home *** CD-ROM | disk | FTP | other *** search
-
- _P_e_t_e_r _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 _T_h_i_s _t_e_s_t _i_s _p_r_o_p_o_s_e_d _i_n _B_i_o_m_e_t_r_i_c_s, _J_u_n_e
- _1_9_7_8.
-
- 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.
-
- Value:
-
- 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 factor,
- 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.
-
- 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 Prentice,
- ranks the data only once at the start. The results are
- usually similar.
-
- References:
-
- O'Brien, Peter, "A Nonparametric Test for Association
- with Censored Data", Biometrics 34: 243-250, 1978.
-
- survdiff
-
- _E_x_a_m_p_l_e_s:
-
- xx <- survobrien(Surv(time, status) ~ age + factor(rx) + ecog.ps,
- data=fleming)
- coxph(Surv(start, stop, event) ~ age, data=xx)
- coxph(Surv(start, stop, event) ~ age + rx + ecog.ps, data=xx)
-
-