_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)