leuk                  package:MASS                  R Documentation

_S_u_r_v_i_v_a_l _T_i_m_e_s _a_n_d _W_h_i_t_e _B_l_o_o_d _C_o_u_n_t_s _f_o_r _L_e_u_k_a_e_m_i_a _P_a_t_i_e_n_t_s

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

     A data frame of data from 33 leukaemia patients.

_U_s_a_g_e:

     leuk

_F_o_r_m_a_t:

     A data frame with columns:

     '_w_b_c' white blood count

     '_a_g' a test result, '"present"' or '"absent"'

     '_t_i_m_e' survival time in weeks

_D_e_t_a_i_l_s:

     Survival times are given for 33 patients who died from acute
     myelogenous leukaemia.  Also measured was the patient's white
     blood cell count at the time of diagnosis.  The patients were also
     factored into 2 groups according to the presence or absence of a
     morphologic characteristic of white blood cells. Patients termed
     AG positive were identified by the presence of Auer rods and/or
     significant granulation of the leukaemic cells in the bone marrow
     at the time of diagnosis.

_S_o_u_r_c_e:

     Cox, D. R. and Oakes, D. (1984) _Analysis of Survival Data_.
     Chapman & Hall, p. 9.

     Taken from

     Feigl, P. & Zelen, M. (1965) Estimation of exponential survival
     probabilities with concomitant information. _Biometrics_ *21*,
     826-838.

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

     Venables, W. N. and Ripley, B. D. (2002) _Modern Applied
     Statistics with S._ Fourth edition.  Springer.

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

     library(survival)
     plot(survfit(Surv(time) ~ ag, data = leuk), lty = 2:3, col = 2:3)

     # now Cox models
     leuk.cox <- coxph(Surv(time) ~ ag + log(wbc), leuk)
     summary(leuk.cox)

