infert               package:datasets               R Documentation

_I_n_f_e_r_t_i_l_i_t_y _a_f_t_e_r _S_p_o_n_t_a_n_e_o_u_s _a_n_d _I_n_d_u_c_e_d _A_b_o_r_t_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     This is a matched case-control study dating from before the
     availability of conditional logistic regression.

_U_s_a_g_e:

     infert

_F_o_r_m_a_t:


       1.  Education              0 = 0-5  years
                                  1 = 6-11 years
                                  2 = 12+  years
       2.  age                    age in years of case
       3.  parity                 count
       4.  number of prior        0 = 0
           induced abortions      1 = 1
                                  2 = 2 or more
       5.  case status            1 = case
                                  0 = control
       6.  number of prior        0 = 0
           spontaneous abortions  1 = 1
                                  2 = 2 or more
       7.  matched set number     1-83
       8.  stratum number         1-63

_N_o_t_e:

     One case with two prior spontaneous abortions and two prior
     induced abortions is omitted.

_S_o_u_r_c_e:

     Trichopoulos et al. (1976) _Br. J. of Obst. and Gynaec._ *83*,
     645-650.

_E_x_a_m_p_l_e_s:

     require(stats)
     model1 <- glm(case ~ spontaneous+induced, data=infert,family=binomial())
     summary(model1)
     ## adjusted for other potential confounders:
     summary(model2 <- glm(case ~ age+parity+education+spontaneous+induced,
                     data=infert,family=binomial()))
     ## Really should be analysed by conditional logistic regression
     ## which is in the survival package
     if(require(survival)){
       model3 <- clogit(case~spontaneous+induced+strata(stratum),data=infert)
       print(summary(model3))
       detach()# survival (conflicts)
     }