cox.zph               package:survival               R Documentation

_T_e_s_t _t_h_e _P_r_o_p_o_r_t_i_o_n_a_l _H_a_z_a_r_d_s _A_s_s_u_m_p_t_i_o_n _o_f _a _C_o_x _R_e_g_r_e_s_s_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     Test the proportional hazards assumption for a Cox regression
     model fit ('coxph').

_U_s_a_g_e:

     cox.zph(fit, transform="km", global=TRUE)

_A_r_g_u_m_e_n_t_s:

     fit: the result of fitting a Cox regression model, using the
          'coxph' function. 

transform: a character string specifying how the survival times should
          be transformed before the test is performed. Possible values
          are '"km"', '"rank"', '"identity"' or  a function of one
          argument. The default is '"km"' for right-censored data and
          '"identity"' for counting-processing data. 

  global: should a global chi-square test be done, in addition to the
          per-variable tests. 

_V_a_l_u_e:

     an object of class '"cox.zph"', with components: 

   table: a matrix with one row for each variable, and optionally a
          last row for the global test. Columns of the matrix contain
          the correlation coefficient between transformed survival time
          and the scaled Schoenfeld residuals, a chi-square, and the
          two-sided p-value. For the global test there is no
          appropriate correlation, so an NA is entered into the matrix
          as a placeholder. 

       x: the transformed time axis. 

       y: the matrix of scaled Schoenfeld residuals.  There will be one
          column per variable and one row per event.  The row labels
          contain the original event times (for the identity transform,
          these will be the same as 'x'). 

    call: the calling sequence for the routine.

          The computations require the original 'x' matrix of the Cox
          model fit. Thus it saves time if the 'x=TRUE' option is used
          in 'coxph'. This function would usually be followed by both a
          plot and a print of the result. The plot gives an estimate of
          the time-dependent coefficient 'beta(t)'. If the proportional
          hazards assumption is true, 'beta(t)' will be a horizontal
          line.  The printout gives a test for 'slope=0'. 

_R_e_f_e_r_e_n_c_e_s:

     P. Grambsch and T. Therneau (1994), Proportional hazards tests and
     diagnostics based on weighted residuals. _Biometrika,_ *81*,
     515-26.

_S_e_e _A_l_s_o:

     'coxph', 'Surv'.

_E_x_a_m_p_l_e_s:

     fit <- coxph( Surv(futime, fustat) ~ age + rx, ovarian)
     temp<- cox.zph(fit)
     print(temp)                  #display the results
     plot(temp)                   #plot curves

