

survobrien(survival5)                        R Documentation

_O_'_B_r_i_e_n_'_s _T_e_s_t _f_o_r _A_s_s_o_c_i_a_t_i_o_n _o_f _a _S_i_n_g_l_e _V_a_r_i_a_b_l_e _w_i_t_h
_S_u_r_v_i_v_a_l

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

     Peter O'Brien's test for association of a single vari-
     able with survival This test is proposed in Biometrics,
     June 1978.

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

     survobrien(formula, data)

_A_r_g_u_m_e_n_t_s_:

 formula: a valid formula for a cox model, without time
          dependent covariates.

    data: a data frame.

_V_a_l_u_e_:

     a new data frame.  The original time and status vari-
     ables are removed, and have been replaced with `start',
     `stop', and `event'.  If a predictor variable is a fac-
     tor or is protected with `I()', it is retained as is.
     Other predictor variables have been replaced with time-
     dependent logit scores.

     Because of the time dependent variables, the new data
     frame will have many more rows that the original data,
     approximately #rows * #deaths /2.

_M_E_T_H_O_D_:

     A time-dependent cox model can now be fit to the new
     data.  The univariate statistic, as originally pro-
     posed, is equivalent to single variable score tests
     from the time-dependent model.  This equivalence is the
     rationale for using the time dependent model as a mul-
     tivariate extension of the original paper.

     In O'Brien's method, the x variables are re-ranked at
     each death time.  A simpler method, proposed by Pren-
     tice, ranks the data only once at the start. The
     results are usually similar.

_R_e_f_e_r_e_n_c_e_s_:

     O'Brien, Peter, "A Nonparametric Test for Association
     with Censored Data", Biometrics 34: 243-250, 1978.

_S_e_e _A_l_s_o_:

     `survdiff'

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

     data(ovarian)
     xx <- survobrien(Surv(futime, fustat) ~ age + factor(rx) + I(ecog.ps),
                                    data=ovarian)
     coxph(Surv(start, stop, event) ~ age, data=xx)
     coxph(Surv(start, stop, event) ~ age + rx + ecog.ps, data=xx)

