

esoph {base}                                 R Documentation

_S_m_o_k_i_n_g_, _A_l_c_o_h_o_l _a_n_d _(_O_)_e_s_o_p_h_a_g_e_a_l _C_a_n_c_e_r

_D_e_s_c_r_i_p_t_i_o_n_:

     Data from a case-control study of (o)esophageal cancer
     in Ile-et-Vilaine, France.

_U_s_a_g_e_:

     data(esoph)

_F_o_r_m_a_t_:

     data frame with records for 88 age/alcohol/tobacco com-
     binations.

      [,1]     "agegp"          Age group               1  25-34 years
                                                        2  35-44
                                                        3  45-54
                                                        4  55-64
                                                        5  65-74
                                                        6  75+
      [,2]     "alcgp"          Alcohol consumption     1   0-39 gm/day
                                                        2  40-79
                                                        3  80-119
                                                        4  120+
      [,3]     "tobgp"          Tobacco consumption     1   0- 9 gm/day
                                                        2  10-19
                                                        3  20-29
                                                        4  30+
      [,4]     "ncases"         Number of cases
      [,5]     "ncontrols"      Number of subjects

_A_u_t_h_o_r_(_s_)_:

     Thomas Lumley

_S_o_u_r_c_e_:

     Breslow and Day (1980).  "Statistical Methods in Cancer
     Research.  1: The Analysis of Case-control studies";
     IARC Lyon.

_E_x_a_m_p_l_e_s_:

     data(esoph)
     summary(esoph)
     ## effects of alcohol, tobacco and interaction, age-adjusted
     model1 <- glm(cbind(ncases, ncontrols) ~ agegp + tobgp * alcgp,
                   data = esoph, family = binomial())
     anova(model1)
     ## Try a linear effect of alcohol and tobacco
     model2 <- glm(cbind(ncases, ncontrols) ~ agegp + codes(tobgp) + codes(alcgp),
                   data = esoph, family = binomial())
     summary(model2)
     ## Re-arrange data for a mosaic plot
     ttt <- table(esoph$agegp, esoph$alcgp, esoph$tobgp)
     ttt[ttt == 1] <- esoph$ncases
     tt1 <- table(esoph$agegp, esoph$alcgp, esoph$tobgp)
     tt1[tt1 == 1] <- esoph$ncontrols
     tt <- array(c(ttt, tt1), c(dim(ttt),2),
                 c(dimnames(ttt), list(c("Cancer", "control"))))
     mosaicplot(tt, main = "esoph data set", color = TRUE)

