dat, nei / (e1i * (nei - e4i)) + nci / (c1i * (nci - c4i)))
dat$v23i <- with(dat, nei / (e2i * (nei - e3i)) + nci / (c2i * (nci - c3i)))
dat$v24i <- with(dat, nei / (e2i * (nei - e4i)) + nci / (c2i * (nci - c4i)))
dat$v34i <- with(dat, nei / (e3i * (nei - e4i)) + nci / (c3i * (nci - c4i)))

### create dataset in long format
dat.long <- data.frame(study=rep(1:nrow(dat), each=4), time=1:4,
                       yi=c(t(dat[c("y1i","y2i","y3i","y4i")])),
                       vi=c(t(dat[c("v1i","v2i","v3i","v4i")])))

### var-cov matrices of the sudies
V <- lapply(split(dat, dat$study),
            function(x) matrix(c( x$v1i, x$v12i, x$v13i, x$v14i,
                                 x$v12i,  x$v2i, x$v23i, x$v24i,
                                 x$v13i, x$v23i,  x$v3i, x$v34i,
                                 x$v14i, x$v24i, x$v34i,  x$v4i), nrow=4, ncol=4, byrow=TRUE))

### remove rows for the missing time points in study 17
dat.long <- na.omit(dat.long)

### remove corresponding rows/columns from var-cov matrix
V[[17]] <- V[[17]][c(2,4),c(2,4)]

### make a copy of V
Vc <- V

### replace any (near) singular var-cov matrices with ridge corrected versions
repl.Vi <- function(Vi) {
   res <- eigen(Vi)
   if (any(res$values <= .08)) {
      round(res$vectors %*% diag(res$values + .08) %*% t(res$vectors), 12)
   } else {
      Vi
   }
}
Vc <- lapply(Vc, repl.Vi)

### do not correct var-cov matrix of study 17
Vc[[17]] <- V[[17]]

### construct block diagonal matrix
Vc <- bldiag(Vc)

### multivariate fixed-effects model
res <- rma.mv(yi, Vc, mods = ~ factor(time) - 1, method="FE", data=dat.long)
print(res, digits=3)

### multivariate random-effects model with heteroscedastic AR(1) structure for the true effects
res <- rma.mv(yi, Vc, mods = ~ factor(time) - 1, random = ~ time | study,
              struct="HAR", data=dat.long)
print(res, digits=3)

# }
# NOT RUN {
### profile the variance components
par(mfrow=c(2,2))
profile(res, tau2=1, xlim=c( 0,.2))
profile(res, tau2=2, xlim=c( 0,.2))
profile(res, tau2=3, xlim=c( 0,.2))
profile(res, tau2=4, xlim=c(.1,.3))
# }
# NOT RUN {
# }
# NOT RUN {
### profile the autocorrelation coefficient
par(mfrow=c(1,1))
profile(res, rho=1)
# }