metadat (version 1.4-0)

dat.dumouchel1994: Nitrogen dioxide data set

Description

Nine studies investigating the effect of NO2 exposure on respiratory illness in children.

Usage

dat.dumouchel1994

Format

The data frame contains the following columns:

study	character	study label
smoke	factor	adjustment for smoking (y/n)
no2	factor	direct measurement of NO2 concentration (y/n)
gender	factor	adjustment for gender (y/n)
or	numeric	odds ratio for childhood respiratory illness
lower	numeric	lower bound of 95 percent CI
upper	numeric	upper bound of 95 percent CI

Author

Christian Roever,

Concepts

medicine, odds ratios, meta-regression

Details

Hasselblad et al. (1992) investigated the effects of nitrogen dioxide (NO2) exposure on the occurrence of respiratory illness in children. Their data were picked up by DuMouchel (1994) as an illustrative example in his article on Bayesian meta-analysis, and were also part of his “hblm” S-Plus software package. DuMouchel's dataset differs slightly from the figures quoted by Hasselblad et al. (1992), apparently because he had additional, more detailed data available.

The data set features three study-level covariables reflecting characteristics of the study designs, namely, whether the quoted estimate had been adjusted for parents' smoking status, whether NO2 exposure had been measured directly (or presence of a gas stove in the household had been used as a proxy instead), and whether the quoted effect had been adjusted for gender. Inclusion of the covariables allows to account for the studies' design features, quantify their effects, and adjust for these.

References

Hasselblad, V., Eddy, D. M., & Kotchmar, D. J. (1992). Synthesis of environmental evidence: Nitrogen dioxide epidemiology studies. Journal of the Air and Waste Management Association, 42(5), 662--671. https://doi.org/10.1080/10473289.1992.10467018

Examples

# show data:
dat.dumouchel1994

if (FALSE) {
# derive effect sizes (log-ORs):
library(metafor)
no2 <- escalc(measure="OR", yi=log(or),
              sei=(log(upper)-log(lower))/(2*qnorm(0.975)),
              slab=study, data=dat.dumouchel1994)
summary(no2)

# compute overall meta-analysis:
library(bayesmeta)
bm01 <- bayesmeta(no2, tau.prior="DuMouchel")

# show results:
bm01
forestplot(bm01)
traceplot(bm01)

# perform meta-regression;
# specify regressor matrix:
X <- model.matrix( ~ smoke + no2 + gender, data=no2)
colnames(X) <- c("intercept", "smoke", "no2", "gender")

# perform regression:
bm02 <- bmr(no2, X=X, tau.prior="DuMouchel")

# show results:
bm02
forestplot(bm02)
#forestplot(bm02, xlab="log-OR",
#           X.mean=rbind("none"      = c(1,0,0,0),
#                        "smoke"     = c(1,1,0,0),
#                        "no2"       = c(1,0,1,0),
#                        "gender"    = c(1,0,0,1),
#                        "all three" = c(1,1,1,1)))
traceplot(bm02)
}