metadat (version 1.4-0)

dat.hackshaw1998: Studies on the Risk of Lung Cancer in Women Exposed to Environmental Tobacco Smoke

Description

Results from 37 studies on the risk of lung cancer in women exposed to environmental tobacco smoke (ETS) from their smoking spouse.

Usage

dat.hackshaw1998

Format

The data frame contains the following columns:

study	numeric	study number
author	character	first author of study
year	numeric	publication year
country	character	country where study was conducted
design	character	study design (either cohort or case-control)
cases	numeric	number of lung cancer cases
or	numeric	odds ratio
or.lb	numeric	lower bound of 95% CI for the odds ratio
or.ub	numeric	upper bound of 95% CI for the odds ratio
yi	numeric	log odds ratio
vi	numeric	corresponding sampling variance

Author

Wolfgang Viechtbauer, , https://www.metafor-project.org

Concepts

medicine, oncology, epidemiology, smoking, odds ratios

Details

The dataset includes the results from 37 studies (4 cohort, 33 case-control) examining if women (who are lifelong nonsmokers) have an elevated risk for lung cancer due to exposure to environmental tobacco smoke (ETS) from their smoking spouse. Values of the log odds ratio greater than 0 indicate an increased risk of cancer in exposed women compared to women not exposed to ETS from their spouse.

Note that the log odds ratios and corresponding sampling variances were back-calculated from the reported odds ratios and confidence interval (CI) bounds (see ‘Examples’). Since the reported values were rounded to some extent, this introduces some minor inaccuracies into the back-calculations. The overall estimate reported in Hackshaw et al. (1997) and Hackshaw (1998) can be fully reproduced though.

Examples

### copy data into 'dat' and examine data
dat <- dat.hackshaw1998
head(dat, 10)

if (FALSE) {
### load metafor package
library(metafor)

### random-effects model using the log odds ratios
res <- rma(yi, vi, data=dat, method="DL")
res

### estimated average odds ratio with CI (and prediction interval)
predict(res, transf=exp, digits=2)

### illustrate how the log odds ratios and corresponding sampling variances
### can be back-calculated based on the reported odds ratios and CI bounds
dat$yi <- NULL
dat$vi <- NULL
dat <- data.frame(dat)
head(dat, 10)
dat <- conv.wald(out=or, ci.lb=or.lb, ci.ub=or.ub, data=dat, transf=log)
head(dat, 10)
}