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

dat.hackshaw1998

Format

The data frame contains the following columns:

studynumericstudy number
authorcharacterfirst author of study
yearnumericpublication year
countrycharactercountry where study was conducted
designcharacterstudy design (either cohort or case-control)
casesnumericnumber of lung cancer cases
ornumericodds ratio
or.lbnumericlower bound of 95% CI for the odds ratio
or.ubnumericupper bound of 95% CI for the odds ratio
yinumericlog odds ratio
vinumericcorresponding sampling variance

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.

Source

Hackshaw, A. K., Law, M. R., & Wald, N. J. (1997). The accumulated evidence on lung cancer and environmental tobacco smoke. British Medical Journal, 315(7114), 980--988. https://doi.org/10.1136/bmj.315.7114.980

Hackshaw, A. K. (1998). Lung cancer and passive smoking. Statistical Methods in Medical Research, 7(2), 119--136. https://doi.org/10.1177/096228029800700203

Examples

### copy data into 'dat' and examine data dat <- dat.hackshaw1998 dat
#> study author year country design cases or or.lb or.ub #> 1 1 Garfinkel 1981 USA cohort 153 1.18 0.90 1.54 #> 2 2 Hirayama 1984 Japan cohort 200 1.45 1.02 2.08 #> 3 3 Butler 1988 USA cohort 8 2.02 0.48 8.56 #> 4 4 Cardenas 1997 USA cohort 150 1.20 0.80 1.60 #> 5 5 Chan 1982 Hong Kong case-control 84 0.75 0.43 1.30 #> 6 6 Correa 1983 USA case-control 22 2.07 0.81 5.25 #> 7 7 Trichopolous 1983 Greece case-control 62 2.13 1.19 3.83 #> 8 8 Buffler 1984 USA case-control 41 0.80 0.34 1.90 #> 9 9 Kabat 1984 USA case-control 24 0.79 0.25 2.45 #> 10 10 Lam 1985 Hong Kong case-control 60 2.01 1.09 3.72 #> 11 11 Garfinkel 1985 USA case-control 134 1.23 0.81 1.87 #> 12 12 Wu 1985 USA case-control 29 1.20 0.50 3.30 #> 13 13 Akiba 1986 Japan case-control 94 1.52 0.87 2.63 #> 14 14 Lee 1986 UK case-control 32 1.03 0.41 2.55 #> 15 15 Koo 1987 Hong Kong case-control 86 1.55 0.90 2.67 #> 16 16 Pershagen 1987 Sweden case-control 70 1.03 0.61 1.74 #> 17 17 Humble 1987 USA case-control 20 2.34 0.81 6.75 #> 18 18 Lam 1987 Hong Kong case-control 199 1.65 1.16 2.35 #> 19 19 Gao 1987 China case-control 246 1.19 0.82 1.73 #> 20 20 Brownson 1987 USA case-control 19 1.52 0.39 5.96 #> 21 21 Geng 1988 China case-control 54 2.16 1.08 4.29 #> 22 22 Shimizu 1988 Japan case-control 90 1.08 0.64 1.82 #> 23 23 Inoue 1988 Japan case-control 22 2.55 0.74 8.78 #> 24 24 Kalandidi 1990 Greece case-control 90 1.62 0.90 2.91 #> 25 25 Sobue 1990 Japan case-control 144 1.06 0.74 1.52 #> 26 26 Wu-Williams 1990 China case-control 417 0.79 0.62 1.02 #> 27 27 Liu 1991 China case-control 54 0.74 0.32 1.69 #> 28 28 Jockel 1991 Germany case-control 23 2.27 0.75 6.82 #> 29 29 Brownson 1992 USA case-control 431 0.97 0.78 1.21 #> 30 30 Stockwell 1992 USA case-control 210 1.60 0.80 3.00 #> 31 31 Du 1993 China case-control 75 1.19 0.66 2.13 #> 32 32 Liu 1993 China case-control 38 1.66 0.73 3.78 #> 33 33 Fontham 1994 USA case-control 651 1.26 1.04 1.54 #> 34 34 Kabat 1995 USA case-control 67 1.10 0.62 1.96 #> 35 35 Zaridze 1995 Russia case-control 162 1.66 1.12 2.45 #> 36 36 Sun 1996 China case-control 230 1.16 0.80 1.69 #> 37 37 Wang 1996 China case-control 135 1.11 0.67 1.84 #> yi vi #> 1 0.1655 0.0188 #> 2 0.3716 0.0330 #> 3 0.7031 0.5402 #> 4 0.1823 0.0313 #> 5 -0.2877 0.0797 #> 6 0.7275 0.2273 #> 7 0.7561 0.0889 #> 8 -0.2231 0.1927 #> 9 -0.2357 0.3390 #> 10 0.6981 0.0981 #> 11 0.2070 0.0456 #> 12 0.1823 0.2317 #> 13 0.4187 0.0796 #> 14 0.0296 0.2174 #> 15 0.4383 0.0770 #> 16 0.0296 0.0715 #> 17 0.8502 0.2926 #> 18 0.5008 0.0324 #> 19 0.1740 0.0363 #> 20 0.4187 0.4839 #> 21 0.7701 0.1238 #> 22 0.0770 0.0711 #> 23 0.9361 0.3982 #> 24 0.4824 0.0896 #> 25 0.0583 0.0337 #> 26 -0.2357 0.0161 #> 27 -0.3011 0.1802 #> 28 0.8198 0.3171 #> 29 -0.0305 0.0125 #> 30 0.4700 0.1137 #> 31 0.1740 0.0893 #> 32 0.5068 0.1760 #> 33 0.2311 0.0100 #> 34 0.0953 0.0862 #> 35 0.5068 0.0399 #> 36 0.1484 0.0364 #> 37 0.1044 0.0664
### random-effects model using the log odds ratios res <- rma(yi, vi, data=dat, method="DL") res
#> #> Random-Effects Model (k = 37; tau^2 estimator: DL) #> #> tau^2 (estimated amount of total heterogeneity): 0.0170 (SE = 0.0171) #> tau (square root of estimated tau^2 value): 0.1305 #> I^2 (total heterogeneity / total variability): 24.21% #> H^2 (total variability / sampling variability): 1.32 #> #> Test for Heterogeneity: #> Q(df = 36) = 47.4979, p-val = 0.0952 #> #> Model Results: #> #> estimate se zval pval ci.lb ci.ub #> 0.2139 0.0471 4.5390 <.0001 0.1215 0.3062 *** #> #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #>
### estimated average odds ratio with CI (and prediction interval) predict(res, transf=exp, digits=2)
#> #> pred ci.lb ci.ub pi.lb pi.ub #> 1.24 1.13 1.36 0.94 1.63 #>
### illustrate how the log odds ratios and corresponding sampling variances ### were back-calculated based on the reported odds ratios and CI bounds dat$yi <- log(dat$or) dat$vi <- ((log(dat$or.ub) - log(dat$or.lb)) / (2*qnorm(.975)))^2