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-
- _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
-
-