Smoking example
Meta-analyses on the effect of smoking on mortality risk.
A data frame with the following columns:
| study | study label |
| participants | total number of participants |
| d.smokers | number of deaths in smokers' group |
| py.smokers | person years at risk in smokers' group |
| d.nonsmokers | number of deaths in non-smokers' group |
| py.nonsmokers | person years at risk in non-smokers' group |
Data have been reconstructed based on the famous Smoking and Health
Report to the Surgeon General (Bayne-Jones S et al., 1964). Data
sets can be used to evaluate the risk of smoking on overall
mortality (dataset smoking) and lung-cancer deaths (dataset
lungcancer), respectively.
The person time is attributed such that the rate ratios are equal to the reported mortality ratios implicitly assuming that the data have arisen from a homogeneous age group; more detailed information by age is not available from the report. Note, the group of "non-smokers" actually consists of all participants except those who are smokers of cigarettes only. Information on real non-smokers is not available from the published Smoking and Health Report.
Bayne-Jones S et al. (1964): Smoking and Health: Report of the Advisory Committee to the Surgeon General of the United States. U-23 Department of Health, Education, and Welfare. Public Health Service Publication No. 1103.
data(smoking)
m1 <- metainc(d.smokers, py.smokers,
d.nonsmokers, py.nonsmokers,
data = smoking, studlab = study)
print(m1, digits = 2)
data(lungcancer)
m2 <- metainc(d.smokers, py.smokers,
d.nonsmokers, py.nonsmokers,
data = lungcancer, studlab = study)
print(m2, digits = 2)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.