_S_h_o_r_t _s_u_m_m_a_r_y _o_f _a _s_u_r_v_i_v_a_l _c_u_r_v_e _P_r_i_n_t _n_u_m_b_e_r _o_f _o_b_s_e_r_v_a_-
	_t_i_o_n_s, _n_u_m_b_e_r _o_f _e_v_e_n_t_s,
	    _m_e_a_n _s_u_r_v_i_v_a_l _a_n_d _i_t_s _s_t_a_n_d_a_r_d _e_r_r_o_r,  _a_n_d _t_h_e _m_e_d_i_a_n
	_s_u_r_v_i_v_a_l
	    _w_i_t_h _c_o_n_f_i_d_e_n_c_e _l_i_m_i_t_s _f_o_r _t_h_e _m_e_d_i_a_n.
	
	     print.survfit(fit, scale=1)
	
	_A_r_g_u_m_e_n_t_s:
	
	          fit:
	     the result of a call to the survfit function.
	
	        scale:
	     rescale the survival time, e.g., if the input data to
	     survfit were in days, scale=365 would scale the prin-
	     tout to years.
	
	     Value:
	
	     x, with the invisible flag set to prevent printing.
	
	     the number of observations, the number of events, the
	     mean survival and its standard error, and the median
	     survival with its confidence interval are printed.  If
	     there are multiple curves, there is one line of output
	     for each.  The mean and its variance are based on a
	     truncated estimator.  That is, if the last
	     observation(s) is not a death, then the survival curve
	     estimate does not go to zero and the mean is undefined.
	     In such a case, the estimator is based on an assumption
	     that the true curve goes to zero just beyond the last
	     observed follow up time; it will systematically
	     underestimate the true mean.  The median and its confi-
	     dence interval are defined by drawing a horizontal line
	     at 0.5 on the plot of the survival curve and it's con-
	     fidence bands.  The intersection of the line with the
	     lower CI band defines the lower limit for the median's
	     interval, and similarly for the upper band.  If any of
	     the intersections is not a point, then we use the smal-
	     lest point of intersection, e.g., if the survival curve
	     were exactly equal to 0.5 over an interval.
	
	     References:
	
	     Miller, Rupert G Jr. (1981) Survival Analysis, Wiley,
	     New York, p 71.
	
	     summary.survfit