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.  

  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 + ecog.ps,  
                  data=ovarian) 
     temp <- cox.zph(fit) 
     print(temp)                  # display the results 
     plot(temp)                   # plot curves